Implicit learning for probable changes in a visual change detection task

Melissa R. Beck a,*, Bonnie L. Angelone b, Daniel T. Levin c, Matthew S. Peterson d,D. Alexander Varakin e

aDepartment of Psychology, Louisiana State University, 236 Audubon Hall, Baton Rouge, LA 70803, USAbDepartment of Psychology, Rowan University, 201 Mullica Hill Rd., Glassboro, NJ 08028, USAcDepartment of Psychology and Human Development, Vanderbilt University, Peabody College # 512, 230 Appleton Place, Nashville, TN. 37203-5701, USAdDepartment of Psychology, George Mason University, MS 3F5, 4400 University Dr., Fairfax, VA 22030, USAeDepartment of Psychology, Knox College, 2 E South St, K60, Galesburg, IL 61401, USA

a r t i c l e i n f o

Article history:Received 21 November 2007Available online 5 August 2008

Keywords:Implicit learningChange detectionVisual attention

a b s t r a c t

Previous research demonstrates that implicitly learned probability information can guidevisual attention. We examined whether the probability of an object changing can beimplicitly learned and then used to improve change detection performance. In a series ofsix experiments, participants completed 120–130 training change detection trials. In fourof the experiments the object that changed color was the same shape (trained shape) onevery trial. Participants were not explicitly aware of this change probability manipulationand change detection performance was not improved for the trained shape versusuntrained shapes. In two of the experiments, the object that changed color was alwaysin the same general location (trained location). Although participants were not explicitlyaware of the change probability, implicit knowledge of it did improve change detectionperformance in the trained location. These results indicate that improved change detectionperformance through implicitly learned change probability occurs for location but notshape.

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1. Introduction

Change blindness, the failure to detect a change in the visual world from one moment to the next, demonstrates that theamount of visual information that can be attended and then retained in visual short-term memory (VTSM) is limited (seeSimons & Rensink, 2005 for review). In a change detection task, it may be useful to attend to information that has a highprobability of changing from one moment to the next. Beck, Angelone, and Levin (2004) demonstrated that probable changes(e.g., a lamp changing from being turned on to being turned off) are detected more frequently than improbable changes(e.g., a green lamp changing into a blue lamp; see also Beck, Peterson, & Angelone, 2007). However, participants were notexplicitly aware that probable changes are preferentially detected (Beck et al., 2004), suggesting that information aboutthe probability of objects changing affects change detection performance implicitly. The current experiments address thequestion of whether the change probability of a novel shape can be learned implicitly during a change detection task andthen used to improve change detection performance.

Participants learn and use many kinds of regularities to improve performance in many tasks, including tasks involvingstrategy selection (Lemaire & Reder, 1999), auditory information (Aslin, Saffran, & Newport, 1998; Creel, Newport, & Aslin,2004; Newport & Aslin, 2004; Saffran, Aslin, & Newport, 1996), the spatial layout of visual objects (Chun & Jiang, 1998; Fiser& Aslin, 2001), and the temporal relationships among visual objects (Fiser & Aslin, 2002; Kirkham, Slemmer, & Johnson,

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* Corresponding author. Fax: +1 225 578 4125.E-mail address: mbeck@lsu.edu (M.R. Beck).

Consciousness and Cognition 17 (2008) 1192–1208

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2002; Olson & Chun, 2001). For example, participants modified their strategies for solving math problems according to baserates of success although they were unaware of these strategy modifications (Lemaire & Reder, 1999). In addition, the phe-nomenon of contextual cueing occurs when participants performing a visual search task are able to find the target fasterwhen it is in distracter arrays that are repeated (Chun & Jiang, 1998). This type of learning is referred to as implicit learningbecause it occurs without the intent to learn and participants are not explicitly aware of the learned information.

Implicit learning can guide visual attention and thereby influence what information is stored in VSTM. For example, whena cue predicts the location of the target, participants are faster to respond to the target (Lambert, Naikar, McLachlan, & Ait-ken, 1999). Furthermore, when a target is more likely to appear on one side of the screen than the other, eye movements goto the predicted side of the screen more readily than the unpredicted side of the screen (Walthew & Gilchrist, 2006). Atten-tional cueing by probability information has been shown to lead to decreased processing of distractors (Awh, Matsukura, &Serences, 2003). Therefore, the target to which attention is drawn is more likely to be stored in VSTM and the distractors areless likely to interfere with this representation. This suggests that if probability information could be learned and used toguide attention in a change detection task, change detection would improve for the probability consistent target.

Research has suggested that probability information can be learned and used implicitly in a change detection task (Olson,Jiang, & Moore, 2005). In this task, the spatial configuration of several boxes predicted the location of a box that was deletedin the post-change array. Participants learned the spatial configuration of the arrays and were able to use that information togive priority to the spatial location that was predicted to change. This study demonstrated the use of a learned associationbetween a spatial configuration and a particular location to guide attention and VSTM. The current studies assess the degreeto which an association between the shape of objects and the likelihood they will change can lead participants to attend andcreate VSTM representations sufficient to detect changes.

Research on implicit learning suggests that participants should be able to learn an association between the shape of anitem and the probability of that shape changing color and use this information to improve color change detection. Implicitlylearning that a particular shape is more likely to change color than other shapes would require the ability to implicitly learntemporal information because change detection involves monitoring objects over time. It would also require the ability toimplicitly learn information about the shape of an attended object. Finally, it would require that implicit learning occur auto-matically for attended information. Previous research suggests that all of these conditions are met and therefore, participantsshould implicitly learn the probability that a particular shape will change, leading to improved change detectionperformance.

Improved change detection performance by implicit knowledge of change probability information would require knowl-edge about the stability of an object across time. Implicit learning of temporal relationships has been demonstrated in par-ticipants’ ability to implicitly learn a predictable relationship among the order in which different shapes are grouped acrosstime (Fiser & Aslin, 2002; Turk-Browne, Junge, & Scholl, 2005). Participants watched a sequence of shapes traveling acrossthe computer screen with a repeated pattern of co-occurring shape triplets. Although participants were not told that a pat-tern would occur, after watching the sequence, they rated the repeated triplets as more familiar than novel triplets, despitenot being aware that patterns had repeated. Furthermore, in a shape identification task, participants responded faster toitems in a repeated triplet than to items in a novel triplet (Turk-Browne et al., 2005). Given that implicit learning for thetemporal relationships among novel shapes does occur, implicit learning could also occur for novel shapes in a change detec-tion task.

Information about the shape of an object can be learned and implicitly used to enhance visual search performance. Chunand Jiang (1998) paired a set of distractors with a particular target. The target and distractor sets were repeated across sev-eral trials, but the location of the target and distractors was randomly determined on each repetition. Search was faster whenthe target appeared with the same distractor set as on previous trials even though all the items were in new positions. There-fore, the shapes of the distractors were used to improve search time for the target. Evidence for implicit learning of temporaland shape information among arrays of novel objects suggests that implicit learning could also occur for novel objects thatare likely to change over time.

Successful change detection and improved performance on a task through implicit knowledge both require attention. Im-plicit knowledge is gained automatically (Fiser & Aslin, 2002), but improves performance on a task only when the relevantinformation is attended (Jiang & Chun, 2001; Jiang & Leung, 2005; Jimenez &Mendez, 1999; Turk-Browne et al., 2005). Whenasked to find a white target in a display containing white and black items, contextual cueing was only found for repeatedconfigurations in the attended color (the white items; Jiang & Leung, 2005). However, when the repeated configurationsin the unattended color were presented in the attended color, search was facilitated immediately. These results indicate thatimplicit learning occurred for the unattended color, but was not used until the configuration was presented in the attendedcolor. Therefore, in order to use implicit knowledge to improve performance, the information relevant to the implicit knowl-edge must be attended.

