SLAM: A connectionist model for attention in visual selection tasks

COGNITIVE

PSYCHOLOGY

22, 273-341 (1990)

SLAM: A Connectionist Model for Attention in Visual Selection Tasks R. HANS PHAF, A.H. C. VAN DERHEIJDEN, AND PATRICK T. W. HUDSON Unit of Experimental

and Theoretical

Psychology,

Leiden University

SLAM, the SeLective Attention Model, performs visual selective attention tasks, an analysis of which shows that two processes, object and attribute selection, are both necessary and sufficient. It is based upon the McClelland and Rumelhart (1981) model for visual word recognition, with the addition of a response selection and evaluation mechanism. The responses may be correct or incorrect and, in particular conditions, SLAM may not make a response at all. Moreover, it allows for the generation of specific responses in time. SLAM’s main characteristics are parallelism restricted by competition within modules, heterarchical processing in a hierarchical structure, and generation of responses as a result of relaxation given the conjoint constraints of stimulation, object, and attribute selection. The model is considered to represent an individual subject performing filtering tasks and demonstrates appropriate selective behavior. It is also tested quantitatively using a single tentative set of model parameters. The study reports simulations of four different filtering experiments, modeling response latencies, and error proportions. Specifications are made to take account of instructions, previous trials, and the effect of a barmarker cue and of asynchronies in stimulus and cue onsets. The model is then extended in order to provide simulations of a number of Stroop experiments, which can be regarded as filtering tasks with nonequivalent stimuli. The extension required for Stroop simulations is the addition of direct connections between compatible stimulus and response aspects. The direct connections do not affect the simulation of simpler filtering tasks. A variety of different experiments carried out by different authors is simulated. The model is discussed in terms of how modular architecture and the interaction of excitation and inhibition generate facilitation or inhibition of response latenties. 0 IWO Academic Press, Inc INTRODUCTION

Attention is required once alternative ways of construing the “world” are possible and must be chosen between. A paradigmatic exemplar of a class of laboratory tasks that demonstrates the operation of attention is a kind of partial report, or filtering, experiment (Kahneman & Treisman, The authors wish to thank Marjolein Vermeij, Kees Verduin, and Gezinus Wolters for their assistance and helpful suggestions. Requests for reprints should be sent to R. Hans Phaf, Unit of Experimental and Theoretical Psychology, Faculty of Social Sciences, Leiden University, P.O. Box 9555, 2300 RB Leiden, The Netherlands, 273 0010..0285/90$7.50 Copyright 0 1990 by Academic Press, Inc. All rights of reproduction in any form reserved.

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1984). A typical stimulus card may consist of a red disk on the left and a blue square on the right. Six elementary attributes are found in this stimulus: red, blue, disk, square, left position, and right position. After instruction the card is shown for a short time (e.g., 150 ms). The subject is asked to name a specific attribute of one of the two objects while that object is specified by another attribute, Some examples of these questions are: name the color of the square, name the form on the left, and name the position of the red object. If only one attribute is asked for and negations are excluded, 12 such questions can be posed. This task is easy for subjects to perform. In one way or another attentional control is directed to one stimulus attribute on the basis of the other attribute. The explanation of such selection would be trivial if relevant objects and attributes were identified beforehand. In the experiment, however, object and attribute can only be extracted after the question is asked. The interaction of question and stimulation results in a single response (i.e., it determines how the world is to be construed and, therefore, reacted to). Our aim is to explain this response by actually producing this type of attentional behavior in connectionist terms. Marr (1977, 1980) distinguished among (a) the computational, (b) the algorithmic or representational levels of explanation, and (c) the hardware implementation of the algorithms. The first requires an analysis of what is computed and why: our computational problem is to understand the essential nature of attention. The algorithm describes how a computation is actually carried out; the problem is to develop an algorithm (expressed in a connectionist representation) to simulate attentional selection. This algorithm, implemented for example as a computer program, should be able to simulate the performance of a typical subject performing an attentional task. As such, the model must extend current connectionist models by being capable of performing more than one task or, alternatively, of treating its inputs in more than one way without changing the hardware. In this paper, we report the theoretical background and basic principles of the model. The notion of attention is first reviewed to uncover the essential functions involved and the ways in which they may interact to produce the behavior required (the computational problem). Next we describe the bases for the model, intended to simulate attentional behavior, stated in a connectionist framework (the representational problem). The simulation can operate in time, thus providing hypothetical response times which may be compared with empirical data, alongside such measures as probability of specific responses and error proportions. In the implementation we find that the selection mechanisms encountered in the definition of the computational problem of attention also fall out naturally at the representational level.

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ATTENTION AT THE COMPUTATIONAL LEVEL Two Mechanisms

in Vision

Attention is the process whereby an abundance of stimuli is ordered and integrated within the framework of current tasks and activities; it integrates ongoing activity and newly arriving information. This integration results in an apparent selection of information. James (189011950) already tried to answer the question of what mechanisms achieve this integration: “. . . two physiological processes . . . suggest themselves as possibly forming in combination a complete reply. . . . 1. the accommodation or adjustment of the sensory organs; and 2. the anticipatory preparations from within of the ideational centres concerned with the object to which the attention is paid.” (James, 1890/1950, p. 434.) At the start of the information processing approach to perception and attention, Treisman (1960, 1964a)proposed a theory involving two mechanisms that are closely related to James’ “physiological processes”: 1. an attenuation-filter that passes relevant messages and attenuates unwanted messages; and 2. anticipatory preparation from within in a dictionary of units with variable thresholds. So, careful introspection (James, 1890/1950)and experimentation and theorization (Treisman, 1960, 1964a) converged on two mechanisms that seemed to be sufficient to explain selective attentional performance. For two reasons this picture became much more complicated. The first reason is that theorists started to distinguish among a large number of selective attention tasks. They suggested that more than two different selection mechanisms need to be distinguished. Treisman (1969), for instance, lists four types of tasks: 1. Input selection tasks (filtering tasks or stimulus set tasks; Broadbent, 1971). The subjects are instructed to name only the relevant items. Relevant items and irrelevant items are presented simultaneously. The relevant items can be distinguished from the irrelevant ones by an obvious physical characteristic (e.g., name the red letters, not the black ones). 2. Target selection tasks. The subjects are instructed to search for a designated target. The target(s) and nontargets are presented simultaneously (e.g., is there an E among the Fs?). 3. Output selection tasks (pigeon-holing tasks or response set tasks; Broadbent, 1971). The subjects are instructed to name only the items of

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one category. Items of two (or more) categories are presented simultaneously. The relevant items are either very difficult or not to be found on the basis of their physical characteristics (e.g., name the vowels, not the consonants). 4. Attribute selection tusks. The subjects are instructed to name one attribute or dimension of the items presented. Of course, in all items, more attributes are combined (e.g., name the colors of the letters). Although more than two types of selection tasks can be distinguished, it is not necessary to assume that there are more than two selective mechanisms involved. First, consider (1) input selection tasks and (2) target selection tasks. Kahneman (1973) already remarked that “The distinction between selection of inputs and selection of targets is that the relevant items are rare and relatively difficult to find in the latter task. However, the mechanism of selection appears to be similar in the two cases” (Kahneman, 1973, pp. 7&71). Recently, however, Kahneman and Treisman (1984) pointed out a relevant difference between the two tasks: “In many search studies, the target is defined by a simple feature; once this has been detected, the response is immediately determined . . . In the more complex filtering design, however, further processing of relevant stimuli is required before a response can be chosen” (Kahneman & Treisman, 1984, p. 34; see also p. SO). So, input selection tasks and target selection tasks seem identical as far as detecting or finding relevant inputs or targets is concerned. The tasks differ, however, as far as further processing is concerned. There are additional selective processes in input selection tasks that are not found in target selection tasks. Because we need a term to indicate the selective process common to both tasks, we use the phrase object selection, with the word object referring to a conjunction of attributes (e.g., form, color, and identity) that occupies a restricted area of visual space. Now consider (3) output selection tasks and (4) attribute selection tasks. There is a very close relationship between these two types of tasks. Generally, specification of the relevant attribute in an attribute selection task simultaneously determines the relevant class of outputs. If color is the attribute to be selected, then “color names” is the relevant output category (see Kahneman, 1973, p. 71). The converse is also generally true. Specification of the relevant output simultaneously determines the relevant attribute. If “vowel” is the relevant category, then “form” is the relevant attribute. Generally, the required output category also determines the relevant attribute (the Stroop task is an exception) and the relevant attribute also determines the relevant output category (nonnaming tasks are an exception). Kahneman and Treisman (1984, p. 45) noticed this close connection: “the priming of a response category” (i.e.,

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output selection) can do most of the work of the selective operation that determines which property will control responses (i.e., of attribute selection) “because different properties of an object are rarely linked to different members of the same class.” They assume that the same selective operation can perform selection of outputs and selection of attributes (see also Kahneman, 1973, p. 111). This is in accord with our analysis. We need a term to indicate the selective process common to these two types of tasks and we will use the phrase attribute selection for this process. Our analysis leaves us with two selection processes: object selection and attribute selection. Object selection is closely related to the first mechanism proposed by James (189011950)and Treisman (1960, 1964). It controls the source of stimuli that determine subsequent responses but not the vocabulary of responses (Broadbent, 1971, p. 177). Attribute selection is closely related to the second mechanism proposed by James (1890/1950) and Treisman (1960, 1964). It controls the vocabulary of responses but not the source of the stimuli (Broadbent, 1971, p. 177). Taken together, it seems that for most selective attention tasks two mechanisms are sufficient and that there is little need to postulate additional mechanisms. The second reason that the picture became much more complicated than James (1890/1950) and Treisman (1960, 1964) initially supposed was that theorists strongly suggested that for some tasks a single selective process would be sufficient. In this context the distinction between “input” (Treisman, 1969) or “unit” (Kahneman, 1973) and “dimension” (Treisman, 1969) or “attribute” (Kahneman, 1973) selection tasks is most relevant. The partial-report task (see, e.g., Sperling, 1960) and the partial-report bar-probe task (see, e.g., Averbach & Coriell, 1961)are prototypical input selection tasks. According to the traditional analysis this task involves only one selective process: “object selection.” In these views “selective attention to inputs” is the operative mechanism in these tasks (Kahneman, 1973, p. 135; see also Treisman, 1969). The Stroop task (see, e.g., Stroop, 1935) is a prototypical attribute selection task. In the traditional analysis this task also involved only one selective process: “attribute selection.” It was suggested that “attention to attributes” is the operative mechanism in this task (Kahneman, 1973, p. 111). More recent theoretical analysis has indicated, however, that the distinction between input selection tasks and attribute selection tasks is not as clear as was initially thought (e.g., Neumann, 1980, pp. 357-362; Van der Heijden, 1981, pp. 85-110; Van der Heijden, La Heij, Phaf, Buijs, & Van Vliet, 1988):In fact, it is not difficult to show that all input selection tasks always involve some form of attribute selection and that all attribute selection tasks always involve one or another form of object selection.

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The tasks mainly seem to differ in what is made explicit to the subject in the instructions. Let us consider the partial-report bar-probe task and the Stroop task in more detail. In the partial-report bar-probe task, the subjects are instructed to name the letter in a row of letters that is indicated by a barmarker. In this “input selection task” two attributes (of the letters) are of importance. The first is the position of the letters. Position either determines whether a letter is relevant for task performance or it serves as a criterion attribute. The second dimension is the form of the letters. Form either determines what response has to be given or it serves as the response attribute. In the Stroop task, subjects are instructed to name the colors in which a set of color words are printed. Also in this “attribute selection task” two attributes (of the colored words) are of importance. The first is the position of the words. Position either determines which word is relevant at a certain temporal position in the response sequence or it serves as the criterion attribute. The second dimension is the color of the words. Color either determines what response must be given or it serves as the response attribute. So, both the “input selection task” and the “attribute selection task” are complex selection tasks in which “. . . two distinct functions . . . are controlled by different aspects of the information presented . . . : stimulus choice, the segregation of relevant items from irrelevant ones, must be guided by some identifying property . . . : response choice, . . . , is controlled by other properties . . .” (Kahneman & Treisman, 1984, p. 31; see also Allport, 1987). Both tasks combine object selection and attribute selection; relevance must be determined by one attribute and the appropriate category of response by another attribute of the same object. In other words, it seems that two mechanisms are always needed and that only one selective mechanism is hardly ever sufficient. Taken all together, two selective processes and their corresponding mechanisms may be necessary and sufficient to perform selective attention tasks in vision: (1) object selection controlling the source of stimuli and (2) attribute selection controlling the vocabulary of responses. One mechanism is needed for object selection; we call this mechanism object set. The other is needed for attribute selection; we call this mechanism attribute set. They cooperate in nearly all selective attention tasks. In other words it seems that both James’ (1890/1950) and Treisman’s (1960, 1964) specifications approximate the necessary and sufftcient conditions for performing selective attention tasks in vision. ATTENTION

AT A REPRESENTATIONAL

LEVEL

Sources of the Model The main source of this model is the general approach to computation

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called “Connectionism” or “Parallel Distributed Processing.” Connectionism-modeling in terms of elements and their connections (i.e., in terms of networks)-has been described extensively by Hinton and Anderson (1981); Feldman and Ballard (1982); Palm (1982); Rumelhart and McClelland (1986); McClelland and Rumelhart (1986); Kohonen (1984, 1988); and Grossberg (1988). The implementation of the model is called SLAM, standing for SeLective Attention Model. From among the many kinds of networks that can be realized in a connectionist fashion we follow the general scheme of the McClelland and Rumelhart (1981) model for letter perception. Their model is useful for the modeling of attentional tasks, because there is a deep analogy between the objectives of both models. The McClelland and Rumelhart model is concerned with the interaction of (word) knowledge and perception. Every attentional model has to be concerned with the interaction of (instructional) preparation and perception. Disambiguation of a letter by a word context is comparable to the selection by way of task context of a response from a stimulus, potentially capable of giving rise to many responses. Therefore, the same mechanisms may also be available for selection. Our model, however, is concerned with attentional tasks rather than with letter perception. Therefore, an appropriate configuration for the tasks in question is necessary. Over and above these task dependent changes, some small simplifications have been made in the assumptions of the McClelland and Rumelhart model (see Phaf, 1986, for more detail). One modification concerns the response side of the model. The response production part of the McClelland and Rumelhart model has been replaced by the sampling and recovery procedures of the SAM model by Raaijmakers and Shiffrin (1980, 1981). Another modification is the removal of thresholds and biases for individual elements. It will become clear later on, that other features of the model serve this function. Outline of the McClelland

and Rumelhart

Model

The construction of the McClelland and Rumelhart (1981) model proceeds from a number of basic assumptions: (1) Processing is basically hierarchical, i.e., in visual word perception several levels of processing can be distinguished: (a) a feature level; (b) a letter level; (c) a word level; (d) higher levels that provide top-down input to the word level. (2) Visual perception involves parallel processing. Two forms of parallel processing can be distinguished:

