On-line Assessment of Statistical Learning by Event-related Potentials

Dilshat Abla1,2,5, Kentaro Katahira1,3,4, and Kazuo Okanoya1,2,3 Downloaded from http://mitprc.silverchair.com/jocn/article-pdf/20/6/952/1759514/jocn.2008.20058.pdf by guest on 18 May 2021

Abstract & We investigated the neural processes involved in on-line sta- was 74.4%, indicating that the tone sequence was segmented tistical learning and word segmentation. Auditory event-related and that the participants learned the tone words statistically. potentials (ERPs) were recorded while participants were ex- Grand-averaged ERPs showed that word onset (initial tone) elic- posed to continuous, nonlinguistic auditory sequences, the ele- ited the largest N100 and N400 in the early learning session of ments of which were organized into ‘‘tritone words’’ that were high learners, but in middle learners, the word-onset effect was sequenced in random order, with no silent spaces between elicited in a later session, and there was no effect in low learn- them. After listening to three 6.6-min sessions of sequences, the ers. The N400 amplitudes significantly differed between the participants performed a behavioral choice test, in which they three learning sessions in the high- and middle-learner groups. were instructed to indicate the most familiar tone sequence in The results suggest that the N400 effect indicates not only on- each test trial by pressing buttons. The participants were divided line word segmentation but also the degree of statistical learning. into three groups (high, middle, and low learners) based on This study provides insight into the neural mechanisms under- their behavioral performance. The overall mean performance lying on-line statistical learning processes. &

INTRODUCTION co-occur with a high probability are usually found within To learn an unknown language, listeners must segment words, whereas low-probability sound pairs tend to span connected speech into constituents and discover how word boundaries. This difference in the likelihood of words are organized. When adults try to cope with an co-occurrence provides potential information on word unknown language or when infants learn their native boundaries, and might contribute to early language acqui- language, they do so by listening to speech before they sition by bolstering the ability to segment the speech know either the words or the grammatical system of that stream into meaningful units. To investigate how infants language, and without receiving explicit instruction. To discriminate high- and low-probability sound pairs with- extract words as well as their organization from the in a corpus of speech, Saffran, Aslin, et al. presented speech stream, humans must possess efficient compu- 8-month-olds with a synthesized speech stream consist- tational procedures. ing of four trisyllabic ‘‘words’’ (e.g., tupiro, golabu, Several behavioral experiments have reported evidence dapiku,andtilado),presentedinrandomorder(e.g., that infants and adults readily learn statistically defined pat- dapikutupirotiladogolabutupiro...)for2min.Theonly terns in auditory input sequences (Pena, Bonatti, Nespor, cues to word boundaries were the transitional probabili- & Mehler, 2002; Seidenberg, MacDonald, & Saffran, 2002; ties (TPs) between syllable pairs. After familiarization, the Brent, 1999; Saffran, Johnson, Aslin, & Newport, 1999; infants were tested to determine their listening prefer- Aslin, Saffran, & Newport, 1998; Christiansen, Allen, & ences for words versus novel words (nonwords and part- Seidenberg, 1998; Brent & Cartwright, 1996; Saffran, Aslin, words of identical stimulus components). The infants & Newport, 1996; Saffran, Newport, & Aslin, 1996; Jusczyk listened significantly longer to the novel words, indicating & Aslin, 1995; Christophe, Dupoux, Bertoncini, & Mehler, they distinguished between the words and other stimuli 1994; Goodsitt, Morgan, & Kuhl, 1993). For example, based on learning the TPs defining word boundaries. Saffran, Aslin, et al. (1996) investigated word segmentation Saffran et al. (1999) have also shown that adults and in- by 8-month-old infants using a corpus of artificial speech. fants can perform the same type of learning with a stream They noted that in natural speech, adjacent sounds that of tones, demonstrating an equivalent ability to use the statistical consistencies among adjacent tones to group 1Brain Science Institute, RIKEN, Wako, Japan, 2Japan Science them into ‘‘tone words.’’ Learners readily group sequences and Technology Agency, Kawaguchi, Japan, 3Chiba University, of auditory events in the same manner, regardless of Chiba City, Japan, 4University of Tokyo, Kashiwa, Japan, 5Xin- whether the input is linguistic (syllables) or nonlinguistic jiang Medical University, Urumchi, China (tones). These behavioral studies suggested that statistical

