Priming by natural category membership in the left and right cerebral hemispheres

Neuropsychologia 42 (2004) 1948–1960

Priming by natural category membership in the left and right cerebral hemispheres Jillian Grose-Fifer, Diana Deacon∗ Department of Psychology, City College of the City University of New York, 138th Street and Convent Avenue, New York, NY 10031, USA Received 21 October 2002; received in revised form 1 March 2004; accepted 27 April 2004

Abstract The cerebral representation of category information was examined in a single word priming paradigm, during which the N400 component of the event-related potential (ERP) was recorded. The visual half-field paradigm was employed in order to selectively stimulate the two hemispheres. To investigate which aspects of category membership are relevant in producing priming, two types of related stimuli were presented. In one condition pairs of exemplars had a higher amount of feature overlap (e.g., MOSQUITO-FLEA) than in the other (e.g., SOFA–VASE). Significant priming was obtained only for stimuli in the high feature overlap condition and then only when these were presented to the left visual field (LVF)/right hemisphere (RH). This finding was interpreted within our recent model of semantic memory wherein the right hemisphere represents items on the basis of distributed individual features, whereas the left hemisphere (LH) represents semantic information locally, within a spreading activation system, where priming occurs exclusively through associative links. It was concluded that knowledge regarding category membership is maintained in the RH, on the basis of feature coding. © 2004 Elsevier Ltd. All rights reserved. Keywords: Category priming; N400; Event-related potentials; Lateralized stimuli

1. Introduction As our knowledge of commonplace objects and beings accumulates, it is eventually organized into a system of taxonomy. Although developed on the basis of personal experience, the recognition of most natural categories and instantiations of each, are surprisingly universal. This is apparent from the high degree of correspondence between norming studies in which category labels and exemplars have been generated by large pools of subjects (c.f. Battig & Montague, 1969; Rosch, 1973) The features attributed to items of a given category are also largely the same, lending support to the hypothesis that items become relegated to a given category because of their similarity (Hinton, 1989; McRae, deSa, & Seidenberg, 1997; Plaut & Shallice, 1993). Most opponents of “similarity based” theories of categorization argue in favor of “rule based” accounts in which higher knowledge and reasoning processes, often specific to the experimental paradigm, are invoked to distinguish between categories (Keil, 1989; Medin, 1989; Rips, 1989). While rule based accounts may adequately describe classification into categories contrived for the purpose of experimentation, ∗

Corresponding author. Tel.: +1 212 650 5680; fax: +1 212 650 6450. E-mail address: [email protected] (D. Deacon).

0028-3932/$ – see front matter © 2004 Elsevier Ltd. All rights reserved. doi:10.1016/j.neuropsychologia.2004.04.024

they seem less germane to the operation of the permanent memory system that maintains knowledge regarding natural categories. However, a third possibility exists. Once our system of taxonomy is in place, exemplars may be permanently associated with a category label. Features would then be less important in recognizing category membership. Our interest in the present investigation extends beyond distinguishing between these alternatives. The manner in which category membership information is represented in the brain impacts the development of several models of semantic memory in which different roles are assigned to the two cerebral hemispheres. 1.1. Recent models of semantic memory in the left and right hemispheres Much of what we know about semantic memory has been gleaned from studies of priming. In single word priming paradigms, a decrease in reaction time is commonly reported when two consecutive words are related. Words may be related by category membership, e.g., CAT–HORSE, by association only, e.g., DOCTOR–HOSPITAL, by both category membership and association, e.g., DOCTOR–NURSE, or can be unrelated in either of these domains but still produce priming if they share physical or functional features,

J. Grose-Fifer, D. Deacon / Neuropsychologia 42 (2004) 1948–1960

e.g., EAGLE–AIRPLANE. It is only within the last decade or so that studies have attempted to separate the effects of relatedness through association, category membership, and shared features. Several divided field studies have demonstrated the presence of semantic priming within each cerebral hemisphere (Chiarello, 1988a,b, 1999). In this type of paradigm, stimuli presented to the left visual field (LVF) access the right hemisphere (RH) directly, whereas those presented to the right visual field (RVF) access the left hemisphere (LH) directly. Words, which are both categorically and associatively related, have been shown to produce equal priming across visual fields (Chiarello, 1988a,b). We have demonstrated, however, that associated words that do not share semantic features (e.g., DOG-BONE) prime each other in the left hemisphere but not in the right hemisphere. Conversely, words that share semantic features but are not associates, and are not in the same semantic category (e.g., TREE-BROCCOLI), prime each other in the RH, but not the LH (Deacon et al., 2004). Semantic representations have been characterized by other authors as being “fine” in the LH and “coarse” in the RH (Beeman, 1998; Beeman et al., 1994) or alternatively “focused” in the left hemisphere and “diffuse” in the RH (Chiarello, 1988a,b). Beeman (Beeman, 1998; Beeman et al., 1994) has proposed that the LH uses relatively fine coding to activate closely related concepts, while the RH uses coarse coding to weakly activate a larger range of related concepts. This view does not distinguish between priming via associative relationship versus by physical/functional feature overlap. In fact, Beeman uses the term “feature” to refer to both associations and attributes. It should be noted that “features” in our usage refers only to physical or functional attributes and not associations. The Beeman theory would predict that associates and words that share semantic features produce equivalent priming in the RH. It cannot account for our finding that associates (that do not share features), produced priming in the LH, but not in the RH (Deacon et al., 2004). Consideration of the visual hemifield data, alongside a number of well documented priming phenomena in the behavioral literature, prompted Deacon et al. (2004) to propose a substantially different model of semantic memory. According to the Deacon et al. model, the hemispheres do not differ with respect to whether their representations of semantic information (or whether the “activation of semantic information,” to use Beeman’s term) are strong/weak or coarse/fine. The distinction made by Deacon et al. regarding the two hemispheres is whether semantic representations are local or distributed. In spreading activation or “local” models all of the information regarding a particular item is mapped onto a single memory node (Collins & Loftus, 1975; Quillian, 1967). Nodes representing related items are usually connected by associative links. Facilitation or priming between items occurs when activation spreads automatically from one node to another. In opposition to local models, distributed systems have been suggested, where nodes