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(a) spatially parallel processing. It is assumed that information covering a region in space, at least large enough to contain a four-letter word, is processed simultaneously; (b) parallel processing at different levels of the model. A process belonging to a particular level can operate simultaneously with a process at a different level. (3) Perception is fundamentally an interactive process. Top-down, or conceptually driven, processing works simultaneously with bottom-up, or data driven, processing. Top-down processing provides the constraints (imposed by word context) on the perception of letters. A number of implementational assumptions are also specified: (i) The basic assumption (1) is implemented by introducing elements in three processing levels: (a) a feature-position level; (b) a letter-position level; (c) a word level. (ii) Up to the word level, letter positions are treated separately. This results in separate modules within these levels. A module contains representations of all features at one position for the feature level and representations of all letters at one position for the letter level. In fact, the first two levels represent topological mappings of features and letters. The upper level (i.e., the word level) forms a (nontopological) module on its own. (iii) Features, letters, or words are represented by the activation of a particular node. At the first level there is a node for every feature-position combination. At the second level there is a node for each letter-position combination. At the third level each word is represented by a single node. (iv) The connections between nodes are governed by a number of general rules: (a) Nodes within the same module only have (horizontal) inhibiting connections to one another. Nodes within the same level, but not within the same module, have no direct connections. (b) Nodes from different levels only have (vertical) connections, if their levels are adjacent. These connections can either be excitatory or inhibitory. (c) The nature of the connections between nodes of different levels is determined by the representations of the respective nodes. Nodes whose representations are compatible (like the node for the letter A on the third position and the node for the word TRAP) have excitatory connections in both directions. Nodes that are incompatible (like that for the letter B on the first position and that for the word TRAP) have inhibitory connections in both directions. We will call the principle that the nature and strength

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of connections between nodes is determined by the compatibility of the representations of the nodes, the compatibility principle. The Elements of SLAM In SLAM we largely followed the activation formula of the McClelland and Rumelhart model, first proposed by Grossberg (1973). We have attempted to minimize the intrinsic structure of the elements, thereby eliminating individual differences between elements. This can illustrate the fact that the behavior of connectionist models arises from the specific wiring and not from some exotic property of an element. In order to interact, the elements must have some uniform medium of communication. This medium is the only intrinsic structure that can be attributed to an element. It is called the activation of the element. It takes on real values within the interval [m,Mj, with M as the maximum and m as the minimum value. M can be viewed as a basic scale factor (see McClelland & Rumelhart, 1981). It was set at 1.0 and m was fixed at 0.0. The activation of element i at time t is denoted by: Ui(t) in the interval [O.O, 1.01for all i at any t.

(1)

The activation of a particular element is built up from the activation it receives from a number of elements by way of input lines. The amount of activation that is subtracted from or added to the activation of an element depends on both the activation of and the connection weights from the elements sending the activation. If the connection contributes to the activation of the next element, it is excitatory and its weight is positive. If it reduces the activation, the connection is inhibitory and the weight negative. The weight will be denoted by:

wij The net input received by element i from all other elements sending activation to it amounts to:

ni(t)= &wij *u,ct>.

(2)

j#i

If the net input is excitatory (ni > O), its effect depends on how far the receiving element is from the maximum activation value M. The effect of the same net input will be far greater if the element is at its minimum activation than if it is near its maximum. The effect of excitatory input can be expressed as: ei(t) = q(t) * (M - Ui(t))

for

If the net input is inhibitory the effect will be:

ni(t)

2 0.

(3)

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q(t) = r+(t) * q(t)

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for ni(t) < 0.

(4)

Besides the effect of input, the activation of a particular element will also change over time as a result of autonomous decay. This decay is characterized by a decay parameter: 8. If no input is present, the activation after a fixed short time interval, At, will decrease with 0 times the old activation (in a representation with continuous time this represents exponential decay). Input activation and decay modify the activation at t + Ar in:

Ui(t+ At)= ai - 0 . ai + ei(t). Representations,

Interactions,

(5)

and Modules

In SLAM the connections are governed by the same three rules given in the McClelland and Rumelhart model [see point (iv) above]. The compatibility between representations of the elements determines the presence and the nature of their connections. An account of the connections in the model, therefore, is only possible after the description of what the elements in SLAM stand for. Our choice of elements is defined in terms of the decision that SLAM should perform the filtering task described in the introduction. An inventory of the elementary features in the filtering experiment suggests the following list of attributes: positions 1 and 2 (e.g., left and right); colors 1 and 2 (e.g., blue and red); forms 1 and 2 (e.g., square and circle). Increasing the number of attributes in the model is straightforward, but because this number is sufficient for our present purposes, we will restrict ourselves to this list. Following the McClelland & Rumelhart model we distinguish three levels: (a) a mapping level; (b) an attribute level; (c) a response level. The representations in the first level consist of all combinations of features in two dimensions. So, three relatively independent modules can be distinguished: Form-Position module (e.g., circle at the left position); Color-Position module (e.g., blue at the right position); Form-Color module (e.g., a red square). These modules are considered as mappings of one feature on another. As the number of features per dimension is limited here to two, the number of combinations within such a mapping will be four (see Fig. 1). At the second level of SLAM single features are extracted from the combina-

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ATTENTION

POSITION

MODULE

COLOUR

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POSITION

MODULE

4-h. I

I

I -

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STIMULI

FIG. 1. The color, form, position, and color-position modules and their interconnections in SLAM. The elements in the color-position mapping stand for the following combinations: color 1 at position 1: (ClPl) (e.g., red, left); color 2 at position 1: (C2Pl) (e.g., blue, left); color 1 at position 2: (ClP2) (e.g., red, right); and color 2 at position 2: (C2P2) (e.g., blue, right). The elements in the second layer stand for: color 1: Cl (e.g., red); color 2: C2 (e.g., blue); form 1: Fl (e.g., circle); form 2: F2 (e.g., square); position 1: Pl (e.g., left); and position 2: P2 (e.g., right).

tions of features. Three modules representing the three kinds of features (color, form, and position) are assumed. Each module consists of two elements. Elements at the third level represent the motor programs needed for the production of verbal responses. The six possible responses in the filtering task are represented at this level. A further seventh element, FTR or pretrial activity node, standing for residual motor program activity preceding the actual response production is also assumed. Before the beginning of an experimental trial the system will generally be engaged in activity not related directly to the stimuli. Upon stimulus presentation this residual activity will interfere to some degree with the execution of the task due to inhibition within the motor program module. The interference disappears because the pretrial activity on its turn will be suppressed by a potential response. The extraction of features from mappings takes place through vertical

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connections that are completely determined by the compatibility principle. Both bottom-up and top-down connections are present between these levels. In Fig. 1 the connections of the color-position mapping with the three modules at the second level are shown. The other two mappings have comparable connections to their corresponding feature modules. Analogous principles apply to the connections between the feature elements and the motor program elements. All between-level connections for the feature and motor program level are shown in Fig. 2. Within level connections are all inhibitory, as in the McClelland and Rumelhart model. Only “equivalent” units will inhibit each other. Therefore, a color-position element can inhibit other color-position elements, color elements can inhibit other color elements, and motor program elements can inhibit other motor program elements. But no color element will directly inhibit a form element or a position element and vice versa. This wiring provides us with a definition of a module. A module is a group of elements at the same level of encoding having inhibitory inter-element connections and no other direct connections to elements at the same level of encoding.

MOTOR

COLOUR

MODULE

FORM

MODULE

POSITION

PROGRAMME MODULE

MODULE

FIG. 2. Modules and interconnections from the second and third level of SLAM. The seven elements at the motor program level together form one module. The elements represent: name of color 1: (RCl) (e.g., red); name of color 2: (RC2) (e.g., blue); name of form 1: (RFI) (e.g., circle); name of form 2: (RF2) (e.g., square); name of position 1: (RPl) (e.g., left); name of position 2: (RP2) (e.g., right); and pretrial residual activity: (PTR) (e.g., green).

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The architecture is derived from the McClelland and Rumelhart (1981) model. There is, however, considerable psychological and neurophysiological evidence to support the general principles, but not necessarily the details, of the model: -Modularity (see e.g., Hubel & Wiesel, 1972; Mountcastle, 1978; Szentagothai, 1975); -1ntramodular inhibition (see e.g., Cowey, 1981; Hatta, Tzumoto, Sato, Hagihara, & Tamura, 1988; Klein, 1988); -Bidirectionality of intermodular connections (see e.g., Zeki & Shipp, 1988); -Segregation of representation at higher levels (see e.g., Cowey, 1985; Livingstone & Hubel, 1988; Van Essen & Maunsel, 1983); -Combination of representations at lower levels (see e.g., Keele, Cohen, Ivry, Liotti, & Yee, 1988; Livingstone & Hubel, 1988; Zeki & Shipp, 1988). In summary, the total computer model we start with consists of 25 elements in 7 modules. Activations at the mapping level will lead to activations at the motor program level through the spread of activation. The transmission of the activations by the network depends upon the gross architecture of the system which eventually determines what the network can do. The total block scheme of the model with a highly schematized wiring pattern is shown in Fig. 3. Responses

For our purposes, the output side of the McClelland and Rumelhart model cannot be used. Our need to provide a response mechanism capable of generating actual RT predictions and not only probabilities led us to adopt a different response generation mechanism than the one used by McClelland and Rumelhart. For this purpose we used the sampling and recovery procedure’ of the SAM model (Raaijmakers & Shiffrin; 1980, 1981). The sampling rule used here corresponds to the Lute (1959) ratio rule applied to activations.

r The sampling and recovery procedures are formulated in a diierent language than the rest of the model. But a mathematical formulation does not, necessarily, constitute a significant departure from the connectionist nature of the model. The sampling and recovery formulae may, actually, be elegant recapitulations of the functioning of a modular network. To show this, a possible method for implementing the sampling and recovery procedures in terms of the same connectionist language is presented in Phaf (1986).

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RESPONSE

MOTOR

PROGRAMME

MODLLL JJ

’

cOLOUR-POSITION MOOULE

u

FIG. 3. Schematic view of the model for filtering tasks.

where L is the set of motor program elements (except the PTR element, which can never be elicited as a response). With this rule the most strongly activated motor program has the greatest chance of being sampled. This rule provides a kind of signal-to-noise ratio for the degree to which a particular activation stands out against the background of competing activations. To estimate reaction times, a recovery procedure is introduced follow-

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ing the sampling stage. The recovery procedure “evaluates” the activation value of the sampled element. The activation of the single unit can be regarded as a measure for the availability of the motor program. The probability of a (complete) recovery of a sampled motor programme i at time t equals: p,(Ri,t)

= 1 - exp[-a#)]

(7)

A small increase in activation, when the activation is low, will lead to a relatively large increase in recovery probability. When the activation value is higher, this increase slows down. The recovery procedure may be considered as a nonabsolute (stochastic) threshold. Motor programs with low activations still have a (small) chance of being produced as a response with this procedure. Moreover, high recovery probabilities cannot be reached in the model because the activation of an element cannot exceed 1.0; the maximum recovery probability equals 0.632. The recovery procedure will tend to produce responses that have higher activations for a longer period of time. Fluctuations or transient activations, even if they are large, will only have a small chance of leading to a response. With this recovery procedure, reaction times for response selection can be defined. SLAM operates by iterating its calculation of the activation of all nodes. A reaction time* is the time t (measured as the number of iterations from the onset of the stimulus) of the first successful recovery of a response. Proportions correct can be derived by counting the correct responses among all produced responses. PROCESSING IN SLAM Simple Stimuli Having constructed SLAM statically we now look at its information processing dynamics and introduce the required mechanisms for performing selection. For this purpose, SLAM is first confronted with “simple stimuli” (in terms of the model), i.e., stimuli that activate a single element in the first, mapping, layer (e.g., a color on a position without a specified form). For modeling human information processing such stimuli may not be realistic, because presentation of any visual stimulus presumably cor’ In the model, all activations are supposed to be calculated in parallel. The continuous evolution of time is approximated by the definition of short time intervals. The fixed short time interval Ar, introduced for the definition of the decay parameter 8, was taken as the unit of time. Within the interval Ar all activations are calculated once (constituting one iteration). These calculations are considered to be simultaneous. In the next iteration the activations are calculated afresh on the basis of the activations of the preceding iteration. Additional information about the actual implementation of SLAM can be found in Phaf (1986).

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responds to the simultaneous activation of many elements on the mapping areas. The use of such two-dimensional stimuli may, nevertheless, simplify matters highlighting all the interesting and relevant features in SLAM. The parameter values (i.e., connection weights) used in all simulations were obtained in an initial “trial and error” process. The search for an adequate parameter set was guided by the desire to obtain a good qualitative tit. Before performing the simulations reported here the exact parameter values were derived by fitting the simulation results quantitatively to the experimental results of a single experiment (Exp. I, Glaser 8z Glaser, 1982). These values were used unaltered for all further simulations, since they represent an arbitrary choice from the range of possible values which may be regarded as implementing a single subject taking part in subsequent experimental conditions. The complete parameter set and some observations on the parameter set are presented in the Appendix. Simple Processing

As the network is completely symmetric with respect to the 12 simple stimuli we can illustrate SLAM’s processing behavior with any one of these stimuli. The presentation of color 1 on position 1 (ClPl: e.g., red at the left position) has been chosen as an example. Two degrees of freedom can be distinguished in the presentation of such a stimulus: the strength of stimulation and its duration. Two arbitrary values for the stimulation strength have been chosen: a low strength of 0.4 and a high strength of 0.8. In both conditions the stimuli were presented for 6 and 80 iterations (80 being the maximum number of iterations performed by the program during one trial). The results after 50 trials per condition, in terms of their average reaction times (RT) and standard errors (SE) (in number of iterations), are given in the first four columns of Table 1. The Table shows that stimulation strength is not a very critical factor. Presentation duration primarily affects the number of responses produced. Very often, no responses at all are produced in the short duration conditions. Nevertheless, in all presentation conditions color 1 (blue) and position 1 (left) have an equal chance of being selected. This simulation demonstrates (1) the simple spread of activation from the initial node PlCl (blue-left), through (2) the analysis in terms of the PI and Cl nodes in their respective modules, followed by (3) the activation of their equivalent motor programs. When there are no clear instructions we can see how the system, as a whole, is incapable of making up its mind. Instructions are needed for attentional selection to take place.

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TABLE 1 Responses with Presentation of ClPl (Blue-left) and with Presentation of ClPl and Attribute Set Simple processing Strength duration RCl RT SE RPl RT SE No resp.

