Incidental learning of secondary attentional cueing

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Acta Psychologica 127 (2008) 459–475 www.elsevier.com/locate/actpsy

Incidental learning of secondary attentional cueing David Navon *, Ronen Kasten Department of Psychology, The University of Haifa, 31905 Haifa, Israel Received 15 April 2007; received in revised form 23 August 2007; accepted 24 August 2007 Available online 24 October 2007

Abstract Subjects instructed to detect targets following moderately valid location cues started being presented at some point in the course of the experiment, without having been informed about it, with a color secondary cue on all invalidly cued trials. In Experiment 1 most subjects quickly learned to use the secondary cue, ending in latency cost being eliminated or even turned negative. The effect failed to manifest only when the secondary cue appeared outside the object serving as imperative cue. Experiment 2 showed that performance with a secondary cue differed significantly from the performance in two control conditions in which colors were not correlated with validity or were not presented at all. On the other hand, it resembled performance of subjects informed beforehand about the secondary cue. Awareness of the contingency as well as of its effect on behavior was probed by a post-test questionnaire. An effect of learning without awareness was not observed in Experiment 1, but was found in Experiment 3, where awareness was probed more shortly after the emergence of incidental learning. Conceivably, subjects first learn to use the contingencies implicitly, and only later do they become aware of the outcome of that learning. Apparently, the attentional system might incidentally learn contingencies detected while being engaged in another task and use them for orienting despite a partial conflict with the following as instructed endogenous cues.  2007 Elsevier B.V. All rights reserved. PsycINFO classification: 2343; 2346 Keywords: Visual attention; Implicit learning

1. Introduction Visual attention is known to be guided by both exogenous and endogenous factors (see reviews by Cave & Bichot, 1999; Yantis, 2000). Whereas exogenously originated guidance is mediated by simple, probably wired-in stimulation, like abrupt onset of a stimulus at the vicinity of the would-be-attended region (e.g., Jonides & Yantis, 1988; Yantis, 2000; Yeshurun & Carrasco, 2000), endogenous guidance requires somewhat more elaborate processing. For example, central cues are believed to affect visual attention by triggering computational processes that determine the destination of a would-be-attentional shift and then control the shift (e.g., Jonides, 1981; Navon & Pearl, 1985; Posner, Nissen, & Ogden, 1978). The computation *

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relies on information known to be functionally related with the target stimuli. For example, the direction of an arrow may tell the subject where a target stimulus is likely to be subsequently presented, in a manner that is specified in the task instructions. The cue-location mapping may even be arbitrary, as when the digits 1 and 2 designate, respectively, right and left (e.g., Downing & Pinker, 1985; Kasten & Navon, in press). Typically, the mapping is acquired by explicit instruction. The issue addressed here is whether or not on top of that subjects can learn to employ information that is pertinent for attentional selection yet is initially unknown to be pertinent. If such incidental learning took place, it would be instructive to know which factors it was sensitive to. A factor that seems a priori relevant is the extent of attention still available for that initially impertinent information. Although the latter is unattended in a sense before it is

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known to be pertinent, the location (or object, or object part) at which the to-be-learned cue is presented might still benefit from some share of visual attention. The bulk of present models of visual attention commonly posit that attention is somehow distributed around fixation at cueing time (e.g., Egly, Driver, & Rafal, 1994; Eriksen & Eriksen, 1974; Hoffman & Nelson, 1981; Logan, 1996) which seems to depend considerably on cueing type – endogenous versus exogenous (Macquistan, 1997), but also on other factors that induce narrowing or spread of attention irrespective of cueing type (Goldsmith & Yeari, 2003). Does such incidental learning depend, and if so in what manner, on the relationship between the element in the field that carries the newly pertinent information and the location (or object) visual attention must be focused at (or distributed around) the time that information could be encoded? It seems a priori plausible that realizing the pertinence of the initially impertinent information depends on the amount of residual attention the element carrying it happens to benefit from at learning time. That in turn might well be determined by the demands of the task as described to the subject by the instructions, presumably on top of the object structure of the stimulus functioning as imperative cue (Yeari, 2003). It is still possible that such learning, because it is incidental, emerges within a representation encoded preattentively, so that its likelihood is fairly indifferent to the amount of attention available to it, including visual attention. A quite related issue is whether or not such incidental learning is also implicit, namely, learning ‘‘. . . that proceeds both unintentionally and unconsciously’’ (Shanks, 2005, p. 202). Presently, quite a vast body of the literature has been compiled on demonstrations of what seems to be implicit learning (see, e.g., Berry & Cock, 1998; Berry & Dienes, 1993; Frensch & Ru¨nger, 2003; Goschke, 1998; Neal & Hesketh, 1997; Reber, 1993; Reed & Johnson, 1998; Schacter, 1987; Shanks & Johnstone, 1998; Stadler & Roediger, 1998). Particularly relevant for our concern are effects of incidentally learned rules on orienting covert attention (Chun & Jiang, 1998, 2003; Lambert, Naikar, Mclachlan, & Aitken, 1999; Lambert & Sumich, 1996). Lambert et al., for example, had subjects respond to a target presented at either of the two locations after they had been briefly exposed to two bilaterally presented letters at the periphery. The exact spatial layout of the letters predicted (with a validity of .80) the target location. Although subjects were not instructed about that contingency, they were found under certain conditions to utilize the cue information for orienting visual attention. Interestingly, most of them were apparently unaware of the cue-target contingency and sometimes even of cue appearance itself. Chun and Jiang (1998) had subjects perform a simple visual search task, where the spatial configuration of the stimuli was varied. They found a benefit in the search performance in the context of old invariant configurations, despite chance level recognition of those configurations. The authors concluded

that implicit learning and memory could have guided visual attention. Whereas the cited findings surely indicate that visual attention may be guided by an incidentally learned rule, it is still unclear whether such rules can be learned and utilized while the attentional system is engaged in directing attention by an explicit rule. This is where the studies reported here come in. To illustrate, suppose subjects are instructed to use the direction of arrows as an attentional cue, yet that cue is not a perfectly valid one. Suppose now an apparently irrelevant feature appears in perfect correlation with cue validity (e.g., when the arrow is pointed, the cue is valid, but when the arrow is obtuse, the cue is invalid), but the subject is unaware of that contingency unless s/he discovers it him/ herself. Utilizing it would thus enable subjects to optimize their orienting behavior, actually turning a probabilistic rule to a deterministic one. Would subjects notice the correlation? Would they be able to use the feature as a secondary cue, namely, a cue that does not indicate location, rather how to relate to the imperative cue at the particular trial? It was recently demonstrated that incidental learning requires that the to-be-learned cue is attended, probably because it is not deemed totally irrelevant (Hoffmann & Sebald, 2005). Hoffmann and Sebald had their subjects respond either to the identity or to the location of either of the two target playing cards presented along with three card distractors following a 1 s preview of the backs of all cards. The backs had on them either of two symbols that were made to correlate perfectly with either of the to-beresponded attributes of the presented target. Only a small number of subjects manifested learning, all of which seemed to have learned the contingency explicitly. In further experiments it was found that it helped when the cue was a feature of the presented target. The authors conclude that even clearly visible and distinct cues cannot give rise to learning, not even implicit, unless being attended. In view of that, it could be reasonably expected that in the experiment envisioned above, a subject’s presumption, entailed by the instructions, that the only clue about target location is the imperative cue (arrow, in the example above), must be likely to lead her to hold any unanticipated feature as irrelevant enough to the location of the subsequent target. Thus, it is far from being clear that a correlation between feature presence and imperative cue validity would be noticed at all. In this study we examine first whether it does, and then several other issues: If it does, could subjects implicitly learn to exploit it without it being consciously noticed? How rapidly can they learn? Would that learning be subsequently applied for guiding attention, considering that for serving the guidance of attention it must override the explicit rule? Would it make a difference what the location of the apparently irrelevant feature was with respect to the region where the imperative cue information is picked up? Incidental learning might not be confined to the focus of visual attention at the time the newly relevant feature is