1949

represent individual features or groups of features (Masson, 1995; McClelland & Rumelhart, 1986; Pulvermüller, 2002; Rumelhart & McClelland, 1986; Seidenberg & McClelland, 1989). Of particular relevance here is a distributed model proposed by Masson (1995) in which items are represented on the basis of their individual features within a Hopfield-like network. Each stimulus attribute maps onto a separate node, e.g., the word CAT will activate the nodes for: has four legs, meows, has fur, etc. Our model proposes that both of these systems are resident within the cerebral cortex: the left hemisphere encodes information holistically within a local spreading activation network, whereas the right hemisphere represents information in a more distributed fashion on the basis of individual features (Deacon et al., 2004).1 This hypothesis is based upon evidence from both electrophysiological and behavioral data that highlight key differences in the circumstances under which priming is obtained from the two hemispheres. In studies using centrally presented stimuli, two priming phenomena have been found to be task specific in ways that implicate hemispheric differences. These are the intervening item effect, which is generally found in lexical decision tasks (LDTs) but not in naming tasks, and the phenomenon of mediated priming, which is usually found in naming tasks but not LDTs. The intervening item effect refers to a disruption of priming that occurs when an unrelated item is interposed between a related prime and target e.g., (SALT–MOON–PEPPER). In a local spreading activation system, one would predict that an intervening item would not disrupt priming, because multiple items can be activated at the same time. However, in a distributed, feature-based network a unique pattern of 1 In lay terms our characterization of the LH system as “local” and the RH system as “distributed “ may appear similar to Chiarello’s characterization of the LH as “focused” and the RH as “diffuse” (Chiarello, 1988a,b). In fact, we are describing entirely different phenomena. It is regrettable that they sound so similar in the context of everyday usage. We are more or less obligated to use these terms because they are the standard terms used by cognitive neuroscientists to describe the two types of systems that we propose exist. Typical local systems are those in which all features of an item are represented in a given node and spreading activation occurs. Typical distributed systems represent items using multiple nodes that code for different features of the item. There is no spreading of activation. By contrast to the Deacon et al model, there are statements in most of Chiarello’s papers that indicate she assumes a spreading activation or “local” system in both hemispheres. Chiarello uses the words focused and distributed to convey the idea that the left hemisphere is able to select relevant meanings whereas the right hemisphere is not. She does not propose any differences in the organization of semantic memory or the mechanisms through which word meanings are represented. On the contrary she explicitly states:

“We make no claim that lexical information is represented (i.e., localized) more focally in the left hemisphere than the right, or that the RH employs a more diffuse (i.e., distributed) means of representing the same information. Our argument rests solely on hemispheric differences in the pattern of activation and inhibition which exists among related word meanings, independent of whether the meanings themselves are represented in a focal or distributed fashion” (Chiarello, 1988, p. 64).

1950

J. Grose-Fifer, D. Deacon / Neuropsychologia 42 (2004) 1948–1960

activated feature nodes codes for each item. Only one item can be represented at a time, because it is the pattern of activation of nodes across the entire network that codes for a given item. Priming is disrupted by an unrelated intervening item because the pattern of activation that it creates has no redundancy with that of the prime or the subsequent target. In naming tasks, priming is relatively unaffected by the interposition of an unrelated item between a related prime and target (Joordens & Besner, 1992; Masson, 1995; Tipper, Weaver, Cameron, Brehaut, & Bastedo, 1991); whereas in LDT, priming tends to be disrupted by the intervening item (Foss, 1982; Gough, Alford, & Holly-Wilcox, 1981; Ratcliff, Hockley, & McKoon, 1985; Ratcliff & McKoon, 1988). If one considers that speech, and therefore naming, are largely controlled by the left hemisphere, but that both right and left hemispheres contribute to the LDT response, then these data can be used to support our model. Priming persists in the LH because activation spreads automatically from the prime to related concepts, and because multiple concepts can remain activated at one time. On the other hand, in the RH, priming only occurs when successive patterns of activation overlap, i.e., when items share common attributes. The pattern of activation representing the prime is reset when the unrelated intervening item is presented. This is necessary for the system to reflect the features of the intervening item. When the target is subsequently presented there is no priming because the last pattern of activation corresponds to the features of the intervening item. Thus, there is no facilitation from the prime because the entire pattern has been reset by the intervening item. Since the RH contributes to lexical decision making, but not to naming, this explains the different results obtained from the two tasks. Mediated priming is produced by primes and targets that are related only through co-association with another item. In the case of LION-STRIPES, the only link is through the co-association of both stimuli with TIGER. It is assumed that activation spreads from LION to TIGER and then to STRIPES through the associative links that are part of a classic spreading activation system. However, in Hopfield-like distributed systems, priming occurs only between concepts that share common features, and thus, common patterns of nodal activation. In the example given, LION shares no features with STRIPES, so one would not expect priming to occur. In naming tasks, mediated priming is commonly reported. On the other hand, in LDTs, no such priming is observed (Neely, 1991). As we have stated above, it is likely that the RH contributes to lexical decision but not to naming. Thus, the mediated priming data also give credence to the idea that activation spreads automatically from one item to another, via associative links, in the LH, but not in the RH. The Deacon et al. model was recently tested by two divided field experiments that examined priming from different types of stimuli (Deacon et al., 2004). In the first ex-

periment, stimuli that were related only by association were used. In the second experiment, the stimuli were related only by virtue of shared perceptual and functional features. The rationale behind the experiments was that if only the LH represents semantic information in a local spreading activation network via associative links, then in the first experiment priming would be obtained for the RVF (LH) stimuli but not for LVF (RH) stimuli. Similarly, if semantic information is represented within a distributed Hopfield-like network on the basis of feature representation only in the RH, then priming would be obtained in the second experiment for LVF (RH) stimuli but not RVF (LH) stimuli. The predicted results were obtained, providing strong evidence for the new model. 1.2. Representation of semantic category membership A remaining issue, regarding which there has been considerable disagreement in the literature, is that of which hemisphere represents category membership per se. There is little consensus, even if the scope of relevant literature is constrained to include only those studies where the related but nonassociative category exemplars were lateralized to the same visual field. Chiarello, Burgess, Richards, and Pollock (1990) and Chiarello and Richards (1992) reported that non-associative categorical priming occurs only in the RH. Conversely, Abernethy sand Coney (1990, 1996) maintained that it occurs only in the LH. However, some of the stimuli used by Abernethy and Coney appear to have also been associates, which according to Deacon et al. (2004), would have produced LH priming. Other investigators have examined whether priming from non-associated category exemplars might occur in both hemispheres, but under different circumstances (Abernethy & Coney, 1990, 1996; Chiarello et al., 1990; Chiarello & Richards, 1992; Collins, 1999; Koivisto, 1997, 1998; Koivisto & Laine, 1999, 2000). In two studies category exemplars were presented in a lexical decision task under different experimental conditions: one, designed to invoke automatic processing, had a low proportion of related pairs and used a short stimulus onset asynchrony (SOA). The other, intended to invoke controlled processing, had a higher proportion of related pairs and a longer SOA (Collins, 1999; Koivisto & Laine, 2000). Although elaborate interpretations were offered in each study, there is virtually no agreement between them. Moreover, the data in several studies do not seem to support the very detailed post-hoc interpretations provided. By way of example, one study claimed to have found evidence of LH representation of category information at short SOAs (Koivisto & Laine, 2000). However, when we recalculated their means, collapsing across everything except visual field and relatedness, there was an overall greater amount of priming in the LVF (RH), when the related and unrelated items were compared directly to each other. A more recent paper (Shears & Chiarello, 2003) found significant differences in hemispheric processing of