0.4 6 10 34.1 8.6 14 37.0 6.3 26

0.4 80 21 29.9 4.1 22 29.4 4.5 I

0.8 6 10 32.5 7.1 9 36.9 6.8 31

With attribute set 0.8 80 21 27.3 3.0 17 37.0 4.4

6

0.4 6 50 12.7 0.88 _

0.4 80 50 11.3 0.57 _

0.8 6 50 12.7 0.90 -

0.8 80 50 12.1 0.89 _

Note. See text for further explanation. RCI, the number of times that color 1 (blue) is given as a response; RPl , the number of left responses; RT, the response time in iteration time units; and SE, the Standard Error of RT.

Attribute

Set

In real experimental situations, typical instructions are: “name the color” or “name the position.” How can such an instruction be implemented in the model without favoring the as yet unknown response (i.e., the name of the color or position that is presented)? The instruction to name the color or the position should comprise all colors or all positions, because the specific color or position that is presented is not known beforehand. In the model the attribute set has to consist of all colors, positions, or forms. Since instructions are generally presented auditorily, instructional effects in visual tasks have to arise from crosstalk with auditory processing modules. But auditory processing modules are not included in the model. We treat spoken instructions as a form of quasi-input from auditory processing modules left unspecified. Attribute set instruction is operationalized by providing a continuous input (0.4) to all elements in the relevant module throughout the trial (80 iterations). Because the network is completely symmetrical, we can illustrate the effect of attribute set by applying this set either to the colors or to the positions. We applied the attribute set to the color module. The results of the simulations are presented in the right part of Table 1. Attribute set activation together with the simple stimulus ClPl resulted in a full 100% correct response production and a strong reduction of reaction times and of standard errors. Providing an attribute set, and not favoring any one of the available color responses in particular, apparently eliminates all ambiguity from this single stimulus and leads to the production of the appropriate color response.

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Inside Information, Part 1 One of the nice things about a model like this is that you can do more than just look at its behavior from the outside as if it were a human subject: you can actually open up the “black box” and see what is happening inside. Though every activation may be interesting on its own account, we select those that best illustrate the workings of the model. The most striking feature of the simulations with only a simple stimulus is the competition between attributes of the same stimulus object. In SLAM only the third level can display competition between different attributes of the same object. On the first level, the stimulus is not yet decomposed into separate attributes. On the second level, different attributes are represented in different modules and, therefore, cannot inhibit one another. Only when the object has been sufficiently split up, in order to be able to produce more than one response, may competition occur. Figure 4 shows the competition at the third level in the simple processing conditions (without attribute set). Such competition is indicated in the model, running with discrete timesteps, by oscillations of the activations of both RCl and RPl. The periodic variation may be used in the model as a detector of strong competition within a module. With discrete time steps the oscillations are characteristic of the negative feedback the equally activated elements provide to each other. If the mutual

0.025

c 0.020 2 t .; : 0.015

0.010

0.005

0.000

FIG. 4. Activation of RCl = RPl nodes (red and left) versus time in the high strength, short duration, simple processing condition.

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291

inhibition is reduced, the oscillation disappears, but the competition can no longer be solved completely. How, then, does the attribute set resolve this competition? This can best be seen from the activations at the second level. Figure 5 shows that with presentation of both a simple stimulus and an attribute set, the activations of Cl and C2 start off at the same level of activation (the attribute set activation) initially showing some competitive oscillation. The contribution of the stimulus to the activation of Cl gives Cl a lead over C2. The initial, unstable equilibrium between Cl and C2 is disturbed and Cl rises, while C2 is totally extinguished. The uninhibited Cl activation is then transmitted to the third level. We will call the process whereby two activations stemming from different sources (stimulation and instruction) are integrated within a module and eventually a relatively stable pattern of activation emerges: relaxation.3 The competing attribute (Pl in the position module) will only receive stimulus activation and remains at much lower values than the activation of Cl. The activation of RPl at the third level is totally inhibited by the higher RCl activation and only RCl will be produced as a response. In summary, selection is effected by the attribute set because it enhances the color activation above the position activation. Moreover, the attribute set activation of the other color does not lead to the production of this response, because it lacks stimulus activation. Competing Simple Stimuli and Attribute Set The presentation of a simple stimulus without any clear instructions led to indecision which could have been resolved by including instructions as attribute set activation. However, as we have argued before, one selection mechanism is not sufficient.4 This can be illustrated in the filtering experiment. In this experiment there is a second source of indecision. Here two forms with different colors are presented. One of the two competing colors (or forms or positions) has to be named. To show this second source of indecision-this time the indecision between the same 3 The instability is a result of the fact that the initial equilibrium arises from two strong counteracting inhibitions. It may be likened to a ball lying perfectly still on top of a hill, not knowing which side to roll off. Only when a small force, like an air current, is applied, will the unstable equilibrium be permanently disturbed and the ball will try to reach a stable equilibrium at some lower level. The development from an unstable equilibrium to a stable equilibrium may be called relaxation. 4 In this model, attribute selection alone may be sufficient for responding if only one object is presented, a situation that is impossible in the real world. This is because objects are already included in the mode1 as representations, segregated from their background. In the real world, object selection is also necessary when only one stimulus is presented, because of the ubiquitous presence of a background.

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I

I

I

1

I

I

2

4

6

0

IO

time

(iterations)

FIG. 5. Activations of Cl (red) and C2 (blue) versus time in the low strength, long duration, simple stimulation condition with an attribute set for color.

type of attribute of two different stimulus objects-let us consider SLAM’s behavior when confronted with two simple stimuli and an attribute (color) set. In the simulation, color 1 at position 1 (ClPl, with strength 0.8 and duration 6) and color 2 at position 2 (C2P2, with strength 0.8 and duration 6) were presented with the instruction to name the color (attribute set activation: Cl = C2, with strength 0.4 and duration 80). The left part of Table 2 gives the results. Again the unresolved competition is characterized by a relatively large number of no-response trials, approximately equal numbers of both competing responses, equally high reaction times, and high standard errors. Though the attribute set is effective in selecting the correct response category (no position responses are given), it does not help at all in selecting the specific color of one of the two stimuli. A stimulus object, to which a response can be made, must also be specified. Competing Simple Stimuli, Attribute Set, and Object Set Selection of the color of one of the two competing simple stimulus objects must be brought about by disturbing the balance between the activations at the first level, where representations are combined. This balance hinders the transmission of activations to higher levels. The basis

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TABLE 2 Responses (Number, RT, and SE) in the Simulation of a Filtering Experiment, both without and with Selection Cue for 50 Presentation Runs per Condition With selection cue

No cue

Presentation RCl RT SE RC2 RT SE RPl RT SE No response

22 31.7 4.8 17 30.1 3.8 11

Pl 0.1,0,80

Pl 0.05,0.80

FlPl 0.4,6,6

48 15.4 0.6 0 2 10.0 -

50 14.8 0.6 0 0 -

47 22.6 0.7 3 12.0 1.5 0 -

Note. FlPl stands for presentation of form 1 (e.g., circle) at position 1 (left) at the first layer of the network. P1(0.1,0,80) stands for presentation of position 1 (left) at the position module with strength 0.1, beginning at iteration 0 and for a duration of 80 iterations.

for this selection in SLAM, therefore, lies at the earliest stage of encoding (i.e., at the stage where combined, rather than unanalyzed, encodings are available). Object selection in SLAM is early selection (see e.g., Van der Heijden, 1981, 1984, 1987). The activation causing the imbalance, however, cannot come directly from the stimuli themselves and, because there are no inter-module connections on the same level, an element at a higher level must be the source of this activation. The higher level element in turn derives its activation from the cue that indicates what stimulus to select: the object selection cue. Two kinds of object selection cues can be distinguished in a typical filtering experiment: verbal selection cues and visual selection cues. The subject can be instructed beforehand to name the color on the left or to name the color of the square, etc. Alternatively, a visual cue (an arrow or a barmarker indicating a position) can be used. We assumed that auditory information, such as spoken instructions, activates the second layer. This was used for attribute set activation and should, for consistency, be used to represent spoken instructions to direct attention to forms, positions, or colors of particular objects. A barmarker, visually presented, has the same format as the stimuli and should, therefore, be consistently treated as a presentation of a form at a position in layer one. The operation of the model in a filtering task will be illustrated by three kinds of presentations. Competing simple stimuli (ClPl and C2P2 with strength 0.8, beginning at 0 and of duration 6; henceforth, we denote the

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latter triple with: 0.8,0,6) and an attribute set (Cl and C2 with 0.4,0, 80) will be combined with either a strong verbal instructional selection cue (Pl with 0.1, 0, 80), a weak verbal selection cue (Pl with 0.05, 0, HO),or a short duration (6) barmarker immediately following the stimuli (FlPl with 0.4, 6, 6). The results of these simulation runs (over 50 trials each) are shown in Table 2 under the heading of “with selection cue.” Table 2 shows that virtually all competition has been resolved by both kinds of instructional input and that only a few errors persist in two of the three conditions. Reaction times and standard errors are also considerably reduced. Inside Information,

Part 2

Both verbal and visual selection cues specifically increase the activation of one position in the position module at the second level (see Fig. 6). By recurrent connections downward to the color position module, the competition within this first level mapping is resolved, and the winning activation is then retransmitted upward to the color module. The resolution of competition (relaxation) at the first level is demonstrated in Fig. 7. The identity of the color that is transmitted depends on the combination of features presented as stimuli. If ClPl is presented and Pl is given as a selection cue, only Cl is transmitted. Activation of C2P1, though also excited by Pl, is prevented by the intramodular inhibition from CIPI.

0.5

g ‘Z .-2

0.4

$

0.3

--_-- _..__._

C2 (red)

------

Pl (left)

/ 2

I 4

I 6 time

I

I 10

(iterations)

FIG. 6. Second level activations for nodes Cl (red), C2 (blue), and Pl (left) with simple, competing, stimuli and attribute set and a strong verbal selection cue.

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ClPl

MODEL

FOR

295

ATTENTION

(blue-left)

C2P2 (red-right)

I 2

I 4

I 6 time

I a

/ 10

J

(iterations)

7. First level activations of the ClPl (blue-left) and C2P2 (red-right) nodes with simple, competing, stimuli and attribute set and a strong verbal selection cue. FIG.

This mode of operation represents a dynamic, state-dependent, threshold mechanism in SLAM. Within this configuration of connections the temporary activation that results from a stimulus acts as a kind of short-term memory for that stimulus (e.g., see Miyashita & Chang, 1988). At the second level the activations of both Cl and C2 are amplified by the attribute set activation, but that of C2 is inhibited by the greater Cl activation (see Fig. 6). Moreover, Pl stays at lower activation values than Cl and also RCI will inhibit RPl at the motor program level. The activation of the selection cue is sufficient to disturb the unstable balance at the first level, but not high enough to be produced as a response itself (see Fig. 8). We may conclude that in SLAM two selection mechanisms are sufficient for performing a filtering task with simple stimuli. Stimulation by Multiple Input Up to now each stimulus was presented to the model as the activation of a single element in the first layer. A realistic stimulus, however, generally possesses form as well as color and position. The presentation of a blue square on the left should lead in SLAM to the activation of the elements belonging to blue on the left, square on the left, and blue square. The analysis of the preceding sections can be repeated with this more realistic multiple input. This will, however, yield little new information, as all the operational principles remain unchanged. To illustrate this point,

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AND

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o.oa: .2z 0.0650

___-r_____--R(-2(“red”)

0.04-

0.02-

o.oo-

time

(iterations)

FIG. 8. Third level activations (RCl and RPl; all other are 0 for all t) with simple,

competing stimulation and attribute set and a strong verbal selection cue.

two further simulations are run. The first is a single stimulus consisting of ClP1, FlP1, and FlCl in layer one. Then the corresponding elements to ClPl, FlPl, and FlCl were all activated for six iterations with a high strength (0.8). The results (over 50 trials) are shown in Table 3. From this simulation it becomes clear that the multiple input leads to the same kind of indecision as the single, implausible input. Attribute uncertainty is now reflected by three feature modules with equal activations. The correspondence of Table 3 with Table 1 is quite clear. Only the number of no-response trials is reduced by the multiple input. This is a result of the increase in activation values at the second level due to the doubling of input to these elements caused by the multiple stimuli. A final simulation of an example of a complete filtering task with realistic stimuli shows these points very well. Presentation of realistic comTABLE

3

Responses with Presentation of a “Realistic” Presentation CIPl,FIPl,FlCl RCl RT SE No response

9 40.1 10.2

RF1 RT SE

Stimulus

(0.8,0,6) 14 40.9 6.0 17

RPl RT SE

10 43.2 6.4

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297

peting stimuli (ClPl, FlPl, FlCl and C2P2, F2P2, F2C2 with strength 0.4, beginning 0 and duration 6 iterations), an attribute set (Cl, C2 with 0.4, 0, 80), and a verbal selection cue (Fl with 0.05, 0, 80) leads to a 98% correct response production of color 1 with a reaction time of 14.3 iterations and a standard error of 0.66 iterations. This simulation shows that the uncertainty can be resolved by attribute set activation in one module, irrespective of the number of concurrently active modules. Competition among stimulus objects can be resolved by a selection cue as in the simple input conditions. The selection cue will enhance one of the two stimulus objects and the attribute set will select the response mode of the selected stimulus. Thus, the operation of SLAM does not change significantly if the network is provided with a stimulus consisting of multiple feature combinations instead of a single combination. Inside Information,

Part 3: Operational

Principles

In the last sections the model was run in a number of conditions and its processing in these specific instances was briefly discussed. Let us here distill the more general processing principles of SLAM: (i) Restricted parallelism because of modularity and competition. In SLAM, processing of stimuli and instructions is characterized by a restricted parallelism. Simultaneous, relatively independent, processing can only take place in separate modules. Within a module no parallel activity can persist for very long because of intramodular competition. If independent activations (e.g., belonging to stimuli and instructions) arrive at a module, activity in elements compatible with both inputs is amplified and activity in other elements is damped out. Instruction and stimuli are thus integrated at a modular level. This is a type of relaxation taking place in a finite time; (ii) Heterarchical processing in a hierarchical network. Encoding within the network is hierarchical. Each hierarchical level consists of a number of modules which are not directly interconnected. Modules from adjacent levels are, however, interconnected by bidirectional lines. All communication between modules at the same level must go via connections to modules at other levels. This allows for some within-level parallel activity. Communication between levels may proceed in either direction. Therefore, processing in SLAM is heterarchical, traversing a hierarchical structure; (iii) Early object selection. In the model, selection of one of two competing stimulus objects takes place at the lowest level where objects are encoded as combinations of attributes. It results from intramodule coordination of direct stimuli-related activation and indirect activation from instructions;