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encoded. However, spatial distance or other perceptual relationships might facilitate or impede it, conceivably because they modulate the amount of visual attention at the location of that feature. That might not be true if that learning is not only incidental but also implicit. Implicit learning, in the strict sense, is often presumed to be quite insensitive to how attention is deployed at the moment learning is affected as well as to what the cognitive load is at that time (e.g., Stadler, 1995; but see Rowland & Shanks, 2006). Hence, to the extent that learning to use an apparently irrelevant feature for guiding attention is implicit, it must be very little affected by the focus of visual attention at cue encoding. Would incidental learning of the contingency between an apparently irrelevant feature and cue validity manifest such insensitivity or would it substantially depend on factors such as spatial distance or whether or not the feature and the imperative cue are part of the same object? For studying that issue, we used a paradigm in which subjects that were instructed to use a location cue were at some point in the course of the experiment, without having been informed about it, presented with a pink color spot on all invalidly cued trials. The presence/absence of that color, made to correlate perfectly with the validity of the imperative one, could then be relied on as a secondary cue, possibly serving to reduce or eliminate latency cost. 2. Experiment 1 Experiment 1 was meant (a) to test whether or not incidental learning of a secondary cue is possible, (b) to manipulate systematically attention condition, defined as the combination of imperative cue shape and secondary cue location, in order to separate the effects of space-based and object-based attention on secondary cue learning and utilization. We used a task of detecting a luminance change. The change could occur randomly at one of two different locations. A spatial cue that pointed to one of two directions indicated to the subject where the change was expected to occur. The cue was valid in 2/3 of the experimental trials, invalid in 1/6 of them, and neutral in the remaining 1/6. At some point in the course of the experiment, without the subject having been informed about it, a secondary cue started being administered. That cue was the color of some element in the field, and it was made to correlate perfectly with the validity of the imperative cue. In other words, in invalid experimental trials the color was not the standard one used in any other trial. If subjects learned to use the secondary cue, implicitly or explicitly, that would be reflected in the cost of invalid trials. In theory, it could be nullified or even turned into a negative cost, which might be termed secondary benefit. If the application of the incidentally learned rule conflicted too much with applying the explicit rule, the application of the former might be inhibited at some point after learning, so that an initial reduction in cost might be later

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reversed. All that would shed light on the dependence of the learning and/or the use of an incidentally learned rule on the cognitive load embodied by applying the explicit rule. To examine the sensitivity of incidental learning, were it found, to the extent of visual attention that the secondary cue may capitalize on, we manipulated its spatial relationship with the focus of visual attention at imperative cue encoding. That was done by placing it either within, or outside of, the object serving as the imperative cue, and either at, or far away from, the part of it that pointed the location. 2.1. Method 2.1.1. Apparatus and setting Stimulus presentation and data acquisition were controlled by an O2 SiliconGraphics computer. Stimuli were presented on the screen of a 22 inch computer display. Each subject sat in front of the display and responded by pressing on a designated keyboard key (actually, one of the control keys) with the index finger of his or her dominant hand. Viewing distance was about 130 cm. 2.1.2. Design and procedure A go/no-go task of detecting a brief change in the luminance of a dot was used. The change could occur randomly in one of two dots that appeared at two opposite sides of the fixation point. A direction-pointing cue (henceforth called the imperative cue) indicated to the subject where the change was expected to occur. The cue could either be a simple arrow or a closed form pointing in one direction. In each experimental trial a fixation cross was presented for 500 ms, then a cue was presented for 700 ms, then a change occurred for 66 ms. The subject was allowed 2 s for responding to the change by pressing on a designated keyboard key. The subsequent trial started 900 ms after the subject responded or after 2 s has elapsed. In addition, there were catch trials where no change occurred and subjects were asked not to respond but to wait for the subsequent trial. In such a case, 2 sec elapsed from cue onset to the start of the subsequent trial. Overall, each experimental session consisted of 1080 trials, of which 120 were catch trials. Out of the remaining trials, the cue was valid (namely, pointed to the location where the change occurred) in 2/3 of them, invalid in 1/6 of them, and neutral (namely, a rhombus that did not point anywhere) in the remaining 1/6. At some point in the course of the experiment, without the subject having been informed about it, a secondary cue was administered. The secondary cue was the color of some element in the field that correlated perfectly with the validity of the cue – pink in invalid experimental trials, white as usual in any other trial. The element in the field that carried the secondary cue was manipulated between subject groups, referred to henceforth as attention conditions. The

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set of attention conditions was designed as a factorial combination of imperative cue shape (arrow, closed pointed form) and secondary cue location (at the element of the imperative cue that pointed location, or quite far – back of and above – it). It thus comprised of the following four conditions: (a) the secondary cue was placed at the arrow head, (b) the secondary cue was placed at the rectangle above the arrow ‘‘tail’’, (c) the secondary cue was placed at the head of the closed form pointing to the cued location and (d) the secondary cue was placed at a location identical with the one used in condition b, yet now located within the upper back portion of that closed form. The four alternative combinations are shown in Fig. 1. In the initial block of 216 trials, used to obtain a baseline for the cueing effect, only imperative cues were presented. The first 54 trials of the baseline block were considered as practice trials and were not included in the statistical analysis. In the following four experimental blocks, of 216 trials each, the secondary cue was presented in each invalid experimental trial. There were three breaks, between every two consecutive experimental blocks. 2.1.3. Stimuli The imperative location cue could be either an arrow (in attention conditions a and b) or a closed form pointing to the cued location (in attention conditions c and d), both white on black background.

The length of the arrow was 25 mm (subtending 1.10 deg of visual angle for a viewing distance of 130 cm). It comprised of two sections: a head – a small triangle (with each side having 7 mm, and a height of 4 mm) and a body – a straight line 15 mm in length (.66 deg of visual angle). When the imperative cue was an arrow, a small square (each side – 5 mm) was presented along with it, 35 mm (1.54 deg of visual angle) above its middle. The height of the closed pointed form was 8 cm (3.52 deg of visual angle). Its head had the same form as the arrow head in the arrow cue conditions and was equal to it in location and area. The uppermost and lowermost square-like parts of the back of that form were equal in location and area to the square in the arrow cue condition. The upper front ribs that connected these squares to the head were 25 mm each. The neutral cue was a small white diamond (each of its sides – 7 mm in length). The luminance change (the target event) occurred in one of two white dots (8 mm in diameter) located at the opposite sides of the fixation cross. The distance between fixation and the middle of each circle was 85 mm (3.75 deg of visual angle). The target event was a brief color change (66 ms) from white to black. Only an inner portion of the white dot (of 5 mm diameter) changed its color. The color of the secondary cue element was reddish pink. Its RGB values (weights of red, green and blue in

Fig. 1. The four combinations of imperative cue shape and secondary cue location, each used in a different attention condition of Experiment 1. The location of the secondary cue is marked in gray. The remaining area of the imperative cue – in white.