Accurate change detection requires attention to the pre- and post-change information (Hollingworth & Henderson, 2002;Levin & Simons, 1997; Rensink, O’Regan, & Clark, 1997; Simons & Levin, 1998a, 1998b). According to feature integration the-ory when an object is attended the features of the object are bound together into an object file in memory (Kahneman, Tre-isman, & Gibbs, 1992; Treisman & Gelade, 1980). Therefore, if a color change to an object is detected, the object was attendedand the features of the object (shape and color) were bound together. However, contrary to feature integration theory, it hasbeen proposed that color and shape are stored independently and that attention can be selectively directed to one but notthe other (Garner, 1974). If attention to an object’s color leads to a bound representation of all of the objects features, then

M.R. Beck et al. / Consciousness and Cognition 17 (2008) 1192–1208 1193

detection of a color change implies that the object’s shape was also represented in VSTM. It follows that detecting a colorchange should lead to implicit learning of an association between the shape of an object and its probability of changing color.

In the current studies, we examined whether change probability information could be learned during a change detectiontask and then lead to improved change detection performance. In Experiments 1–3 and 5, participants completed a set oftraining trials in which the same shape (trained shape) changed color on every trial. Training trials were followed by aset of test trials in which the trained shaped changed on half of the trials and untrained shapes changed on the other halfof the trials. Performance on the test trials was examined to see whether changes to the trained shape were detected at ahigher rate than changes to the untrained shapes. If participants learned during the training phase that a given shape wouldalways be the shape that changed color, then the load on VSTM could be lessened by only attending to that shape. Therefore,on the test trials, performance for the trained shape should be higher than performance for the untrained shapes.

2. Experiment 1

In Experiment 1, we tested participants’ ability to implicitly learn the probability that a shape will change color and usethis information to improve change detection performance. Participants first completed a set of 130 training trials in which 1of 9 objects changed color. Across all of the training trials the object that changed color was always the same shape (trainedshape). Then 24 test trials were completed in which the object that changed was the trained shape (consistent test trials) onhalf of the trials and the object that changed was an untrained shape (inconsistent test trials) on the remaining trials. If im-plicit learning occurs and improves performance, then change detection accuracy should be higher on the consistent test tri-als than on the inconsistent test trials.

2.1. Methods

2.1.1. ParticipantsThirty undergraduates at Kent State University participated in exchange for class credit. The average age was 20 years and

20 of the participants were female.

2.1.2. MaterialsFour sets of pre-change arrays containing 9 objects were created from a set of 16 possible objects. The 16 objects were 4

novel shapes appearing once in each of 4 colors (red, yellow, blue, or green). Each object was a unique shape–color combi-nation that could appear in any one of the nine locations of the array. The objects and their locations in the array were chosenrandomly with the constraints that each shape had to appear at least once in each array and at least three of the four colorshad to appear in each array. The objects were arranged in a 3 ! 3 grid (see Fig. 1). A post-change array was created for eachpre-change array by changing the color of one of the objects in the pre-change arrays. The post-change color was randomlychosen with the constraint that the change did not result in only two of the four colors remaining in the post-change array.There were four sets of pre- and post-change array pairs that were used during the training phase of the experiment. Withineach set, the shape of the object that changed color was always the same (the trained shape). However, the object that chan-ged color could be in any of the nine locations, could be any one of the four colors and could change into any three of the

1 2 3 4

Which color was new?

Pre-change array:2 seconds

ISI:350 ms

Post-change array:2 seconds

4AFC post-change recognition question:2 seconds

Fig. 1. The sequence of events in Experiment 1. Objects were presented in colors red, yellow, green, or blue represented in shades of grey here.

1194 M.R. Beck et al. / Consciousness and Cognition 17 (2008) 1192–1208

remaining colors. Each of the four sets of pre- and post- change pairs used during the training phase of the experiment waspaired with a set of 24 pre- and post-change pairs used during the test phase of the experiment. Within each set of 24 testpairs, the object that changed color was the same as the trained shape for 12 of the arrays (consistent test trials) and for theremaining 12 arrays, each of the other 3 shapes changed color in 4 of the test arrays (inconsistent test trials).

The arrays were presented onMacOS computers with 14-in. monitors set at a resolution of 1024 ! 768 (92 dpi) and at 16-bit color depth (thousands of colors). They were 16 ! 8.5 cm, presented at the center of a 28 ! 21 cm screen, and subtended a14.7" ! 7.9" visual angle from a viewing distance of 61 cm from the screen (viewing distance was not constrained).

2.1.3. ProcedureParticipants completed the experiment in small groups ranging in size from one to four and were tested on individual

computers in the same room. Participants were alternately assigned to receive one of the four training-test sets. All partic-ipants completed 130 training trials (randomly ordered) and then 24 test trials (randomly ordered). For each trial, the pre-change array was presented for 2 s and then after a 350 ms white screen ISI, the post-change scene was presented for 2 s. Afour-alternative forced-choice (4AFC) recognition question for the post-change color was then presented. The four colorsused in the arrays were presented on the screen and the participant was asked to choose which of the four colors wasthe post-change color. Participants pressed a number 1–4 corresponding to the order of the four colors on the screen (seeFig. 1). Following their response the next trial would begin.

Participants were instructed to find the object that changed color on each trial and then identify the post-change color.Each participant received one of the four sets of pre- and post-change pairs for the training phase and then the correspondingpre- and post-change pairs for the test phase in which the shape that changed on 100% of the training trials, changed on 50%of the trials during test. There was no break between the training and test phases and participants were not told of this dis-tinction between the trials.

After completing the change detection task, participants filled out a post-experiment questionnaire to assess their aware-ness of the probability manipulation. The first question asked participants to list any strategies they used to detect thechanges, to see if participants would spontaneously mention the probability manipulation. Then participants were asked,‘‘Do you think that any of the shapes changed color more frequently than the others?” They responded by choosing oneof two options: ‘‘Some more often” or ‘‘Equally likely”. Finally, participants were asked ‘‘What percentage of trials did eachof the shapes changed color across all the trials?” They then wrote down percentages for each of the four shapes.

2.2. Results

Data from two participants was excluded because their performance (proportion correct) on the training trials was at orbelow chance (.25). Accuracy in identifying the post-change color on the training trials for the remaining 28 participants was.80 (SE = .03). Average performance ranged from .49 correct to 1.0 correct, and the distribution of performance on the train-ing trials did not differ from a normal distribution [Kolmogorov–Smirnov (K–S) test, p = .994].

2.2.1. Test trialsAccuracy on the 4AFC questions for the test trials was entered into a 2 ! 4 repeated measures ANOVA with consistency

(consistent or inconsistent with training) as a within-subjects factor and training set (one for each of the four shapes) as abetween-subjects factor. Accuracy on the consistent test trials (M = .83; SE = .03) was not significantly different than accu-racy on the inconsistent test trials (M = .80; SE = .03), F(1,24) = .809, MS = .012, p = .38, partial g2 = 0.033 (see Table 1). Inaddition, there was no main effect for training set, F(3,24) = .435, MS = .022, p = .73, partial g2 = 0.052. The interaction be-tween consistency and training set was also not significant, F(3,24) = 2, MS = .03, p = .12, partial g2 = 0.21.

In order to examine the extent to which performance was affected by the modification in change probability from thetraining trials to the test trials, accuracy in the last 12 training trials was compared to accuracy on the consistent and incon-sistent test trials. Accuracy on the consistent test trials was not significantly different than accuracy on the last 12 trainingtrials (M = .8; SE = .04), t(27) = .9, p = .34. Accuracy on the inconsistent test trials was also not significantly different thanaccuracy on the last 12 training trials, t(27) = .08, p = .93.

Table 1Proportion correct in Experiments 1 and 2

Last 12 training trials Consistent test trials Inconsistent test trails

Experiment 1 .79 (.19) .83 (.18) .80 (.18)Experiment 2 .77 (.18) .73 (.16) .75 (.20)

Standard deviation presented in parentheses.

1 For all experiments, these same analyses were conducted on the test trials for only those participants scoring in the top "50% on the test trials and for onlythose participants scoring in the top "25%. Using only these participants who scored exceptionally well on the training trials did not change the results.

M.R. Beck et al. / Consciousness and Cognition 17 (2008) 1192–1208 1195

2.2.2. Post-experiment questionnaireNone of the participants spontaneously mentioned the difference in change probabilities (question 1) on the post-exper-

iment questionnaire. Only 27% (8 out of 30) of the participants chose the ‘‘some more likely to change” option on question 2.Of these 8 participants, 4 estimated that one of the untrained shapes changed more often than the others. The remaining 4gave an estimate of change probability greater than chance (25%) to the trained shape, but none of them correctly estimatedthat the trained shape changed on all of the training trials. Averaged across these 4 participants, the predicted probability ofchange for the shape that changed on 100% of the training trials was 45%2. Excluding these participants did not meaningfullychange the analyses presented above.