J. Grose-Fifer, D. Deacon / Neuropsychologia 42 (2004) 1948–1960

category material as a function of task (go/no-go versus choice RT). This finding underscores the fact that much of the variability in behavioral data is extraneous to the cognitive processes under investigation. In the study reported here, a physiological measure was used that circumvented the variability associated with decision and response processes. The N400 component of the human event-related potentials (ERPs) was recorded in a single word priming paradigm in order to further examine how category membership information is coded for in the cerebral hemispheres. 1.3. The N400 component of the human ERP The N400 is a negative going wave that is elicited during the processing of words and pronounceable pseudowords. When two consecutive words are related, the N400 wave elicited by the second is attenuated. In ERP priming experiments, the N400 elicited by a target that is related to a preceding word (when words are presented in lists or in pairs) is compared to the N400 elicited by an unrelated target. The N400 wave elicited by the related target word is attenuated. That is, the N400 is less negative-going, with an accompanying decrease in mean amplitude. When both RT and N400 are recorded to the same stimulus, a decrease in RT is usually associated with a decrease in N400 amplitude. The attenuation of N400 thus appears to reflect facilitation (i.e., priming) of word processing. The N400 is a more direct index of the modulation of semantic processing than behavioral measures since it can be recorded in the absence of a behavioral response (Deacon, Hewitt, Yang, & Nagata, 2000; Kutas & Hillyard, 1989), thus precluding the influence of post-lexical response strategies. 1.4. Aims and predictions regarding the present investigation A goal of the present study was to further tease apart how, and in which hemisphere, priming between nonassociated category exemplars might occur. Many of the category exemplars used in other studies shared common semantic features, i.e., had similar global shape, and other physical attributes, such as comparative size, color, texture, etc. In a previous publication (Deacon et al., 2004), we reported that stimuli that are related only by virtue of their shared features, e.g., TREE-BROCCOLI, prime each other in the RH but not the LH. It is possible that categorical priming may be produced by semantic feature overlap and not the knowledge that a given item is a member of any particular category, i.e., it may be degree of feature overlap that produces priming rather than category membership, per se. Of course, by definition, category members share certain common features, however, when one considers members of a particular category, it is quite evident that some exemplars are perceptually and functionally more similar than others. For instance,

1951

in the Battig and Montague (1969) category of “TOYS”, the exemplars BALL and BALLOON share many more attributes than do the exemplars DOLL and CRAYONS. In order to address this issue, two sets of category exemplars were chosen using the Battig and Montague category norms that were not associatively related to each other, as indexed by free association norms (Nelson, McEvoy, & Schreiber, 1998; Palermo & Jenkin, 1964). One set consisted of primes and targets that shared few physical or functional features (e.g., SOFA–VASE). In these pairs there was relatively little similarity in terms of shape, texture, constituent material, function, etc. The other set was comprised of primes and targets that shared multiple features (e.g., MOSQUITO-FLEA). In this particular example, there are marked similarities in physical form and action between the two words (small, biting, blood-sucking, pests, fast-moving). Within Deacon et al.’s model (Deacon et al., 2004) category membership could be represented in two ways. In the LH, associative links might be maintained between commonly recognized category labels and their exemplars. Spreading activation would permit priming between exemplars that are not direct associates of each other, because of their co-association with the category label. If category membership were coded for in this way, then in the present study priming would be significant for RVF (LH) stimuli and there would be no difference in the amount of priming between items that shared many features and items that shared few features. The other possibility would be that category membership is encoded entirely on the basis of overlapping feature representations. In this instance, priming would be expected from the RH only, since it is the distributed system of the RH that is sensitive to feature overlap. The amount of priming would be expected to increase as a function of the number of feature shared by exemplars.

2. Methods 2.1. Participants Eleven healthy adults (four males and seven females) between the ages of 18 and 23 years (mean age 19.8 years (1.6 S.D.) participated in this study as paid volunteers. All were monolingual native English speakers who had been raised in homes where only English was spoken. Prospective participants were excluded who did not have normal, or corrected to normal vision, reported current use of psychoactive drugs, or had significant psychiatric or neurological histories. All were strongly right-handed and scored greater than 86% (mean 95.6 (5.9 S.D.) on the Edinburgh Handedness Inventory (Oldfield, 1971). Informed consent was obtained prior to testing. We adhered to these criteria in order to minimize inter-participant variability. In pilot studies that we have conducted even native English speakers showed different ERP priming patterns between the hemispheres if they had

1952

J. Grose-Fifer, D. Deacon / Neuropsychologia 42 (2004) 1948–1960

early experience with a second language. Similarly, participants who did not score very highly on the Edinburgh Handedness questionnaire also showed different priming patterns from those with higher scores (>85%). 2.2. Stimuli Concrete English nouns were presented in black on a gray background on a video monitor. The viewing distance to the screen was 67 cm. For each trial, lateralized stimuli were presented centered on a point 4◦ to the left or right of a central fixation cross. Each trial consisted of two words presented consecutively, followed by a question mark and then a third, probe word. All the words within one trial were presented to the same visual field. The first word (S1) was presented for 185 ms, followed at an SOA of 250 ms by the second word (S2) which was presented for 185 ms, followed at an SOA of 500 ms by a question mark presented for 185 ms, followed at an SOA of 500 ms by the third word (probe) presented for 500 ms. The inter-trial interval was 2.5 s (see Fig. 1). On 50% of the trials, the probe word was a repeat of S1 or S2. With the exception of these repeated probe words, no other words were used twice in this study. S1 and S2 were category exemplars selected from the Battig and Montague category norms (1969). These norms have been widely used to look at categorical priming (Chiarello et al., 1990; Chiarello & Richards, 1992; Collins, 1999). Twenty-three categories from the Battig and Montague norms were chosen (see Table 1). None of the words within a trial were associatively related as indexed by free association norms (Nelson et al., 1998; Palermo & Jenkin, 1964). A total of 480 trials were presented. The semantic relatedness of S1 and S2 was manipulated such that in 120 of the trials the words were categorically related but shared few, if any, features (low feature overlap condition), and in a further 120 trials words were categorically related and shared many features (high feature overlap condition). Examples of both these kinds of stimuli are given in Table 1. In order to quantify the number of semantic features shared by the items in each condition, a norming procedure was conducted on another 10 subjects. The stimulus pairs were randomized with respect to condition and presented to

Fig. 1. An example of a typical trial in the low feature overlap condition.