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tionnaire comprised of six questions (see Appendix A), of which the last five probe some aspects of awareness. Thirty-eight of the 91 subjects whose data were analyzed also filled the post-experiment questionnaire.

the additive mixture) were 255, 80, and 80. It appeared at one of two places, depending on attention condition – at the arrow head (in condition a) or at the square above the arrow ‘‘tail’’ (in condition b). In the case of the closed pointed form, both places were inside it, at identical locations as in the arrow cue conditions, namely, at its head pointing to the cued location (in condition c), or at the uppermost square-like part of its back (in condition d).

2.2. Results and discussion 2.2.1. Mean latency and error percentages Trials with latencies shorter than 150 ms or longer than 1000 ms were excluded from analysis (1.7% of all trials). The data were cast into a mixed four-way ANOVA with two within-subject factors (blocknumber and validity) and two between-subject factors (secondary cue location and imperative cue shape). Significant main effects were found for the factors validity, F(2, 174) = 82.46, p < .0001, MSE = 2070, and blocknumber, F(4, 348) = 72.31, p < .0001, MSE = 1653. Significant effects were found for three pairwise interactions: blocknumber · validity, F(8, 696) = 11.03, p < .0001, MSE = 327, imperative cue shape · validity, F(2, 174) = 3.69, p < .05, MSE = 2070, and secondary cue location · validity, F(2, 174) = 4.05, p < .05, MSE = 2070. One triple interaction, secondary cue location · blocknumber · validity, was also found significant, F(8, 696) = 2.06, p = .05, MSE = 327. The quadruple interaction, secondary cue location · imperative cue shape · blocknumber · validity, was found significant as well, F(8, 696) = 2.58, p < .01, MSE = 327. Mean latencies as a function of validity, attention condition (broken down to secondary cue location and imperative cue shape) and blocknumber are presented in Table 1. The overall error rate in target-present trials, namely, misses, was small (2.29%). Analysis of the arcsine square root transforms of percentages of incorrect responses yielded a main effect for validity, F(2, 174) = 12.03, p < .0001, MSE = .0156. All other effects were non-significant.

2.1.4. Subjects One hundred and twenty three subjects participated in the experiment, all first-year students at the Department of Psychology of the University of Haifa, for partial fulfillment of their course credit. They were further sorted by two criteria – (a) making no more than 30% errors in catch trials; (b) surpassing a reasonably moderate effect of imperative cue validity (mean latency in invalid trials minus mean latency in valid ones) in the baseline block. For that purpose we calculated an individual t-value, the numerator of which was the mean difference between valid and invalid trials for each subject. The minimal t-value for meeting the criterion was set to 1. These criteria were meant to exclude subjects who did not respond adequately to the imperative cue. Five subjects were excluded by the first criterion, and 26 more subjects – by the second criterion. One more subject was excluded because of extreme low accuracy level. The data of all other 91 subjects were analyzed. About a third of all the subjects were given a detailed post-experiment questionnaire to assess awareness of the secondary cue and of its contingency with imperative cue validity. If no evidence for awareness is found despite a systematic change in behavior, it may be seen as the sort of dissociation required for diagnosing an unconscious process (cf. e.g., Dixon, 1971, 1981; Holender, 1986; Merikle & Reingold, 1998; Stadler & Roediger, 1998). The ques-

Table 1 Mean latency in Experiment 1 as a function of attention condition (see text), validity and blocknumber Attention condition

Validity

Blocknumber

Mean 1–4

Baseline

1

2

3

4

(a) N = 20

Invalid Neutral Valid

405 374 338

357 356 324

326 332 305

320 327 299

309 331 296

328 337 306

(b) N = 20

Invalid Neutral Valid

391 369 320

354 337 300

344 321 287

338 317 281

340 318 281

344 323 287

(c) N = 23

Invalid Neutral Valid

376 356 331

333 327 311

322 323 304

322 315 296

314 320 294

323 321 301

(d) N = 28

Invalid Neutral Valid

366 344 320

335 321 301

316 307 284

300 305 277

300 306 278

313 310 285

Total N = 91

Invalid Neutral Valid

383 359 327

344 334 308

326 320 294

318 315 287

314 318 287

326 322 294

Number of subjects in each condition is given below its label.

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The overall pattern of results was similar to the one observed in the mean latency analysis. No sign of speedaccuracy tradeoff was evident. As can be seen in Table 1, in all attention conditions (namely, combinations of secondary cue location and imperative cue shape), an effect of imperative cue validity was, as expected, obtained in the baseline block. In three conditions, mean latency in invalid trials decreases considerably starting with the first experimental block. This tendency is most salient in attention condition a, in which the imperative shape was an arrow and the secondary cue appeared at its head. In contrast, in attention condition b, in which the secondary cue appeared at the rectangle above the arrow, there was no sign of any change in invalid trials in the experimental blocks. In the invalid condition, the decline from the baseline block to the last experimental one was larger than the corresponding declines in valid trials and in neutral ones. As a result, the benefit from a valid cue (namely, the difference in mean latency between neutral and valid trials) was basically constant throughout the blocks, whereas the cost of an invalid cue (namely, the difference in mean latency difference between invalid and neutral trials) decreased considerably with blocknumber, probably as the subject learns to anticipate the presence of the target in the noncued location in invalid trials. This can be readily seen in Fig. 2, in which costs (in each of the four attention conditions) and benefit (across attention conditions) are presented as a function of blocknumber. To examine the effects of factors on costs and benefits, mixed three-way ANOVAs for the factors blocknumber, secondary cue location and imperative cue shape were conducted. The analysis on costs yielded significant main effects for blocknumber, F(4, 348) = 9.86, p < .0001, MSE = 876, and secondary cue location, F(1, 87) = 4.72,

p < .05, MSE = 3141. The pairwise interaction blocknumber · secondary cue location was also found significant, F(4, 348) = 2.54, p < .05, MSE = 876, as well as the triple interaction blocknumber · secondary cue location · imperative cue shape, F(4, 348) = 3.39, p < .01, MSE = 876. A similar ANOVA applied to benefits yielded significant effects for blocknumber F(4, 348) = 3.35, p < .01, MSE = 318, and imperative cue shape, F(1, 87) = 6.54, p < .025, MSE = 2432. No other main effect or interaction was found significant. As can be seen in Fig. 2, the small effect of blocknumber on benefit was non-monotonic and hard to make sense of. Anyhow, the benefit in the last experimental block was about the same as the benefit in the baseline block. With regard to costs (actually presented in the figure as negative RT differences), interesting differences were manifested between attention conditions. In condition a, in which the imperative shape was an arrow and the secondary cue appeared at its head, cost changed from 31 ms in the baseline block to 23 ms in the last experimental block. In contrast, in attention condition b, in which the secondary cue appeared at the rectangle above the arrow, the cost stayed about the same throughout blocks. When the imperative cue shape was a closed pointed figure, the location of the secondary cue did not matter for the trend manifested across trials by cost: When the color appeared at the head of the closed figure (attention condition c), cost changed from 19 ms in the baseline block to 5 ms in the last experimental block. When color appeared at the upper extreme of the figure’s back (attention condition d), cost changes from 22 ms to 6 ms. In order to explore the significant triple interaction observed, we conducted several further analyses. When the imperative cue shape was an arrow, a two-way ANOVA on costs yielded a significant effect of the interac-

35 30

Overall benefit

25 20

RT difference

15 10 5 0 -5 -10

Attention Overall benefit condition a

-15 -20

-

b

-

c

-

d

-25 Baseline

1

2

3

4

Blocknumber

Fig. 2. Mean costs in each of the four attention conditions and mean benefits across attention conditions as a function of blocknumber in Experiment 1.