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(iv) Object selection by recurrent activations. In the model, instructional activations at the lower level of encoding come from higher levels of encoding. So recurrent connections are used here for disambiguating concurrent stimuli; (v) Attribute selection by simple intramodule coordination. SLAM performs attribute selection at the second level of encoding on the basis of intramodule coordination of direct instruction-related activation and indirect activation from the stimuli; (vi) Response times determined by modular relaxation. Response production times are based on the relaxation times of the intramodule coordination underlying both object and attribute selection. Transmission times, which play an important role in serial models, are only of minor importance. SIMULATION From Qualitative

to Quantitative

OF FILTERING Simulation

TASKS

Results

In the type of filtering task introduced earlier, three kinds of conditions can be distinguished: an incongruent condition, a congruent condition, and a control or single stimulus condition. In the incongruent condition two (or more) different attributes belonging to the attribute set are presented (e.g., the task is color naming and the stimulation consists of blue-left and red-right). In the congruent condition the stimuli are equal with respect to the attribute belonging to the attribute set (e.g., blue-left and blue-right). Finally, in the control condition either only one stimulus is presented or a relevant stimulus (e.g., blue-left) together with an irrelevant stimulus (a color, not belonging to the attribute set, right or a form right). A general indication of the results, obtained from SLAM, can be derived from some qualitative considerations. The activation patterns of the relevant modules for the three conditions are shown in Fig. 9. Two modules are shown: the color-position module from the first level with the color module at the second level above it. In the incongruent condition mutually inhibiting elements are activated in both the first and the second level modules. In the congruent condition mutually inhibiting elements will only be activated at the first level. At the second level no inhibition occurs for this condition. One element belonging to the attribute set even receives double input as a result of the congruency of stimulation. In the single condition neither inhibition nor double activation takes place, because no competing stimuli are presented. The large amount of inhibition in the incongruent condition leads to a considerable disadvantage in reaction times relative to the single condition. The double activation of the second level element, that is only slightly reduced by the intramodule

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299

INCONGRUENT:

SINGLE:

CONGRUENT:

FIG. 9. Activations in the color-position module and color module (see also Fig. 1) in different congruency conditions (only active lines are plotted).

competition at the first level, will, however, lead to a small advantage of the congruent condition over the single condition. The facilitation in the congruent condition will also be smaller than the interference in the incongruent condition as a result of the excitation/inhibition battle in these modules. These predictions are only qualitative. To arrive at quantitative results for the reaction times we must elaborate the model slightly. The reaction times in number of iterations have to be transformed to get reaction times in ms. If every iteration is assumed to represent a constant amount of time, the transformation will be linear: RT=A.m+B

(8)

The constant time per iteration will be called A. The intercept B represents an (approximately) constant amount of time consumed by processing in parts of the system that are not included in the model, such as, for

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instance, (nonselective) preparatory stages for response production. A coarse estimate of parameter B can be derived from filtering experiments that have different stimulus onset asynchronies (SOA) between the competing stimuli. The logic is as follows: the arrival of an interfering stimulus after time RT - B (when selection has been completed) should no longer influence reaction times for the target stimulus. Reaction time curves for the incongruent and single conditions will then meet at SOA = RT - B and stay at a constant level for longer SOAs. Fortunately, an experiment that provides relevant information has been performed by Glaser and Glaser (1982, Experiments 1 & 3). At a SOA of between 200 and 300 ms the incongruent and single conditions converge on reaction times between 400 and 500 ms. Fitting Experiment 1 of Glaser and Glaser (1982) to a simulation results in the following values for A and B. A = 25 ms per iteration B = 200 k 20 ms The A parameter performs two functions. It determines the number of iterations a stimulus has to be presented given the presentation duration in the actual experiment. It also calculates the reaction times in ms. The B parameter is only used for the latter purpose. For this reason the error margin of both parameters has been concentrated in the B parameter, so that presentation times could be completely accurate but confidence intervals can be specified for the reaction times. The effects of experimental variables that are not under control in the simulation, such as small changes in color, sizes, intensities, and distances, are assumed to be incorporated in this, rather large, interval. In the next sections, the behavior of the model in a number of filtering experiments is investigated in a quantitative manner. For every condition the program was run for 50 trials, with a maximum of 80 iterations per trial. This is equivalent to a single subject’s performance on the various experimental conditions. Different individuals may be represented by individual values of A and B and different connection weights. In all following simulations identical parameters are used: A and B as specified above and the rest of the parameters as specified in the Appendix. The overall standard error for the reaction times was obtained by combining the error in B and the standard error in the simulation results according to the standard formula for the propagation of errors with a linear relation (see Squires, 1968). S,, = (A* . S,* + Ss2)o.5 Goodness-of-fit of individual experiments will be assessed by comparing experimental and simulation results in terms of the overall standard error.

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The total fit will be evaluated by correlating all simulation and experimental results. Filtering with Attribute Set and Instructional Selection Cue A paradigmatic example of a filtering experiment with a verbal selection cue that uses no more than two alternatives per attribute class is Experiment IV of Van der Heijden (1981). In this experiment, both a disk (in the simulation: form 2) and a half-disk (form 1) were presented for 150 ms at different positions. The task was always to name the color of the half-disk. The combination of colors with disks and half-disks was either incongruent, congruent, irrelevant, or single. The irrelevant condition (the disk has a color not belonging to the attribute set) cannot be simulated in the model in its present form, because the model does not possess an irrelevant third color. For the simulation of this experiment the verbal selection cue enhanced the activation of form 1 in the form module. The attribute set always consisted of both colors (see Table 4). The confidence interval for the experimental results were estimated from the reaction times for the two positions of the three groups of subjects (Van der Heijden, 1981, p. 141). In Table 5 both simulation and experimental results are presented in terms of average reaction times, confidence intervals, and error percentages. Agreement of the experimental and simulation results in terms of reacTABLE 4 Presentation Scheme for Simulating Exp. IV of Van der Heijden (1981) Incongruent

Congruent

stim

str

sta

dur

stim

ClPl c2P2 FlPl F2P2 FlCl F2C2

0.4 0.4 0.4 0.4 0.4 0.4

0 0 0 0 0 0

6 6 6 6 6 6

ClPl ClP2 FlPl F2P2 FlCl F2Cl

Cl C2 Fl

0.4 0.4 0.05

0 0 0

80 80 80

str

sta

Stimulation 0.4 0 0.4 0 0.4 0 0.4 0 0.4 0 0.4 0

Single dur

stim

str

sta

dur

6 6 6 6 6 6

ClPl

0.4

0

6

FlPl

0.4

0

6

FlCl

0.4

0

6

Cl C2 Fl

0.4 0.4 0.05

0 0 0

80 80 80

Instruction and preparation Cl 0.4 0 80 C2 0.4 0 80 Fl 0.05 0 80

Note. Input patterns represent the three experimental conditions. All other elements (except PlX, the residual pretrial activity) have zero activation for t = 0 (stim, nature of stimulus; str, stimulation strength; sta, beginning of stimulation; and dur, duration of stimulation). For an explanation of symbols used for the representations (ClPl, Fl, etc.), see text.

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5

Simulation and Experimental Results of Exp. IV of Van der Heijden (1981) in Terms of RT (in ms), SE, and Error Percentage Simulation

Experimental

Incongruent RT SE % error

557 26 2

541 15 13.9

Congruent RT SE % error

487 22 0

474 22 0.7

Single RT SE % error

517 22 0

475 28 1.0

tion times, as far as the congruent and incongruent conditions are concerned, is reasonably good. The incongruent condition has higher reaction times and error percentages than the other two conditions. The simulated value for single stimuli does deviate considerably. Inspection of Table 5.1 in Van der Heijden (1981) and the standard errors in Table 5 show that such a deviation is not statistically surprising. Moreover, the simulation results show that SLAM is more accurate and somewhat slower, suggesting a different speed-accuracy trade-off. Barmarker Experiments

A filtering experiment with two alternatives per attribute class and a barmarker as a selection cue can also be found in Van der Heijden (1981, Exp. II). In this experiment competing stimuli consisted of either colorcolor, color-word, or word-word combinations. The words were all color names. The color-word combination, thus, constituted a Stroop task. The color or the word that had to be named was indicated by a barmarker. There were three conditions: incongruent, congruent, and neutral. The present simulation only concerns the color-color part of the experiment. Colors were always presented as colored disks (form 1). The barmarker was represented as form 2 at the position of the stimulus that had to be named (F2Pl). Both stimuli and barmarker started off at the same time and lasted for 150 ms (6 iterations). The three conditions (incongruent, congruent, and neutral) were simulated as in the previous section, except that the single condition was replaced by a neutral condition that had a form presented on the other position. This form, however, did not possess a definite color as in the congruent and incongruent conditions. The pre-

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MODEL FOR ATTENTION

sentation scheme for the three conditions is shown in Table 6. The experimental and simulation results are shown in Table 7. Values for the standard error of the experimental results were estimated from the differences between reaction times for the left and the right positions. Error percentages for the experimental results unfortunately could not be determined . The qualitative agreement between experimental and simulation results is good. At first sight quantitative agreement seems poor. The reaction times for the congruent and neutral condition do not lie within one standard error from the simulation results. The simulation reaction times, however, are on average about 40 ms less than the experimental reaction times. This trend may be due to the fact that an important factor in the experiment was neglected in the simulation. The color-color trials were embedded in other kinds of trials. This may have led the experimental subjects to be somewhat more cautious. In model terms the color-word trials may have left some residual activity. The activity, not compatible with stimuli presented in the new trial, will then have to be suppressed. We incorporated such an effect of nonhomogeneous trials in a simulation by introducing pretrial activity elements (representing residual activity from nonrepresented elements) at the mapping level as well, in order to attenuate the spread of activation through the network. The pretrial activity elements in the color position mapping (PCP), the form-position mapping (PFP), and the form-color mapping (PFC) were set at 1.Ofor one iteration before the start of the actual presentation. In order to synchronize the pretrial activation in the network the activation of the PTR eleTABLE 6 Presentation Scheme for Simulating Exp. II of Van der Heijden (1981) Incongruent

Congruent

stim

str

sta

dur

stim

ClPl C2P2 FlPl FlP2 FlCl FlC2 F2Pl

0.4 0.4 0.4 0.4 0.4 0.4 0.4

0 0 0 0 0 0 0

6 6 6 6 6 6 6

ClPl ClP2 FlPl FlP2 FlCl

Cl c2

0.4 0.4

0 0

80 80

F2Pl

str

sta

Stimulation 0.4 0 0.4 0 0.4 0 0.4 0 0.4 0 0.4

0

Neutral dur

stim

str

sta

dur

6 6 6 6 6

ClPl

0.4

0

6

FlPl FlP2 FlCl

0.4 0.4 0.4

0 0 0

6 6 6

6

F2Pl

0.4

0

6

Cl C2

0.4 0.4

0 0

80 80

Instruction and preparation Cl 0.4 0 80 c2 0.4 0 80

Note. Input patterns represent the three experimental conditions (all other elements, except PTR, have zero activation for t = 0). F2Pl is the barmarker presentation.

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TABLE 7 Simulation and Experimental Results of Exp. II of Van der Heijden, (1981) Simulation

Experimental

Incongruent RT SE % error

608 25 8

624 22 -

Congruent RT SE % error

511 26 0

568 22 -

Neutral RT SE % error

541 34 0

585 22 -

ment was also prolonged with one iteration (the PTR always automatically starts at 1.0 for f = 0). In Table 8 the new presentation scheme can be found. Simulation with this presentation scheme now yields higher reaction times (see Table 9). (All reaction times were taken from t = 1 instead of t = 0). Both qualitative and quantitative correspondence are TABLE 8 Input Patterns Representing the Three Experimental Conditions, with the Introduction of Modular Residual Activity, for the Simulation of Exp. II of Van der Heijden (1981) Incongruent stim

Congruent

str

sta

dur

stim

ClPl c2P2 FIPI FlP2 FlCl FlC2 F2Pl

0.4 0.4 0.4 0.4 0.4 0.4 0.4

1 1 1 1 1 1 1

6 6 6 6 6 6 6

CIPI ClP2 FIPI FlP2 FICI

Cl c2 PCP PFP PFC PTR

0.4 0.4 1.0 1.0 1.0 1.0

0 0 0 0 0 0

80 80 1 1 1 1

Neutral

sta

dur

stim

str

sta

dur

Stimulation 0.4 1 0.4 1 0.4 1 0.4 1 0.4 1

6 6 6 6 6

CIPI

0.4

1

6

FlPl FlP2 FICI

0.4 0.4 0.4

1 1 1

6 6 6

6

F2Pl

0.4

1

6

Instruction and preparation Cl 0.4 0 80 c2 0.4 0 80 PCP 1.0 0 1 PFP 1.0 0 1 PFC 1.0 0 1 PTR 1.0 0 1

Cl c2 PCP PFP PFC PTR

0.4 0.4 1.0 1.0 1.0 1.0

0 0 0 0 0 0

80 80 1 1 I 1

F2Pl

str

0.4

1

SLAM: A CONNECTIONIST

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MODEL FOR ATTENTION

TABLE 9 Simulation and Experimental Results of the Second Simulation of Exp. II of Van der Heijden (1981) Simulation

Experimental

Incongruent RT SE % error

632 22 2

624 22 -

Congruent RT SE % error

543 34 0

568 22 -

Neutral RT SE % error

559 33 0

58.5 22 -

now good. The introduction of pretrial activity raised reaction times by an average of 25 ms. An experiment with two alternatives per attribute class and an advance barmarker has been performed by Hagenaar and Van der Heijden (1986, Exp. II). The barmarker was presented first for 150 ms, followed immediately by the stimulus (duration 50 ms). During simulation an interesting problem became apparent. SLAM produces a small number of responses during barmarker presentation. In the actual experiment the subject receives an implicit additional instruction to name the color at the position of the barmarker only after the stimulus has been presented. The instruction to the subject to only engage in vocal activity after presentation of stimuli will have to be operationalized in some way. An obvious choice for this instruction is to prolong the pretrial motor program activity (F’TR element) during barmarker presentation to hinder response production during that interval. Because this activation is confined to the third level, activation belonging to the barmarker may show a considerable buildup at the lower levels during barmarker presentation. In fact, it is expected that the bat-marker activations will be well established after 150 ms, so that upon stimulus presentation interference from competing stimuli will be reduced relative to the preceding experiments. Therefore, differences between reaction times for the conditions are expected to be smaller, reaction times faster, and error percentages lower than, for instance, in Experiment II of Van der Heijden (1981; see Table 7). SLAM was confronted with the three experimental conditions in Hagenaar and Van der Heijden (1986, Exp. II): an incongruent, an irrelevant, and a single condition (see Table 10). As in the previous simulation