Table 1 Category labels and sample exemplars Category title

Low feature overlap

High feature overlap

Precious stone Metal 4-Footed animal Cloth Kitchen utensil Furniture Human body part Fruit Weapon Human dwelling (Alcoholic) beverage (Non-alcoholic) beverage Food flavoring Natural earth formation Article of clothing Part of building Vehicle Toy Vegetable Footwear Insect Flower Type of sport

Crystal–turquoise Tin–cobalt – Satin–tweed Spatula–toaster Sofa–vase Tongue–foot Berry–banana Bayonet–missile Mansion–igloo – Milk–lemonade Onion–nutmeg Desert–waterfall Muffler–bathrobe Chimney–Porch Boat–tram Marbles–seesaw Broccoli–potato Clogs–stockings – – Archery–rugby

Ruby–garnet Silver–steel Fox–squirrel Gabardine–canvas Ladle–spoon Bookcase–cabinet Esophagus–intestine Apricot–peach Tank–rifle Dorm–barracks Beer–champagne Coke–coffee Turmeric–mustard Rock–mountain Shirt–coat Skylight–door Tricycle–tractor Ball–balloon Lettuce–spinach Galoshes–flippers Mosquito–flea Marigold–sunflower Golf–hockey

subjects as printed lists of word pairs. Subjects were told that the pairs of words were drawn from the same category of items, and given examples of the types of categories (e.g., fruit, kitchen utensils, toys, etc.). Subjects were asked to write down, in the space provided beside each pair, all of the common features that the items shared. It was explained that the features could be physical or functional, and that some items would have many features in common, e.g., MOUSE and DOG share the features of having fur, four legs, tail, claws, being pets, whereas other pairs would have few or no shared features. Examples were given of each. No time limit was imposed for writing responses to individual items, but subjects were told that they should not spend more than a minute or so thinking about what features each item shared. This part of the instructions was intended to discourage more esoteric, idiosyncratic responses from being generated, as opposed to more obvious generally agreed upon features. According to the data collected in the norming procedure, subjects generated an average of 0.49 shared features per word pair in the low feature overlap condition (S.E.M. = 0.11) and an average of 2.16 shared features (S.E.M. = 0.32) in the high feature overlap condition. A paired samples t-test showed these scores to be significantly different [t(1,9) = 5.59, P < 0.001]. Additionally, subjects were asked to use a three-point rating scale to decide how similar exemplars were in terms of their functional and physical attributes (very similar = 2, somewhat similar = 1, not similar = 0). For the high feature overlap stimuli, 63.5% were rated very similar and only 4% not similar at all. For the low feature overlap stimuli, only 2.33% were rated very similar, while 67% were rated

J. Grose-Fifer, D. Deacon / Neuropsychologia 42 (2004) 1948–1960

1953

Table 2 Mean SFI and word frequency for the stimuli. Primed high feature overlap

Primed low feature overlap

Unprimed

RVF

LVF

RVF

LVF

RVF

LVF

Prime (S1)

Word length Word frequency

6.05 45.63

6.20 45.16

6.08 45.07

6.09 45.96

6.09 45.96

6.00 45.39

Target (S2)

Word length Word frequency

6.08 45.80

6.28 45.52

6.02 45.59

6.03 45.90

6.03 45.90

6.01 45.19

not similar at all. Using the rating score for each subject, the mean score was 1.44 (0.008 S.E.M.) for the high feature overlap stimuli and 0.44 (0.008 S.E.M.) for the low feature overlap stimuli. A paired samples t-test shows these scores to be significantly different [t(1,9) = 8.16, P < 0.001]. The words in the remaining 240 trials were not related in any way. Within each of these three conditions, half of the trials were presented to the RVF (LH) and half were presented to the LVF (RH). The order in which different types of stimuli were presented was randomized and stimuli were presented in 6 blocks of 80 trials. Words varied in length from 3 to 11 letters (mean 6.1). The angular subtense of an average letter was 0.86◦ vertically by 0.46◦ horizontally. The mean angular subtense of a word was about 3.28◦ horizontally (range: 1.4–4.6◦ ). The mean angular subtense from fixation to the nearest edge of the word was 2.4◦ (range: 1.7–3.3◦ ). Word length and frequency (Carroll, Davies, & Richman, 1971) were balanced across conditions (see Table 2) and mean standard frequency index (SFI) was 45.6. 2.3. Recording procedures The electroencephalogram (EEG) was recorded using an electrode cap (Electrocap International) from 18 standard scalp locations (International 10:20 system), Fp1, Fp2, Fz, F3, F4, Cz, C3, C4, Pz, P3, P4, T3, T4, T5, T6, Oz, O1, and O2 using a Neuroscan 32 channel amplifier. These sites were referenced to the tip of the nose. Horizontal and vertical electrooculograms (EOGs) were recorded from electrodes placed at the left and right outer canthi and the infraorbital and supraorbital ridges of the right eye, respectively. All electrode impedances were below 5 k. EEG and EOGs were recorded with a bandpass of 0.1–35 Hz. Prior to the study, we recorded eye movements by asking the participant to fixate upon an asterisk that moved from the center to a position 4◦ to the right of this, then back to center and then 4◦ to the left. This was repeated several times in order to provide a horizontal EOG calibration. An average saccade when the subject moved their eyes to fixate the calibration target was between 40 and 60 ␮V. Offline inspection of the EOG allowed for more stringent filtering (1–20 Hz), which was not used for EEG. Saccadic movements of 5 ␮V were discernable within the EOG recordings. The relationship between EOG and eccentricity has been shown to be

linear (for eccentricities of less than 20◦ ). We calculated that since a 4◦ eye movement is equivalent to 40 ␮V, then a 5 ␮V deflection would correspond to a 0.5◦ saccade. As far as we know, calibrations are not typically performed on such small eccentricities as we use, and it has been suggested by Joyce, Gorodnitsky, King, & Kutas (2002) that error rates tend to increase with larger eye movements. Joyce et al. state that their mean accuracy for the 18◦ horizontal eye movement was 1.2◦ . Therefore, it would seem credible to us that greater accuracy might be attained for smaller saccades. During the study, the participant was required to determine whether the third (probe) word was a repeat of one of the two previous words and to press a mouse button accordingly. They were asked to perform the task as quickly and accurately as they could, and were reminded to keep movements and blinking to a minimum. Two different blocks of practice trials were run as many times as necessary, until the EOG channels showed accurate fixation and the participant felt comfortable with the procedure. The individual horizontal EOG calibration allowed the exclusion of trials in which the participants moved their eyes by as little as 0.5◦ . Pilot studies in our laboratory have demonstrated the importance of having sufficient practice in order for participants to be able to read stimuli comfortably, without moving their eyes. If a horizontal eye movement was made during stimulus presentation, the experimenter reminded the participant to keep looking at the fixation cross. This resulted in very few discarded trials due to inaccurate fixation. A visual inspection was made of the continuous EOG files and if the participant moved their eyes laterally during a trial, the entire trial was rejected. Similarly, if they blinked when a stimulus was presented the trial was rejected. The remaining data were then epoched as described below. Sweeps consisted of 775 data points, which were sampled from 200 ms preceding S1 to 1500 ms after this word. Baseline correction was performed using the averaged EEG in the 200 ms prior to S1 onset in the epoch. Off-line artifact rejection was used to exclude any sweeps where the EEG or EOG exceeded plus or minus 50 ␮V. Individual averaged files were created for each S1–S2 stimulus type, within each visual field. Average files were re-baseline corrected using the averaged EEG in the 200 ms prior to the critical stimulus (S2) onset, i.e., the period from 50 to 250 ms after S1. Trials where stimuli were categorically related were considered primed trials and were

1954

J. Grose-Fifer, D. Deacon / Neuropsychologia 42 (2004) 1948–1960

of two types: (1) the high feature overlap condition, where S1 and S2 shared many features and (2) the low feature overlap condition, where S1 and S2 shared few, if any, features. Trials on which S1 and S2 were unrelated were considered unprimed. A grand average was created for each condition for each visual field. Behavioral data were sorted according to condition and tabulated off-line. Repeated measures ANOVAs were employed. A Greenhouse–Geisser correction was applied where appropriate. Planned pairwise comparisons were made between the primed and unprimed conditions, for each visual field. The alpha level was 0.05.