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a

465

30

Mean cost

20 10 0 -10 -20 Baseline Baseline

1

Baseline

b

1

1

2

3

4

5

6

7

8

9

5

6

7

8

9

5

6

7

8

9

Octuple

80 60

Mean cost

40 20 0 -20 -40 -60 -80

c

2

3

4

Octuple

80 60

Mean cost

40 20 0 -20 -40 -60 -80 2

3

4

Octuple

Fig. 3. Mean costs as a function of ordinal number of octuple in the first half of the first experimental block in Experiment 1. Panel a presents mean cost across all subjects that manifested cost decrease over the period (see text). Panels b and c present individual curves illustrating gradual and abrupt decrease, respectively.

tion blocknumber · secondary cue location, F(4, 152) = 4.02, p < .005, MSE = 970. Similar analysis on benefits did not show any sign of an interaction (F < 1). Significant effect of blocknumber was evident only when the secondary cue appeared at the arrow head, F(4, 76) = 7.02, p < .0001, MSE = 1099, but not when it appeared above the ‘‘tail’’ (F < 1). When the imperative cue was a closed pointed form, post-hoc analysis did not show a significant interaction between blocknumber and secondary cue location, neither for costs, F(4, 196) = 1.21, p = .31, MSE = 803, nor for benefits (F < 1). Across the two conditions, however, the analysis on costs yielded a significant effect of blocknumber, F(4, 196) = 6.28, p < .0001, MSE = 803. 2.2.2. The learning curve To further examine the shape of the learning curve, a more fine-grained analysis was applied to the data from the first half of the first experimental block (108 trials in

total) of attention condition (a) in which learning was most manifest. A running average of cost for that span of trials was obtained in the following way: Mean latencies were calculated for octuples of partly overlapping consecutive invalid trials (starting with the first to the eighth, then the second to the ninth, etc.) and for octuples of partly overlapping consecutive neutral trials. The mean costs are presented in Fig. 3a as a function of ordinal number of octuple. It can be readily seen that a practically null cost is observed already in the first octuple. It thus appears that, at least when the secondary feature is particularly salient, some learning that helps to eliminate the cost may be already evident after very few invalid trials with secondary cues.1 The further monotonic rise may well be due to 1

In theory, one invalid trial in which the non-mandatory feature is present followed by several valid ones (in which that feature does not appear, of course) might be enough for suggesting a contingency, especially since within a context as sterile as a laboratory experiment is, nothing looks accidental.

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Table 2 Distribution of number of errors per subject in answering the questions that assess awareness (in Experiment 1), and the frequencies of errors in each of the questions conditional on the overall numbers of errors Number of errors

Question 2

Question 3

Question 4

Question 5

Distribution

0 1 2 3 4 5

0 0 0 0 2 –

0 0 0 1 1 –

Question 6 0 0 2 8 3 –

0 1 3 9 3 –

0 0 5 9 3 –

20 1 5 9 3 0

Total

2

2

13

16

17

38

averaging across subjects. Inspecting learning curves of 14 individual subjects who seemed to have learned during the examined 108 trials (at least 30 ms decrease in cost) revealed that seven had a gradual decrease and seven had an abrupt one. Two examples of individual learning curves, one for each type, are presented in Fig. 3b and c. Note, however, that the rate of decrease reflects the degree to which the rule is implemented. The acquisition of the contingency underlying the rule might have been learned early and abruptly even by subjects who exhibit a gradual decrease. 2.2.3. False alarms analysis Rates of false alarms in catch trials might suggest criterion shifts. Accordingly, we conducted analyses of false alarm rates to examine whether or not effects on mean latency attributed to attention shifts could not be alternatively due to some criterion shift presumably induced by the anticipation of target location following the appearance/non-appearance of the secondary cue or just by its mere appearance. In this analysis we used raw rates rather than arcsine square root transforms.2 We conducted two analyses. The first one was aimed to explore whether subjects react differently to the appearance of the red color when no target follows. In this analysis we omitted the neutral condition and the base line block. A significant main effect of blocknumber; F(3, 261) = 6.26, p < .001, MSE = 109.67, was found. This effect reflects a tendency toward an overall increase in false alarms from the first experimental block (3.3%) to the last one (7.4%). No other effect was found significant. No main effect of the appearance of color was found (F < 1). The interaction between this factor and blocknumber also did not reach significance, F(3, 261) = 1.38, p > .25, MSE = 93. In another analysis, arrow trials (across colors) were compared with neutral trials, in all blocks including the baseline one. The analysis yielded a significant main effect of arrow presence, F(1, 87) = 13.50, p < .001, MSE = 87.42, where the false alarm rate was higher in trials with 2 The transformation, used to normalize the distribution of rates in case it is extremely asymmetric, is not needed when a substantial number of rates assume the values 0 or 1 and other rates are not extreme. That is true in the present case, where the false alarms rates, especially in the case of neutral trials and invalid trials, are based on very small number of cases (only four catch trials on each block).

arrows (5.8%) compared with neutral trials (3.6%). The interaction between this factor and blocknumber was just close to significance, F(4, 348) = 2.40, p = .06, MSE = 49.37, suggesting that the effect was perhaps smaller in the first two experimental blocks. 2.2.4. Analysis of awareness Thirty-eight of the 91 subjects, whose data were analyzed, were also asked to fill the post-experiment questionnaire (see Appendix A). Table 2 presents both the distribution of number of errors in answering the last five questions that assess awareness, and the frequencies of errors in each of those questions conditional on overall number of errors. The pattern suggests a Guttman scale, where questions 2, 3, 6, 4 and 5 indicate, in this order, increasing degrees of awareness (see further analysis in Section 4.1.3). In theory, a subject who was fully aware of the process required for the application of secondary cue utilization could be reasonably supposed to have noticed the occasional appearance of the stimulus which served as a secondary cue, thought that it helped him/her, could explain how, and picked the correct explanation (alternative b) in the multiple-choice question, hence to have no error. Yet, to allow for some slips of memory or comprehension, a subject was counted as having full awareness if s/he answered at least four questions right. Actually, none of those failed in the open question that asked for recalling the learned rule (no. 5). The overall number of subjects who did not seem to have awareness was 17 (45%). Thirteen of the latter did not show any sign – neither in the open question (no. 5) nor in the multiple-choice one (no. 6) – that they understood how the secondary cue was related to the validity of the imperative cue. Sixteen did not realize that the cue could be helpful (question 4). However, all but two claimed (question 2) to have been aware of the occasional presence of the stimulus serving as the secondary cue. All but two appeared to know (question 3) the color of the secondary cue (including one who claimed earlier to have been unaware of its very presence). Although it is conceivable that some of the unaware subjects had been aware during the experimental blocks, or at least during part of them, but have forgotten that by the time the questionnaire was administered, that does not seem very likely.