2.3. Discussion

In Experiment 1, there was no evidence that the probability manipulation in the training trials lead to improved changedetection performance by directing attention toward this shape. Participants detected changes during the test phase whenthe consistent shape changed just as accurately as they did when the inconsistent shape changed. Furthermore, when par-ticipants were asked if they had any awareness that the same shape changed on every trial, the majority reported that all ofthe shapes were equally likely to change.

Improved change detection for the consistent shape may have failed to occur because the shapes were not familiar or welllearned. Research indicates that implicit learning is less likely to occur under conditions of high attentional load (Shanks,Rowland, & Ranger, 2005). Processing the novel shapes may have interfered with learning the change probability. In Exper-iment 2, we used familiar namable objects (letters and numbers) to further examine the ability to implicitly learn changeprobability information and use it to improve performance on a change detection task.

3. Experiment 2

In Experiment 2, participants completed a change detection task similar to Experiment 1 except the stimuli were lettersand numbers instead of novel shapes. In addition, the change probability information was contained within a familiar cat-egory (letters or numbers) rather than an individual shape or identity. During the training trials the object that changed colorwas always in the same category. For half of the participants a letter always changed color and for the other half a numberalways changed color. During the test trials, a letter changed color on half of the trials and a number changed color on theother half of the trials for all participants. If participants can implicitly learn that objects within one category are more likelyto change color, they could direct attention to objects belonging to this category and detect more changes. We measured thisability by examining whether change detection performance during the test trials was better for the category consistent withtraining.

3.1. Methods

3.1.1. ParticipantsTwenty-seven undergraduates at Kent State University participated in exchange for class credit. The average age was 19

years and 20 of the participants were female.

3.1.2. MaterialsThe materials were the same as those used in Experiment 1 except for the modifications listed here. The 16 objects that

could appear in the arrays were the letters A and B and the numbers 8 and 9 each appearing once in each of four colors (red,yellow, blue, or green; see Fig. 2). There were two sets of pre- and post-change array pairs that could be used during thetraining phase of the experiment. Within each set the object that changed color was always from the same category (lettersor numbers). The same set of 24 test pre- and post-change array pairs was used with each training set. Within the set of testarray pairs the object that changed color would be from one of the categories in 50% of the test pairs and from the othercategory in the remaining test pairs.

3.1.3. ProcedureThe procedure was the same as in Experiment 1 except for the modifications noted here. Each participant received one of

the two training sets of pre- and post-change array pairs for the training phase and then all participants received the sameset of pre- and post-change array pairs for the test phase. After completing the change detection task, participants filled out apost-experiment questionnaire to assess their awareness of the probability manipulation. The first question asked partici-pants to list any strategies they used to detect the changes. The second question asked participants ‘‘Do you think that eitherthe numbers or the letters were more likely than the other to change color?” They responded by choosing one of two op-tions: ‘‘One changed more often” or ‘‘Both were equally likely to change”. Finally, participants were asked ‘‘What percentage

2 Averaged across all of the trials, the probability of the trained shape changing was 92%. This takes into account the 12 test trials when an untrained shapechanged. The estimates of change were not accurate even when taking into account this overall probability of the trained shape changing.

1196 M.R. Beck et al. / Consciousness and Cognition 17 (2008) 1192–1208

of trials did the letters and the numbers change color across all of the trials?” They then wrote down an estimate of the prob-ability of each category changing across all of the trials.

3.2. Results

All participants scored above chance (.25). Accuracy in identifying the post-change color on the training trials was .76(SE = .02). Average performance ranged from .52 to 1, and the distribution of performance on the training trials did not differfrom a normal distribution (K–S test, p = .994).

3.2.1. Test trialsAccuracy on the 4AFC questions for the test trials was entered into a 2 ! 2 repeated measures ANOVA with consistency

(consistent or inconsistent with training) as a within-subjects factor and training set (letters or numbers) as a between-sub-jects factor. Accuracy on the consistent test trials (M = .73) was not significantly different than accuracy on the inconsistenttest trials (M = .75), F(1,25) = .533, MS = .005, p = .47, partial g2 = 0.021. In addition, there was no main effect for training set,F(1,25) = 1.54,MS = .085, p = .23, partial g2 = 0.058. The interaction between consistency and training set was also not signif-icant, F(1,25) = .001, MS < .001, p = .98, partial g2 < 0.001 (see Table 1).

In order to examine the extent to which performance was affected by the modification in change probability from thetraining trials to the test trials, accuracy in the last 12 training trials was compared to accuracy on the consistent and incon-sistent test trials. Accuracy on the consistent test trials (M = .73) was not significantly different than accuracy on the last 12training trials (M = .77), t(26) = #1.12, p = .28. Accuracy on the inconsistent test trials (M = .75) was also not significantly dif-ferent than accuracy on the last 12 training trials, t(26) = #.53, p = .6.

3.2.2. Post-experiment questionnaireNone of the participants spontaneously mentioned the difference in change probabilities (question 1) on the post-exper-

iment questionnaire. Only 30% (8 out of 27) of the participants chose the ‘‘one changed more often” option on question 2. Ofthese 8 participants, 2 estimated that the untrained category changed more often than the trained category. The remaining 6gave an estimate of change probability greater than chance (50%) to the trained category, but only 1 of these 6 correctly esti-mated that the trained category changed on all of the training trials. Averaged across these 6 participants, the predictedprobability of change for the category that changed on 100% of the training trials was 70%. Excluding these participantsdid not meaningfully change the analyses presented above.

3.3. Discussion

Even when the identity that predicted the change object was a common, well-learned category (letters or numbers),thereby lessening the attentional load for processing the objects, participants were unable to improve change detection per-formance by guiding attention toward the trained shape. Therefore, the failure to improve change detection performancethrough implicitly learned change probabilities in Experiment 1 was not due to participants being unfamiliar with the novelshapes. Furthermore, the items within each category were easily identifiable because they varied by low-level features. Theletters (A and B) both contain 90-degree edges and horizontal lines while the numbers (8 and 9) do not contain these

1 2 3 4

Which color was new?

Pre-change array:2 seconds

ISI:350 ms

Post-change array:2 seconds

4AFC post-changerecognition question:2 seconds

B B 8A B 9B 9 A

B B 8A B 9B 9 A

Fig. 2. Sequence of events in Experiment 2. Letters and numbers were actually shown in colors red, yellow, green, and blue.

M.R. Beck et al. / Consciousness and Cognition 17 (2008) 1192–1208 1197

features. This feature difference should have improved the ability to use shape information to direct visual attention to theshape most likely to change. Perhaps change detection performance was not improved for the consistent shape in Experi-ments 1 and 2 because the change detection task does not require participants to effortfully process the shapes of the objects.Research on implicit learning suggests that the feature containing the probability information must be processed effortfullyor implicit learning will not occur (Hartman, Knopman, & Nissen, 1989; Jiang & Chun, 2001; Jimenez & Mendez, 1999). Toaddress this possibility, in Experiment 3, we used stimuli that would encourage more effortful processing of the items inthe change detection arrays.

4. Experiment 3

In Experiment 3, we used the same shapes as used in Experiment 1 except each shape was composed of two colors insteadof one. Using shapes composed of two colors instead of one may not only increase attention to the shapes, but may also in-crease the attentional load of the change detection task. In order to minimize the increase in overall attentional load, thearrays in Experiment 3 contained only 6 shapes instead of 9. The color change in each array pair involved a change in bothcolors. However, it was possible for one color to simply move from one side of the shape to the other. Therefore, in order tosuccessfully complete the task it was necessary to either remember both colors for each shape or remember the side of theshape a given color appeared.

In addition to using change detection arrays that encouraged more effortful processing of the items, we also included anequal-probability training condition. Participants in this condition completed the same number of trials as participants in theprobability training condition except the color change occurred equally often for each shape. The probability training con-dition was similar to that in Experiment 1: the color change always occurred to the same shape during the training trials. Inaddition to comparing performance on the consistent and the inconsistent test trials in the probability training condition,performance on the last 24 trials (‘test trials’) in the equal-probability training condition was compared to performanceon the consistent test trials in the probability training condition. This allowed for an analysis of a probability training effectwhile factoring out a general learning effect on the change detection task.