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PHAF, VAN DER HELIDEN, AND HUDSON

TABLE 10 Presentation Scheme for Simulating Exp. II of Hagenaar and Van der Heijden (1986) Incongruent

Irrelevant

stim

str

sta

dur

stim

ClPl C2P2 FlPl FlP2 FlCl FlC2 F2Pl

0.4 0.4 0.4 0.4 0.4 0.4 0.4

6 6 6 6 6 6 0

2 2 2 2 2 2 6

ClPl

Cl c2 PTR

0.4 0.4 1.0

0 0 0

80 80 6

str

sta

Stimulation 0.4 6

Single dur

stim

str

sta

dur

2

ClPl

0.4

6

2

FlPl FlP2 FlCl

0.4 0.4 0.4

6 6 6

2 2 2

FlPl

0.4

6

2

FlCl

0.4

6

2

F2Pl

0.4

0

6

F2Pl

0.4

0

6

Instruction and preparation Cl 0.4 0 80 C2 0.4 0 80 PTR 1.0 0 6

Cl C2 PTR

0.4 0.4 1.0

0 0 0

80 80 6

Note. Input patterns represent the three experimental conditions.

the representation for the irrelevant condition was a form without a definite color at the position not indicated by the barmarker. In the experiment the disk at this position, however, had a color that did not belong to the attribute set. Agreement between simulation and experimental results was not unreasonable (see Table 11). In accordance with expectations the difference between incongruent and single conditions has been reduced by the preceding barmarker presentation. Furthermore, there are fewer errors and TABLE 11 Simulation and Experimental Results of Exp. II of Hagenaar and Van der Heijden (1986) Simulation

Experimental

Incongruent RT SE % error

520 27 0

524 5 2

Irrelevant RT SE % error

499 22 0

500 5 1.6

Single RT SE % error

490 22 0

491 5 1.6

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FOR ATTENTION

307

faster reaction times than in the simulation represented in Table 7 (Van der Heijden, 1981, Experiment II). SOA and Filtering

As seen above, SLAM is sensitive to the timing of events. An important experimental factor that can be simulated with the model is the stimulus onset asynchrony (SOA) between competing stimuli. Experiment III of Glaser and Glaser (1982) manipulated the SOA between two competing colors. The subject was instructed to name either the first or the second stimulus. The position of the stimulus to be named must be derived from the temporal order and, therefore, a new kind of “selection cue,” besides the instructional and the barmarker selection cue, would have to be introduced. We will, however, only simulate Glaser and Glaser’s experiment for positive SOAs, so no selection cue is needed. If the color to be named is presented first, this color will be named in the absence of a selection cue. The time course of interference caused by the later arrival of a competing stimulus can then be determined. There are a number of relevant differences between Glaser and Glaser’s (1982) experiment and the experiments we simulated in previous sections of this paper. In Glaser and Glaser’s experiment stimuli occupied a considerable part of the visual field. Because it is likely that no localized forms were recognized in this experiment, the stimuli were not presented to SLAM as multiple input, but as colors on positions. Moreover, in the experiment stimuli were not presented for fixed durations, but stayed on until response onset. Because SLAM automatically ends a trial when a response is produced, it is possible to simulate this feature by presenting all stimuli for the maximum number of iterations (80). In Glaser and Glaser’s experiment color-color trials were again mixed with other trials (word-word trials). Therefore, we again need preactivation in the first (PCP) and the third (PTR) level for one iteration. Finally, the control condition was operationalized by presenting an irrelevant stimulus (a form) on the other position. The input patterns used for simulating the three conditions in Glaser and Glaser’s (1982) experiment are given in Table 12. Simulation and experimental results for this experiment are shown in Figs. 10a and lob. In the simulation runs no errors were made. In the actual experiment only a small number of errors (2.6%) was made. The standard error for the simulation results was about 27 ms and for the experimental results about 10 ms. Agreement between both kinds of results was reasonable (r = 0.9; N = 12). Nevertheless, the pattern exhibited by the incongruent stimuli differs importantly. SLAM is not susceptible to interference from the incongruent stimulus at longer SOAs. Also, it does not make a great difference whether an irrelevant stimulus helps the stimulus to be named (i.e., is congruent) or is neutral. A possible

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DER HEIJDEN, TABLE

AND

HUDSON

12

Input Patterns Representing the Three Experimental Conditions Incongruent

Congruent

stim

str

sta

dur

stim

str

ClPl C2P2

0.8 0.8

1

SOA + 1

80 80

ClPl ClP2

Stimulation 0.8 1 0.8 SOA + 1

Cl c2 PCP FrR

0.4 0.4 1.0 1.0

0 0 0 0

ii8 1 1

St8

Control dur

stim

str

sta

dur

i8

ClPl F2P2

0.8 0.8

1 SOA + 1

80 80

Cl C2 PCP PT’R

0.4 0.4 1.0 1.0

0 0 0 0

80 80 1 1

Instruction and preparation Cl 0.4 0 80 c2 0.4 0 80 PCP 1.0 0 1 P-CR 1.0 0 1

reason for the latter is that a single stimulus dominates the network so rapidly that a subsequent stimulus can no longer exert any great influence after about 100 ms. With the inhibitory pathway from level 2 to level 1 in place, a second stimulus is rapidly attenuated directly both from within by the previous stimulus itself and from above by the attributes associated with the previous stimulus. The elimination of this recurrent inhibition (see Appendix), as Grossberg (1984) suggests, may prolong the interfering effect of the subsequent stimulus. !iYMULATION OF THE STROOP TASK Task and Related Model

A task that can be considered as a filtering task, but now with non-

50

loo

200 SOA imsl

300

FIG. 1Oa. Simulation results for Experiment III of Glaser and Glaser (1982).

SLAM: A CONNECTIONIST ,

b fj50,

_

m * X-X

MODEL FOR ATTENTION

1

I

I

1

309

I

incongruent control congruent

600. -

500

-

LSO. I 50

I 100

I

I 200

I

1 300

SOA lmsi

FIG. lob. Experimental results from Experiment III of Glaser and Glaser (1982).

equivalent stimuli, is the Stroop task (Jaensch, 1929; Stroop, 1935). A Stroop stimulus consists of a color word printed in a color that does not correspond to the color word (e.g., the color word “red” printed in blue). Stroop (1935) compared the time required to name the colors on a card with a large number of Stroop stimuli (incongruent condition) with the total time required to name all colors on a card with the same number of color patches (control condition). Color naming was much slower in the incongruent condition than in the control condition. This delay is called the Stroop effect. Stroop also showed that reading the color words was not affected by the presence of incongruent colors. Dalrymple-Alford and Budayr (1966) showed the same effects for single Stroop stimuli. Sichel and Chandler (1969) included a congruent condition, in which the name of the color of the letters was the same as the word the letters spelled. Relative to the control condition the latencies were reduced for color naming, but no effect of congruency was observed in reading the color words. Dyer (1973a) transformed the task into a real filtering task by splitting up the Stroop stimulus into two separately located objects. He presented a black color word and colored Xs and Ss symmetrically spaced on either side of the fixation point. Effects of congruency were comparable, though smaller in size than with integral Stroop stimuli. The Stroop effect also shows up in other domains. Position words (top, bottom) interfere with the naming of positions, but not the reverse (Dyer, 1972; Hudson, 1977; Seymour, 1969; Shot-, 1970; Virzi & Egeth, 1985;

310

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DER HELIDEN,

AND

HUDSON

White, 1969). Flowers, Warner, and Polansky (1979) found interference of numerals on the naming of numerosity and no reverse effect. What these tasks seem to have in common is that there is (1) one particular stimulus aspect that is more closely related to the response mode (e.g., high compatibility) and (2) another stimulus aspect which is less strongly related (e.g., low compatibility). Responses to the more compatible aspect are not interfered with by responses to the less compatible aspect, while the response to the less compatible aspect is strongly hampered by responses to the compatible aspect. The diversity of the Stroop effect suggests a very general information processing structure that may be replicated many times in the system, not only in centers concerned with color naming and word reading, but also in centers that are concerned with such diverse activities as pushing buttons, locating objects, or telling pitch. It is clear that SLAM in its present form (i.e., for performing filtering tasks) is not immediately suited for performing Stroop tasks. No terms are present for sufficiently describing Stroop stimuli and, more importantly, nothing produces asymmetrical interference. The problems in adjusting the model are not, however, fundamental; any adjustments will be extensions rather than modifications. The two features of the Stroop task that must be incorporated in the model are: (a) Representations for congruency effects. Consistent with the filtering task both stimulus aspects of an orthodox Stroop stimulus (i.e., the color and the word) can lead to the same color name (congruent condition) or they can lead to different color names (incongruent condition). Motor program elements for naming both colors and color words are available in the present model (RCI and RC2). Only representations of color words at a visual level are missing and have to be included in the model. (b) Connections for asymmetry in the congruency effects. Large congruency effects are only observed with color naming, and not with word reading. Moreover, word reading is generally quicker than color naming. In the present form of the model all stimulus aspects are equivalent. The model is perfectly symmetrical. To simulate Stroop tasks, a stimulus aspect with a privileged status must be introduced. This special status (e.g., of words) reflects the compatibility with the type of response (e.g., reading aloud) that has been chosen and must be realized as a special relation between some stimulus representation and some response representation. Since relations in the model are expressed as connections, the Stroop asymmetry is implemented by a highly compatible connection between the visual representation of a word and its motor program. This “privileged link” is strongly related to and probably a part of the concept of the “privileged loops” of McLeod and Posner (1984). The introduction of visual representations of color words in the model is quite straightforward. All visual features are entered into the network

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311

FIG. 11. Schematic model of Van der Heijden (1981). V, Visual code, S, Semantic code, and A, Articulatory code.

through activation of nodes in the feature-feature mappings. At this visual level (color) words, or at least their visual word forms, must be encoded as new feature dimensions, because words can also be considered to have a particular visual form independent of their meaning, color, etc. (for neurophysiological evidence for visual word forms, see Posner, Petersen, Fox, dz Raichle, 1988). In line with our earlier assumptions we assume that every feature dimension is mapped to every other feature dimension. This then results in three new mappings at the first level: a position-color word mapping (e.g., left-“blue”); a color word-color mapping (e.g., “blue’‘-blue); a color word-form mapping (e.g., “blue’‘-square). Specific connections for implementing the Stroop asymmetry can be introduced along several lines (see Phaf, 1986). The theoretical model of Van der Heijden (1981) (see also Fraisse, 1980; and Warren & Morton, 1982, for identical proposals’) consists of three processing domains (see Fig. 11): a visual domain, a semantic domain, and an articulatory domain. In SLAM these domains may be mapped onto the three levels of the model. Visual code Semantic code Articulatory code

=> => =>

feature-feature mapping feature module motor program module

Most connections depicted in Fig. 11 are already implemented in SLAM. ’ Recently, a model containing similar modules and direct connections between visual word forms and articulatory codes has been proposed by Petersen, Fox, Posner, Mintum, and Raichle (1988) on the basis of Positron Emission Tomography of the working brain during single-word processing.

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PHAF, VAN DER HEUDEN,

AND HUDSON

For instance, the bidirectional connections between visual and semantic codes (bl in Fig. 11) are responsible for the actual filtering behavior and the dashed arrows leading to the semantic domain correspond to the instructional preparation in SLAM. The only connection that is missing is the direct connection between visual and articulatory domains (a in Fig. 11). Bypassing the feature level competition during “reading” may indeed be the most obvious way to explain the pattern of results in the Stroop task. Due to the modularity the extended model for Stroop tasks (Fig. 12) will continue to perform the filtering tasks in exactly the same manner as in the previous sections, without any need for weight modifications. Stimuli and instructions automatically address the relevant modules and additional irrelevant modules do not influence the behavior of the model. In this way SLAM conforms nicely to the contention of Van der Heijden (1981) that Stroop and filtering tasks are based on the same central selection mechanism. Stroop Performance

We are concerned with the type of Stroop task in which either one stimulus (i.e., a colored word) or two stimuli with nonoverlapping attributes (e.g., a black word and a color patch) are presented at a time. In apparent contradiction with our computational treatment of attention,

MOTOR PROGRAMME

MODULE

FIG. 12. The full model with schematized representation of all excitatory and inhibitory connections. See Appendix for all connection weights. New mappings at the first level as well as asymmetrical connections between the first and third level are added.

SLAM: A CONNECTIONIST

313

MODEL FOR ATTENTION

only one kind of instruction-the attribute set instruction-is sufftcient for SLAM to perform adequate selections in this task. It is important to see that this is a consequence of the “unrealistic” stimulus presentation in the model. Stimuli are presented as coherent sets of attributes (objects) that are already separated from their background. In many instances only one object is presented. In the model, competition between stimulus objects and other objects in the background has been eliminated by this manner of presentation. Only when experimental stimulus objects compete does the model need object selection. In the real world object selection is always needed, because the experimental stimulus also has to be selected from experimentally irrelevant objects. If the model contained parts for the initial analysis of the total visual field, the need for object selection in all cases would be obvious. Our wish to build the simplest possible attentional model (i.e., without initial visual analysis), therefore, leads to an artificial reduction of competition in the execution of the Stroop task. The preparation of the model for performing Stroop tasks will mostly consist of only one kind of attentional set: either (a) activation of both colors at the feature level (the color-naming instruction) or (b) of activation of both color names at the motor program level (the wordreading instruction). The first simulation concerns a generalized experiment with integral (color name with colored letters) Stroop stimuli and with reaction times to single stimuli in both reading and color-naming conditions. The presentation scheme is given in Table 13. Because we limited the number of elements (i.e., 2) per dimension, representations of neutral (noncolor) words are not available to the model. The neutral condition with color naming has been implemented by taking one of the two forms as the TABLE 13 Presentation Scheme for a Single Integral Stroop Task Incongruent

Congruent

stim

str

sta

dur

stim

ClPl PlW2 W2Cl

0.4 0.4 0.4

0 0 0

4 4 4

ClPl PlWl WlCl

str

sta

Stimulation 0.4 0 0.4 0 0.4 0

Neutral dur

stim

str

sta

dur

4 4 4

ClPl FlPl FlCl

0.4 0.4 0.4

0 0 0

4 4 4

0 0

80 80

Instruction Color naming Cl c2

0.4 0.4

0 0

Reading 80 80

RCl RC2

0.4 0.4

Note. W stands for word representation, which are color names in the model.