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A mixed three-way ANOVA with two within-subject factors (blocknumber and validity) and one between-subject factor (awareness) was conducted. Significant effects were found for the factors validity, F(2, 72) = 34.97, p < .0001, MSE = 2025, and blocknumber, F(4, 144) = 26.24, p < .0001, MSE = 1902. On the other hand, no significant main effect of awareness was found (F < 1). All pairwise interactions were found significant: validity · blocknumber, F(8, 288) = 5.89, p < .0001, MSE = 384, awareness · validity, F(2, 72) = 4.04, p < .05, MSE = 2025, and awareness · blocknumber, F(4, 144) = 2.93, p < .05, MSE = 1902. Most important for our concern, the triple interaction validity · blocknumber · awareness was found significant, F(8, 288) = 5.17, p < .0001, MSE = 384. Analysis on the arcsine square root transforms of percentages of misses yielded significant main effect for validity, F(2, 72) = 4.49, p < .05, MSE = .021. No other main effect or interaction was found significant. No sign of speed-accuracy tradeoff was evident. Mean costs as a function of attention condition, awareness and blocknumber (where all experimental blocks are collapsed) are presented in Table 3. Since the number of observations in the cells was small, the analysis was conducted on data collapsed across all attention conditions. A mixed two-way ANOVA applied to costs yielded significant effects for the factors awareness, F(1, 36) = 11.30, p < .002, MSE = 2309, blocknumber, F(4, 144) = 5.28, p < .0005, MSE = 952, as well as for their pairwise interaction, F(4, 144) = 7.82, p < .0001, MSE = 952. A similar ANOVA applied to benefits did not yield any significant main effect or interaction. In further analyses conducted within awareness values, it was found that whereas a clear reduction in cost was manifested in the group of subjects who seemed to be aware, F(4, 80) = 11.49, p < .0001, MSE = 1165, no such

Table 3 Mean cost in Experiment 1 as a function of attention condition, awareness, and blocknumber (for subjects who filled the post-test questionnaire) Attention condition

Awareness

Cost

(a)

Aware N = 6 Unaware N = 4

34 33

19 2

(b)

Aware N = 7 Unaware N = 5

34 9

6 27

(c)

Aware N = 5 Unaware N = 2

33 18

24 41

(d)

Aware N = 3 Unaware N = 6

35 12

40 15

Total

Aware N = 21 Unaware N = 17

34 17

15 19

Baseline block

Mean across experimental blocks 1–4

Numbers of subjects in each condition that were aware or unaware are given below the awareness level.

467

effect was obtained in the group of subjects who seemed to be unaware (F < 1). 3. Experiment 2 To get a better idea of the magnitude of the learning effect demonstrated in Experiment 1 and to examine the possibility that the incidental learning effect was artifactual, it seemed instructive to explore lower and upper boundary conditions for performance in it. For that purpose we conducted another experiment comprised three control conditions administered to three different subject groups: one in which subjects were not given any secondary cues throughout the experiment; one in which subjects were presented with the same number of stimuli used as secondary cues in Experiment 1, only without any contingency with imperative cue validity; and another one in which subjects were presented with secondary cues that are perfectly contingent with validity, but were explicitly told in the instructions about that. Comparing costs in Experiment 1 with corresponding costs in these control groups may serve as a measure of the magnitude of the incidental learning effect in the former. 3.1. Method 3.1.1. Subjects Eighty-two subjects participated in the experiment, all first-year students at the Department of Psychology of the University of Haifa, for partial fulfillment of their course credit who had not participated in Experiment 1. They were further sorted by the two criteria that were employed in Experiment 1 – (a) making no more than 30% errors in catch trials; (b) surpassing a reasonably moderate effect (t P 1) of the imperative cue validity (valid versus invalid) in the baseline block. Six subjects were excluded by the first criterion. Thirteen more subjects were excluded by the second criterion. Two more subjects were deleted because of extremely low accuracy level. The data of all other 61 subjects were analyzed. 3.1.2. Design The design and procedure of this experiment were similar to those of Experiment 1, in the arrow head attention condition, with the following group-specific differences. One control condition (labeled henceforth no-color) was designed to delineate the effect of blocknumber on cost when attention was supposed to be directed only by imperative cues. The subjects in this condition performed the same task used in Experiment 1, except that they were not presented with any secondary cue at all throughout the experiment. Another control condition (labeled random-color) was meant to examine whether or not some or all of the secondary cue effect could be due to the mere occasional presence of color stimuli regardless of any possible contingency with

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imperative cue validity. The subjects in this condition were presented, starting with the first experimental block, with the same number of color stimuli used as secondary cues in Experiment 1, presented on the arrow head, only without any contingency with validity. Specifically, color stimuli were presented at random on 8 of the 32 invalid trials and on 36 of the 128 valid trials in each experimental block – amounting to a correlation that is very close to 0 (r = .06). Finally, yet another control condition (labeled explicitcolor-instructions) was designed to find out the performance of subjects that were known to be fully aware of both secondary cueing and the manner in which it could be utilized. A third group of subjects was thus presented with secondary cues that were perfectly contingent with validity, exactly as they were in Experiment 1, but was explicitly foretold immediately after the baseline block about that contingency. 3.2. Results and discussion To enable comparisons, data from the present experiment were analyzed along with the data of the subjects in Experiment 1 in the condition where the secondary cue appeared at the arrow head and the imperative cue was an arrow (labeled henceforth contingent-color). The analysis was carried out on 81 subjects who have met the two criteria – 20, 27, 19, and 15, in the conditions contingent-color, no-color, random-color and explicitcolor-instructions, respectively. Mean latency data were cast into mixed three-way ANOVA with two within-subject factors (blocknumber and validity) and one between-subject factor (condition). Two significant main effects were found for the factors blocknumber, F(4, 308) = 25.88, p < .0001, MSE = 3642, and validity, F(2, 154) = 102.98, p < .0001, MSE = 3068. Two pairwise interactions, condition · blocknumber and validity · blocknumber, were found significant,

F(6, 154) = 11.21, p < .0001, MSE = 3068 and F(8, 616) = 5.35, p < .0001, MSE = 456, respectively. Most important, the triple interaction, condition · blocknumber · validity, was found significant as well, F(24, 616) = 6.20, p < .0001, MSE = 456. Mean latencies as a function of validity, condition, and blocknumber are presented in Table 4. The overall error rate in target-present trials, namely, misses, was small (4.1%). Analysis on the arcsine square root transforms of percentages of incorrect responses yielded significant results for the effects of validity, F(2, 154) = 19.86, p < .0001, MSE = .029, as well as for the pairwise interactions condition · validity, F(6, 154) = 5.70, p < .0001, MSE = .029, and blocknumber · validity, F(8, 616) = 4.39, p < .0001, MSE = .0082, as well as for the triple interaction, condition · blocknumber · validity, F(24, 616) = 2.93, p < .0001, MSE = .0082. The patterns of the accuracy data and the latency data were similar. No signs of speed-accuracy tradeoff were found. Separate analyses were carried out for mean costs and mean benefits. The mixed two-way ANOVA applied to benefits yielded no significant effect. On the other hand, a similar ANOVA applied to costs yielded significant main effects for blocknumber, F(7, 413) = 3.31, p < .01, MSE = 3580, and condition, F(3, 77) = 29.12, p < .0001, MSE = 1178, as well as for the pairwise interaction blocknumber · condition, F(12, 308) = 7.10, p < .0001, MSE = 11,178. Mean cost, as a function of blocknumber and condition, is presented in Fig. 4. As can be readily seen, in the conditions with no secondary cue, cost did not decrease. There seemed to be even an increase in cost in them. In a further analysis, the randomcolor condition that increase was found significant, F(4, 72) = 2.67, p < .04, MSE = 1087. It was found to be only close to significance in the no-color condition, F(4, 104) = 2.11, p < .09, MSE = 973. In contrast, in both the contingent-color condition and the explicit-color-instructions condition, cost decreased