D 2008 Massachusetts Institute of Technology Journal of Cognitive Neuroscience 20:6, pp. 952–964 Downloaded from http://www.mitpressjournals.org/doi/pdf/10.1162/jocn.2008.20058 by guest on 27 September 2021 learning is an important process in word segmentation. The aim of our study was to elucidate the neural cor- However, these studies did not provide direct evidence of relates of on-line word segmentation and statistical learn- the temporal resolution of the neural activity involved in ing using ERPs. We recorded 32-channel auditory ERPs word segmentation. while adult participants were exposed to continuous, Few neurophysiological studies have examined how we nonlinguistic auditory sequences, in three 6.6-min ses- can discover the boundaries of words when listening to sions consisting of elements organized into ‘‘tritone continuous speech in an unfamiliar language and what words.’’ Using the sequence stimuli from the three ses- neural processes are involved in on-line sequential learn- sions, we were able to compare ERPs with the continu- ing and word segmentation. One way of addressing these ous stimuli when they were and were not segmented as

questions is with scalp-recorded event-related potentials ‘‘words,’’ thereby elucidating the neuronal processes of Downloaded from http://mitprc.silverchair.com/jocn/article-pdf/20/6/952/1759514/jocn.2008.20058.pdf by guest on 18 May 2021 (ERPs) used as a direct, on-line measurement of brain ac- on-line statistical learning. tivity with millisecond temporal resolution. Recently, ERPs have been used to investigate language segmentation (Cunillera, Toro, Sebastian-Galles, & Rodriguez-Fornells, METHODS 2006; Kooijman, Hagoort, & Cutler, 2005; Sanders & Neville, 2003a, 2003b; Sanders, Newport, & Neville, 2002). Participants Sanders et al. (2002) reported that while adults listened to Twenty-eight adult participants (12 men, 16 women) six trisyllabic pronounceable nonwords (babupu, bupada, with normal hearing participated in this experiment. In dutaba, patubi, pidapu,andtutibu) presented as con- order to avoid possible effects of musical expertise on tinuous speech (Saffran, Aslin, et al., 1996), word onset performance, participants who identified themselves as elicited larger negative potentials (N100 and N400). In that nonmusicians and had not taken musical instrument study, however, the participants performed a behavioral lessons in primary school were used. Their ages ranged pretest with 36 pairs of trisyllabic nonsense words, and the from 20 to 45 years (mean = 25.96; SD = 7.2). Five participants were trained with the six nonsense words for additional participants were tested but were excluded 20 min before exposure to the continuous speech and ERP from the analysis due to sleepiness (n = 2) or equip- measurement. This procedure gave participants a hint that ment failure (n = 3). Twenty-seven participants were the continuous speech stream contained trisyllabic words. right-handed and one was left-handed (measured using Therefore, the ERP results in this case may not actually the Edinburgh Inventory; Oldfield, 1971). The partici- reflect the process of on-line segmentation. Cunillera et al. pants had no history of neurological disease. All proce- (2006) conducted a similar ERP study in which ERPs were dures were approved in advance by the RIKEN Ethics recorded for a task that was the same as a previous task Committee, and all participants gave prior written in- (Saffran, Aslin, et al., 1996). However, their study used an formed consent before each experiment. on-line measurement approach and also found evidence that nonsense words elicited a larger N400 component. Stimuli Both studies clearly demonstrated that the N400 com- ponent is involved in the process of learning nonsense Tone sequences were constructed from 11 pure tones words and regarded this component as an indicator of within the same octave (starting at middle C within a speech segmentation. chromatic set) as Saffran et al. (1999) used in their study. However, two important issues remained unresolved Sine-wave tones were generated using signal generator by these ERP studies. First, sequential changes of the software (Avisoft-SASLab Pro) at 22 kHz. Tones had a N400 component while participants are listening to the duration of 550 msec, including 25-msec rise and 25-msec stimulus stream must be described. Does this compo- fall times; this duration was substantially longer than that nent reflect the process of learning or the results of used by Saffran et al. to allow ERP analysis for each tone. learning? Although Cunillera et al. (2006) recorded ERPs We combined three tones into one word and prepared during an on-line segmentation task, they did not ask two language sets, each having six tone words (Language 1: this specific question because the ERP components be- ADB, DFE, GG#A, FCF#,D#ED, CC#D; Language 2: AC#E, tween successive sessions were not compared. Second, F#G#E, GCD#,C#BA, C#FD, G#BA). The statistical struc- ERP studies of word segmentation tasks have used only ture of these words exactly mirrored that of the words linguistic syllables as stimuli, and thus provided no data used by Saffran et al. For L1, the TPs between tones with- on nonlinguistic streams. It is unknown whether the in words averaged 0.64 (range: 0.25–1.00), whereas the TPs N400 component indexing word segmentation is elicited between tone words averaged 0.14 (range: 0.05–0.60). The only during a linguistic segmentation task or also during statistical structure of L2 was very similar to L1. TPs be- nonlinguistic tone segmentation. Given that a common tween tones within words averaged 0.71 (range: 0.33–1.00), learning device uses the statistical consistencies among whereas TPs between tones spanning word boundaries adjacent tones to create groups of ‘‘tone words,’’ the averaged 0.18 (range: 0.07–0.53). The tone words con- ERP indicator of word segmentation might also be ob- tained no constructs involving standard musical compo- served during segmentation of tone sequences. sition or melodic fragments (Saffran et al., 1999).