4.1. Methods

4.1.1. ParticipantsFifty-four undergraduates at George Mason University participated in exchange for class credit. Twenty-nine participated

in the probability training condition and 25 participated in the equal-probability training condition. In the probability train-ing condition the average age was 19 years and 20 of the participants were female. In the equal-probability training condi-tion, the average age was 21 and 14 of the participants were female.

4.1.2. MaterialsFive sets of 154 pre- and post-change arrayswere used. Each array contained six objects arranged in a 2 ! 3 grid (see Fig. 3).

The objects in each pre-change array were chosen from a set of 16 objects. The 16 objects were four novel shapes appearing

Pre-change array: 2 seconds

ISI: 350 ms

Post-change array: 2 seconds

Which shape changed? Which shape changed color?

1 2 3 4Experiment 3 response screen Experiment 4 response screen

Fig. 3. Sequence of events in Experiments 3 and 4. Shapes were actually shown in color combinations red–blue, yellow–red, blue–green, or green–yellow.

1198 M.R. Beck et al. / Consciousness and Cognition 17 (2008) 1192–1208

once in each of four colors combinations (red–blue, yellow–red, blue–green, or green–yellow). The objects in the pre-changearrays and their locations were chosen randomly with the constraints that each of the four shapes had to appear at least oncein each array and at least three of the four color combinations had to appear in each array. A post-change array was created foreach pre-change array by changing the color combinations of one of the objects in the pre-change arrays. The post-changecolor combination was randomly chosen with the constraint that the change did not result in only two of the four color-com-binations remaining in the post-change array.

Of the five sets of 154 pre- and post-change array pairs, 130 were presented during the training phase and the remaining24 were presented during the test phase. Four of the five sets were used in the probability training condition and one set wasused in the equal-probability training condition. Within each set of 130 pre- and post-change array pairs used in the trainingphase of the probability training condition, the object that changed color was always the same shape (one set for eachshape). For the set of 130 trials used in the training phase of the equal-probability training condition, each shape had anapproximately equal probability of changing (2 shapes changed on 33 trials and the other two changed on 32 trials). In boththe probability training set and the equal-probability training sets, the object that changed color could be in any of the sixlocations, could be any one of the four color-combinations and could change into any of the three remaining color-combi-nations. Each set of training pairs used in the probability training condition was paired with a set of 24 test pre- and post-change array pairs. Within each set of test pairs in the probability training condition, the object that changed color was thetrained shape for 12 of the array pairs (consistent test pairs) and for the other 12 pairs the other 3 shapes each changed colorin 4 of the test array pairs. The training set used in the equal-probability training condition was paired with a set of 24 testpair arrays in which each shape changed color in 6 of the array pairs.

4.1.3. ProcedureThe procedures in Experiment 3 were the same as those used in Experiment 1 except for the changes noted here.

Participants completed the experiment either individually or in groups of two and were tested on individual computersin the same room. After the post-change scene was presented, a six-alternative forced-choice (6AFC) recognition ques-tion for the location of the change was presented. The six locations occupied in the arrays were presented on the screenand the participant was asked to choose in which of the six locations the color change occurred. They pressed a number1–6 corresponding to the order of the six locations on the screen (see Fig. 3). Following their response the next trialwould begin.

Participants in the probability training condition were alternately assigned to receive one of the four training sets inwhich the same shape changed on each trial. Participants in the equal-probability training condition all received the sametraining trials. Across these training trials each shape changed color on 25% of the trials. All participants completed 130 train-ing trials (randomly ordered) and then 24 test trials (randomly ordered). After completing the change detection task, partic-ipants filled out a post-experiment questionnaire similar to the one used in Experiment 1.

4.2. Results

All participants scored above chance (.17) on the training trials. In the probability training condition, participantswere .61 (SE = .02) accurate in detecting the color change on the training trials. Performance ranged from .46 to .82 cor-rect, and the distribution of performance on the training trials did not differ from a normal distribution (K–S test,p = .99). In the equal-probability training condition, participants were .59 (SE = .03) accurate in detecting the color changeon the training trials. Performance ranged from .27 to .88 correct, and the distribution of performance on the trainingtrials did not differ from a normal distribution (K–S test, p = .996). Overall accuracy did not differ between the proba-bility and equal-probability training trials, t(52) = .61, p = .54. Therefore, receiving training trials in which the same shapechanged on every trial did not improve performance compared to participants who received training trials in which eachshape was equally likely to change.

4.2.1. Test trialsAccuracy on the 6AFC questions for the test trials was entered into a 2 ! 4 repeated measures ANOVA with consistency

(consistent or inconsistent with training) as a within-subjects factor and training set (one for each of the 4 shapes) as a be-tween-subjects factor. Accuracy on the consistent test trials (M = .66: SE = .03) was not significantly different than accuracyon the inconsistent test trials (M = .60: SE = .04), F(1,25) = 2.8, MS = .064, p = .11, partial g2 = 0.101 (Table 2). In addition,there was no main effect for training set, F(3,25) = 1.9, MS = .086, p = .15, partial g2 = 0.189. The interaction between consis-tency and training set was also not significant, F(3,25) = 1, MS = .023, p = .41, partial g2 = 0.108.

Table 2Proportion correct in Experiments 3 and 4

Equal-probability test trials Consistent test trials Inconsistent test trails

Experiment 3 .64(.21) .66(.15) .60(.22)Experiment 4 .67(.13) .74(.14) .59(.20)

Standard deviation presented in parentheses.

M.R. Beck et al. / Consciousness and Cognition 17 (2008) 1192–1208 1199

Performance on the training trials was split into the first half (first 65 trials) and the second half (second 65 trials). A 2 ! 2repeated measures ANOVA was conducted with training half (first, second) as a within subjects factor and training condition(probability, equal-probability) as a between subjects factor. The main effect for training condition was not significant,F(1,52) = .094, MS = .003, p = .76, partial g2 = 0.002. The main effect for training half was also not significant, F(1,52) = .345, MS = .002, p = .56, partial g2 = 0.007. The interaction between training half and training condition was alsonot significant, F(1,52) = .024, MS < .001, p = .88, partial g2 < 0.001.

Accuracy on the consistent test trials was not significantly different from accuracy on the equal-probability test trials(M = .64), t(52) = .37, p = .71. Accuracy on the inconsistent test trials was also not significantly different from accuracy onthe equal-probability test trials, t(52) = #.72, p = .43.

4.2.2. Post-experiment questionnaireNone of the participants in the probability training condition spontaneously mentioned the difference in change proba-

bilities (question 1) on the post-experiment questionnaire. Over half (58%; 17 out of 29) of the participants chose the ‘‘somemore likely to change” option on question 2. Of these 17 participants, 6 estimated that one of the untrained shapes changedmore often than the others. The remaining 11 gave an estimate of change probability greater than chance (25%) to thetrained shape, but none of them correctly estimated that the trained shape changed on all of the training trials. Averagedacross these 11 participants, the predicted probability of change for the shape that changed on 100% of the training trialswas 43%.

The 6% difference found between consistent and inconsistent test trials is larger than found in Experiments 1 and 2. Todetermine whether this larger difference was caused by a greater number of the participants expressing some awareness ofthe change probability manipulation (n = 11 in Experiment 3 versus n = 4 and n = 7 in Experiments 1 and 2, respectively), werecalculated the test trial analysis excluding these 11 participants. Accuracy on the consistent test trials (M = .64: SE = .04)was not significantly different than accuracy on the inconsistent test trials (M = .63: SE = .06), F(1,14) = .45, MS = .012,p = .51, partial g2 = 0.031. In addition, there was no main effect for training set, F(3,14) = 1.22, MS = .078, p = .34, partialg2 = 0.207. The interaction between consistency and training set was also not significant, F(3,14) = .67, MS = .017, p = .58,partial g2 = 0.126. Therefore, the larger difference between inconsistent trials and consistent trials in this experiment wasdriven by a sub set of participants who expressed some awareness of the change probability manipulation.