314

PHAF, VAN DER HEUDEN,

AND HUDSON

neutral word. The neutral condition with reading cannot be represented in the model in its present form. Table 14 shows the simulation results. The qualitative behavior of the model resembles the actual behavior of a human subject. The reading of the color words is appreciably faster than the naming of the colors (e.g., Gholson & Hohle, 1968). Naming of colors is strongly affected by interfering color words, with the largest latencies and error percentages in the incongruent condition. Reading of color words is not affected by interfering colors. Quantitative comparison of these results with experimental results is hindered to some degree by the absence of completely comparable experiments. No experimental study could be found that included reading conditions in the single integral Stroop task. Moreover, all Stroop stimuli in the experiments considered were presented until the response onset and thus had variable presentation times. In our simulation all stimuli were presented for a constant time (100 ms). Table 15 presents a number of comparable, but not completely equivalent, experimental results. Quantitative agreement between our simulation results and the experimental results is difficult to assess. Though the different experimental results cover a large range of latencies, experimental latencies are generally somewhat longer than in the simulation. In many of the experiments of Table 15 the results were determined on the basis of 4-choice reaction times, instead of the 2-choice reaction times found in the simulation. Moreover, there seems to be a somewhat larger incongruency effect in the simulation than that found in most of the experiments. Dyer’s (1973a) variation of the Stroop task (i.e., the separate bilateral presentation of colors and words) can also be simulated in the model. Two TABLE 14 Simulation Results for Single Integral Stroop Stimuli Color naming

Reading

Incongruent RT SE % error

655 26 6

353 21 0

Control RT SE % error

466 23 0

-

Congruent RT SE % error

436 23 0

343 21 0

SLAM: A CONNECTIONIST

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MODEL FOR ATTENTION

TABLE 15 Experimental Results for Single Integral Stroop Stimuli Color naming

Incongruent

Dalrymple-Alford et al. (1966) Dyer (1970) Dyer (1973b) Hintzman et al. (1972) Dalrymple-Alford (1972a) Dalrymple-Alford (1972b) Dyer (1974) Regan (1978)

Control

RT

S

% er.

RT

S

404 803 600

-

-10 -

381 658 510

-

799

-

694

-

881

54

<0.5

685

39

842 653 666

-

17.5 10

649 580 554

-

13.2

Congruent % er.

RT

S

-2 -

662 490

-

-

653

-

1.1

687

42

618 515 532

-

1.9 <<0.5 0.6 co.3

% er.

<<0.5 1.7 <0.3

presentation durations were used: 100 ms and 150 ms (4 and 6 iterations, respectively). The presentation schemes are shown in Table 16. Once again, the control conditions for reading cannot be represented. The control condition for color-naming instruction is represented by taking the other form as the neutral stimulus (e.g., a row of Xs). Table 17 presents the simulation results. Results obtained in the reading conditions were reported by Van der Heijden (1981, Exp. 1; see Table 18). The absence of congruency effects in the reading conditions is especially clear in the experimental results. In the simulation, however, there is a small difference but the times for both congruent and incongruent stimuli lie within the (standard error) confidence interval. TABLE 16 Presentation Scheme for Separate, Bilaterally presented, Stroop Stimuli Incongruent stim ClPl FlPl P2W2 FlCl

str 0.4 0.4 0.4 0.4

sta 0 0 0 0

Congruent dur 4; 6 4; 6 4; 6 4; 6

stim ClPl FlPl P2Wl FlCl

str

sta

Stimulation 0.4 0 0.4 0 0.4 0 0.4 0

Control dur

stim

str

sta

dur

4; 6 4; 6 4; 6 4; 6

ClPl FlPl F2P2 FlCl

0.4 0.4 0.4 0.4

0 0 0 0

4; 6 4; 6 4; 6 4; 6

Instruction Color naming Cl c2

0.4 0.4

0 0

Reading 80 80

RCl RC2

0.4 0.4

0 0

80 80

316

PHAF, VAN DER HELIDEN, AND HUDSON TABLE 17 Simulation Results for Single Separate Stroop Stimuli Color naming

Reading

Presentation

100 ms

150 ms

100 ms

150 ms

Incongruent RT SE % error

513 25 2

530 25 2

373 22 12

369 22 6

Control RT SE % error

472 23 0

503 25 0

-

-

Congruent RT SE % error

447 23 0

483 25 0

350 21 0

351 21 0

The results of a number of Stroop experiments with separate bilateral presentations of word and color, and color naming as the task are listed in Table 19. A comparison of Tables 17 and 19 shows that the qualitative behavior of the model is in close agreement with the experimental results. Naming times observed in the experiments are somewhat higher than in the simulation results. Part of this difference can be explained by the longer presentation times in the actual experiments. Both in the simulations and in the experiments the overall latencies increase with increasing presentation time. In SLAM the resolution of intramodular competition is delayed by the sustained activation of the competing elements. Prolonging the stimulation, therefore, leads to longer reaction times. Integral

vs. Separate Stroop Stimuli

An interesting phenomenon apparent in both experiment and simulation is that the effects of congruency are greater with integral than with separate Stroop stimuli. We first attempt to replicate this phenomenon in the model by using the simple presentation form and then explaining it in TABLE 18 Experimental Results of Van der Heijden for the Reading Condition in a Separate Stroop Task (1981, Exp I; 150 ms Presentation)

RT SE % err.

Incongruent

Control

Congruent

369 6.3

364 3.1

367 4.7

SLAM: A CONNECTIONIST

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MODEL FOR ATTENTION

TABLE 19 Experimental Results for Single Separate Stroop Stimuli Color naming

Incongruent

Presentation time Dyer (1973a) (100 ms) Kahneman & Chajczyk (1983) (200 ms) Kahneman & Chajczyk (1983) (200 ms) Kahneman & Chajczyk (1983) (until response)

Control

Congruent

RT

SE

% er.

RT

SE

% er.

RT

SE

% er.

558

-


510

-


484

-


682

-

2.6

610

-

0.8

561

-

0

692

-

-

595

-

-

539

-

-

706

18

4.6

637

14

0.6

588

-

0.1

terms of the model. A simple stimulation form leads to simpler activation patterns and so the cause of the effect can be demonstrated more clearly. Integral and separate Stroop stimuli were presented to the model for 150 ms (see Table 20). Figure 13 shows that irrelevant attributes, belonging to the same object, display more interference than irrelevant attributes belonging to another object. Preventing selection of irrelevant attributes is more difficult when they belong to the same object, Selection is apparently object-based. Object selection in the model depends upon the fact that at the first level there is a combined representation of attributes (e.g., a combination of color word and color representations). The increasing activation of the winning color in the second level color module enhances, through the recurrent connections, not only the relevant color but also the activation of the irrelevant color word, because color and word are combined at the TABLE 20 Simple Presentation of Integral and Separate Stroop Stimuli Incongruent

Congruent

stim

str

sta

dur

ClPl P2w2

0.4 0.4

0 0

6 6

ClW2

0.4

0

6

stim

str

sta

Control dur

stim

str

sta

dur

Separate stimulation ClPl 0.4 0 P2Wl 0.4 0

6 6

ClPl FlP2

0.4 0.4

0 0

6 6

Integral stimulation ClWl 0.4 0

6

FlCl

0.4

0

6

Color-naming instruction Cl 0.4 0 80 c2 0.4 0 80

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625. -

575. is r

525. -

k L75. -

I

congruent

I

incongruent

I

neutral

FIG. 13. Simulated reaction times with simple stimuli for the Congruent, Incongruent, and Neutral (or control) conditions with simple integral (same position) and separate (different position) Stroop stimuli.

first level. In a sense the presentation time of the irrelevant attribute is prolonged by the feedback from the relevant attribute. In the separate condition such a feedback does not occur because relevant and irrelevant attributes are already split up at the first level. The activations of the visual word forms at the first level are allowed to decay independently of the color activations. The conclusion that selection is object-based has been drawn before from several experiments (Allport, 1987; Duncan, 1984; Kahneman & Henik, 1981; Kahneman & Treisman, 1984; Neumann, 1987; Van der Heijden, Hagenaar & Bloem, 1984). We simulated the three experiments reported by Van der Heijden et al. (1984) which were in part replications of Kahneman and Henik (1981). They are suited for simulation by SLAM because only two relevant colors were used. In these experiments integral and separate presentation were combined within one stimulation. A colored word and a “noncolored” (black or brown) word were presented simultaneously for 150 ms above and below the fixation point. Subjects had to name the color of the colored word. If only the color were to be selected, the position of the irrelevant words should not matter much; interference of noncolored words would be the same as the interference of colored words. With object selection, however, interference from the latter would be much greater than from the former. A small problem in preparing the network for this task is how accuracy can be maintained as it is explicitly instructed to the subject. To introduce

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FOR ATTENTION

a more cautious criterion, the pretrial activation (FTR) at the third level was extended during a part of stimulus presentation. This should prevent the buildup of motor program activations for some time and allow competition at lower levels to proceed a stage further. This preparation will be maintained in all conditions, because the conditions are mixed and the subject cannot know beforehand which condition will be presented. The construction of the remaining part of the presentation scheme is quite straightforward (see Table 21). The congruency of the relations between color and word of the colored word is called internal congruency; the relation between the color of the colored word and the name of the distant word is called external congruency. Mean latencies and error percentages (in parentheses) are presented in Table 22. All standard errors equalled about 25 ms. The experimental results are shown in Table 23. As could be expected from previous simulations the internal congruency has by far the greatest effect on color naming. These experiments thus warrant the conclusions of Kahneman and Henik (1981) cited by Van der Heijden et al. (1984, p. 460) that: “(1) Attention can be directed only to preattentively defined perceptual objects. It is extremely difficult or impossible to restrict attention to the relevant properties of an object. (2) Attention facilitates all the responses associated with properties or elements of the selected object.” Kahneman and Henik (1981), however, also present a third conclusion: Presentation

TABLE 21 Scheme for Exp. I of Van der Heijden et al. (1984)

Internal incongruent (Ii) stim

str

sta

Internal congruent (Ci) dur

stim

str

Internal neutral @Ii)

sta

dur

stim

str

sta

dur

0 0 0 0

6 6 6 6

ClPl FlPl l’2W2 FlCl

0.4 0.4 0.4 0.4

0 0 0 0

6 6 6 6

External incong. W

ClPl PlW2 P2w2 ClW2

0.4 0.4 0.4 0.4

0 0 0 0

6 6 6 6

Stimulation ClPl 0.4 PlWl 0.4 F2w2 0.4 ClWl 0.4

External neutral We)

ClPl FlP2 PlW2 ClW2

0.4 0.4 0.4 0.4

0 0 0 0

6 6 6 6

ClPl FlP2 PlWl ClWl

0.4 0.4 0.4 0.4

0 0 0 0

6 6 6 6

ClPl FlPl FlK! FlCl

0.4 0.4 0.4 0.4

0 0 0 0

6 6 6 6

External congr. (Ce)

ClPl PlW2 F2Wl ClW2

0.4 0.4 0.4 0.4

0 0 0 0

6 6 6 6

ClPl PlWl P2Wl ClWl

0.4 0.4 0.4 0.4

0 0 0 0

6 6 6 6

ClPl FlPl l’2Wl FlCl

0.4 0.4 0.4 0.4

0 0 0 0

6 6 6 6

Color -naming instruction Cl 0.4 0 80 c2 0.4 0 80

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TABLE 22 Simulation Results of Exp. I of Van der Heijden et al. (1984)

Ie Ne Ce Average

Ii

Ni

Ci

Average

736 (2) 718 (0) 732 (0) 729 (0.7)

634 (0) 627 (0) 622 (0) 628 (0)

609 (0) 603 (0) 565 (0) 592 (0)

660 (0.7) 649 (0) 640 (0)

Note. Ii, Ni, and Ci are the internal congruency conditions and Ie, Ne, and Ce are the external congruency conditions.

“(3) The focussing of attention on the relevant visual object prevents the involuntary reading of a distant color word.” This conclusion was made on the basis of their finding that external congruency had no effect or only a minor effect on latencies and it is corroborated by Van der Heijden et al. (see Table 23). In the simulations the effect was of the same order of magnitude: very small (see Table 22) and within the standard error interval. Van der Heijden et al. (1984) explained their remaining small effect by assuming that in some instances the wrong object was selected initially. This explanation was tested by removing the uncertainty about which object has to be selected. Van der Heijden et al. (1984) did so by preexposing a barmarker pointing to the position of the colored word. Simulating this experiment is quite simple: the presentation scheme is essentially the same as for the preceding experiment. A bar-marker (F2Pl) pointing for 2 iterations (50 ms) at the position of the colored word was added and pretrial activity was extended to 8 iterations (50 + 150 ms). All other presentations were delayed until barmarker offset (i.e., started at t = 50). Table 24 shows the simulated reaction times with a standard error of about 25 ms and error percentages (in brackets). Table 25 shows the experimental results. In both experiment and simulation the resolution of spatial uncertainty removes the (possible) interference caused by distant words. In the simulation this is accomplished by the extra activation provided to a position by the barmarker. Through recurrent connections TABLE 23 Experimental Results of Exp. I of Van der Heijden et al. (1984)

Ie Ne Ce Average

Ii

Ni

Ci

Average

738 (IO) 737 (5.2) 742 (10) 739 (8.7)

667 (1) 646 (3.1) 646 (3.1) 653 (2.4)

635 (2) 605 (0) 603 (1) 614 (1)

680 (4.5) 663 (2.8) 664 (4.8)

SLAM:

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Ii Ne Ce Average

321

FOR ATTENTION

TABLE 24 Results of Exp. II of Van der Heijden et al. (1984)

Simulation

Ie

MODEL

728 762 760 750

Ni (2) (2) (2) (2)

630 625 628 628

Ci (0) (0) (0) (0)

596 584 566 582

Average (0) (0) (0) (0)

651 (0.7) 657 (0.7) 651 (0.7)

this position enhances all attributes that are connected to this position. Competing stimuli (e.g., distant words) at other positions are quickly inhibited by attributes at the indicated position. The activation of the colored word is amplified by the barmarker at the expense of the activation of the distant word. Purely on the basis of the last two experiments, Kahneman and Henik’s (1981) third conclusion that distant color words are not read can still be upheld. However, inspection of the model’s behavior shows that for the model at least this conclusion is not correct. The second experiment merely shows that with an appropriate focussing of attention the distant color word no longer affects the relevant activations. It does not, however, show that all involuntary reading of distant color words is prevented after selection of an object. Van der Heijden et al. (1984) set out to demonstrate in a third experiment that the distant word was indeed read. They argued that the distant color word is initially available as a response, but that it subsequently decays during postidentification central processing and, therefore, goes unnoticed. By reducing the processing time, the effect of the distant word may become visible. This reduction can be achieved by eliminating the internal congruency conditions (e.g., Keele, 1972; Klein, 1964). In their experiment, all colored words were now neutral. The rest of the experiment was the same as the second experiment. The prediction is clear. If conclusion (3) of Kahneman and Henik is valid, no effect of external congruency will be found. If, however, an effect is found, conclusion (3) is invalidated. TABLE 25 Results of Exp. II of Van der Heijden et al. (1984)