3. Results 3.1. Behavioral data The function of the behavioral task was to induce participants to process the words as opposed to merely fixating the central cross. The accuracy data provided a gross measure of compliance. Accuracies were 91.6% (S.D. 6.2) for RVF (LH) stimuli and 88.3% (S.D. 4.4) for LVF (RH) stimuli. The slightly higher accuracy of the RVF (LH) is consistent with the RVF advantage for word recognition reported in other studies (see Chiarello, 1991 for review).

3.2. ERP data Within each visual field, the primed trials were compared to the unprimed trials. In the LVF (RH), an area of separation of the waves was identified in N400 time region, 250–612 ms after the onset of S2 (see Fig. 3). For the RVF (LH), there was no clear N400 priming effect (see Fig. 2). Mean amplitude was measured for each condition, in each participant, in an epoch extending from 250 to 612 ms after S2 (see window 2, Fig. 3). This time window matched the area of separation for the high feature overlap condition, and also encompassed the priming produced by low feature overlap stimuli (see Fig. 3). Electrode sites Fp1 and Fp2 were excluded from these analyses as they did not reflect priming and are often contaminated with eye movements. A three-way repeated measures ANOVA with factors of visual field (2 levels), priming (3 levels) and electrode site (16 levels) revealed a significant field by priming interaction, F(2, 20) = 4.31, P = 0.039. Therefore, two-way ANOVAs with factors of priming (3 levels) and electrode (16 levels) were performed for each visual field separately. There was a main effect of priming only for the LVF (RH), F(2, 20) = 4.34, P = 0.027, for RVF (LH) F(2, 20) < 1. This is borne out by the grand averages shown in Figs. 2 and 3. Two-way ANOVAs with factors of electrode (16) and priming (2) revealed a main effect of priming for the LVF (RH) data, only for the comparison of unprimed and high

Fig. 2. The N400 priming effect for the stimuli presented to the RVF (LH). The ERPs to unprimed words are represented by the solid thick black line, primed (high feature overlap) by the thin black line and primed (low feature overlap) by the dotted line.

J. Grose-Fifer, D. Deacon / Neuropsychologia 42 (2004) 1948–1960

1955

Fig. 3. The N400 priming effect for the stimuli presented to the LVF (RH). The ERPs to unprimed words are represented by the solid thick black line, primed (high feature overlap) by the thin black line and primed (low feature overlap) by the dotted line.

feature overlap trials, F(1, 10) = 10.62, P = 0.009. Differences between unprimed and low feature overlap trials did not reach significance, F(1, 10) = 2.64, P = 0.14. It was apparent from Fig. 3 that the window of priming elicited by low feature overlap stimuli was of shorter duration. Therfore, mean amplitudes were also measured using a more restricted window of 250–510 ms (see window 1, Figs. 2 and 3). The same comparisons were performed and the priming elicited by low feature overlap stimuli still failed to reach significance. These results indicate that categorical priming takes place only when stimuli are presented to the LVF (RH), and the effect is greater when the stimuli share common features. To directly compare the priming elicited by the two different types of related stimuli, subtraction waves were made for each participant by subtracting the primed waves from the unprimed waves. This enabled a direct comparison between the two conditions that incorporates a baseline measurement. Statistical analyses revealed that the two primed conditions differed significantly in the later part of the N400 (450–612 ms), for the LVF (RH) only [F(1, 10) = 6.57, P = 0.028]. 3.3. Early priming effect Although there was no significant priming effect in the N400 region for stimuli presented to the RVF (LH), visual inspection of the grand average revealed an early area of separation between the primed and unprimed conditions

(see Fig. 2). The area from 170 to 400 ms (window a) was measured for all participants. The separation was seen to be largest at C3, Cz, C4, P3, Pz, P4, O1, Oz, and O2. These nine electrodes were used in the statistical analysis. A three-way ANOVA with factors of field (2), priming (3), and electrode (9) revealed no main effect of priming F(1, 20) = 2.36, P = 0.12 and no field by priming interaction (F(2,20) = 1.97, P = 0.17. Despite the appearance of the grand average ERP, this separation was not present in all participants.

4. Discussion Priming occurred only when exemplars had many shared features (i.e., the high feature overlap condition), and were presented to the LVF (RH). There were no significant effects of priming for stimuli in the low feature overlap condition for either the LVF (RH) or RVF (LH) stimuli. Thus, category membership per se was not sufficient to produce significant priming of the N400 component in either hemisphere. Rather, the tendency for category exemplars to share semantic features may be what causes them to prime each other. In the RH, priming approached statistical significance in the N400 latency range for stimuli with low feature overlap. This may have been due to some basic semantic feature overlap that is inherent to membership of a particular category. It could also perhaps be argued that category

1956

J. Grose-Fifer, D. Deacon / Neuropsychologia 42 (2004) 1948–1960

membership is encoded as a feature in the RH, and that the non-significant priming found in the low feature overlap condition was due to S1 and S2 being co-exemplars (i.e., priming from category membership per se). We are reticent to spend time considering these alternatives, however, as the effect was not significant in the present study. An earlier study from our laboratory demonstrated that stimuli that were related only by common semantic features (i.e., were structurally or functionally similar, or both) produced priming exclusively for LVF (RH) presentations (Deacon et al., 2004). Several behavioral studies have also demonstrated the presence of priming for nonassociated category exemplars in the RH, albeit at longer SOAs than were used in this study (Chiarello, 1988a,b; Chiarello & Richards, 1992; Collins, 1999; Koivisto, 1997). This is not surprising since the category exemplars used in these various studies all shared a high degree of feature overlap. Chiarello (1988a,b) and Chiarello and Richards (1992) acknowledged that feature overlap may influence categorical priming and even ranked their stimuli accordingly, however the degree of feature overlap between most exemplars was quite considerable. Because of this, the category exemplars used in Chiarello’s study and all other behavioral category priming studies were probably more similar to our high feature overlap stimuli than our low feature overlap exemplars. This would explain why they were able to elicit RH priming. The concept that priming occurs in the RH due to feature overlap is also supported by data from Koivisto and Laine (1999), even though the authors interpreted their findings quite differently than we interpret the present data. In their semantic categorization tasks, typicality effects were greater in the RH than in the LH, perhaps due to semantic feature overlap being more salient to the RH. The results obtained in this experiment provide further evidence for the model of semantic representation suggested by Deacon et al. (2004). Deacon et al. proposed that word meanings are represented locally, in a spreading activation system in the LH, but are represented in the RH in terms of feature encoding within a distributed network. In the RH, word meanings are represented by the activation of nodes, with each word having its own distinctive pattern of activation. Each node may represent a feature of the stimulus, such as one for fur, one for the presence of a tail, etc. Words that share overlapping features will also share some similarities in their pattern of node activation. When two words with overlapping features are presented consecutively, the second word will be activated more quickly since some of the nodes have already been activated by the first word. Therefore, from Deacon et al’s model, one would predict that words that share common features would show strong priming when presented to the RH and words that share a minimum of common features would show less or no priming. Our data bear this out. Since it is proposed that the LH encodes words holistically, and not on the basis of individual features, one would not expect to elicit priming from the LH with either of the types of stimuli used in this study,