4.3. Discussion

Even when the change detection task required more attention to the objects, participants were not able to implicitly learnthe change probability information and use it to improve change detection performance. Across three experiments we havefailed to find evidence that participants are able to use change probability information to guide attention and improve per-formance on a change detection task. These results are somewhat surprising given that Olson et al. (2005) were able to findimplicit learning in change detection task. In Olson et al.’s (2005) studies, the participants’ task was to detect the disappear-ance of an object. When the spatial configuration of the items and the change location were both repeated, change detectionperformance improved. This change detection task differed from ours in several ways. First, the probability informationcould only be used on a portion of the trials (repeated arrays) and could not be used on non-repeated arrays. Second, par-ticipants were detecting a location change. Third, the probability information was presented in the location domain. Finally,the type of change was in the same domain as the probability information: location. In Experiment 4, we focused on one ofthese differences, the domain of the probability information. In Experiment 4, we used a task in which the probability infor-mation was in the location of the change object rather than the shape of the change object to test whether change probabilityinformation for location can be used to guide attention in a change detection task.

The domain of the probability information is a likely candidate for the critical difference between Olson et al.’s (2005)findings and our findings in Experiments 1–3 because selective attention may be directed to location but not shape informa-tion. Research has shown that selective attention is necessary for implicit learning of probability information to occur (Jiang& Chun, 2001; Jiang & Leung, 2005; Jimenez &Mendez, 1999; Turk-Browne et al., 2005). A closer examination of participants’responses to the first question on the post-experiment questionnaire revealed that most participants use a location-colorbinding strategy to complete the change detection task. This question asked participants to describe any strategies they wereusing to perform the change detection task. Only 7% of the participants reported using a strategy that involved binding theshapes with their colors, while 72% of participants reported using a strategy of binding the colors to specific locations in thearray. Therefore, to examine the possibility that implicit learning can occur for location and be used to guide attention in achange detection task, in Experiment 4 the probability information during training was in the location of the change objectrather than the shape of the change object.

5. Experiment 4

In Experiment 4, we tested the hypothesis that implicit learning in change detection occurs for change probability infor-mation in the location of an object. The pre- and post-change arrays consisted of the same 6 dual colored objects as used inExperiment 3. However, the object that changed color was always in the same general location, either the top row of objects

1200 M.R. Beck et al. / Consciousness and Cognition 17 (2008) 1192–1208

or the bottom row of objects. Participants then reported which of the 4 shapes corresponded to the change object. If locationchange probability information can be implicitly learned and used to improve change detection performance, then changedetection accuracy on the test trials should be higher for changes in the trained location.

5.1. Methods

5.1.1. ParticipantsFifty-seven undergraduates at George Mason University participated in exchange for class credit. Thirty participated in

the probability training condition and 27 participated in the equal-probability training condition. In the probability trainingcondition the average age was 20 years and 25 of the participants were female. In the equal-probability training condition,the average age was 21 and 17 of the participants were female.

5.1.2. MaterialsAll of the materials in Experiment 4 were the same as those used in Experiment 3 except for the modifications noted here.

Three sets of 120 pre- and post-change array pairs were created for the training phase of the experiment and one set of 24pre- and post-change array pairs was created for the test phase of the experiment. Two of the three training sets were used inthe probability training condition and one set was used in the equal-probability training condition. Within each set of pre-and post-change array pairs used in the training phase of the probability training condition, the object that changed was al-ways in the same general location. In one of the sets, the object that changed color was always in the top row of objects andin the other set, the object that changed was always in the bottom row of objects. For the set of training arrays used in theequal-probability training condition, the color change was equally likely to occur in all of the locations. In all three trainingsets, the object that changed color could be any of the four shapes, could be any one of the four color-combinations and couldchange into any of the three remaining color-combinations. All three training sets were paired with the same set of 24 testpre- and post-change array pairs. Within the set of test arrays, the object that changed color was in the top row for 12 of thearray pairs and in the bottom row for the remaining 12 array pairs.

5.1.3. ProcedureThe procedure in Experiment 4 was the same as in Experiment 3 except for the changes noted here. After the post-change

array was presented, a 4AFC recognition question for the shape of the change object was presented. The four shapes thatappeared in each array were presented in a vertical line on the screen and the participant was asked to choose which ofthe four shapes changed color. They pressed a number 1–4 corresponding to the order of the four shapes on the screen(see Fig. 3). Following their response the next trial would begin.

Participants in the probability training condition were alternately assigned to receive one of the two training sets. Partic-ipants in the equal-probability training condition all received the same training trials. After completing the 120 training tri-als, all participants completed the same 24 test trials.

After completing the change detection task, participants filled out a post-experiment questionnaire to assess their aware-ness of the probability manipulation. The first question asked participants to list any strategies they used to detect thechanges. Then participants were asked ‘‘Do you think that any of the changes were more likely to occur in certain locations?”They responded by choosing one of two options: ‘‘Some more often” or ‘‘Equally likely”. Finally, participants were asked‘‘What percentage of trials did changes occur in the top row of shapes versus the bottom row of shapes?” Participants wrotedown percentages for the top row and for the bottom row.

5.2. Results

All participants scored above chance on the training trials (.25). In the probability training condition, participants were .61(SE = .02) accurate in detecting the color change on the training trials. Average performance ranged from .40 correct to .82,and the distribution of performance on the training trials did not differ from a normal distribution (K–S test, p = .97). In theequal-probability training condition, participants were .55 (SE = .02) accurate in detecting the color change on the trainingtrials. Average performance ranged from .33 correct to .82, and the distribution of performance on the training trials did notdiffer from a normal distribution (K–S test, p = .6). Performance on the training trials in the probability training condition wassignificantly higher than performance in the equal-probability training condition, t(55) = 2.08, p = .04. Therefore, receivingtraining trials in which the object that changed color was in the same general location on every trial improved performanceduring training as compared to participants who received training trials in which the change was equally likely to occur in allof the locations.

5.2.1. Test trialsAccuracy on the 4AFC questions for the test trials in the probability training condition was entered into a 2 ! 2 repeated

measures ANOVA with the consistency (consistent or inconsistent) as a within-subjects factor and training set (top row orbottom row) as a between-subjects factor. Accuracy on the consistent test trials (M = .74: SE = .03) was significantly higherthan accuracy on the inconsistent test trials (M = .59: SE = .04), F(1,28) = 12.4,MS = .327, p = .001, partial g2 = 0.307 (see Table

M.R. Beck et al. / Consciousness and Cognition 17 (2008) 1192–1208 1201

2). There was no main effect for training set, F(1,28) = .27, MS = .01, p = .61, partial g2 = 0.009, and the interaction betweenconsistency and training set was also not significant, F(1,28) = .09, MS = .002, p = .76, partial g2 = 0.003.

Performance on the training trials was split into the first half (first 65 trials) and the second half (second 65 trials). A 2 ! 2repeated measures ANOVA was conducted with training half (first, second) as a within subjects factor and training condition(probability, no probability) as a between subjects factor. The main effect for training condition was significant,F(1,55) = 8.91, MS = .3, p = .004, partial g2 = 0.139. The main effect for training half was not significant, F(1,55) = .86,MS = .003, p = .36, partial g2 = 0.015. The interaction between training half and training condition was significant,F(1,55) = 4.43, MS = .017, p = .04, partial g2 = 0.075.

Accuracy on the consistent test trials in the probability training condition was compared to accuracy on the test trials inequal-probability training condition. Accuracy on the consistent test trials was significantly higher than accuracy on theequal-probability test trials (M = .67), t(55) = 2, p = .05. Accuracy on the inconsistent test trials was not significantly differentthan accuracy on the equal-probability test trials, t(55) = #1.67, p = .1.

5.2.2. Post-experiment questionnaireNone of the participants in the probability training condition spontaneously mentioned the difference in change proba-

bilities (question 1) on the post-experiment questionnaire. Only 30% (9 out of 30) of the participants chose the ‘‘some morelikely to change” option on question 2. Of these 9 participants, 4 estimated the untrained location changed more often thanthe trained location. The remaining 5 gave an estimate of change probability greater than chance (50%) to the trained loca-tion, but none of them correctly estimated than the trained location changed on all of the training trials. Averaged acrossthese 5 participants, the predicted probability of change for the general location that changed on 100% of the training trialswas 68%. Excluding these participants did not meaningfully change the analyses presented above.

