Oculomotor and manual indexes of incidental and intentional spatial sequence learning during middle...

Journal of Experimental Child Psychology 96 (2007) 107–130

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Oculomotor and manual indexes of incidental and intentional spatial sequence learning during middle childhood and adolescence

Canan Karatekin a,¤, David J. Marcus b, Tonya White c

a Institute of Child Development, University of Minnesota, Minneapolis, MN 55455, USAb Children’s National Medical Center, Washington, DC 20010, USA

c Division of Child and Adolescent Psychiatry, University of Minnesota, Minneapolis, MN 55455, USA

Received 10 August 2003; revised 24 May 2006Available online 7 July 2006

Abstract

The goal of this study was to examine incidental and intentional spatial sequence learning duringmiddle childhood and adolescence. We tested four age groups (8–10 years, 11–13 years, 14–17 years,and young adults [18+ years]) on a serial reaction time task and used manual and oculomotor mea-sures to examine incidental sequence learning. Participants were also administered a trial block inwhich they were explicitly instructed to learn a sequence. Replicating our previous study with adults,oculomotor anticipations and response times showed learning eVects similar to those in the manualmodality. There were few age-related diVerences in the sequence learning indexes during incidentallearning, but intentional learning yielded diVerences on all indexes. Results indicate that the searchfor regularities and the ability to learn a sequence rapidly under incidental conditions are mature by8 to 10 years of age. In contrast, the ability to learn a sequence intentionally, which requires cognitiveresources and strategies, continues to develop through adolescence.© 2006 Elsevier Inc. All rights reserved.

Keywords: Development; Sequence learning; Incidental learning; Intentional learning; Serial reaction time(SRT); Eye movements; Anticipatory responses; Visual–spatial attention

* Corresponding author. Fax: +1 612 624 6373.E-mail address: [email protected] (C. Karatekin).

0022-0965/$ - see front matter © 2006 Elsevier Inc. All rights reserved.doi:10.1016/j.jecp.2006.05.005

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108 C. Karatekin et al. / Journal of Experimental Child Psychology 96 (2007) 107–130

Introduction

The ability to extract sequential regularities from complex environmental input and toexecute sequential actions based on what one has learned underlies many cognitive andmotor skills. The goal of the current study was to compare age-related changes in inciden-tal and intentional spatial sequence learning during middle childhood and adolescence.

One of the most commonly used measures of spatial sequence learning, the serial reac-tion time (SRT) task, was devised by Nissen and Bullemer (1987) to investigate the role ofawareness and attention in learning. On this task, series of stimuli are displayed in one ofseveral (typically three or four) locations on a computer screen. Participants respond bypressing keys corresponding to the stimulus locations. Although participants are notinformed of this, the stimulus locations sometimes occur in a repeating sequence (com-monly 10 or 12 repeats). Learning is inferred if manual response times (RTs) (a) decreaseacross repeated exposures to the sequences (initial learning) and (b) increase for interven-ing random stimuli (interference). The initial decrease in RTs reXects a combination ofgeneral visual–motor learning and sequence-speciWc learning. Therefore, the interferenceeVects on random stimuli provide a clearer index of sequence-speciWc learning (Knopman& Nissen, 1987). Because participants usually report little sequence-speciWc knowledge ondirect recall or recognition measures, the SRT task has been interpreted as a form of learn-ing without explicit awareness.1

To our knowledge, there are only four published studies of age-related diVerences inspatial sequence learning in older children. In the Wrst SRT study, magnitude of learningwas similar across 6- to 10-year-olds and adults (Meulemans, Van der Linden, & Perruchet,1998). In addition, both groups showed similar retention of the sequence 1 week later.

Thomas and colleagues (2004) used functional brain imaging to compare 7- to 11-year-olds with young adults on a task adapted from Meulemans and colleagues (1998). Thelearning eVect was greater in adults than in children. The authors also reported age-relateddiVerences in cortical and subcortical activation. The results were interpreted as indicatingthat implicit and explicit learning systems develop in parallel.

In a previous study, Thomas and Nelson (2001) found a similar magnitude of learningacross 4-, 7-, and 10-year-olds. The authors concluded that measures of sequence learning

1 Although many deWnitions of implicit learning have been oVered (Frensch, 1998), it is generally agreed that inthis form of learning the participant does not intentionally try to learn new information, is not aware of or able toverbalize what has been learned, and learns relatively complex information—not merely simple associations(Seger, 1994). There are unresolved issues in the research literature regarding the deWnition and measurement ofimplicit learning such as the degree to which implicit learning excludes conscious awareness and use of attentionalresources and the diYculties involved in measuring learning that proceeds without participants’ full awareness(e.g., Destrebecqz et al., 2003; Perruchet & Amorim, 1992; Robertson & Pascual-Leone, 2003; Schvaneveldt &Gomez, 1998; Shanks, Rowland, & Ranger, 2005; Sun, Slusarz, & Terry, 2005). These problems are compoundedwhen the participants are children. The focus of our work is not on implicit learning in general or on the dissocia-tion between implicit and explicit learning; rather, it is on spatial sequence learning under diVerent types of in-structions. Therefore, we chose to label our conditions as incidental versus intentional (e.g., as in Rüsseler,Henninghausen, Münte, & Rösler (2003)) rather than as implicit versus explicit. We use the term incidental learn-ing to reXect the fact that participants were instructed to make visual–motor responses on Blocks 1–5 and thatany learning that occurred, consciously or not, is incidental to this primary task. In addition, as noted in theResults section, the majority of the participants were aware that there was some kind of regularity to the stimuli,although most of them could not articulate more than three steps of the sequence. Therefore, it would not beaccurate to refer to the learning eVects we observed as reXecting learning without any awareness of a regularity.

C. Karatekin et al. / Journal of Experimental Child Psychology 96 (2007) 107–130 109

based on manual RTs do not develop between 4 and 10 years of age but that more subtlemeasures, such as anticipatory button presses to correct locations, show evidence of devel-opmental change.

Finally, De Guise and Lassonde (2001) examined SRT performance under unimanualand bimanual conditions in four age groups (6–8, 9–11, 12–14, and 15–16 years). Allgroups learned the sequence under unimanual conditions, but only the two older groupslearned the sequence in the bimanual condition. However, the youngest group made a largenumber of errors in both conditions. Thus, their failure to show learning eVects in thebimanual condition could be attributed to a diYculty in learning the visual–motor require-ments of the task (Goedert & Willingham, 2002; Howard & Howard, 1997; Howard et al.,2004). In addition, even the youngest children appeared to show interference eVects in theWrst half of the blocks in the bimanual condition (see Fig. 1 in De Guise and Lassonde(2001)), although this eVect was not tested statistically.