Abla, Katahira, and Okanoya 953 Downloaded from http://www.mitpressjournals.org/doi/pdf/10.1162/jocn.2008.20058 by guest on 27 September 2021 The six tritone words were presented in random times. Each of the three 6.6-min streams was random- order, with no silent spaces between words, to produce ized separately. After listening for a total of 19.8 min, the a 6.6-min continuous stream of tones (e.g., DFEFCF# participants performed the behavioral choice test. The CC#DD#EDGG#A...). Each word was repeated 40 times participants were instructed to indicate the most familiar in one stream, and particular tone words were never tone sequence in each test trial by pressing either button presented twice in a row. The tone sequence was con- 1 or 2, corresponding to whether the familiar sequence structed using Gentask in Stim software (Neuroscan). was played first or second in that trial. Half of the par- Only higher-order statistics (bitone frequency, tritone ticipants exposed to each language received one ran- frequency, or TP) provided cues to tone words. domized test order, whereas the other half received a

To prevent pitch differences between tone positions different test order. Downloaded from http://mitprc.silverchair.com/jocn/article-pdf/20/6/952/1759514/jocn.2008.20058.pdf by guest on 18 May 2021 within tone words from influencing the ERPs, we calcu- lated the mean frequency of the initial, middle, and final tones of the tone words. The mean frequencies for L1 EEG Recording were 341.3 Hz (SD = 66.2), 321.1 Hz (SD = 56.5), and 369.8 Hz (SD = 81.9) for the initial, middle, and final Electroencephalograms (EEGs) were recorded during tones, respectively. For L2, these means were 361.6 Hz each of the three 6.6-min listening sessions from the scalp (SD = 69.6), 381.8 Hz (SD = 102.6), and 357.3 Hz (SD = by a 32-channel Ag–AgCl electrode cap (10–20 system) 65.4), respectively. There were no significant differences using the Scan 4.2 acquisition system (SynAmps; Neuro- between the frequency of the tones at each position Scan) with a 0.15–30 Hz band-pass filter and a sampling within tone words in either L1 [F(2, 15) = 0.75, p = .48] rate of 500 Hz. All the electrodes were referenced to linked or L2 [F(2, 15) = 0.15, p = .85]. The frequency of electrodesplacedontheleftandrightearlobes.Theim- occurrence of initial, middle, and final tones of tone pedance of the electrodes was maintained at 5 k or less. words were 12, 12, and 14 for Language 1 and 13, 11, and The vertical and horizontal electrooculograms (EOGs) 12 for Language 2. Thus, across all six tone words, the were recorded simultaneously to eliminate EEG data con- frequency of occurrence of each tone was not a function taminated by eye movements. The continuous recordings of its position in the tritone words. were then cut into epochs of 750 msec (ranging from a For the behavioral test designed to assess learning, we 100-msec prestimulus to 650-msec poststimulus), with the constructed 36-pair items and followed the test used by 100-msec prestimulus serving as the baseline. A semiauto- Saffran et al. (1999). Each test item consisted of two matic artifact-rejection procedure was applied to the con- tritone sequences: one word and one nonword. If par- tinuous data. First, epochs containing amplitude changes ticipants were exposed to L1 in a continuous listening exceeding 100 AV for the EOG and the EEG channels were session, the words were extracted from L1, and the non- excluded. Next, all epochs and channels were scanned words were extracted from L2 and vice versa. All six manually for any additional disturbances. words from each of the two languages were paired with each of the other, generating 36 test trials. The two stimuli (word and nonword) separated by a 1.2-sec pause Data Analysis were presented in random order in each trial. We used an intertrial interval of 3 to 4 sec. The behavioral performance of all participants was mea- sured. A correct button press within 1000 msec after the test trial offset was regarded as a hit. An incorrect but- ton press during this period was classified as an error, Procedure and trials with no response were classified as misses. The The participants sat in a comfortable chair with a head- participants were divided into three groups based on rest, in an electrically shielded, airconditioned room. their mean behavioral performance (MBP) and standard They were asked to look at the fixation point on the deviation (SD). If the performance was above MBP + TV monitor placed 120 cm in front of them and to not 0.5 SD, the participant was categorized as a high learner; move their eyes, and were told that they would listen to below MBP À 0.5 SD was considered the performance of continuous tones through headphones. The participants a low learner; and intermediate scores indicated middle were asked to relax and to avoid consciously analyzing learners. the tone sequence while listening. The participants were Average ERPs were computed separately for each not given any information regarding to the nature of participant, stimulus position (initial, middle, and final stimuli including the statistical structure of the tone se- tones within a tone word), and session (three 6.6-min quences and were not told which language set would be recording sessions) using the EDIT module of Scan 4.3 tested for them. The participants then listened to the (NeuroScan). Table 1 lists the mean number of epochs 6.6-min-long recording of one of the two tone streams for the single-subject averages for each group, session, (L1 or L2) described above. The stimulus presentation and stimulus position. Across participants, an average of was followed by a short break and was repeated three 70.5 (29.4%) of the epochs in a stimulus position were