Experimental Ii Ie Ne Ce Average

786 772 784 781

(7.3) (10) (19) (12.2)

Ni 690 681 695 689

(4.1) (0) (2.1) (2.1)

Ci 657 652 656 655

(0) (1) (3.1) (1.4)

Average 711 (3.8) 702 (3.8) 712 (8.0)

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In the simulation there is no more need to provide pretrial activity during stimulus presentation. Because the internal congruency conditions have been replaced by neutral ones the most difficult condition (Ii, Ie) has been eliminated. Adopting a less cautious criterion lowers reaction times considerably and can, therefore, have the effect that was postulated by Van der Heijden et al. (1984): reduction of postidentification central processing time. The presentation scheme is the same as for the Ni condition in the previous experiment, except that the pretrial activity (PTR) now continues only during barmarker presentation (2 iterations: 50 ms). Both simulation and experimental results (see Table 26) clearly falsify conclusion (3) of Kahneman and Henik (1981). Distant color words are read involuntarily and can lead to measurable effects if their influence is not inhibited by “closer” color words. Comparison of the results of Experiment III of Van der Heijden et al. (1984) with the results in the Ni condition of Experiment II shows a remarkable difference, given the fact that the stimuli for both were completely the same. In the experiments the difference was in the combination of conditions presented to the subjects; Ni, Ii, and Ci in Experiment II and only Ni in Experiment III. In the simulations only preparation differed. In Experiment II the pretrial activation was continued throughout stimulus presentation and in Experiment III pretrial activation was only administered during barmarker presentation. The application of a more cautious criterion leads to longer latencies and fewer effects of external congruency. SOA and Stroop

A model that produces the behavior in time for Stroop tasks should also be able to account for the interaction between relative timing of stimuli, interference, and instruction. The relation between these variables was examined by Glaser and Glaser (1982, Exp. I). They varied both the SOA between color and word and the instruction (to name either colors or words) as well as the congruency between word and color. Some aspects of the presentation scheme (see Table 27) that must be discussed before presenting the results of the simulation are: TABLE 26 Simulation and Experimental Results of Exp. III of Van der Heijden et al. (1984)

Ie Ne Ce Average

Simulation

Experimental

Ni

Ni

517 (0) 484 (0) 480 (0) 494 (0)

594 (6.1) 560 (4.1) 555 (2.8) 570 (4.3)

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TABLE 21 Presentation Scheme for Simulation of Exp. I of Glaser & Glaser (1982) Congruent

Incongruent stim

str

sta

dur

stim

C2P2 PlWl

0.8 0.8

SOA SOA

80 80

C2P2 PlW2

Control

sta

dur

stim

str

sta

dur

Stimulation 0.8 SOA 0.8 SOA

80 80

C2P2 FlPl

0.8 0.8

SOA SOA

80 80

str

Instruction Reading

Color naming Cl c2 ITR

0.4 0.4 0.2

0 0 0

80 80 SOA

RCl RC2 PTR

0.4 0.4 0.2

0 0 0

80 80 SOA

(i) The color in the experiment of Glaser and Glaser (1982) occupied a large part of the visual field and actually formed the background for the words. A color was, therefore, presented on a position without specifying the form of the color. A stimulation strength of 0.8 was taken for these stimuli in order to keep the resulting activations at a comparable level. (ii) In Experiment I of Glaser and Glaser (1982) all stimuli were presented until response onset. Therefore, in the simulation all stimuli were presented for the maximum number of iterations. (iii) SOA in Table 27 stands for zero iterations for whichever stimulus has been chosen as relevant. For the other, irrelevant, stimulus SOA takes the value appropriate to the number of iterations between irrelevant and relevant stimuli. Glaser and Glaser allowed for SOAs of - 400, - 300, - 200, - 100, 0, 100,200,300, and 400 ms. With color naming the word is the irrelevant stimulus. With reading the color is the irrelevant stimulus. The first stimulus, whether relevant or irrelevant, always starts at zero time (SOA = 0), the second starts at t = SOA. Reaction times are of course defined as the time since (relevant) stimulus onset. (iv) Again no control condition with reading instructions can be included in the simulations, because of the lack of a representation for a third word. The reading conditions have, therefore, only been simulated in the incongruent and the congruent conditions. (v) A pretrial activation has been applied during the SOA whenever the irrelevant stimulus came first. In the experiments this additional preparation was induced implicitly by means of the instructions; if with color naming a color word was presented first, the subject was implicitly instructed to refrain from producing a response until the color was presented. The simulation results with color naming and word reading are shown

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in Figs. 14a and 14b. All standard errors were about 25 ms. In the colornaming conditions all error percentages remained below the 2% level. In the reading conditions these error percentages were somewhat higher due to the direct application of attribute set activation to the motor program level. The experimental reaction times show a comparable pattern (see Figs. 14 and 15, r = 0.968; df = 39). In Fig. 14 we see a slightly reversed Stroop effect for negative SOAs. This effect is, however, smaller than the normal Stroop effect. A similar tendency is present in the Glaser and Glaser results. An effect for negative SOAs, more similar in magnitude to the simulations, has been reported by Neumann (1980). Nevertheless, the effect in the simulation does stand out in comparison to the original Glaser and Glaser results. Stroop’s (1935) original explanation was a Horse-Race model involving differences in timing for different stimulus dimensions (i.e., faster word reading than color naming). Many Stroop studies have had similar explanations (e.g., Cohen & Martin, 1975; Klein, 1964; Morton, 1969; Morton & Chambers, 1973; Palef & Olson, 1975; Poker & Snyder, 1975; Proctor, 1978; Regan, 1978, and Seymour, 1977), most of which have three assumptions in common: (a) processing of color and word occurs in parallel in separate channels up to a late stage (usually the response stage); I

a

I

I

I

I

I

I

750.-

650.-

350. -

n I x-x

incongruent control congruent I

I

I

4

-3

-2

i

I

I

I

-1 0 1 2 3 SOA (x100ms) FIG. 14a. Simulated reaction times for the color-naming conditions of Experiment I of Glaser and Glaser (1982).

SLAM: A CONNECTIONIST b

I

I

I

MODEL FOR ATTENTION I

I

I

I

325

1

L50. -

LOO. -

300.

-

250.

-

o--o x---x I -L

Incongruent congruent 1 I I I I I -3 -2 -1 0 1 2 SOA (X lOOmsI

I 3

FIG. 14b. Simulated reaction times for the word-reading conditions of Experiment I of Glaser and Glaser (1982).

(b) words are processed faster than colors; and (c) interference occurs at the stage where color and word meet (i.e., when the channels converge) only when the incorrect response arrives first. In these explanations color naming is interfered with because the incorrect word response generally arrives first and has to be overcome. Word reading is not interfered with because the word response arrives first. The three assumptions are also features of SLAM: (i) Though word and color representations are combined at the first level (i.e., the attribute-attribute level), they are subsequently processed in parallel separate channels and meet again at the response stage. (ii) The simulations confirm that words are processed faster than colors (e.g., Table 14). (iii) The simulations also show that interference at the response level (and at lower levels) plays an important role in the Stroop effect. Two important questions are: (A) Is the Horse-Race model a sufficient explanation for the phenomena observed in the Stroop task, and especially for the asymmetry in interference between color naming and word reading? (B) Are the conditions specified by Horse-Race models sufficient to

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D-------o incongruent -control *congruent

600

reading

450.

LOO.

350; -LOU -300 -200

-100 0 100 200 300 SOA (ms.1 FIG. 15. Experimental reaction times for the reading and color-naming conditions of Experiment I of Glaser and Glaser (1982).

explain the Stroop asymmetry when SLAM performs color-naming and word-reading tasks? It is particularly the experimental results which effectively falsify the Horse-Race model, because such a model predicts substantial congruency effects in the reading conditions for negative SOAs. A recent overview of much of the experimental literature that contradicts models based solely on relative speeds of processing can be found in Dunbar and MacLeod (1984). In this context it is interesting to see that while SLAM fulfills the three necessary conditions for a Horse-Race model, it shows relatively little racing behavior. Reaction times in the model are not based on transmission times (as in a Horse-Race model) but on the relaxation times of several intramodule competitions. The shorter reaction times to words (with reading aloud) are caused not so much by the shorter connections (which would account for a time difference of only 1 iteration: 25 ms) but by the absence of an intermediate level as a potential source of competition.

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Reversing the Stroop Asymmetry

In terms of the model, a tentative explanation for the asymmetry in Stroop interference may be that the word produces so much activation at the motor program level that the color simply does not get the chance to interfere. On the other hand the (color) word activation may be so big that it will always interfere with color naming. This explanation can be tested by reducing the stimulation strength of the word which should then lead to a reduction of the normal Stroop effect and the development of a reversed Stroop effect. Experimental testing of this explanation requires the identification of an experimental manipulation (e.g., reduced legibility of words) with reduced activations of word representations in the model. Legibility of words has been reduced experimentally in a number of ways. Gumenik and Glass (1970) and Dyer and Severance (1972) simply masked the words; Van der Heijden (1981, Exp. III) shortened presentation time of the words to 50 ms. Dunbar and MacLeod (1984) presented words upsidedown and backwards; Gatti and Egeth (1978) and Merikle and Gorewich (1979) varied legibility of the words by varying the distance to the fixation point. Because of the speculative nature of the identification of legibility reduction with activation reduction we have not simulated particular experiments, but only investigated the qualitative effects of activation reduction. We will not present the detailed simulations here (see Phaf, 1986). The simulations with simple integral Stroop stimuli showed the normal Stroop interference vanishing with decreasing presentation strength and even some indication of Stroop reversal. The difference between reading times of color words with incongruent and congruent colors was consistent yet very small, but it showed a trend to increase with decreasing activation. Reading times increased considerably with reduced activation. Much of the experimental evidence points in the same direction. There is no really convincing proof for a Stroop reversal in these experiments. Gumenik and Glass (1970) did find a Stroop reversal, but their experiment suffered from methodological flaws, which were corrected by Dyer and Severance (1972). They found a smaller (nonsignificant) trend towards Stroop reversal. Both studies showed an increase in reading times with masking. Moreover, Gumenik and Glass (1970) also found a clear increase in color-naming times and a decrease in the normal Stroop effect with masking. Van der Heijden (1981, Exp. III) found no Stroop reversal but a small reduction of the normal Stroop effect and a considerable increase in reading times. Finally, Dunbar and MacLeod (1984) did find a reversed Stroop effect. In all experiments, reading times increased with reducing legibility, but the normal Stroop effect remained completely unaffected by their legibility manipulation. Therefore, the experimental evidence for the

328

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production of a reverse Stroop effect by a reduction of legibility is inconelusive . Obviously an important factor in Stroop interference is the instructional preparation. The instruction to name colors produces interference, while the instruction to name color words produces no interference. Stimulation as well as preparation may be changed in the model. What will happen if, in the reading conditions of the Stroop task, not only the color words but also the colors are made relevant for task performance? Translated in SLAM’s terms, what will happen in the reading conditions when there is also attribute set activation (though less than that found in the motor program module) in the color module? Because the preparational factor in attentional tasks is not generally recognized, it is hard to find experiments that manipulate that factor. We are aware of only one pure attempt to reverse the Stroop effect along these lines: the study of Virzi and Egeth (1985). This study was, however, not concerned with color naming but with naming positions. The color words were replaced by position names (right and left). The simulations concerned a largely comparable experiment with color naming. The task simulated was reading the color words from integral Stroop stimuli and involved attempting to make the irrelevant dimension more relevant. The Stroop stimuli were preceded by color patches at the fixation point that perfectly predicted the color of the Stroop stimulus. The double preparation was simulated by providing both attribute set activations to the relevant set and (with smaller strength) to the irrelevant set for the first response. A detailed account of the simulations can be found in Phaf (1986). The different nature of experiments and simulation only allowed for qualitative comparison of both results. Both experimental and simulation results, however, showed a consistent reversal of the Stroop effect both for the reaction times and the error percentages. Another experimental example of such Stroop Reversal may be found in Glaser and Glaser (1982, Exp. 2; see also Logan & Zbrodoff, 1979). If the irrelevant color was made more relevant by having it correspond to the relevant word (congruent condition) in 80% of the trials, it was found to interfere with reading in the incongruent conditions. Preparational factors may be more important in reversing the Stroop effect than stimulus factors such as relative timing and presentation strength. Inside Information,

Part 4: Further

Operational

Principles

Extending SLAM with additional modules to perform Stroop tasks does not change its behavior in filtering tasks. In simulations of filtering tasks no activations are presented to first level modules that contain word representations. Any activation that reaches these first level modules must come from the color, position, or form modules and will affect both

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word representations equally. Due to the mutual inhibition these activations will be at a low level. Because the direct connection between first and third level only contributes to the activation of a particular (color) word at the response level after this word has won the competition at the first level, no effects of the extension are expected. The possibility for making quantitative simulations in such a simple connectionist model may only follow as a consequence of the intrinsic modularity of this approach. The incorporation of limited parts of the total system in the model is sufficient to simulate a specified range of tasks completely. Modules not implemented in a particular model may receive activations, but they do not influence task execution as a consequence of the indirect nature of these activations and the large amounts of competition within it. Put in other words: we do not need to specify the total network in order to perform a restricted class of tasks. From these considerations it will be clear that the operational principles presented earlier also remain valid for the extended model. For the successful execution of Stroop tasks by SLAM two further operational principles can be formulated: (i) Activations belonging to stimulus dimensions compatible to the response are passed through directly (without much intermediate processing) to levels concerned with initiation of response production. (ii) Interference with a noncompatible stimulus dimension mainly takes place at the second level of encoding. Interference effects at the third level are found only when some of the corresponding second level activations are enhanced by attribute set activation. If attribute set activation is applied to the third level, no effects of second-level interference can be found on response production. DISCUSSION SLAM demonstrates attentional behavior. It can produce orderly behavior on the basis of ambiguous stimulation. Like an experimental subject, the model only requires stimuli and instruction to arrive at a decision, Thus, a mechanistic model of selection has been implemented in SLAM. A linear transformation on the number of iterations was shown to produce realistic reaction times. Our intention was to construct a model of a single subject which would demonstrate appropriate qualitative behavior in filtering and Stroop tasks. The addition of quantitative interpretations may be tested in a variety of ways. Given that the parameter values were the same for all simulations, we find the overall correlation between experimental and simulation results to be quite high. Over the total of 15 simulated experiments the correlation between results and simulations is 0.88 (df = 14, slope = .91, intercept = 19.6 ms). The experi-