Thus, there is inconsistency in the child SRT studies regarding whether sequence learn-ing is age invariant during middle childhood. Two unresolved methodological issues mayaccount for the discrepancies. First, only two studies controlled for overall frequency ofstimulus locations in the random stimuli (Meulemans et al., 1998; Thomas et al., 2004), andnone of the studies controlled for frequency of Wrst-order transitions in the control condi-tions.2 Meulemans and colleagues (1998) conducted a post hoc analysis comparing RTs torandom and sequence trials to rule out the possibility that the learning eVects were due tolearning of Wrst-order transitions. In other child SRT studies, however, the learning andinterference eVects observed on the sequence blocks could not be unambiguously attrib-uted to learning of higher order transitions.

Second, there was no consistency in how researchers addressed age-related diVerences inbaseline RTs and in how learning indexes were deWned. All analyses were based on rawRTs in De Guise and Lassonde (2001). Meulemans and colleagues (1998) used diVerencesbetween random and sequence trials as well as proportional and raw scores. Thomas andNelson (2001) used proportional change in RTs, and Thomas and colleagues (2004) usedthe diVerence between z scores on random trials and z scores on sequence trials. Interpreta-tion of diVerence scores between groups on experimental versus control conditions can beproblematic when there are baseline diVerences among groups (Chapman, Chapman,Curran, & Miller, 1994; Faust, Balota, Spieler, & Ferraro, 1999; Knight & Silverstein, 2001;MacDonald & Carter, 2002; Miller, Chapman, Chapman, & Collins, 1995; Salthouse &Hedden, 2002). DiVerent ways of handling baseline diVerences can result in diVerentinterpretations. Thus, it is not clear whether discrepancies across studies may be due todiVerences in the statistical methods.

For the purposes of the current study, there were two other unaddressed issues in thedevelopmental studies of the SRT task. First, we wished to examine eye movements duringsequence learning. Eye movements reXect overt shifts of visual–spatial attention and havealready been shown to be informative in investigating processes of visual–spatial sequence

2 Zero-order learning refers to the learning of the overall frequency of locations, and Wrst-order learning refersto learning to predict the subsequent stimulus based on the previous stimulus. Second-order learning refers tolearning to predict the location of the subsequent stimulus based on the location of the previous two stimuli,although there are diVerences in whether this is deWned as being able to predict the third stimulus in the sequencefrom the Wrst stimulus or from the combination of the Wrst two stimuli (Curran, 1998; Howard et al., 2004; Reed& Johnson, 1994; Remillard & Clark, 2001).


learning in monkeys (Miyashita, Rand, Miyachi, & Hikosaka, 1996), human infants(CanWeld & Haith, 1991; Clohessy, Posner, & Rothbart, 2001; Wentworth & Haith, 1998),and adults (Kawashima et al., 1998).

In a prior study of the SRT task with healthy young adults (Marcus, Karatekin, & Mark-iewicz, 2006), we found that participants spontaneously made anticipatory eye movements onone third to one half of all the trials and that these eye movements showed the same learningeVects as did manual responses. We interpreted these results as indicating that participantsovertly shifted visual–spatial attention to likely target locations prior to the onset of the stim-uli. Following intentional learning instructions, these shifts probably reXected, at least tosome extent, conscious hypothesis-testing strategies. On the sequence and pseudorandomblocks, however, they seemed to reXect an obligatory search for regularities in the environ-ment, whether these regularities existed or not (cf. Huettel, Mack, & McCarthy, 2002; Shankset al., 2005). Thus, participants appeared to constantly anticipate stimulus locations from thebeginning (Block 1), and learning on the sequence blocks consisted largely of improvementsin the speed and accuracy of these oculomotor anticipations.

In addition, we were interested in examining whether explicitly paying attention to thesequence would help or hurt performance and whether this eVect would depend on age. Previ-ous studies of the SRT task in adults had indicated that being instructed to learn the sequencecan enhance or hinder performance depending on several factors such as the salience of thesequence structure (Howard & Howard, 2001; Sun et al., 2005). For young children, incidentalsequence learning may make small but nontrivial demands on resources, and the additionaltask of explicitly learning the sequence may overwhelm their limited cognitive resources(cf. Howard & Howard, 2001). None of the SRT studies in children had included a direct com-parison of incidental learning and intentional learning, and no studies had compared theeVects of explicit learning instructions on sequence learning at diVerent ages.

The goal of the current study was to examine age-related changes in performance on thesame task3 during middle childhood and adolescence. We tested four age groups (8–10years, 11–13 years, 14–17 years, and young adults [18+ years]) to address three questions.First, we asked whether there were age-related changes in the magnitude of the learningindexes in the manual modality during incidental learning. Second, we examined whetherthere were age-related changes in the frequency and pattern of anticipatory eye movementsduring incidental learning. Third, we investigated age-related diVerences during intentionallearning in terms of manual and oculomotor responses.

Method

Participants

The demographic information for each age group is summarized in Table 1. Partici-pants were recruited from three sources. In the oldest age group, 20 participants

3 There were several minor diVerences in the methods used for the push-sequence group in Marcus and col-leagues (2006) and the current study. First, in the current study, we included Wve incidental blocks rather than sev-en. Second, in our previous article, anticipations were included in the analyses only if no further saccades wereinitiated for 1000 ms after the stimulus onset; in the current study, we eliminated this criterion to make the deWni-tion less restrictive. Third, in the current study, we used a recognition task rather than a free recall task to measuresequence awareness.


(16 women and 4 men) were students at the University of Minnesota (mean ageD248months, SDD 27, rangeD220–327). They were recruited from psychology classes and fromnotices posted on campus. Criteria for inclusion in the study, based on participant self-reports, were as follows: normal or corrected-to-normal vision, native language of English,no history of signiWcant neurological or psychiatric conditions, no use of psychoactivemedications or recreational drugs during the week prior to testing, and no more than onedrink of alcohol during the 24 h prior to testing. Participants were provided with monetarycompensation or extra credit class points for their participation.

In the younger age groups, 16 children (9 boys and 7 girls) were recruited from a data-base of potential research participants maintained by the Institute of Child Developmentat the University of Minnesota. The participants from this pool were selected for the cur-rent study on the basis of gender and date of birth. They were contacted by phone by theresearch staV and were asked about their willingness to participate in the study. Each childwas tested within 1 to 2 months of his or her 10th birthday (mean ageD 121 months,SDD0.5, rangeD 120–122). Inclusionary criteria (based on parent reports during phonescreening interviews) were as follows: no current or past signiWcant neurological or psychi-atric conditions, no reading disorder, no use of psychoactive medications, normal or cor-rected-to-normal vision, and native language of English. The children were provided withsmall prizes to motivate them to remain on task, and the families received monetary com-pensation on completion of the testing.