unless LH categorical priming between exemplars occurred through co-association with the category label. We saw no evidence of this, however. While providing further evidence for the Deacon et al. model of semantic memory (Deacon et al., 2004), our data cannot as clearly be accounted for by Beeman’s model (Beeman, 1998). According to Beeman, the LH uses relatively fine coding to activate closely related features, while in the RH a broader range of features is activated more weakly. Beeman’s theory does not differentiate between the nature of features represented by each hemisphere. In the schematic provided in both of Beeman’s relevant publications (Beeman, 1998; Beeman et al., 1994) closely related “features” appear centrally in the diagram and more “distantly related features” appear more peripherally. Physical and functional features of the type considered here constitute some of those depicted as closely related. Yet data showing only RH priming from nonassociated category members (Chiarello, 1988a,b; Chiarello & Richards, 1992) are interpreted by Beeman and Chiarello (Beeman, 1998; Beeman et al., 1994; Chiarello, 1988a,b; Chiarello & Richards, 1992) as reflecting that the stimuli used were weakly related. It was not essential for the purpose of the studies by Chiarello and colleagues that the category exemplars share features. The stimuli in their studies may in fact have been more weakly related than our high feature overlap condition (although the appendices provided suggest a considerable degree of similarity between items). However, as the related category exemplars in our high feature overlap condition were specifically chosen to share as many salient characteristics as possible, Beeman’s model would have predicted priming in both hemispheres, at least for that condition. The absence of LH priming is thus problematic for Beeman’s account, as were the data from our previous study mentioned above (Deacon et al., 2004). In that study (Deacon et al., 2004), items that were associates but not in the same category, produced priming exclusively in the LH, whereas items that were neither associatively nor categorically related but shared physical or functional features produced priming in the RH. Beeman’s model would have predicted priming from both hemispheres for associates. The present data essentially replicate our previous finding that semantic coding is feature based in the RH but not the LH, and suggest further that category membership is coded for almost exclusively on the basis of feature similarity. Data from patients with right or left hemisphere lesions are also consonant with the possibility that category processing is localized to the RH. In several ERP studies, little or no N400 priming from items related purely by category, was observed in patients with RH lesions (Hagoort, Brown, & Swaab, 1996; Kotz & Friederici, 2003; Kotz, Friederici, & von Cramon, 1999; Swaab, Baynes, & Knight, 1998). Behavioral studies of patients with RH lesions have found them to be impaired at making judgments of semantic similarity and to be unable to recognize that two words were exemplars of the same category (Chiarello & Church, 1986).

J. Grose-Fifer, D. Deacon / Neuropsychologia 42 (2004) 1948–1960

1957

Table 3 A summary of the main findings of behavioral studies that have examined categorical priming, restricted to those where prime and target were lateralized to the same visual field Study

SOA

Task

LQ

Results

Chiarello et al. (1990) (Exp. 1)

575 ms Low % related words

LDT Yes/no

M = +0.73 (Bryden) At least +0.30

RH priming only

Chiarello et al. (1990) (Exp. 2)

600 ms Low % related words

Naming

M = +0.76 (Bryden)

RH priming only

Chiarello and Richards (1992)

600 ms Low % related words

LDT Yes/no

M = +0.79 (Bryden) At least +0.30

RH priming only did not vary with prime dominance

Abernethy and Coney (1990)

250 ms

Abernethy and Coney (1996)

250 and 450 ms Medium % related words

LDT Go/no-go

Collins (1999) (Exp. 1) (designed to elicit automatic processing)

250 ms Low % related words

Collins (1999) (Exp. 2) (designed to elicit controlled processing)

Comments

LH priming

Used associates

M = +0.74 (Bryden) S.D. 0.25

LH priming RH no priming

Used associates

LDT Go/no-go

+0.80 (Bryden) S.D. 0.24

LH priming only

RTs recorded in neutral condition (which affected all statistical comparisons) were much more variable than in either the related or unrelated conditions. If the related and unrelated trials had been compared directly, different conclusions would likely have been drawn, since there was actually more RVF (LH) priming in both the automatic and controlled conditions.

750 ms High % related words Categorical relationship pointed out

LDT Go/no-go

As above

RH priming only No inhibition from either hemisphere

A repetition of categories may have provided a context for the LH to elicit priming.

Koivisto (1997)

(1) 165 ms (2) 250 ms (3) 500 ms 4) 750 ms Low % related words

LDT Yes/no

M = +0.88 (Edinburgh)

(1) (2) (3) (4)

Koivisto (1998) (Exp. 1)

(1) 165 ms (2) 750 ms

LDT Go/no-go

M = +0.89 (Edinburgh)

Koivisto (1998)

(1) 165 ms (2) 750 ms Low % related words

LDT Yes/no to P and T

M = +0.89 (Edinburgh)

LH priming at both SOAs, no RH priming (1) RH priming (2) No priming

Koivisto and Laine (2000)a

600 ms 1) High NW % (57%) 2) Low NW % (15%)

LDT Yes/no

M = +0.86 (Edinburgh)

(1) RH priming (2) LH priming (however this only developed in 2nd block)

Koivisto and Hämmäläinen (2002)

1000 ms+ Low % related words

LDT Yes/no to P and T

M = +0.92 (Edinburgh)

LH priming only

Shears and Chiarello (2003)

510 ms High % related words

LDT (Yes/no) neutral baseline

M = +0.87 (Bryden)

LH & RH priming

LH priming No priming No priming RH priming

1958

J. Grose-Fifer, D. Deacon / Neuropsychologia 42 (2004) 1948–1960

Table 3 (Continued ) Study

SOA

Task

Medium % related words

LDT (yes/no) unrelated baseline LDT (go/no-go) neutral baseline (Go/no-go unrelated baseline

High % related words

Medium % related words

LQ

Results

Comments

No significant priming

No significant priming

No significant priming

a

The predicted effects were observed only in the second experimental block and the authors claim that this was because it took time for “performance strategies to stabilize”. However, as Chiarello (2000) points out there is no precedent for blocking effects in priming experiments and Koivisto and Laine’s results were based upon an insufficient number of trials (5–8 responses per block of each condition). According to our own calculations, when the data are collapsed across blocks, there appeared to be greater priming in the LVF(RH) than in the RVF(LH) in both nonword proportion conditions. Additionally, in the first block there seemed to be more RH than LH priming in the lower nonword proportion condition. If this condition truly reflects automatic processes, then there is no logical reason for these effects to reverse in the second experimental block. The authors nevertheless concluded that priming from categorically related exemplars is due to automatic processing in the LH but is generated predominantly from post-lexical semantic matching or meaning integration in the RH.