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mental means range between 320 and 700 ms. In most studies we take little notice of absolute differences between experiments attributing these to local variations in set-up and experimenters. Ignoring these variations SLAM gives a reasonably accurate description of a number of different experiments with a wide range of response latencies using only a single parameter set. Taking all conditions within experiments into account the correlation is 0.92 (& = 92, slope = 0.97, intercept = 12.7 ms). Such correlations do not, of course, always do justice to the detailed patterns of reaction time results. One should not place too high a value on the agreement in absolute reaction time levels. Ultimately, the qualitative results as shown in the total pattern of results can demonstrate that the mechanisms postulated are adequate. Realistic patterns of errors were found over experimental conditions, insofar as individual subjects may produce such error rates. Error rates and reaction times are positively correlated. Nevertheless, SLAM’s error rate is considerably lower than the average error rate. This may reflect a particular position on the speedaccuracy trade-off function. Multiplying the maximum allowable activation level and all weights by a constant will produce higher error rates and faster reaction times in SLAM. The recovery procedure then becomes more sensitive to transient activations. Such a manipulation, however, alters an individual’s overall relationship between errors and reaction times, but not the pattern of errors over conditions and the positive correlation with reaction times over experimental conditions (see Hunt & Lansman, 1986, for a further exploration of these issues in a productionsystem model). Considerable changes in excitatory connection weights may be carried through without affecting the qualitative behavior of the model. Overall strengthening somewhat increases the activations and alters the speedaccuracy position but not the amount of competition in the modules. This may be compared to the observation made previously that altering the stimulation strength does not greatly affect the performance of the model. SLAM’s behavior is viewed on the basis of intramodular competition and does not change very much as long as the pattern of competition is preserved. Differential weight changes are also not very critical as long as the ordering of the magnitudes is maintained. Weight variations between networks may reflect variations in performance that may also exist between individuals (see Yee & Hunt, 1988, for individual differences in Stroop performance). The magnitude of the intramodule inhibition is, however, important and should not come above a level of about - 1.O in order not to prevent the complete resolution of competition. Above this level the oscillation, which serves as an indicator for strong competition, also vanishes. Without strong inhibition, for instance, the integration of preparation and stimulation cannot be achieved properly. The number of errors

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would increase intolerably and RT differences would become much smaller. Other parameters, such as the maximum and minimum activation levels, are arbitrary scale constants or have effects that can easily be compensated by adjustments to other parameters (e.g., the decay parameter affects the time per iteration A). Some characteristics of SLAM that are due to the gross architecture of the model and that cannot be altered by parameter changes may be seen from the comparison to alternative models. A model, somewhat similar to our own, but couched in a combined production system/connectionist framework, is the attentional and problem-solving model of Hunt and Lansman (1986). Similarities lie in the role of competition, combined representations of words and colors, and direct connections between visual and articulatory representations for words as opposed to the indirect connections via the semantic system for colors. In terms of SLAM, attribute selection can be performed successfully with the Hunt and Lansman model, but object selection can not. It lacks positional representations and tasks that specifically require object selection cannot be simulated. From the tasks simulated here, only the Stroop task can be simulated with the Hunt and Lansman model. If the Stroop task also requires selection from among a number of objects (see the experiments of Van der Heijden et al., 1984), the model has no selection mechanism to do so. Moreover, in the Hunt and Lansman model, color naming in the Stroop task is performed as quickly as reading, especially in the congruent and neutral conditions; whereas in SLAM, color naming is slowed down due to the necessary buildup of activations in intermediate modules. Another connectionist model that simulates the Stroop task is the model of Cohen, McClelland, and Dunbar (1987). Here the Stroop asymmetry results, not from some architectural property such as direct connections, but from weight differences which arise through differential practice. There is, however, not much evidence that the Stroop asymmetry (found under normal Stroop conditions) is a consequence of differential learning. If this were so, a reverse Stroop effect could easily be obtained with learning and certainly some subjects would show such effects. Practice does not seem to affect the Stroop behavior of an individual subject very much (Yee & Hunt, 1988). There may, however, be an effect of learning on the magnitude of the Stroop effect. Practice has been shown (e.g., Neill, 1977, Exp. II) to reduce interference effects. A number of architectural constancies, which have important functional consequences, can be distinguished in the model. SLAM consists of three layers which are subdivided into modules. The specific organization of excitation and inhibition is quite uniform throughout the model. Excitatory connections are only found in the vertical direction of the

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(hierarchical) network and cause the activation to spread through the network in a parallel fashion. The spread of parallel activations is hindered by the inhibition that is mainly to be found in the horizontal direction. The inhibition is not evenly distributed over all simultaneous activations within a layer, but only over the activations within one module. Due to the competition within modules, parallel processing within a layer is limited to the parallel processing distributed over different modules. The modules serve as bottlenecks that restrict the parallel processing and prevent every kind of processing from interfering with any other kind of processing. In the simulation of the filtering task, for instance, it prevents interference of forms with colors at the second level. Thus, the gross architecture of the model determines what kind of interference can be found in the simulations. The interplay of excitation and inhibition eventually gives rise to a stationary and consistent set of activations in the modules. The prevalence of either facilitatory or inhibitory effects on reaction times will depend on the exact experimental setup and the preparation of the network. The relaxation in SLAM can be viewed as an extensive constraint satisfaction process (e.g., see Ballard, 1986; Hopfield 62 Tank, 1986) with the connections serving as simultaneous constraints. After a perturbation has been introduced in the network by the stimuli and instruction, coherence is achieved between formerly unrelated activations as a consequence of constraint satisfaction. Coordination of parallel processing and selection of a response is seen here merely as a relaxation process by which one coherent state is eventually selected from among numerous (mostly incoherent) states. Activations may either spread rapidly through the network or may encounter much inhibition and proceed rather slowly. When parallel activations do not fall within the same modules the buildup of activations will be fast and responses will be produced without delay. When parallel activations fall within the same modules, the resulting competition has to be resolved first and response times will become dependent upon the time needed to do so. These times increase with the number of competing alternatives (with a linear activation function this increase can be approximated to a logarithmic dependence), thus providing the illusion of serial behavior in a massively parallel system (e.g., see Pashler, 1987). The idea that reaction time is not dependent upon the time it takes to travel from input to output, but rather is due to the time needed for a network to settle, was implicit in the original McClelland and Rumelhart model. We now have three models relating response times to memory retrieval. One relates directly to the time taken to retrieve one particular item. The second allows for a degree of parallelism with multiple candidates independently entering into a horse race but with each individual retrieval being essentially of the first type. The third model, in contrast, allows

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333

candidates to interact mutually so that response time is a function of the time the system takes to settle, regardless of the individual “length” from input to selected item. In SLAM an essentially uninhibited spread of activation does not require instructional activation because there is no competition to be resolved. In the alternative kind of activation spread, however, buildup is so slow and troubled by competition that instructional activation is needed to produce responses. The distinction between a fast, parallel, mainly facilitatory, and uninstructed spread of activation and a spread which is slow, quasi-serial, both facilitatory and inhibitory, and requiring instruction, comes very close to the automatic versus controlled processing distinction of Shiffrin and Schneider (1977). In the model, however, there is no strict distinction between the two. Processing without inhibition and processing with much inhibition only represent extremes on a continuum, The relation between stimuli and preparation on one side and the organization on the other side determines which kind of processing will take place. Moreover, changes in weights due to learning may reduce the competition and induce a gradual transition from one kind of processing to the other. SLAM also represents a choice for a particular theoretical position in the Psychology of Attention. First, it shows that a theory-derived combination of a few ideas about attention can be built into an operational model. In this way SLAM shows that object selection and attribute selection are sufftcient at least to perform the kind of filtering tasks that are simulated. It also demonstrates that these two processes may be applicable to a much wider class of tasks than those simulated in this paper. For instance, in comparing simple (with two attribute categories) to more realistic stimuli (with three attribute categories) we found that both attribute and object selection were independent of the number of attribute categories that were activated by the stimulation. There is abundant evidence that the human information processing system decomposes its stimuli into a much larger number of attribute categories (see e.g., Barlow, 1981; Cowey, 1979, 1981, 1985; Livingstone & Hubel, 1988). Our findings now imply that mechanisms of attribute and object selection will work irrespective of the precise nature of the stimuli and irrespective of the specific representations chosen to encode those stimuli. Second, the model implements the theoretical standpoint of the combination of early selection and postcategorical filtering in object selection. This combination has only been proposed quite recently in the literature (see Allport, 1987; Van der Heijden, 1981, 1984, 1987) and requires high-level processing in order to select low-level encodings. SLAM performs this feat quite easily, by assuming that, while encoding is hierarchical, processing is not. It offers a more articulated account of postcategorical filtering than is

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usually found in the literature. In the model, top-down processing is required for the implementation of both early selection and postcategorical filtering. Of course, the fact that an operational model can be built in this way does not prove that this is the only possible combination. Nevertheless, hierarchical encoding and heterarchical processing allow for a minimalization of the number of levels, because everything can be used more than once, but needs to be represented only once. Moreover, segregated attributes do not have to be combined again at higher levels. In terms of representations this particular combination of early selection and postcategorical filtering is, therefore, very parsimonious. Attentional bias is directed towards a particular attribute class or stimulus object by providing extra activations to subsets of nodes in the model. Such bias alone is, however, not sufficient for selection to occur. Within the attended class a further selection must be made on the basis of stimulational activations. Mutual inhibition of all nodes within a module enables such finer selection. After resolution of competition the initial boost to all attended nodes results in a higher activation for the winning node and stronger inhibition to the losing nodes. The process can be seen as a kind of filtering with an initial processing of all stimuli followed by the active inhibition of irrelevant information. The selective mechanism in SLAM is fully compatible with the findings of Spitzer, Desimone, and Moran (1988) that state that attended cells show an increase in responsiveness and a narrowing of bandwidth, while the response of a cell to an ignored stimulus is greatly attenuated. These cell recordings were obtained from the V4 area in the visual cortex of a rhesus monkey. In SLAM we do not want to identify specific modules with specific areas in the brain, but we do wish to identify relevant mechanisms that lead to behaviors that are comparable to those found in psychological experiments. SLAM endorses the view that selective attention entails the excitation of relevant areas and the subsequent inhibition of distracting information in this area. This is in contrast to the view of attention as a general inhibition of processing unattended signals caused by the commitment of limited processing resources to an attended signal (Posner & Snyder, 1975) and as an attenuation of all unattended information (Treisman, 1964b). Selective inhibition has also a more specific action than just the passive decay of distracters when not selected (Van der Heijden, 1981). In SLAM, attention to targets also actively decreases the activations belonging to irrelevant distracters. Actually, a large range of experimental results that demonstrate such active inhibition may probably also be simulated with SLAM. Neil1 (1977, Exp. I), for instance, found that when two successive Stroop stimuli were related as a consequence of the matching of the distracting color-word of the first to the color name of the second stimulus (color naming), the

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second presentation took longer than when both stimuli were unrelated (the distractor-suppression effect). In SLAM such results may be obtained simply by not initializing activations between trials. Object activations at the first level, combining target color and distractor color-word information remaining from the previous presentation, will both inhibit the second target color (at the first and second level) and facilitate the specific response (at the third level) to the new presentation. The second distractor word will primarily reduce the facilitation leaving the inhibition by the first target color to produce a suppression effect on the second Stroop stimulus. If, however, the second stimulus only consists of a color patch, the balance of facilitation and inhibition may tip the other way. Due to the direct connections between first and third level the congruent color-word activation will then prevail over the incongruent color activation. This result was indeed found by Lowe (1979, Exp. III) and was then interpreted as contradicting an inhibitory account of selective attention. In the model, such contradictory results follow from the asymmetric connections for compatible and less-compatible attributes. If SLAM produces these results, it shows that selective attention should be explained by a careful combination of spreading activation and selective inhibition. We feel that more attentional phenomena may be investigated and described naturally in connectionist terms, although SLAM was originally set up to describe a limited range of tasks. SLAM offers some basic principles and is capable of providing quantitative results in this restricted task environment. The validity for the human system of the conclusions we derived from the model is based primarily on comparison of empirical and simulated behavior. As far as we can see, SLAM behaves in much the same way as a human subject. In some respects SLAM can actually pass for a human subject; if the simulation results of SLAM were to be mixed with experimental results of real human subjects, not even experts would be able to recognize the artificial origin of these results. APPENDIX The Parameter Set

The set of parameter values was obtained by a process of fitting the simulation results to the experimental data from Experiment 1 of Glaser and Glaser (1982). We have not found a systematic method for obtaining all parameters simultaneously. Because of the heuristic nature of the parameter fitting and the stochastic nature of the model, however, a large error margin has to be observed in using these parameters. Indeed a totally different set of parameters may exist that yields better results. Thus, no claims are made about the uniqueness or optimality of the obtained parameter set. As this parameter set gives a good demonstration of

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the operational principles of SLAM and a satisfactory quantitative agreement can be reached with it, we have not made further attempts to improve upon this parameter set. The parameters are: Intramodule inhibition in the first level: - 1.0 Excitatory weight to the first level: 0.5 Inhibitory weight to the first level: -0.5 Intramodule inhibition within the second level: - 1.5 Excitatory weight to the second level: 0.2 Inhibitory weight to the second level: 0.0 Intramodule inhibition within the third level: - 1.5 Excitatory weight to the third level: 0.2 Inhibitory weight to the third level: 0.0 Decay parameter: 0.5 Excitatory weight from first to third level and back: 0.08 Inhibitory weight from first to third level and back: -0.08 Two important observations should be made about this parameter set. First, the model could not be made to work with non-zero values for the inhibitory weights to second and third level. This finding may further strengthen Grossberg’s (1984) cogent criticism of the McClelland and Rumelhart (1981) model that, under learning conditions, the model would be unstable due to the inhibitory between-level connections. His arguments cannot, however, be evaluated in the framework of our study. If Grossberg’s criticism is completely justified and generalizable to all between-level connections, this would imply that, possibly, an even better parameter set could be found also with zero inhibition leading to the first level. A second observation should be made about the very large intramodule inhibition. Since intramodule competition (as shown by oscillation) is almost the single most important feature of SLAM, the model cannot resolve the competition and, therefore, function properly with smaller values for intramodule inhibition weights. REFERENCES Allport, D. A. (1987). Selection for action: Some behavioral and neurophysiological considerations of attention and action. In H. Heuer & A. F. Sanders (Eds.), Perspectives on perception and action. Hillsdale, NJ: Erlbaum. Averbach, E., & Coriell, A. S. (1961). Short-term memory in vision. Bell System Technical Journal,

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