All remaining participants (nD64, 37 girls and 27 boys, age rangeD132–241 months)were tested as controls for studies on cognitive functioning in clinical disorders. These con-trol participants were recruited from advertisements in local newspapers, schools and com-munity centers, and friends of other participants. Potential participants were excluded ifthey were taking psychotropic medications, if they had a history of signiWcant neurologicalconditions (e.g., seizures, moderate to severe head injuries), if they had been adopted, or iftheir native language was not English or they had learned English after 3 years of age.After a screening interview on the phone, the parents and participants underwent a diag-nostic evaluation. All diagnoses were made using DSM-IV criteria (American PsychiatricAssociation, 1994) and were based on semistructured interviews with the adolescents andat least one parent using the Kiddie Schedule for AVective Disorders and Schizophrenia(Kaufman, Birmaher, Brent, Rao, & Ryan, 1996). Parents and teachers also Wlled out ques-tionnaires about the medical and developmental history and current functioning of theirchildren. The participants were administered the diagnostic measures and a set of cognitivetasks, including the one presented in the current article, on the Wrst day of testing, and asecond set of tasks was administered on the second day. Participants were also adminis-tered the Vocabulary and Block Design subtests from the Wechsler Intelligence Scale for

Table 1Characteristics of the participants

Note. Standard deviations are in parentheses.a Male:female data are percentages.

8–10 years 11–13 years 14–17 years 18+ years

n 35 28 13 24Age (months) 119 (6) 152 (12) 185 (12) 244 (26)Gender (male:female)a 51:49 50:50 23:77 21:79Estimated IQ 115 (14) 111 (13) 112 (13) 113 (14)


Children, Third Edition (WISC-III) (Wechsler, 1991), or the Wechsler Adult IntelligenceScale, Third Edition (WAIS-III) (Wechsler, 1997), to obtain an estimate of IQ. The familieswere provided with monetary compensation as well as feedback on the results of the diag-nostic evaluation.

One 14-year-old boy was excluded because he developed an unusually explicit and rapidawareness of the sequence and performed far above the level of the other participants onthe recognition (17/20) and prediction (17/20) tasks. Two boys (9 and 12 years of age) wereexcluded due to behavior problems during testing.

Apparatus

Stimulus presentation

Custom software controlled stimulus presentation and linked the timing of stimuluspresentation with the recording of eye movements. Stimuli were displayed on a VGA colormonitor (53 cm diagonal). Participants were seated 69 cm in front of the monitor.

Recording manual responses

A custom-built button box had four buttons (1.5 cm2) arranged horizontally with 1.5 cmbetween each pair of buttons. Participants rested the middle and index Wngers of their lefthand on the Wrst and second buttons and rested the index and middle Wngers of their righthand on the third and fourth buttons. Custom software recorded manual responses andmerged this information with the eye movement data.

Eye monitoring

The horizontal and vertical coordinates of the center of gaze were collected with avideo-based eye monitor (ISCAN Eye Tracking Laboratory, Model ETL-400), which has atemporal resolution of 60 Hz and a spatial resolution of 1° over the range of visual anglesused in the study. A camera with an infrared light source to illuminate the pupil was posi-tioned in front of the monitor, below eye level, and 40 cm from each participant. Becausethe camera automatically compensated for small head movements, no head restraint wasused. However, the participant rested his or her head against a padded headrest. The exper-imenter sat behind the participant, in front of the computer that controlled the video cam-era, and collected the eye movement data. Gaze position was calibrated for the participantat the beginning of the session by focusing the camera on the participant’s left eye and hav-ing him or her look at small visual stimuli in the center and four corners of the screen.These positions were recorded as the targets of gaze. Calibration was repeated betweenblocks of trials if necessary due to excessive head movement.

Procedure

The same participants also took part in two other eye-tracking tasks during this session.The SRT task was administered as the second or third of these measures, in counterbal-anced order, and took approximately 35 min to complete. The SRT task consisted of Wveblocks of trials in a standard incidental learning condition, followed by three direct


measures of sequence awareness: verbal response, recognition, and prediction. Finally, par-ticipants completed one block of trials in an intentional learning condition in which theywere explicitly directed to learn a sequence.

Incidental learning

Four boxes (2.9£ 2.9 cm, each separated by 7 cm) were displayed horizontally in thecenter of the screen. The boxes were outlined in black against a white background and sub-tended 27° of visual angle. The stimulus, a colored image of a butterXy, was displayed for1000 ms, followed by a 500-ms intertrial interval. Participants were instructed to look at thebutterXy and to press the corresponding button as quickly as they could without makingmistakes. The experimenter monitored the eye movements to the stimuli on a separatescreen, and if it appeared that participants were performing the task using peripheralvision, participants were reminded to look at the butterXy.

Each block of trials consisted of 100 stimuli. In the sequence blocks (second, third, andWfth blocks), a 10-item sequence was presented 10 times. The order of the sequence was 3-2-4-3-1-4-2-3-4-1 (based on Beldarrain, Grafman, Pascual-Leone, & Garcia-Monco, 1999),where the numbers correspond to the boxes from left to right. This sequence was chosenbecause transitions between consecutive locations (Wrst-order or pairwise transitions) werenot repeated during the sequence and because it did not contain any salient fragments suchas 1-2-3-4. In the pseudorandom blocks (Wrst and fourth blocks), the stimuli were pre-sented in a pseudorandom order that was constructed so that the overall frequency of loca-tions matched that of the sequence trials. In addition, for every 20 trials (1–20, 21–40, etc.)of the pseudorandom blocks, the frequency of Wrst-order transitions matched that of thesequence block. The same 100 trial order was used for the pseudorandom blocks. The Wrstblock was preceded by 10 practice trials. Participants were not informed that a sequencewas present.

Because the trials were so highly constrained, diVerences between the sequence and pseu-dorandom blocks cannot be explained by the frequency of the locations or the Wrst-ordertransitions. Instead, they must reXect learning of more complex aspects of the sequence suchas segments of three or more consecutive elements (second-order transitions or higher).

Sequence awareness

After the Wfth block, participants were queried to determine whether they were aware ofthe repeated sequence. First they were asked, “Did you notice anything about the order inwhich the butterXies appeared?” If they responded positively, they were asked to describewhat they noticed. Regardless of their response, they were informed that the stimuli some-times had appeared in a repeating sequence. Then they were shown series of four stimuliand were asked to judge whether these were part of the sequence they had seen (recogni-tion task). Here 20 series were shown, 10 of which were from the sequence. Then a stimuluswas displayed in one of the boxes, and participants verbally predicted the box in whichthey thought the next stimulus would appear (prediction task). They were instructed torefer to the boxes as 1, 2, 3, or 4 (from left to right). Participants did not receive direct feed-back about their guesses, but they could use the location of the subsequent stimulus toinfer whether their predictions were correct. A total of 20 trials were administered, consist-ing of two repetitions of the 10-item sequence.


Intentional learning

After the awareness tasks, participants were administered a Wnal block of 100 trials (Block6). They were informed that this block would contain a new repeating sequence and that thestimulus would be a picture of a crab. They were instructed to look at the stimulus and topress the corresponding button as quickly as they could but without making mistakes. Theywere instructed to try to learn the sequence and were informed that they would be asked todescribe it at the end of the block. They were again asked to label the boxes with the numbers1 through 4. At the conclusion of this block, participants were asked to describe the patternand what they did to try to learn it. The new 10-item sequence (2-1-4-3-1-3-2-1-2-4) wasrepeated 10 times. Thus, it was identical to the sequence in the incidental condition in that italso contained a 10-step ambiguous deterministic sequence. In both cases, the stimulusappeared 30 times in two locations and 20 times in the other two locations. In both cases,each location could be followed by two or three other locations. In both cases, the blockscontained 10 triplets: 8 occurring 10 times and 2 occurring 9 times. The only diVerence wasthat the sequence blocks in the incidental condition contained 10 pairwise transitions: 9occurring 10 times and 1 occurring 9 times. The explicit block contained 9 pairwise transi-tions: 1 occurring 20 times, 7 occurring 10 times, and 1 occurring 9 times.