Table 4 Mean latency in Experiment 2 as a function of condition, validity and blocknumber Condition

Validity

Blocknumber Baseline

1

2

3

4

Contingent-color N = 20

Invalid Neutral Valid

405 374 338

357 356 324

326 332 305

320 327 299

309 331 296

328 337 306

No-color N = 27

Invalid Neutral Valid

422 387 346

418 365 331

427 370 325

409 362 315

400 354 308

413 363 320

Random-color N = 19

Invalid Neutral Valid

389 384 332

369 338 308

354 328 292

354 321 282

350 315 281

357 325 290

Explicit-color-instructions N = 15

Invalid Neutral Valid

428 403 347

361 376 322

346 384 319

323 380 320

316 366 314

337 378 319

Number of subjects in each condition is given below its label.

Mean 1–4

D. Navon, R. Kasten / Acta Psychologica 127 (2008) 459–475

469

70 60 50 40 30 20

Mean cost

10 0 -10 -20 -30 -40

Random Color

-50

Explicit instructions

-60

No color

-70

Contingent color

-80 Baseline

1

2

3

4

Blocknumber

Fig. 4. Mean costs as a function of blocknumber and condition in Experiment 2.

with blocknumber, F(4, 99) = 7.02, p < .0001, MSE = 1099 and F(4, 56) = 9.16, p < .001, MSE = 1783, respectively. The difference between mean costs in these two conditions was found significant, F(1, 33) = 18.67, p < .0001, MSE = 1563. On the other hand, the interaction between condition and blocknumber was not found significant, F(4, 132) = 1.77, p > .14, MSE = 1389. Finally, in further analyses devised to directly compare the contingent color condition with the random-color condition and the no-color one, both pairwise interactions of blocknumber and condition were found significant: F(4, 148) = 8.86, p < .0001, MSE = 1093, in the comparison with the former, and F(4, 180) = 7.45, p < .0001, MSE = 1026, in the comparison with the latter. Thus, the manipulation of contingency of color with imperative cue validity in Experiment 1 affected the cost in a way that must be related with it. It brought about an incidental learning effect that was similar to, though smaller than, the effect generated by explicit instructions. Absence of any similar trend in the other two control conditions show that the effect was due neither to some inhibitory or facilitative effect of color presence nor to some color-independent change in behavior with blocknumber.

implicitly learn the contingency between the secondary cue appearance, we ran more subjects in the condition most amenable to obtain learning (the arrow head condition, marked a), and administered post-experiment questionnaires to all of them. In addition, since it is conceivable that subjects do learn implicitly the contingency but grow aware of it later in the course of hundreds of experimental trials, we probed awareness after a much smaller number of trials, presumably not long after the emergence of learning.

4.1. Method 4.1.1. Subjects Thirty-seven subjects participated in the experiment, all first-year students at the Department of Psychology of the University of Haifa, for partial fulfillment of their course credit who had not participated in Experiment 1. They were further sorted by two criteria that were employed by us in Experiment 1 – (a) making no more than 30% errors in catch trials; (b) surpassing a reasonably moderate (t P 1) the imperative cue validity (valid versus invalid) in the baseline block. Two subjects were excluded by the first criterion, then three more – by the second criterion. The data of all other 32 subjects were analyzed.

4. Experiment 3 The awareness analysis of subjects in Experiment 1 suggested that no learning was manifest by subjects who seemed to be unaware of the contingency between the secondary cue and validity. However, most of those subjects were from groups that were presented with attention conditions that yielded moderate learning or none at all (b, c and d). To further explore the possibility that subjects can

4.1.2. Design The design and procedure of this experiment were similar to the arrow head condition in Experiment 1. The only difference was the smaller number of trials: The questionnaire was administered after 378 experimental trials (in which secondary cue was introduced) instead of 864 trials in Experiment 1.

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factor awareness was far from significance (F < 1). The pairwise interaction, validity · blocknumber was found significant, F(2, 60) = 18.80, p < .0001, MSE = 294. Most important for our concern, the triple interaction validity · blocknumber · awareness was not found significant, F < 1. Mean latencies as a function of validity, blocknumber and awareness are presented in Table 6. The overall error rate in target-present trials, namely, misses, was small (5.15%). Analysis on the arcsine square root transforms of percentages of misses yielded significant main effect for validity, F(2, 60) = 4.94, p < .01 MSE = .0142. The interaction between awareness and validity was significant, F(2, 60) = 3.16, p < .05, MSE = .0090. No other main effect or interaction was found significant. No sign of speed-accuracy tradeoff was evident. Let us now focus on mean costs and mean benefits in latency. A mixed two-way ANOVA applied to costs yielded significant effects for the factor blocknumber, F(1, 30) = 19.48, p < .0001, MSE = 815. Most important,

4.2. Results and discussion Trials with latencies shorter than 150 ms or longer than 1000 ms were excluded from analysis (1.1% of all trials). Table 5 (panel A) presents both the distribution of number of errors in answering the last five questions that assess awareness and the frequencies of errors in each of those questions conditional on overall number of errors. The overall number of subjects who did not seem to have awareness was 18 (about 56%, considerably more than in Experiment 1). All of the latter did not show any sign in the open question (no. 5) that they understood how the secondary cue was related to the validity of the imperative cue, yet six did answer correctly (partly by chance, presumably) the multiple-choice question (no. 6). Seventeen did not realize that the cue could be helpful (question 4). However, all but three claimed (question 2) to have been aware of the occasional presence of the stimulus serving as the secondary cue, though one of those identified (question 3) the color of the secondary cue as yellow. An overall analysis of all the questionnaire data across Experiments 1 and 3 (see Table 5, panel B) confirmed that questions 2–6, in the order given within the table, constitute a Guttman scale with a .98 coefficient of reproducibility. A mixed three-way ANOVA with two within-subject factors (blocknumber and validity) and one between-subject factor (awareness) was conducted. In this analysis, all trials from the point in which secondary cue was introduced were considered as one experimental block to be compared with trials from the baseline block. Significant effects were found for the factors validity, F(2, 72) = 46.00, p < .0001, MSE = 678, and blocknumber, F(1, 30) = 12.80, p < .001, MSE = 2538. The effect of the

Table 6 Mean latency in Experiment 3 as a function of awareness, validity and blocknumber Awareness

Validity

Baseline

Experimental block

Aware N = 14

Invalid Neutral Valid

391 362 325

348 348 317

Unaware N = 18

Invalid Neutral Valid

411 387 358

360 371 338

Numbers of subjects in each condition that were aware or unaware are given below the awareness level.