In order to determine whether participants preferentially used a strategy of binding location to color to perform thechange detection task, we examined participants’ reported strategy in the first question on the post-experiment question-naire. Only 17% of the participants reported using a strategy that involved binding the shapes with their colors in orderto perform the task, while 73% of participants reported using a strategy of binding the colors to specific locations in the array.

5.3. Discussion

When the change consistently occurred in the same general location during training trials, participants were better atdetecting changes in this general location than they were in another general location. However, the post-experiment ques-tionnaire suggests participants were not aware that the change always occurred in the same general location during thetraining trials. These results suggest that participants improved change detection performance through implicitly learnedknowledge about the probable locations of changes. Experiments 1–4 suggest that the ability to implicitly learn and thanuse change probability information in a change detection task occurs for the location of the object but not the shape ofthe object3. Experiments 5 and 6 examine the extent to which participants can learn and use explicit knowledge of change prob-ability in shape (Experiment 5) and location (Experiment 6).

6. Experiment 5

It is possible that change probability information for shape failed to improved change detection performance because thecomplexity of the shape information prevents participants from using this information. In Experiments 5 and 6, we exam-ined this question by testing participant’s ability to explicitly learn and use change probability information for the shape orfor the location of an object. Half of the participants were assigned to an explicit training condition in which they were told atthe beginning of the experiment that the color change would always occur to the same shape (or location), but they were nottold which shape (or location) would change color on each trial (explicit training condition). The other half of the participantsreceived no instruction about the systematic relationship between the color change and the shape (or location) of the objectthat changed (implicit training condition). If participants in the explicit training condition are able to explicitly learn and usechange probability information for the shape of an object, we can conclude that the failure to improve change detection per-formance for the consistent shape in Experiments 1–3 is not caused by an inability to use shape information to directattention.

In addition to the explicit training condition, two additional modifications were made to Experiments 5 and 6. In Exper-iments 1–4, the configuration of the shapes repeated across all trials (e.g., 3 shapes on top and 3 shapes on bottom) this rep-etition of configuration may have made it easier for participants to bind color to location rather than binding color to shapeduring the change detection task. In Experiment 5 and 6, the six shapes were randomly placed in one of 16 locations so thatthe configuration of the objects did not repeat across trials. Another issue addressed in Experiments 5 and 6 is that chanceperformance varied between Experiments 3 and 4 and could have lead to the differences in performance found between

3 The Cohen’s d effect size in Experiment 3 was 28.This is only a small effect according to Cohen (1977, 1988). Alternatively, the effect size in Experiment 4was .65, which falls in between what is considered to be a medium (.5) and a large effect size (.8). Furthermore, in order for an effect size of .28 to result in asignificant difference between consistent and inconsistent trials in Experiment 3, we would need 102 subjects. Therefore, although there may be a slightadvantage for consistent test trials in Experiment 3, the effect is clearly smaller than the effect in Experiment 4.

1202 M.R. Beck et al. / Consciousness and Cognition 17 (2008) 1192–1208

these experiments. In Experiment 3 participants choose which of 6 locations contained the change and in Experiment 4, par-ticipants choose which of 4 shapes contained the change. In both Experiments 5 and 6, there are 4 possible responses to thechange detection question.

6.1. Methods

6.1.1. ParticipantsFifty-one undergraduates at Louisiana State University participated in exchange for class credit. The average age was 20

years and 37 of the participants were female. Twenty-four participated in the implicit training condition, and 27 participatedin the explicit training condition.

6.1.2. MaterialsOne set of 144 pre-change arrays containing 6 objects was created from a set of 16 possible objects. The 16 objects were

the same as those used in Experiment 1. For each array, 6 objects were randomly chosen and placed in one of 16 locations ina 4 ! 4 grid (see Fig. 4). Post-change arrays were created by changing the color of one of the objects in the pre-change arrays.Constraints in creating the pre and post-change arrays were the same as reported in Experiment 1. There were 4 sets of 120post-change arrays in which the change always occurred to the same shape (training trials). The remaining 24 arrays wereused for the test trials. Within each of 24 test trials, the object that changed color was the same as the trained shape for 12 ofthe arrays (consistent test trials) and for the remaining 12 arrays, each of the other 3 shapes changed color in 4 of the testarrays (inconsistent test trials). The 12 test trials for each shape were rotated through participants such that across partic-ipants all 12 trials were presented as inconsistent test trials.

Stimuli were presented on iMac computers with 20-in. (diagonal) wide-screen monitors set at a resolution of1680 ! 1050 and at 24-bit color depth (millions of colors). The arrays were 16 ! 9.7 cm, presented at the center of a43.5 ! 27 cm white screen, and subtended a 18.8" ! 10.3" visual angle from a viewing distance of approximately 47 cm fromthe screen (viewing distance was not constrained).

6.1.3. ProcedureThe procedures in Experiment 5 were the same as those used in Experiment 1 except for the changes noted here. Partic-

ipants in the implicit training condition were not given any information about the association between the shape of the ob-ject and the color change. After the instructions about the change detection task were given, participants in the explicit

Which shape changed?

1 2 3 4

Where was the object that changed? Choose a number between 1-4 corresponding to the column.

Pre-change array: 2000 ms

Post-change array: 2000 ms

ISI: 800 ms

Experiment 5 response screen Experiment 6 response screen

Fig. 4. Sequence of events in Experiments 5 and 6. Objects were presented in colors red, yellow, green, or blue represented in shades of grey here.

M.R. Beck et al. / Consciousness and Cognition 17 (2008) 1192–1208 1203

training condition were presented with a screen with the following sentence presented above the four shapes: ‘‘the changewill always occur in the same shape of the array. The four shapes that will appear in each array are shown below. Once youknow which shape changed for one trial, you will know which shape will change for all of the trials.” For each trial, the pre-change array was presented for 2 s and then after a 800 ms white screen ISI, the post-change scene was presented for 2 s.Participants were then presented with a response screen asking them to choose which of the four columns changed color(see Fig. 4). All participants completed the post-experiment questionnaire after the change detection trials were completed.

6.2. Results

All participants scored above chance on the training trials (.25). In the implicit training condition, average accuracy was.80 (SE = .02) on the training trials. Accuracy ranged from .67 correct to .92, and the distribution of performance on the train-ing trials did not differ from a normal distribution (K–S test, p = .82). In the explicit training condition, accuracy was .89(SE = .02) on the training trials. Accuracy ranged from .49 correct to .98, and the distribution of performance on the trainingtrials did not differ from a normal distribution (K–S test, p = .19). Performance on the training trials in the implicit trainingcondition was significantly lower than performance in the explicit training condition, t(49) = 3.59, p < .001. Therefore, receiv-ing explicit instruction that the color change would always occur to the same shape improved performance on the trainingtrials.

6.2.1. Test trialsAccuracy on the 4AFC questions for the test trials was entered into a 2 ! 2 repeated measures ANOVA with the consis-

tency (consistent or inconsistent) as a within-subjects factor and training condition (implicit or explicit) as a between-sub-jects factor. There was main effect of consistency, F(1,49) = 22.5, MS = .732, p < .001, partial g2 = 0.315 (see Table 3), but nomain effect for training condition, F(1,49) = 3.03, MS = .137, p = .09, partial g2 = 0.058. Most importantly there was a signif-icant interaction between consistency and training condition, F(1,49) = 15.8,MS = .512, p < .001, partial g2 = 0.243. This inter-action was driven by a significant difference between the consistent (M = .90, SE = .02) and inconsistent trials (M = .58,SE = .05) in the explicit training condition, t(26) = 5.1, p < .001, but no difference between the consistent (M = .83, SE = .03)and inconsistent trials (M = .80, SE = .04) in the implicit training condition, t(23) = .92, p = .37. For the consistent trials, per-formance was not significantly different between the implicit training condition and the explicit training condition,t(49) = 1.8, p = .07, but the difference was significant for the inconsistent trials, t(49) = 3.1, p = .003.