Dependent variables

Manual responses

Eye movement data were merged with stimulus data, and an algorithm was used to calcu-late the speed and accuracy of manual responses. Because the sampling rate of the eye moni-tor determined the temporal resolution of the merged data, manual RTs were accurate to16.67 ms. Mean manual RT for each block was calculated based on the correct trials. Antici-patory manual responses were deWned as responses initiated prior to, or within 100ms after,the stimulus onset. This value is consistent with deWnitions in previous SRT studies (e.g., Will-ingham, Nissen, & Bullemer, 1989). The average number of trials on which a manual responsewas recorded within each group ranged from 92 to 99 across the six blocks.

Oculomotor responses

An algorithm automatically removed eye blinks and identiWed saccades from the rawdata. Blinks were deWned as (a) the pupil diameter falling below 1.86 mm or above 5.96 mm,(b) the horizontal or vertical positions of the eye falling outside the limits of the screen, or(c) the diameter of the pupil changing by more than 0.74 mm over 16.7 ms. Saccades weredeWned as eye movements with a velocity between 90 and 800°/s over at least 33.3 ms (twosamples). The saccadic RT was calculated for each trial; however, several factors reducedthe utility of saccadic RTs on this task. First, because no central Wxation point was used,there was no consistent resting position for the eye gaze between trials. Second, becausethis algorithm did not identify saccades occurring prior to stimulus onset, a saccadic RTwas not recorded for trials on which the stimulus was correctly anticipated. For these rea-sons, the RT to initiate the initial saccade was not a reliable measure.

To provide a better measure of oculomotor responding, the time at which the partici-pant’s gaze was directed at the correct target location was calculated. To be counted as


correct, the horizontal location of the center of gaze needed to fall within 23 mm of theedges of the correct box for at least 33.3 ms prior to the onset of the next stimulus. Thesetemporal and spatial buVers were used to account for slight imprecision in calibration andgaze recording. This measure allowed us to calculate oculomotor RTs for trials on whichthe gaze was directed at the correct location both after stimulus onset (resulting in positiveRTs) and prior to stimulus onset (resulting in negative RTs). Oculomotor anticipationswere deWned as oculomotor responses with negative RTs. We added the stipulation that ananticipation could not occur more than 1350 ms prior to stimulus onset because thesemight be confused with responses to the previous trial.

The average number of trials with valid oculomotor responses within each group rangedfrom 92 to 100 across the six blocks. Trials on which oculomotor responses were notrecorded could reXect lack of gaze to the target or loss of eye tracking integrity such as aparticipant closing his or her eye or looking away from the monitor.

The mean oculomotor RT and the proportion of oculomotor anticipations (i.e., numberof trials with anticipations divided by total number of trials on which a valid oculomotorresponse was recorded) for each block were used as dependent variables for statistical anal-yses.

Measures of incidental sequence learning

Two a priori indicators were used to evaluate expected patterns of learning. The Wrstwas the diVerence between Block 1 (random) and Block 3 (sequence); this diVerence reXectsmotor facilitation and learning after the Wrst 20 repetitions of the sequence and was labeledas the initial learning index. The second measure was the diVerence between Block 4 (ran-dom) and the average of Blocks 3 and 5 (sequence). This worsening of performance onBlock 4 relative to the adjacent blocks measures the degree of interference due to the pseu-dorandom trials and was labeled as the interference index. Both indexes were calculated formanual and oculomotor RTs and frequency of anticipatory responses.

As is typical in the research literature on the SRT task, Block 4 was compared with bothBlock 3 and Block 5 (not simply with Block 3) because (a) the average of two blocks ismore reliable than data based on one block and (b) it is as important to show thatsequence-speciWc learning reappears on Block 5 as it is to show that interference eVects areobserved on Block 4 compared with Block 3.

Sequence awareness tasks

Responses to the initial verbal query were recorded verbatim and coded dichotomouslybased on whether participants spontaneously reported any awareness of a pattern. Thescore for the recognition task was the number of correct judgments on the 20 trials.The scores for the prediction task were the longest string of responses matching any part ofthe sequence and the total number of correct responses on the 20 trials.

Intentional sequence learning (Block 6)

Recording and calculation of manual and oculomotor measures are described above.The learning score for the verbal report after Block 6 was the longest string of recalledresponses matching any part of the 10-item sequence.


Statistical analysis

Because the accuracy of manual responses and the frequency of anticipatory manualresponses were not distributed normally, they were transformed with an arcsine transforma-tion (Kirk, 1995; p. 106). For the incidental blocks, all dependent variables initially were sub-jected to 4 (Age)£5 (Block) mixed-participants analyses of variance (ANOVAs). Thencomparisons of the magnitudes of the two learning indexes across ages were conducted usingone-way ANOVAs. Main eVects of age and block on the omnibus ANOVAs and eVects ofage on the two learning indexes were followed up with Tukey’s HSD or Tukey–Kramer testson all pairwise comparisons. Sequence awareness tasks were analyzed with chi-square tests,ANOVAs, or t tests. For Tukey’s HSD and Tukey–Kramer tests, only the signiWcant results(pD .05) are reported. For all other tests, all p values less than .05 are reported as signiWcant,all p values between .05 and .10 are reported as trends, and all p values greater than .10 arereported as nonsigniWcant. Finally, we present the results on raw data Wrst before presentingdata on transformed scores to enable direct comparisons with other studies.

Results

Incidental learning (Blocks 1–5)

Accuracy of manual responses

Average accuracy in each group ranged from 92 to 99% across blocks. The ANOVA onaccuracy yielded an eVect of block, F (4, 384)D3.43, pD .009, and age, F (3, 96)D18.06,p < .001, but no Age£Block interaction. Follow-up tests of the block eVect showed thataccuracy was lower on Block 4 than on Blocks 2, 3, and 5, indicating an interference eVect.The lack of an initial learning eVect is likely due to ceiling eVects on Block 1. Follow-uptests of the age eVect indicated that the 8- to 10-year-olds were less accurate than the threeolder groups and that the 11- to 13-year-olds were less accurate than the young adults.

The initial learning index did not diVer across ages, but the interference index did,F (3, 96)D4.64, pD .005. The youngest participants showed greater interference on Block 4than did the young adults; no other pairwise comparison reached signiWcance.