They have also been shown to be deficient in producing lists of semantically related items and seem to use little or no category clustering in retrieval tasks (Villardita, 1987; Welte, 1993). Converging evidence from an fMRI study, published since the submission of the present article, found that “pure categorical relations produce differential activity in the right isthmus gyrus cinguli, the right precuneus, and the right cuneus.” The authors concluded that “the processing of categorical relations. . . depends on RH areas” (Kotz, Cappa, von Cramon, & Friederici, 2002, p. 1769). Behavioral studies that have examined category priming have produced conflicting results. A summary of the main findings of each, as well as our comments where applicable, is provided in Table 3. The data of Chiarello (1988a,b) and Chiarello and Richards (1992) are consistent with those presented here, in that they also suggested RH processing of category information. Other behavioral studies would superficially appear to contradict the present findings in that they reported LH priming (see Table 3). However, in one of these (Koivisto & Laine, 2000), we calculated overall greater priming in the RH when conditions other than visual field and relatedness were collapsed and the primed and unprimed trials were compared directly. The Collins study (Collins, 1999) included many items that were associatively related (e.g., CARPET–FLOOR, PILLOW–SHEET, CLOCK–RADIO, BEEF–PORK, DESK–LAMP) which, according to our earlier study, would have produced LH priming. In addition, Collins used a very small number of categories (as many of her categories could be collapsed) and exemplars were repeated. Others (Chiarello & Richards, 1992) have suggested that LH priming is more likely to occur under these circumstances (in our view perhaps due to expectancy-based priming). The LH priming that was present in these studies might be attributed to the factors discussed above as well as lax control of horizontal eye movements in some studies (of particular concern at

longer SOAs), or participant characteristics (see methods section). A further caveat concerns the limitations of behavioral measures. As we have pointed out, reaction time includes decision and response related variability that is not present in the N400. Several authors have, in fact, attributed their behavioral findings to post-lexical checking (Koivisto, 1998) or response requirements (Shears & Chiarello, 2003). Shears and Chiarello (2003) for example, obtained different results using a go/no-go task than when a binary decision was required. The operation of such influences might well account for much of disparity in the behavioral data, but would not influence the present data. The present study is of value in that in addition to using a very stable physiological measure, the data were recorded to category exemplars that were not associates, eye movements were monitored, only strongly right handed monolingual subjects were selected, and statistical analyses were based upon more trials than several of the behavioral studies. In another physiological study, now in preparation in our laboratory, priming between non-associated category exemplars was also observed for items delivered to the LVF (RH) but not to the RVF (LH). The data generated by these two ERP studies are congruent with the findings of the physiological and neurological studies reviewed in the introduction (the fMRI data, and the data derived from neurological patients). We conclude from this, in accordance with “similarity based” theories (Hinton, 1989; McRae et al., 1997; Plaut & Shallice, 1993) that category membership is for the most part coded for by consistency between features. Further, the feature representations relied upon are maintained by the RH.

Acknowledgements This research was supported by NIH grant RO1 DC00895-11 to Diana Deacon.

J. Grose-Fifer, D. Deacon / Neuropsychologia 42 (2004) 1948–1960

References Abernethy, M., & Coney, J. (1990). Semantic and phonemic priming in the cerebral hemispheres. Neuropsychologia, 28, 933–945. Abernethy, M., & Coney, J. (1996). Semantic category priming in the left cerebral hemisphere. Neuropsychologia, 34, 339–350. Battig, W. F., & Montague, W. E. (1969). Category norms for verbal items in 56 categories: a replication and extension of the Connecticut norms. Journal of Experimental Psychology Monographs, 80, 1–45. Beeman, M. (1998). Coarse semantic coding and discourse comprehension. In M. Beeman, & C. Chiarello (Eds.), Right hemisphere language comprehension: Perspectives from cognitive neuroscience (pp. 255–284). Mahwah, NJ: Lawrence Erlbaum Associates. Beeman, M., Friedman, R. B., Grafman, J., Perez, E., Diamond, S., & Lindsay, M. B. (1994). Summation priming and coarse semantic coding in the right hemisphere. Journal of Cognitive Neuroscience, 6, 26–45. Carroll, J.B, Davies, P., & Richman, B. (1971). The American heritage word frequency book. New York: American Heritage Publishing Co. Chiarello, C. (2000). Inferring the nature of semantic processes by varying priming procedure: A reply to Koivisto and Laine. Laterality, 5, 23–27. Chiarello, C. (1991). Interpretation of word meanings by the cerebral hemispheres: One is not enough. In P. Schwanenflugel (Ed.), The psychology of word meanings (pp. 251–278). Hillsdale, NJ: Lawrence Erlbaum Associates. Chiarello, C. (1988). Lateralization of lexical processes in the normal brain: A review of visual half-field research. In H. H. Whitaker (Ed.), Contemporary reviews in neuropsychology (pp. 59–69). New York: Springer-Verlag. Chiarello, C. (1999). Parallel systems for processing language: Hemispheric complementarity in the normal brain. In M. T. Banich, & M. Mack (Eds.), Mind, brain, and language: Multidisciplinary perspectives. Mahwah NJ: Lawrence Erlbaum Associates. Chiarello, C. (1988). Semantic priming in the intact brain: Separate roles for the right and left hemispheres? In C. Chiarello (Ed.), Right hemisphere contributions to lexical semantics (pp. 59–69). Heidelberg: Springer-Verlag. Chiarello, C., Burgess, C., Richards, L., & Pollock, A. (1990). Semantic and associative priming in the cerebral hemispheres: Some words do, some words don’t . . . sometimes, some places. Brain and Language, 38, 75–104. Chiarello, C., & Church, K. L. (1986). Lexical judgments after right- or left-hemisphere injury. Neuropsychologia, 24, 623–630. Chiarello, C., & Richards, L. (1992). Another look at categorical priming in the cerebral hemispheres. Neuropsychologia, 30, 381–392. Collins, M. (1999). Differences in semantic category priming in the left and right cerebral hemispheres under automatic and controlled processing conditions. Neuropsychologia, 37, 1071–1085. Collins, A. M., & Loftus, E. F. (1975). A spreading activation theory of semantic processing. Psychological Review, 82, 407–428. Deacon, D., Grose-Fifer, J., Yang, C., Stanick, V., Hewitt, S., & Dynowska, A. (in press). Evidence for a new conceptualization of semantic representation in the left and right cerebral hemispheres. Cortex. Deacon, D., Hewitt, S., Yang, C., & Nagata, M. (2000). Event related potential indices of semantic priming using masked and unmasked words: evidence that the N400 does not reflect a post-lexical process. Cognitive Brain Research, 9, 137–146. Foss, D. J. (1982). A discourse on semantic priming. Cognitive Psychology, 14, 590–607. Gough, P. B., Alford, J. A., & Holly-Wilcox, P. (1981). Words and contexts. In O. H. L. Tzeng, & H. Singer (Eds.), Perception of print: Reading research in experimental psychology. Hillsdale, NJ: Lawrence Erlbaum Associates. Hagoort, P., Brown, C. M., & Swaab, T. Y. (1996). Lexical-semantic eventrelated potential effects in patients with left hemisphere lesions and aphasia, and patients with right hemisphere lesions without aphasia. Brain, 119, 627–649.