Table 5 Distribution of number of errors per subject in answering the questions that assess awareness, and the frequencies of errors in each of the questions conditional on the overall numbers of errors Number of errors

Question 2

Question 3

Panel A 0 1 2 3 4 5

Question 6

Question 4

0 0 0 0 0 3

0 0 0 0 1 3

0 1 1 7 1 3

0 0 6 7 1 3

0 0 7 7 1 3

13 1 7 7 1 3

Total

3

4

13

17

18

32

Panel B 0 1 2 3 4 5

0 0 0 0 2 3

0 0 0 1 2 3

0 1 3 15 4 3

0 1 9 16 4 3

0 0 12 16 4 3

33 2 12 16 4 3

Total

5

6

26

33

35

70

Panel A relates only to data from Experiment 3. Panel B relates to pooled data from both Experiments 1 and 3.

Question 5

Distribution

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the pairwise interaction – blocknumber · awareness was far from significance (F < 1). A further test for a simple effect confirmed that there was a clear, significant cost reduction effect (35 ms) in the group of unaware subjects: The main effect of block was highly significant in this group, F(1, 18) = 26.87, p < .0001, MSE = 411. Interestingly, a marked effect of blocknumber indicating learning is observed also in a small group of three subjects that not only reported to be unaware of the contingency but also attested to not having noticed at all the occasional appearance of the stimulus serving as secondary cue. Actually, the cost in the baseline block in this small group (20 ms) turned into secondary benefit in the experimental trials (12 ms), much like it did in the group of other 16 subjects in the unaware group (a cost of 25 ms that turned into a 13 ms secondary benefit). Although that cannot be confirmed by a statistical test due to the small number of subjects, it at least suggests, at the anecdotal level, that implicit learning might not always require even awareness of the cue itself. One may doubt the credibility of the reports made by those subjects, all or part of them. On the other hand, it is not implausible that a subject who perceives both color and color change would habituate to it for some reason, hence not aware of the contingency. Were the subject habituated, a failure to confirm in the post-experiment questionnaire that there was a color change is not terribly surprising. Learning curves were plotted as they were in Experiment 1. Seventeen subjects seemed to have learned during the 108 trials examined for plotting the curve (at least 30 ms decrease in cost). Interestingly, whereas 12 out of the 18 unaware subjects (67%) manifested learning as early as that, only 5 out of 14 aware subjects (36%) manifested such learning. Inspecting the individual learning curves of those 17 subjects revealed that six had a gradual decrease (5 of which were unaware) and 11 (7 of which were unaware) had an abrupt one. Thus, the percentage of unaware subjects that manifested both early and abrupt learning (39%) was at least not smaller than the corresponding percentage of aware subjects (29%). 5. General discussion The results indicate that non-imperative rules can be incidentally learned and utilized for guiding attention. That clearly strengthens the conclusion suggested by results like those reported by Jiang and Chun (2003) and Lambert et al. (1999), yet in a paradigm designed to lend much confidence that the locus of the effect is endogenously controllable visual attention. Unlike in previous studies in which attention was probably controlled exogenously from the outset, here attention was to be guided, and actually was guided to a great extent, by an endogenous rule. The incidentally learned rule was inherently a secondary one. That made the application of

471

the incidentally acquired rule dependent on a modification of the procedure for allocating visual attention prescribed by the imperative rule. That procedure is more complex, since it determines the direction of attention shift by a combination of features – arrow direction and secondary cue presence. Perceiving that combination in itself might require attention (see, e.g., Treisman & Sato, 1990). That seems to be corroborated by the finding that incidental learning of the secondary cue was not observed when the latter was clearly outside the beam of visual attention (Experiment 1). Moreover, the procedure actually calls for orienting attention sometimes to the direction that is opposite to the one indicated by the arrow, a quite unnatural operation, which might be expected to have generated some conflict. On top of that, it squarely conflicts with the instructions. Furthermore, acquisition of the incidental rule, as well as its application, must have been especially difficult, since the rule was detected despite the cognitive load of having to process an endogenous cue, apply the imperative rule and respond to the target. Finally, the advantage in using the incidental cue was limited, since unlike in previous studies, here spatial uncertainty was small to begin with. Despite all these considerations, the difference between mean latency in ‘‘invalid’’ and valid trials was toward the end small or negligible, at least in some conditions. In those conditions, performance in ‘‘invalid’’ trials was found to be not much different from that of control subjects told beforehand about the contingency (Experiment 2). That finding seems to be a particularly strong evidence that the effect of incidental learning on performance was mediated by some change in the orienting of attention rather than to some criterion shift or selective alerting. This was not manifested just by a handful of subjects. Actually, for 17 out of 20 subjects in the color-contingent condition, cost was considerably reduced from the baseline block to the last experimental one. For 14 out of these 17, the cost even turned into secondary benefit. It, thus, seems that processes guiding attention can be quite sophisticated. On top of pre-wired dispositions and explicit rule following, typically referred to as exogenous and endogenous guidance, respectively, the attentional system might also resort to, or be affected by, contingencies recovered from the stimulation, even while the system is being guided by other cues. 5.1. Implicit learning? A different, albeit not unrelated, issue is whether or not the incidentally learned rule was acquired implicitly. If incidental learning of contingencies like the one manipulated here could be implicit, that would demonstrate particularly a surprising feat of automatic processing – fooling consciousness to believe that attention was guided by the explicit rule, though in fact the latter was totally overridden in trials in which the implicit cue appeared. This might be likened to a person at a cocktail party

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setting taking place at a dark room who deliberately tries to listen to the distinctive voice of a certain speaker constantly changing positions, yet is actually using some implicitly learned clue for telling where the speaker is without having noticed that at all. In both cases, the regulation of attention is not just unintentional and unconscious but rather sheer incompatible with both intention and awareness. The results of the questionnaires administered to subjects of Experiment 3 seem to indicate (as much as questionnaires of that sort can) that learning was indeed quite often implicit (though with further use of the secondary cues, meta-cognitive awareness of the learned rule and of its use floats up to consciousness or is gradually formed there, as indicated by the results of the questionnaires administered to subjects of Experiment 1). One may choose whether to be impressed by the demonstrated ability of involuntary, hence presumably automatic, processes to take control of learning and behavior without leaving any traces in conscious experience for a while, or to keep on doubting that those processes are fully automatic. Even if it is granted that many subjects were really unaware of what their attention system learned to do, there are some other grounds to wonder whether indeed the processes giving rise to the acquisition of the incidental rule were automatic in the strict sense. First, it depended considerably on the amount of visual attention available from the outset to the secondary cue. Specifically, though distance between the secondary cue and the element serving as direction pointer was not found to have a significant effect, learning failed to manifest when the secondary cue appeared outside the object containing, or acting as, the imperative cue. That incidentally seems quite compatible with object-based attention models (see, e.g., Bundesen, 1990; Desimone & Duncan, 1995; Duncan, 1984; Kahneman & Treisman, 1984; Kramer & Jacobson, 1991; Vecera & Farah, 1994). More important for our concern, the learning and application of that rule, however unconscious they might have been, could not have been strongly automatic (for another example of attention-sensitive implicit learning see Rowland & Shanks, 2006). If it did not absolutely require attention (as Hoffmann & Sebald (2005) conclude based on their data), it was at least partly automatic, by the terminology proposed by Kahneman and Treisman (1984), in the sense that it could be improved by expending attention on it – probably both visual attention and central processing capacity. As pointed out before (e.g., Kahneman & Treisman, 1984; Navon, 1989a, 1989b), not every computation to which attention contributes must be accessible to awareness. Second, the complexity of the augmented mapping rule does not quite fit the common wisdom about automatic processes as being capable only of performing simple operations unless they are habitual. Indeed, awareness was found here to have a considerably larger effect than it had in studies of incidental learning of simpler rules (Lambert et al., 1999).