Manual RTs

As can be seen in Fig. 1, manual RT results across blocks and age groups were consis-tent with expectations. All groups showed learning eVects from Block 1 to Block 3 andinterference eVects on Block 4. The ANOVA showed eVects of block, F (4,384)D79.87,p < .001, and age, F (3, 96)D15.20, p < .001, as well as an Age£Block interaction,F (12, 384)D3.01, p <.001. All pairwise comparisons across blocks were signiWcant, reveal-ing both initial learning and interference eVects. Pairwise comparisons between ages indi-cated that the 8- to 10-year-olds had longer RTs than did the 11- to 13-year-olds, who inturn had longer RTs than did both the 14- to 17-year-olds and the young adults.

There was an age eVect for initial learning, F (3,96)D 2.63, pD .054, with the youngergroups showing larger eVects than the older groups. However, none of the pairwise com-parisons reached signiWcance. There was also an age eVect for interference, F (3, 96)D2.97,


pD .036, with follow-up analyses showing greater interference in the youngest group thanin the oldest group.

Frequency of manual anticipations

Manual anticipations were rare, with maximum frequency ranging between 0 and 8across blocks within each age. The ANOVA showed a block eVect, F (4,384)D 6.05,p < .001, and a trend toward an age eVect, F (3,96)D2.37, pD .075. Follow-up tests indi-cated that manual anticipations were more frequent on Block 3 than on Blocks 1 and 2,decreased from Block 3 to Block 4, and increased from Block 4 to Block 5. There were alsomore anticipations on Block 5 than on both Block 1 and Block 2. Thus, although rare,manual anticipations showed learning and interference eVects. None of the pairwise com-parisons between ages was signiWcant, and there was no eVect of age on the learningindexes.

Oculomotor RTs

As can be seen in Fig. 2, oculomotor RTs paralleled the pattern of manual RTs, show-ing both learning and interference eVects in all age groups. However, the oculomotor RTsconsisted mostly of negative values, indicating the preponderance of anticipatory eyemovements. The omnibus ANOVA showed eVects of block, F (4, 384)D95.47, p < .001, andage, F (3, 96)D 3.14, pD .029, but no Age £ Block interaction. All pairwise comparisonsacross blocks were signiWcant, whereas none of the pairwise comparisons between agesreached signiWcance. There were also no age eVects on the learning indexes.

Frequency of oculomotor anticipations

As shown in Fig. 3, the proportion of trials with anticipations followed similar patternsacross blocks as the manual and oculomotor RTs, increasing with repeated exposure to the

Fig. 1. Mean manual RTs across blocks as a function of age. Error bars are 95% conWdence intervals. Seq,sequence.

325

375

425

475

525

575

Random Seq Seq Random Seq Intentional

Man

ual R

T (

ms)

8 to 1011 to 1314 to 1718+


sequence and decreasing on the pseudorandom block. The omnibus ANOVA showed aneVect of block, F (4, 384)D101.55, p < .01, but no eVect of age and no Age£Block interac-tion. All pairwise comparisons between blocks were signiWcant except that between Block 3and Block 5. There were no age eVects on the two learning indexes.

Baseline diVerences among groups

The learning and interference indexes are based on raw diVerence scores, and as notedabove, interpretation of diVerence scores between groups can be problematic when thereare baseline diVerences between the groups. Thus, before interpreting age-related diVer-ences on the learning indexes, we conducted additional analyses. First, we used one-wayANOVAs to test whether there were indeed diVerences among the age groups on the base-line conditions (Block 1 for the initial learning index and the average of Blocks 3 and 5 forthe interference index). The results conWrmed age eVects on Block 1 for both accuracy,F (3, 96)D12.10, p < .001, and RTs, F (3, 96)D17.61, p <.001. Similarly, there were age eVects

Fig. 2. Mean oculomotor RTs across blocks as a function of age. Error bars are 95% conWdence intervals. Seq,sequence.

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on scores averaged across Blocks 3 and 5 for accuracy, F (3, 96)D9.65, p < .001, and RTs,F (3, 96)D 9.96, p < .001. Thus, age-related diVerences on the learning indexes could simplybe due to the fact that younger children were less accurate and slower.

Therefore, we conducted analyses on log proportional scores for manual accuracy andRTs for all age groups. This transformation was chosen (a) to increase homogeneity of var-iance across groups and (b) to allow for multiplicative eVects (i.e., the worse the baselineperformance, the greater the diVerence score is expected to be). Log proportional scores forthe learning indexes were calculated by taking the log of Block 1 divided by Block 3. Logproportional scores for interference were calculated by taking the log of Block 4 divided bythe average of Blocks 3 and 5. Results remained the same for manual accuracy regardlessof whether raw or log-transformed diVerence scores were used; there was no age eVect onthe learning index, but there was an age eVect on the interference index, F (3, 96)D 4.93,pD .003. However, age eVects on the learning and interference indexes disappeared whenlog-transformed values were used (ps > .47).4

Manual RTs on trials with and without oculomotor anticipations

Anticipating where the stimulus will appear should speed up manual responses. We exam-ined whether this eVect was present and whether it was similar across the age groups by con-ducting a 4 (Age)£2 (Anticipation: with vs. without)£5 (Block) ANOVA on manual RTs.Manual RTs were faster on trials with oculomotor anticipations, F(1,96)D592.70, p < .001,and this factor interacted with age, F (3,96)D9.39, p< .001. There were no other two- orthree-way interactions involving anticipations. In other words, similar learning and interfer-ence eVects were obtained regardless of whether participants made anticipatory eye move-ments. Follow-up ANOVAs on manual RTs showed age eVects for trials both withanticipations, F (3,96)D10.04, p < .001, and without anticipations, F (3,96)D16.66, p < .001. Inboth cases, the two younger groups diVered signiWcantly from each of the two older groups,but the two older groups did not diVer from each other. As can be seen in Fig. 4, the diVer-ence between the two younger groups was signiWcant on trials without anticipations but nolonger reached signiWcance on trials when they correctly anticipated the target location. Thus,manual RT diVerences between these two younger groups appeared to be related more toattentional factors than to irreducible diVerences in execution of motor responses.

Sequence awareness

Verbal response

When asked whether they noticed anything about the order in which the stimuliappeared, a majority of participants indicated that there was some regularity or pattern inthe stimuli (Table 2). Although the older adolescents appeared to be more aware than theother three groups, a �2 test showed that the proportions of participants indicating someawareness of a pattern were not signiWcantly diVerent across groups. Nevertheless, whenasked to elaborate on what they noticed, most participants were unable to describe the

4 Results were the same for proportional scores as for log-transformed scores, with proportional scores for thelearning and interference indexes being deWned as the diVerence divided by the baseline.


sequence extensively. The longest string described correctly ranged from two to Wve acrossgroups, although the median value was three in all age groups. These results suggest thatalthough many participants developed some awareness that the stimuli followed a pattern,they did not gain explicit knowledge of the constituent elements of that sequence beyondthree steps.