1959

Hinton, G. E. (1989). Implementing semantic networks in parallel hardware. In G. E. Hinton, & J. A. Anderson (Eds.), Parallel models of associative memory (pp. 161–188). Hillsdale, NJ: Erlbaum. Joordens, S., & Besner, D. (1992). Priming effects that span an intervening word: Implications for models of memory representation and retrieval. Journal of Experimental Psychology: Learning Memory and Cognition, 3, 483–491. Joyce, C. A., Gorodnitsky, I. F., King, J. W., & Kutas, M. (2002). Tracking eye fixations with electroocular and electroencephalographic recordings. Psychophysiology, 39, 607–618. Keil, F. C. (1989). Concepts, kinds, and cognitive development. Cambridge, MA: MIT Press. Koivisto, M. (1997). Time course of semantic activation in the cerebral hemispheres. Neuropsychologia, 35, 497–504. Koivisto, M. (1998). Categorical priming in the cerebral hemispheres: Automatic in the left hemisphere, post-lexical in the right hemisphere? Neuropsychologia, 36, 661–668. Koivisto, M., & Laine, M. (1999). Strategies of semantic categorization in the cerebral hemispheres. Brain & Language, 66, 341–357. Koivisto, M., & Laine, M. (2000). Hemispheric asymmetries in activation and integration of categorical information. Laterality, 5, 1–21. Kotz, S. A., Cappa, S. F., von Cramon, D. Y., & Friederici, A. D. (2002). Modulation of the lexical-semantic network by auditory semantic priming: An event-related functional MRI study. Neuroimage, 17, 1761–1772. Kotz, S. A., & Friederici, A. D. (2003). Electrophysiology of normal and pathological language processing. Journal of Neurolinguistics, 16, 43–58. Kotz, S. A., Friederici, A. D., & von Cramon, D. Y. (1999). Auditory word list priming in left and right temporal lobe lesion patients. Brain and Language, 69, 294–296. Kutas, M., & Hillyard, S. A. (1989). A neurophysiological probe of incidental semantic association. Journal of Cognitive Neuroscience, 1, 38–49. Masson, M. E. J. (1995). A distributed memory model of semantic priming. Journal of Experimental Psychology: Learning Memory and Cognition, 21, 3–23. McClelland, J. L., Rumelhart, & D. E. (Eds.). (1986). Parallel distributed processing: Explorations in the microstructure of cognition: Vol. 2. Psychological and biological models. Cambridge, MA: MIT Press. McRae, K., deSa, V., & Seidenberg, M. S. (1997). On the nature and scope of feature representations of word meaning. Journal of Experimental Psychology: General, 126, 99–130. Medin, D. L. (1989). Concepts and conceptual structure. American Psychologist, 44, 1469–1481. Nelson, D. L., McEvoy, C. L., & Schreiber, T. A. (1998). The University of South Florida word association rhyme and word fragment norms. http://www.usf.edu/FreeAssociation. Neely, J. H. (1991).Semantic priming effects in visual word recognition: A selective review of current findings and theories. In D. Besner, & G. W. Humphreys (Eds.), Basic processes in reading: Visual word recognition (pp. 264–336). Hillsdale, NJ: Lawrence Erlbaum Associates. Oldfield, R. C. (1971). The assessment and analysis of handedness: The Edinburgh Inventory. Neuropsychologia, 9, 97–113. Palermo, D. S., & Jenkins, J. J. (1964). Word association norms: Grade school through college. Minneapolis: University of Minneapolis Press. Plaut, D. C., & Shallice, T. (1993). Deep dyslexia: A case study of connectionist neuropsychology. Cognitive Neuropsychology, 10, 377– 500. Pulvermüller, F. (2002). A brain perspective on language mechanisms: From discrete neuronal ensembles to serial order. Progress in Neurobiology, 67, 85–111. Quillian, M. R. (1967). Word concepts: A theory of simulation of some basic semantic capabilities. Behavioral Science, 12, 410–430. Ratcliff, R., Hockley, W., & McKoon, G. (1985). Components of activation: Repetition and priming effects in lexical decision and

1960

J. Grose-Fifer, D. Deacon / Neuropsychologia 42 (2004) 1948–1960

recognition. Journal of Experimental Psychology: General, 114, 435– 450. Ratcliff, R., & McKoon, G. (1988). A retrieval theory of priming in memory. Psychological Review, 95, 385–408. Rips, L. J., 1989. Similarity, typicality and categorization. In S. Vosniadou, & A. Ortony (Eds.), Similarity and analogical reasoning (pp. 21–59). Cambridge: Cambridge University Press. Rosch, E., 1973. On the internal structure of perceptual and semantic categories. In T. E. Moore (Ed.), Cognitive development and the acquisition of language (pp. 111–144). New York: Academic Press. Rumelhart, D. E., & McClelland, J. L. (Eds.). (1986). Parallel distributed processing: Explorations in the microstructure of cognition: Vol. 1. Foundations. Cambridge, MA: MIT Press. Seidenberg, M. S., & McClelland, J. L. (1989). A distributed, developmental model of word recognition and naming. Psychological Review, 96, 523–568.

Shears, C., & Chiarello, C. (2003). No go on neutrals? An interhemispheric account of semantic category priming. Laterality, 8, 1–23. Swaab, T., Baynes, K., & Knight, R. T. (1998). Coarse semantic coding in the right hemisphere: An ERP study. In Proceedings of the 5th Annual Meeting of the Cognitive Neuroscience Society Supplement, 34. Tipper, S. P., Weaver, B., Cameron, S., Brehaut, J. C., & Bastedo, J. (1991). Inhibitory mechanisms of attention in identification and localization tasks: Time course and disruption. Journal of Experimental Psychology: Learning Memory and Cognition, 17, 681–682. Villardita, C. (1987). Verbal memory and semantic clustering in right brain-damaged patients. Neuropsychologia, 25, 277–280. Welte, P. O. (1993). Indices of verbal learning and memory deficits after right hemisphere stroke. Archives of Physical Medicine and Rehabilitation, 74, 631–636.