Performance on the training trials was split into the first half (first 60 trials) and the second half (second 60 trials). A 2 ! 2repeated measures ANOVA was conducted with training half (first, second) as a within subjects factor and training condition(implicit, explicit) as a between subjects factor. The main effect for training condition was significant, F(1,49) = 4.64,MS = .17, p = .04, partial g2 = 0.087. The main effect for training half was not significant, F(1,49) = 1.49,MS = .014, p = .23, par-tial g2 = 0.029. The interaction between training half and training condition was not significant, F(1,49) = .24, MS = .002,p = .62, partial g2 = 0.005.

6.2.2. Post-experiment questionnaireNone of the participants in the implicit training condition spontaneously mentioned the difference in change probabilities

(question 1) on the post-experiment questionnaire. Only 21% (5 out of 24) of the participants chose the ‘‘some more likely tochange” option on question 2. Of these 5 participants, 2 estimated that an untrained shape changed more often than thetrained shape. The remaining 3 gave an estimate of change probability greater than chance (25%) to the trained shape,but none of them correctly estimated than the trained shape changed on all of the training trials. Averaged across these 3participants, the predicted probability of change for the shape that changed on 100% of the training trials was 38%. Excludingthese participants did not meaningfully change the analyses presented above.

Twelve of the twenty-seven participants in the explicit training condition spontaneously mentioned the difference inchange probabilities (question 1) on the post-experiment questionnaire. Furthermore, 63% (17 out of 27) of the participantschose the ‘‘some more likely to change” option on question 2. All of these 17 participants estimated that the trained shapechanged more often than the untrained shapes, and the average estimate of change probability for the trained shape was 79%(SE = 3.6). Awareness of the change probability information appears to be an important factor in the consistency effect foundin the test trials. When comparing only participants that were aware of the change probability information (n = 17), perfor-mance on the consistent test trials (M = .92, SE = .02) was significantly higher than performance on the inconsistent test trials(M = .44, SE = .06), t(16) = 6.87, p < .001. However for participants that were not aware (n = 10), performance on the consis-

Table 3Proportion correct for implicit and explicit training conditions in Experiments 5 and 6

Implicit consistent test trials Implicit inconsistent test trails Explicit consistent test trials Explicit inconsistent test trails

Experiment 5 .83(.16) .80(.20) .90(.11) .58(.28)Experiment 6 .83(.19) .42(.16) .85(.13) .33(.17)

Standard deviation presented in parentheses.

1204 M.R. Beck et al. / Consciousness and Cognition 17 (2008) 1192–1208

tent test trials (M = .84, SE = .04) was similar to performance on the inconsistent test trials (M = .81, SE = .04), t(9) = .84,p = .42.

7. Experiment 6

Experiment 6 was the same as Experiment 5 except the probability information was in the location of the change objectrather than the shape of the change object. For the 120 training trials, the color change always occurred in the same columnof the 4 ! 4 grid of possible object locations.

7.1. Methods

7.1.1. ParticipantsFifty-six undergraduates at Louisiana State University participated in exchange for class credit. The average age was 20

years and 40 of the participants were female. Twenty-eight participated in the implicit training condition and 28 participatedin the explicit training condition.

7.1.2. Materials and procedureThe same pre-change images used in Experiment 5 were used in Experiment 6. Four sets of post-change images were cre-

ated in which the change always occurred in the same column of the 4 ! 4 grid for 120 of the post-change arrays (trainingtrials). Within each of 24 test trials, the object that changed color was in the same column as the trained location for 12 of thearrays (consistent test trials) and for the remaining 12 arrays, the object that changed color occurred equally often in each ofthe three untrained columns (inconsistent test trials). The 12 test trials for each column were rotated through participantssuch that across participants all 12 trials were presented as inconsistent test trials.

The procedures in Experiment 6 were the same as those used in Experiment 5 except for the changes noted here. Partic-ipants in the explicit training condition were presented with a screen with the following sentence presented above a diagramof the four columns: ‘‘the change will always occur in the same column of the array. The four columns that will appear ineach array are shown below. Once you know which column changed for one trial, you will know which column will changefor all of the trials.” After each post-change array, participants were presented with a response screen asking them to choosewhich of the four shapes changed color (see Fig. 4). All participants completed the post-experiment questionnaire after thechange detection trials were completed.

7.2. Results

All participants scored above chance on the training trials (.25). In the implicit training condition, accuracy was .79(SE = .02) on the training trials. Average performance ranged from .63 correct to .94, and the distribution of performanceon the training trials did not differ from a normal distribution (K–S test, p = .99). In the explicit training condition, accuracywas .90 (SE = .02) on the training trials. Average performance ranged from .65 correct to .99, and the distribution of perfor-mance on the training trials was significantly different from a normal distribution (K–S test, p = .049). Performance in thiscondition was largely skewed toward higher scores. Twenty-one of the twenty-eight participants performed at .90 or higher.The remaining 7 participants scored between .84 and .65. Performance on the training trials in the implicit training conditionwas significantly lower than performance in the explicit training condition, t(54) = 4.4, p < .001. Therefore, receiving explicitinstruction that the color change would always occur in the same column improved performance on the training trials.

7.2.1. Test trialsAccuracy on the 4AFC questions for the test trials was entered into a 2 ! 2 repeated measures ANOVA with the consis-

tency (consistent or inconsistent) as a within-subjects factor and training condition (implicit or explicit) as a between-sub-jects factor4. There was a main effect of consistency, F(1,54) = 211.4, MS = 5.9, p < .001, partial g2 = 0.797 (see Table 3), but nomain effect for training condition, F(1,54) = 1.03, MS = .026, p = .32, partial g2 = 0.019. The interaction between consistency andtraining condition was not significant, F(1,54) = 3.37, MS = .094, p = .07, partial g2 = 0.059. There was a significant difference be-tween the consistent (M = .85, SE = .03) and inconsistent trials (M = .33, SE = .03) in the explicit training condition, t(27) = 11.4,p < .001, and a significant difference between the consistent (M = .82, SE = .04) and inconsistent trials (M = .42, SE = .03) in theimplicit training condition, t(27) = 9.1, p < .001. For the consistent trials, performance was not significantly different betweenthe implicit training condition and the explicit training condition, t(54) = .63, p = .53, but the difference was significant forthe inconsistent trials, t(54) = 2.0, p = .047.

Performance on the training trials was split into the first half (first 60 trials) and the second half (second 60 trials). A 2 ! 2repeated measures ANOVA was conducted with training half (first, second) as a within subjects factor and training condition

4 In Experiment 6, for 10 participants in the implicit training condition and 12 participants in the explicit training condition, one inconsistent test trial wasexcluded from analysis because the wrong post-change image was presented. For 6 participants in the implicit training condition and 7 participants in theexplicit training condition two consistent test trials were excluded from analysis because the wrong post-change scene was presented.

M.R. Beck et al. / Consciousness and Cognition 17 (2008) 1192–1208 1205

(implicit, explicit) as a between subjects factor. The main effect for training condition was significant, F(1,54) = 19.35,MS = .33, p = < .001, partial g2 = 0.264. The main effect for training half was also significant; performance in the first half(M = .81) was significantly lower than performance in the second half (M = .87), F(1,54) = 17.6, MS = .1, p < .001, partialg2 = 0.246. The interaction between training half and training condition was not significant, F(1,54) = .004, MS < .001,p = .95, partial g2 = 0.001.

7.2.2. Post-experiment questionnaireNone of the participants in the implicit training condition spontaneously mentioned the difference in change probabilities

(question 1) on the post-experiment questionnaire. Only 29% (9 out of 29) of the participants chose the ‘‘some more likely tochange” option on question 2. Of these 9 participants, 5 estimated that an untrained column contained the changed objectmore often than the trained column. The remaining 4 gave an estimate of change probability greater than chance (25%) to thetrained column, but none of them correctly estimated than the trained column contained the changed object on all of thetraining trials. Averaged across these 4 participants, the predicted probability of change for the column that containedthe object that changed on 100% of the training trials was 40%. Excluding these participants did not meaningfully changeany of the analyses presented above.