Recognition task

As can be seen in Table 2, the average scores of the groups on this task were approxi-mately 10 to 11 of 20 and fairly similar to each other. A one-way ANOVA on correctresponses did not yield an age eVect. However, although the 8- to 10-year-olds did not per-form above chance on this test, the oldest three groups did, ts 72.51, p <.02.

Prediction task

As shown in Table 2, average scores within the age groups were approximately 9 to 11of 20 on this test. ANOVAs showed no eVect of age for the total number or the longeststring of correct responses.

Fig. 4. Mean manual RTs on trials with (solid lines) and without (dotted lines) oculomotor anticipations acrossblocks as a function of age. Error bars are 95% conWdence intervals. Seq, sequence; anticip., anticipations.

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Table 2Results on the sequence awareness tasks

Note. Standard deviations are in parentheses.a Out of 20.b Out of 10.

8–10 years 1–13 years 4–17 years 18+ years

Verbal report of awareness (%) 66 61 92 63Recognition: Total correcta 10.5 (2.2) 11.0 (2.1) 11.3 (1.5) 11.2 (1.7)Prediction: Total correcta 9.3 (2.9) 9.8 (3.1) 10.5 (2.9) 10.1 (2.6)Prediction: Longest stringb 3.6 (1.5) 3.7 (1.7) 4.2 (2.2) 4.3 (2.5)Block 6: Longest stringb 4.0 (1.7) 5.4 (2.4) 6.0 (2.6) 5.5 (2.5)


Intentional learning (Block 6)

On Block 6, participants were instructed to learn the sequence. As on Blocks 1–5, a one-way ANOVA showed an age eVect for accuracy of manual responses, F (3,96)D 11.67,p < .001. The 8- to 10-year-olds were less accurate than the older groups, who did not diVerfrom each other. There was also an age eVect on manual RTs, F (3, 97)D 20.92, p < .001. The8- to 10-year-olds had longer RTs than the 11- to 13-year-olds, who in turn had longer RTsthan the young adults.

Manual anticipations were slightly more common on this block than on Blocks 1–5.Although the modal value was still 0 in all groups, anticipations ranged from approxi-mately 0 to 19 of 100 trials across all participants. A one-way ANOVA on transformedscores yielded no age eVects.

There was an age eVect for oculomotor RTs, F (3, 96)D13.52, p < .001. The youngestgroup had longer RTs than did the three older groups, and the 11- to 13-year-olds hadlonger RTs than did the young adults. There was also an age eVect for oculomotor antici-pations, F (3,96)D6.89, p < .001. The 8- to 10-year-olds had fewer anticipations than didthe 11- to 13-year-olds and the young adults.

As on Blocks 1–5, a 4 (Age)£2 (Anticipation) ANOVA showed that manual RTs wereshorter on trials with anticipations than on those without anticipations, F (1, 96)D250.94,p < .001. However, this eVect did not interact with age.

On the verbal report of the sequence as well, there was an age eVect, F (3, 96)D 3.80,pD .013, with the youngest group scoring signiWcantly lower than the 14- to 17-year-olds.

Incidental versus intentional learning (Block 2 vs. Block 6)

To explore diVerences between incidental and intentional learning conditions in moredetail, we compared performance on each subset of 20 trials on Block 6 and Block 2 (the Wrsttime the sequences were presented) using 4 (Age)£2 (Block: 2 vs. 6)£5 (Subset) ANOVAs.The data are presented in Figs. 5–7. Results showed a Block£Subset interaction for manualRTs, F (4,384)D17.25, p <.001, but no three-way interaction. In contrast, there were three-way interactions for both oculomotor RTs, F (12,384)D2.57, pD .003, and oculomotor antic-ipations, F(12,384)D2.24, pD .010 (other signiWcant results are not presented here).

Then we conducted analyses of linear trends in each block. The linear trend for manualRTs was not signiWcant on Block 2, and Tukey’s HSD tests comparing each pair of subsetsindicated that only the Wrst and last subsets diVered from each other. In contrast, lineartrends were signiWcant for manual RTs on Block 6 and for both oculomotor RTs (Fig. 6)and anticipations (Fig. 7) on both Block 2 and Block 6 (all ps <.001). In addition, lineartrends were obtained within each age group for both oculomotor RTs and anticipations inboth blocks (all ps <.01).

To explicate the three-way interactions for the oculomotor measures, we compared lin-ear trends among age groups. For oculomotor RTs on Block 2, the 8- to 10-year-olds hadshallower slopes (or showed a trend in that direction) than did the 11- to 13-year-olds,F (1, 61)D 3.64, pD .061, the 14- to 17-year-olds, F (1, 46)D 8.17, pD .006, and the adults,F (1, 57)D 3.53, pD .065. The oldest three groups did not diVer from each other. On Block 6,the 8- to 10-year-olds had shallower slopes than did the 11- to 13-year-olds,F (1, 61)D 12.00, pD .001, the 14- to 17-year-olds, F (1, 46)D5.16, pD .028, and the adults,F (1, 57)D 13.18, pD .001. The oldest three groups did not diVer from each other.


For interactions involving linear trends of the oculomotor anticipations on Block 2,none of the results was signiWcant. On Block 6, the 8- to 10-year-olds had shallower slopes(or showed a trend in that direction) than did the 11- to 13-year-olds, F (1, 61)D13.49,pD .001, the 13- to 14-year-olds, F (1, 46)D2.99, pD .09, and the adults, F (1, 57)D15.02,p < .001. The oldest three groups did not diVer from each other.

It is important to note that the four age groups were fairly close to each other on theWrst 20 trials for both oculomotor measures on both blocks and that age diVerencesbecame more apparent with increasing exposure to the sequences.

We also compared performance in the last subset of trials on Blocks 2 and 6 within eachage group. In the 8- to 10-year-olds, the average diVerence in manual RTs between blockswas 4 ms and was not signiWcant. However, Block 6 led to longer oculomotor RTs,t (34)D2.27, pD .03, and fewer anticipations, t (34)D 3.71, pD .001. In 11- to 13-year-olds,

Fig. 5. Mean manual RTs within each 20 trial subset across blocks as a function of age. Error bars are 95% conW-dence intervals. Seq, sequence.

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Fig. 6. Mean oculomotor RTs within each 20 trial subset across blocks as a function of age. Error bars are 95%conWdence intervals. Seq, sequence.

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manual RTs were faster on Block 6, t (27)D 2.63, pD .014, but the oculomotor RTs andanticipations did not diVer between blocks. In 14- to 17-year-olds, none of the comparisonsreached signiWcance. In young adults, Block 6 led to shorter manual RTs, t (23)D 2.93,pD .008, and oculomotor RTs, t (23)D2.45, pD .022, as well as a trend toward more antici-pations, t (23)D 1.75, pD .094.