Third, the incidental learning in the present study was found to be quite early and sometimes precipitous (though the magnitude of its impact on behavior developed more gradually after that), even when the subject seemed to be unaware of the contingency (see Section 4.1.3). That might give rise to some doubts of whether learning was automatic. Although some evidence that implicit learning may be abrupt has been previously found (e.g., Stanley, Mathews, Buss, & Kotler-Cope, 1989), one might wonder how a rule that spontaneously emerges out of indirect evidence (as is assumed to be true of all cases of implicit learning; see Whittlesea & Dorken, 1997) and then applied involuntarily has an early onset of application and precipitous rise in it. Learning to use a discriminative cue is typically believed to be gradual (see, e.g., tutorial exposition in Schwartz & Robbins, 1995; but see methodological critique by Gallistel, Fairhurst, & Balsam, 2004), thus probably reflects incremental changes in performance that are not preceded by any process that may be likened to a rational, informed decision. Precipitous changes in performances are mostly manifest in cases in which learning can be attained by deciding between two hypotheses on the basis of some simple, crucial test (e.g., discovering under which of the two different objects a peanut is regularly hidden). Thus, if the incidental learning required in this study was accomplished without any mediation of mechanisms that support higher-level processing, it might be expected to be gradual, presumably due to some automatic, reasoning-free strengthening of neural pathways. Nonetheless, from inspecting the rates of abrupt learners it looks as if quite frequently, among aware and unaware subjects alike, some process starts using the rule as soon as it becomes sufficiently ‘‘confident’’ of the adequacy of the rule without really trying, a strategy quite reminiscent of a deliberate, informed decision that characterizes some types of intentional learning (though not highly prevalent even in it). It is tempting to still insist on disbelieving that the operation of that process depends on attention. The reason seems to be that it has turned to be common wisdom that a good deal of processing is simply triggered and driven by stimulus presence and is carried out with no intent and no need for any attention (but see Besner & Care, 2003, for an interesting counterexample; cf. also Navon, 1990). Could it be, then, that there are two decision processes, one explicit and one implicit yet smart? In our view, that possibility is not more plausible, and certainly less parsimonious, than the possibility that actually there is only one such process, one that does a pretty sophisticated job of evidence-based reasoning (cf. Gallistel et al., 2004), possibly relying on provisions that may be regarded as regulated by attention. On the other hand, the access to the activity or to its output of that process by reflective processes, especially those invoked for reporting awareness, especially in retrospect, may be sometimes

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limited,3 in which case those processes may infer ‘‘intuitions’’ about rules followed by the decision process or about its products. The level of veridicality of those feelings seemingly emanating from privileged sources may not surpass much the veridicality typical of hypotheses proposed by an outside observer (see, e.g., Bjork, 1999; Koriat, 1998; Simon & Bjork, 2001). Implicit learning may differ from explicit learning primarily, or even exclusively, in that sort of access.

Appendix A. Post-experiment questionnaire

6. Conclusion

2.

We thank you for participating in the experiment. Now you are being asked to answer a short questionnaire relating to the experiment in which you have just participated. Circle the correct answer. Please, answer the questions in the order given and do not skip any of them. 1.

Have the arrows helped you, in course of the experiment, perform the task you

were asked to perform (namely, detection of the flash)? Yes

No

Have you noticed that the color of the [

]* occasionally changed in the course of

the experiment?

One way or the other, incidental learning found in the setting of the present paradigm suggests that the process that guides attention is open to acquisition of rules learned on the fly. Note that despite the salience of the to-be-incidentally-learned feature, the kind of learning here is quite complex – noticing the contingency between the appearance of an apparently irrelevant feature and the actual mapping of imperative cue to stimulus location, which helps to turn an initially probabilistic cue–stimulus relationship into a deterministic one. In view of that, it is interesting that the incidental learning observed here was found to be often very early and sometimes abrupt. It is also of some interest that many of those early, sometimes abrupt, learners provided no evidence that they were aware of that learning, though, as argued above, the interpretation of that is not unequivocal. More studies are required to examine whether, and to what extent, such learning is sensitive to secondary cue delay, imperative cue delay, processing load and degree of contingency between the secondary cue and imperative cue validity. It may be also illuminating to study how reversible that learning is and how robust it is to late modification of cue appearance. For the time being, it seems apt to conclude that the process guiding attention, even in a standard experiment on endogenous cueing, is quite adaptively flexible.

Yes 3.

No

What was the color of the [

]* (when it was not white)? __________

[flip to new page] 4.

Have the differences in the color of the [ ]* (white vs. red) helped you in any way perform the task you were asked to perform (namely, localization of the flash)?

5.

If you answered “yes” on question 4, please describe the way in which the color

Yes

No

differences helped you: ___________________________________________________________ ___________________________________________________________ ___________________________________________________________ [flip to new page] 6.

Below are two sentences relating to the experiment in which you have just participated. Only one of them is correct. You are being asked to circle the one you think is correct. If you do not know, do not guess, rather circle option c.

a.

The color of the [

]* was red4 only when the arrow pointed to the direction where

the target (flash) did appear. Accordingly, whenever a red [

]* appeared, it was

possible to know with absolute certainty that the arrow points to the direction where the target (flash) would appear. b.

The color of the [

]* was red whenever the arrow pointed to the direction opposite

to where the target (flash) appeared. Accordingly, whenever a red [

]* appeared,

it was possible to know with absolute certainty that the the target (flash) would appear in the direction opposite to where the arrow pointed. c.

I do not know.

* The brackets above were not shown in the actual questionnaire, and rather stand here for a phrase denoting the object and/or location where the secondary cue element appeared, according to attention condition. 4

Acknowledgements The experiments reported in this paper were supported in part by a Grant No. 883/03 from the Israeli Science Foundation. The early version of the experiments reported here was supported in part by the ZEIT Bucerius foundation. We are indebted to Ziziana Lazar for programming the experiments. We are also indebted to Jonathan Dvash, Noa Shalev and Kobi Walder for running them. Finally, thanks are due to Tom Beckers, Artem Belopolsky, Raymond Klein, Johan Wagemans and an anonymous reviewer for lots of useful comments and suggestions. 3 This claim makes particularly good sense, if awareness is viewed not as an outcome of the commitment of some special provision of processing to an object, but rather as the spread of information about that object within a distributed system (Navon, 1989a, 1989b).

We used the word “red” since we felt that most subjects would tend to categorize the color as “red” rather than as “pink” or “reddish pink”.

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