Ten of the 28 participants in the explicit training condition spontaneously mentioned the difference in change probabil-ities (question 1) on the post-experiment questionnaire. Furthermore, 82% (23 out of 28) of the participants chose the ‘‘somemore likely to change” option on question 2. One of these 23 participants estimated that an untrained column contained thechanged object more often than the trained column. The remaining 22 participants estimated that trained column changedmore often than any of the untrained columns, and the average estimate of change probability for the trained column was70% (SE = 3.7). Awareness of the change probability information does not appear to be an important factor in the consistencyeffect found in the test trials. When including only those participants that were aware of the change probability information(n = 22), performance on the consistent test trials (M = .83, SE = .03) was significantly higher than performance on the incon-sistent test trials (M = .36, SE = .04), t(21) = 10.2, p < .001. The same is found for those participants that were not aware(n = 6), performance on the consistent test trials (M = .94, SE = .04) was higher than performance on the inconsistent test tri-als (M = .23, SE = .07), t(5) = 7.1, p < .001.

7.3. Discussion

Experiments 5 and 6 demonstrate that when participants are explicitly told that a color change will always occur in thesame shape or the same location, they are able to use this information to direct attention to the probable shape or locationand improve change detection performance. Furthermore, the results from Experiment 5 demonstrate that the inability toimprove change detection performance for the consistent shape in Experiments 1, 2, and 3 did not occur because participantsare incapable of using shape information to direct attention and VSTM in a change detection task. Experiment 6 demon-strates that the use of explicit information about the probability of a change occurring in a particular location leads to similarperformance as the use of implicit information. Finally, Experiments 5 and 6 replicate the findings from Experiment 3 and 4with arrays that do not repeat the same spatial configuration on each trial. This further strengthens the conclusion thatchange probability information can be implicitly learned and then used to improve change detection performance when thisinformation occurs in the location of the change object, but not when the change probability information occurs in the shapeof the object.

8. General discussion

Across four experiments (Experiments 1–3 and 5) participants were unable to improve change detection performancebased on change probability information for the shape of the changed object. Experiments 4 and 6 demonstrated thatimplicit learning and improved change detection performance does occur when the probability information is in thelocation of the changed object. Experiments 4 and 6 support previous research demonstrating that observers are sensi-tive to probability information in the visual environment. However, Experiments 1–3 and 5 suggest that there are limitson the ability to implicitly learn probability information and then use this information to improve change detectionperformance.

The lack of evidence that change probability information presented in the shape of an object is implicitly learned and thenused to improve change detection performance is somewhat surprising because shape information has been found to sup-port implicit learning in other tasks (Fiser & Aslin, 2002; Turk-Browne et al., 2005). Furthermore, change detection duringtraining was relatively high (77% averaged across Experiments 1–3 and 5), suggesting that the change object was attendedon a large percentage of trials. Based on research showing that implicit learning occurs automatically for attended informa-tion (Jiang & Chun, 2001; Jiang & Leung, 2005; Jimenez & Mendez, 1999; Turk-Browne et al., 2005), implicit learning shouldoccur for attended novel shapes. We will explore two possible explanations why we found evidence of improved changedetection through implicit learning for change probability information presented in the location of an object but not forchange probability information presented in the shape of an object. First, implicit learning may occur more readily for loca-tion than shape because location information is encoded into memory more readily than shape information. Second, implicit

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learning requires that the encoded probability information is bound to the feature that is changing and color may be morereadily bound to location than to shape.

It has been argued that location information is processed automatically, but features such as shape require selectiveattention for processing (Aginsky & Tarr, 2000). Consequently, spatiotemporal information may be represented more ro-bustly than shape information (Sagi & Julesz, 1985; Scholl, 2001). For example, in an object tracking task, participants wereable to report the location of a tracked item but not its color or shape (Scholl, 2001). In a visual search task, participantsremember the location of previously searched items but not their identities (Beck, Peterson, & Vomela, 2006). In changedetection tasks, location changes are detected more readily than color changes (Simons, 1996). Therefore, implicit learningmay have occurred for location but not shape because location is more likely to be encoded than shape information.

Change detection performance may be improved through implicit learning for probability information presented in thelocation of an object but not for probability information presented in the shape of an object because location information isautomatically bound to object information but object features are not necessarily bound together. Research shows that theshape and color of an object are accessed in memory by the location of the object (Nissen, 1985; Keele, Cohen, Ivry, Liotti, &Yee, 1988; Treisman & Zhang, 2006). Therefore, spatial location is used as a cue to access the color or shape of an object.Furthermore, in change detection task, when the spatial configuration of colored squares changes from pre- to post- changescene, the ability to detect a color change is impaired (Jiang, Olson, & Chun, 2001). However, if the colors of all of the itemschange, the ability to detect a location change are not impaired, which suggests that location is a cue to feature informationabout an object. When detecting a color change, if the other items also change, color performance declines for a cued targetchange (Vidal, Gauchou, Tallon-Baudry, O’Regan, 2005). However, if the other items change shape, color change detection isnot impaired. In line with these results, it has been suggested that color and shape are stored separately and both are ac-cessed through location information (Johnston & Pashler, 1990).

The current research supports the conclusion that attention to an object does not guarantee that all features of the objectare encoded and bound together in memory. Rather, selective attention to the aspect of the scene providing the probabilityinformation is necessary for implicit learning to improve task performance (Jiang & Chun, 2001; Jiang & Leung, 2005; Jime-nez & Mendez, 1999; Turk-Browne et al., 2005). Participants in the current experiments had to attend to the objects to detectthe change, suggesting that attending to an object during change detection allows implicit learning for the location of theobject but not its shape. This finding seems contrary to the idea that when attention is directed to an object, all of the fea-tures of that object are stored together in VSTM automatically (Vogel, Woodman, & Luck, 2001). If this were the case, thenimplicit learning should occur for an attended object regardless of whether the probability information is contained in thelocation of the object or the shape of the object.

The current research extends the findings of Olson et al. (2005) in several ways. Although, Experiments 4 and 6 and Olsonet al.’s (2005) study found implicit learning for the location in a change detection task, there were several differences be-tween the change detection tasks and how the probability information was presented. First, in Olson et al.’s (2005) study,several different spatial arrays were presented across the task and a particular repeated spatial array would predict an indi-vidual change location within the array. In Experiment 4 of the current studies the same spatial arrangement was repeatedacross every trial and in Experiment 6 the spatial arrangement was different on each trial. Most importantly, the change loca-tion was predicted by which object changed on previous trials, not by the spatial configuration of the distractors. Further-more, the predicted location was not a single location but any one of three (Experiment 4) or four (Experiment 6) possiblelocations. Therefore, Experiments 4 and 6 demonstrate that implicit learning can occur with noise in the predicted changelocation and with and without noise in the repeated spatial array. Second, Experiments 4 and 6 demonstrated that implicitknowledge improves change detection performance for a change detection task in which the domain containing the prob-ability information was not directly relevant to the task. In Olson et al.’s (2005) study, the probability information was inthe location domain and the change was to the location of one of the objects. Alternatively in our Experiments 4 and 6,the change probability information and the change were in different domains; the probability information was in the loca-tion domain and the change was in the color domain. Finally, our studies demonstrate that although implicit knowledge ofchange probability information provided in the location of an object can be used to guide attention, this finding does notnecessarily extend to change probability information provided in other domains such as shape.

More generally, the results of Experiments 4 and 6 are consistent with research demonstrating that in more real worldsituations, observers have knowledge about where items are expected to occur and they use this knowledge to direct atten-tion to the expected location of objects (Beck et al., 2004; Shinoda, Hayhoe, & Shrivastava, 2001). Shinoda et al. (2001) re-ported that while participants are in a driving simulator, they are more likely to notice a stop sign changing into a yieldsign if the stop sign occurs in its expected location (at an intersection) rather than in an unexpected location (the middleof a block). Presumably, expectations of the locations of objects have been learned implicitly through prior experience withthe visual world.

In conclusion, implicit learning in change detection tasks can occur with exposure to change probability information.This ability, however, occurs when the change probability information is provided in the location of the object, but notwhen it is provided in the shape of the object. This is either because location, but not shape, information is automat-ically processed and stored in VSTM or because selective attention to a feature of an object binds that feature to locationinformation for the object but not to other features of the object. Regardless of the mechanism supporting or preventingimplicit learning for change probability information, the current studies show that it can occur for general location butnot for shape.

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