These diVerences between the manual and oculomotor modalities in within-block analy-ses replicate the results of our previous study with adults (Marcus et al., 2006) and raise thequestion as to why the manual RTs do not decrease in tandem with increasing anticipa-tions during the incidental blocks. The answer may lie in measurement problems. FlooreVects are much stronger in the current task for manual RTs than for oculomotor mea-sures. In addition, our measures might not have been sensitive enough to detect decreasesin manual RT on the incidental blocks that occurred along with improvements in anticipa-tions. There were 20 trials in each subset, and the number of trials with anticipations gener-ally ranged from 4 to 10. Given the Xoor eVects for manual RTs, the low temporalresolution of manual RTs in the current study (60 Hz), and the relatively small number oftrials with anticipations, we might not have been able to detect small improvements inmanual RTs on the incidental blocks. On the intentional block, however, the requirementto learn the sequence resulted in initially slow manual RTs, allowing more room for detect-ing improvements. It could also be speculated that there may be a tighter coupling or inte-gration between eye and hand movements under the intentional learning condition thanunder the incidental learning condition.

Discussion

Developmental changes in incidental learning

With increasing exposure to the sequence, oculomotor and manual anticipationsincreased and oculomotor and manual RTs decreased. Interference eVects on Block 4 wereapparent on all measures. Thus, both manual and oculomotor measures were reXecting thelearning of at least second-order transitional probabilities.

Fig. 7. Mean proportion of oculomotor anticipations within each 20 trial subset across blocks as a function ofage. Error bars are 95% conWdence intervals. Seq, sequence.

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The Wrst question was whether the magnitude of the learning indexes in the manualmodality would change with age. Results showed greater learning eVects in younger ageson RTs and greater interference in the youngest group, compared with the oldest group, onaccuracy and RTs. However, when the data were log-transformed to take baseline diVer-ences into account, the age eVect remained for accuracy but not for RTs. Thus, as with theelderly in Howard and Howard (1997), the accuracy of the youngest group may have beenmore susceptible to violation of expectations than that of the adults.

The second question was whether anticipatory eye movements would diVer with age.There was an age eVect on oculomotor RTs. However, none of the pairwise comparisonsbetween ages reached signiWcance. There were no age eVects on the frequency of oculomo-tor anticipations or on the learning indexes based on the oculomotor measures.

These results replicate and extend the Wndings of our study with adults and demonstratethat oculomotor measures can be used as indexes of sequence learning in children and ado-lescents. Furthermore, oculomotor and manual indexes of learning converge on the con-clusion that incidental sequence learning does not change substantially across the agerange under study. However, the 8- to 10-year-olds had a shallower rate of decrease in ocu-lomotor RTs on the Wrst incidental block (Block 2) compared with the older groups, sug-gesting that they may have taken longer to learn the sequence on this block. By 11 years ofage, however, the sequence learning evidenced on the SRT task appears to be mature interms of both the rate of learning and the magnitude of the learning indexes in the oculo-motor and manual modalities.

Taken together with the studies reviewed in the Introduction, the results indicate thatadult-like sequence learning can be observed on the SRT task by 12 years of age. Studiesthat included children younger than age 12 years of age yielded inconsistent results, withsome Wndings suggesting that sequence learning continues to develop until age 12. How-ever, it is important to keep in mind that even pigeons, rats, and mice show evidence ofsequence learning (Bailey & Mair, 2006; Christie & Dalrymple-Alford, 2004; Christie &Hersch, 2004; Froelich, Herbranson, Loper, Wood, & Shimp, 2004). Thus, before conclud-ing that spatial sequence learning is a higher cognitive skill requiring cortical maturation, itis crucial to conduct developmental studies spanning the period from infancy to 12 years ofage to address methodological questions and to obtain a better understanding of the devel-opment of diVerent aspects of spatial sequence learning.

Another issue to consider is that learning occurs very rapidly in adults on the type ofSRT task used in the current study. In our previous study, we attempted to examine trans-fer of learning across eVectors by comparing adults who were exposed to the sequence witha group whose members were exposed only to pseudorandom stimuli. After 600 pseudo-random trials, manual and oculomotor RTs and oculomotor anticipations of the lattergroup on sequence trials were undistinguishable from those of the group who had beenexposed to the sequence. In infants as well (CanWeld & Haith, 1991; Clohessy et al., 2001;Wentworth & Haith, 1998), sequence-speciWc anticipatory eye movements emerge within2 min. Nevertheless, the current results suggest that developmental diVerences in sequencelearning may emerge if the rate of learning is examined more closely during the earlieststages of the task. Age-related declines in learning rates in the elderly have been observed(Krebs, Hogan, Hening, Adamovich, & Poizner, 2001). In addition, although animals haverepeatedly been shown to be sensitive to sequential information, a large number of trials isrequired before this sensitivity is elicited. Therefore, to detect subtle age- or species-relateddiVerences in sequence learning, it may be important to use measures and tasks that are


sensitive enough to detect diVerences in learning curves over very brief periods of time (e.g.,Howard & Howard, 1997; Schvaneveldt & Gomez, 1998).

Also important in examining developmental diVerences in sequence learning is thenature of the sequence. Research on adults shows diVerences in the attentional require-ments and neurobiological bases of learning ambiguous versus unique sequences (BischoV-Grethe, Martin, Mao, & Berns, 2003; Cohen, Ivry, & Keele, 1990; Frensch, Buchner, & Lin,1994), and of deterministic versus probabilistic sequences (Jiménez & Vázquez, 2005; Schv-aneveldt & Gomez, 1998), and in the response programming requirements of short versuslong sequences (Park & Shea, 2005).5 Age-related declines have been found in the elderly inthe ability to learn probabilistic sequences, which make demands on capacity of workingmemory and formation of associations between items (Howard & Howard, 1997; Howardet al., 2004). Conversely, age-related improvements have been observed in infants in theability to learn ambiguous sequences, which make more demands on attention than dounambiguous sequences (Clohessy et al., 2001). In all published studies of the SRT task inchildren, 10-step Wxed ambiguous sequences were used. Therefore, there is a need to exam-ine sequence learning on a wider range of paradigms to better understand how develop-ment of sequence learning is modulated by the cognitive and motor demands of the task.

Developmental changes in intentional learning

The third goal was to examine age-related changes during intentional learning (Block 6).This block yielded age-related diVerences in accuracy and RT of manual responses, oculo-motor anticipations, and RTs as well as in verbal recall of the sequence. DiVerences wereobserved not only between the youngest children and the young adults but also betweenthe adolescents and the young adults.

Within-block analyses of the intentional block showed that manual and oculomotorRTs decreased linearly and oculomotor anticipations increased linearly in all groups. Theadults were initially slowed down, but they improved quickly (as found by Howard &Howard, 2001). In contrast, instructions to learn the sequence explicitly did not beneWt theadolescents. The 8- to 10-year-olds showed a gradual improvement in terms of both oculo-motor measures, but their oculomotor performance by the end of the block was worse thantheir performance at the end of Block 2. Thus, as with the elderly in Howard and Howard(2001), the youngest group was hurt by the requirement to learn the sequence.

The intentional task required the conscious formulation and use of learning strategiesand hypothesis testing. It is diYcult to infer from the verbal reports what kinds of hypoth-eses the children were generating and testing throughout the block. However, based on thechildren’s performance, it is likely that their hypotheses were less eVective than the adults’hypotheses (cf. Fiser & Aslin, 2001; Wilkinson & Shanks, 2004).

These results raise the question of what the best strategies are for teaching children tasksthat involve statistical regularities. When during learning is it more eVective to bring in

5 Deterministic sequences, such as the one used in the current study, are those in which the steps of the sequenceare completely predictable from previous steps. In probabilistic sequences, consecutive elements can be predictedonly probabilistically (Howard et al., 2004; Schvaneveldt & Gomez, 1998; Wilkinson & Shanks, 2004). Unique se-quences contain elements that always are followed by another unique element. In ambiguous sequences, such asthe sequence in the current study, each element can be followed by more than one other element (Frensch et al.,1994).


conscious strategic processes, and when is it more eVective to let children’s skills for pick-ing up regularities guide their learning? How do these considerations interact with thedevelopmental levels of children? Research on motor skill learning indicates that guidingattention to diVerent aspects of performance helps individuals diVerentially depending onwhether they are novices or experts (Beilock, Carr, MacMahon, & Starkes, 2002; Gray,2004; Wulf & Shea, 2002) and on whether the skill is simple or complex (Wulf & Shea,2002). Children’s methods of coping with increasing task diYculty on a task requiringmotor skills interact with the nature and maturity of the strategies available to them (e.g.,Badan, Hauert, & Mounoud, 2000). Research by Siegler (1996) on explicit strategy use dur-ing problem solving in children points to similar conclusions. It would be informative toextend these lines of research to children’s learning of both cognitive and motor skills andto examine the role of statistical learning mechanisms in skill learning.

DiVerences in manifestations of learning between oculomotor and manual modalities

The manual and oculomotor indexes of performance may be reXecting diVerent, per-haps dissociable, aspects of learning. DiVerent aspects of a sequence sometimes can belearned simultaneously (Helmuth, Mayr, & Daum, 2000; Lee, 2000; Shin & Ivry, 2002;Warren & GriYths, 2003). In addition, despite many similarities between the oculomotorand manual systems, there are also subtle diVerences between them in planning and execu-tion of sequential movements (e.g., Pratt, Shen, & Adam, 2004). In the current study, oculo-motor measures could be reXecting overt shifts of visual–spatial attention in preparationfor manual responses (HoVman, Martin, & Schilling, 2003; Willingham, 1999). In our pre-vious study, however, we found very similar eye movement patterns between groupsinstructed to look at the stimuli or to look and press. Thus, we suggest that anticipationsdo not reXect preparation for action but rather reXect a constant search for, and learningof, spatial regularities that occur in conjunction with, or in advance of, manual learning.

Expectations about stimulus locations could also be inferred from frequency of manualanticipations, manual RTs, and anticipatory errors on random trials (e.g., Howard & How-ard, 1997; Schvaneveldt & Gomez, 1998; Thomas & Nelson, 2001). However, becauseanticipatory eye movements reXect expectations prior to stimulus onset, they provide amore compelling measure of sequence learning than do manual responses made after thestimulus. They are also more sensitive and useful from a methodological point of viewbecause they make these internal expectations easily observable and are not as vulnerableto Xoor eVects and motoric limitations as are manual anticipations and RTs. Because eyemovements to a location can be used as a straightforward index of an overt shift of visual–spatial attention regardless of whether a monkey, a human infant, or a human adult ismaking the eye movements, they can also be employed to make direct comparisons acrossspecies and throughout the life span.

Limits

Several factors limit the conclusions that can be drawn from these results. First, individ-ual manual RTs had a relatively low temporal resolution (60 Hz), and this may have pre-vented us from detecting small diVerences between groups and blocks. Second, participantswere instructed to look at the stimuli. Therefore, the eye movements were not spontaneous,although the anticipatory nature of these movements was unprompted.


In addition, the results are limited to spatial sequence learning. Imaging studies in adultsindicate that diVerent brain regions are involved in sequence learning depending on thenature of the stimuli (Robertson, Tormos, Maeda, & Pascual-Leone, 2001). Behavioral andlesion studies further suggest that separable mechanisms are involved in learning spatialversus nonspatial sequences (Koch & HoVmann, 2000; Mayr, 1996), and spatial versusstimulus–response regularities in spatial sequences (Helmuth et al., 2000), and that nonspa-tial stimulus sequences lead to greater explicit awareness than do spatial stimuli (Tubau &Lopez-Moliner, 2004).

Finally, the cognitive architecture and neural substrates of skills shift over the course oflearning. The current Wndings and interpretations are based only on the type of sequencelearning that occurs very rapidly and are limited only to the initial stages of learning.

Implications for studies of statistical and skill learning

We suggest two lines of future research. First, to the extent that statistical learningmechanisms for sequential information are shared across visual and auditory domains(e.g., Kirkham, Slemmer, & Johnson, 2002), eye movements to visual stimulus sequencescan be used to generate and test hypotheses about domain-general mechanisms of learning.Anticipatory eye movements can also be readily applied to exploring sensitivity to nonse-quential regularities, such as extracting statistical properties of visual scenes (Chong & Tre-isman, 2005; Fiser & Aslin, 2001; Turk-Browne, Jungé, & Scholl, 2005) and forming visualcategories (McMurray & Aslin, 2004), and the nature of the statistical mechanisms under-lying sensitivity to these regularities (e.g., Ariely, 2001; Baker, Olson, & Behrmann, 2004;Parkhurst & Niebur, 2003).

Another question involves the process of skill learning. To understand skill learning, weneed to understand what individuals are anticipating under what conditions, how these areshaped during skill learning, how they are modulated by cognitive demands, how theychange with stage of skill learning, and how they interact with maturity of top-down guid-ance of attention and problem-solving skills. For instance, Johnson, Amso, and Slemmer(2003) investigated the development of a cognitive skill, object representation, by present-ing 4- and 6-month-olds with objects moving through a trajectory under occlusion andusing anticipatory eye movements to where the object would reemerge as an index ofobject representation. The 6-month-olds produced more anticipations than did the 4-month-olds. After training, the frequency of the 4-month-olds’ anticipations for occludedobjects approached that of the 6-month-olds and generalized to another orientation. Whatboth that study and the current study show is that skill learning consists in part of learningto make better predictions and that training skills (e.g., through coaching, education, andinterventions) may involve, at least to some extent, training anticipations.

Acknowledgments

Funding for the study was provided by a McKnight Land Grant Professorship from theUniversity of Minnesota, a Young Investigator (Wodecroft Investigator) Award from theNational Alliance for Schizophrenia and Depression, a grant from the National Instituteof Mental Health (1 RO3 MH063150-01A2), and a grant from the University of Minne-sota Center for Neurobehavioral Development. We thank Sanford Weisberg and MichaelHarwell for their comments on data analyses, and Cacy Miranda, M. A., Bonnie Houg,


M. A., Kathryn McGraw-Schuchman, M. A., L. P., and Angie Guimaraes, M. A. for theirhelp with the diagnostic assessments.

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