954 Journal of Cognitive Neuroscience Volume 20, Number 6 Downloaded from http://www.mitpressjournals.org/doi/pdf/10.1162/jocn.2008.20058 by guest on 27 September 2021 Table 1. Number (Mean and SD) of Averaged Trials for Each Session and Stimulus Position Initial Tone Middle Tone Final Tone Total

Mean SD Mean SD Mean SD Mean SD

High learners S1 166.0 40.5 162.5 42.2 158.1 42.2 162.2 41.6 S2 168.4 46.2 168.2 48.6 165.2 46.5 167.3 47.1 S3 168.2 47.5 169.5 51.6 167.2 51.2 168.3 50.1

Middle learners S1 178.7 28.4 177.9 34.3 177.8 30.6 178.1 31.1 Downloaded from http://mitprc.silverchair.com/jocn/article-pdf/20/6/952/1759514/jocn.2008.20058.pdf by guest on 18 May 2021 S2 162.7 32.4 163.4 43.6 157.3 36.8 161.1 37.6 S3 168.2 34.1 166.3 36.6 165.8 29.2 166.8 33.3 Low learners S1 172.9 30.6 166.0 42.0 178.1 33.2 172.3 35.3 S2 172.2 38.9 177.3 41.6 172.2 41.1 173.9 40.5 S3 173.4 43.6 177.7 37.9 173.7 37.4 174.9 39.7

rejected from the averaging process. A comparison of To reveal the relationship between the TPs and ERP the number of epochs rejected in each condition, amplitudes, we calculated TPs across a continuous stream session, and group revealed no significant differences. for each session and divided TPs into four to five sub- The mean ERP amplitudes were measured using the intervals (bins), so that each bin contains roughly 100 following windows, centered on the clear negative peak artifact-free epochs. We made the number of bins 4 or found by visual inspection of the grand-averaged data: 5 depending on the total number of artifact-free epochs. from 80 to 160 msec (40 msec before and after the mean This is because we wanted to make as many bins as peak at around 120 msec) for N100 and 300 to 500 msec possible, yet we needed to make the number of epochs (100 msec before and after the mean peak at around within bins as similar as possible, each containing at least 400 msec) for N400. To account for the topographical 80 epochs. Next, the correlations between the mean distribution of the ERPs in the statistical analysis, the amplitudes of the ERPs (N100 and N400 at FCz) within scalp surface was divided into seven topographical the same bin, and the mean TPs of the bin, were cal- regions of three electrodes each: middle anterior (Fp1, culated for each session and each group. Fz, Fp2), middle central (FCz, Cz, CPz), middle posterior (P3, Pz, P4), left anterior (F3, FC3, F7), right anterior (F4, FC4, F8), left posterior (CP3, TP7, P7), and right poste- RESULTS rior (CP4, TP8, P8). Data were analyzed by repeated-measures analyses Behavioral Data of variance (ANOVAs, a level = .05). The Greenhouse– When asked to identify a familiar tone word in a two- Geisser adjustment for nonsphericity was used when ap- alternative forced-choice task, after three sessions (total propriate, and the corrected p values are reported to- 19.8 min) of continuous exposure, the 28 participants gether with the uncorrected degrees of freedom (Vasey performed with an average correct score of 26.79 out of & Thayer, 1987). To reveal the time course of on-line seg- 36 (mean = 74.37%, SD = 14.01; chance performance = mentation, separate statistical analyses were conducted 18). A single-sample t test revealed that overall perfor- for each time window of each 6.6-min recording session. mance was significantly different from chance [t(27) = The N100 and N400 mean amplitude data were subjected 9.22, p < .0001]. The mean score of high learners was to ANOVA with stimulus position (initial, middle, or final 32.5 out of 36 (n = 10; mean = 90.24%; minimum: tone of a word) and electrode site (seven regions) as >81.37%). The mean score of middle learners was 26.11 within-subject factors. Only main effects or interactions (n = 9; mean = 72.5%; range: 67.38–81.37%). The mean including the stimulus position factor are reported. Mea- score of low learners was 21.11 (n = 9; mean = 58.62%; sures of effect size (Eta squared, h2) and observed power maximum: <67.38%). A single-sample t test revealed ( pw) for a single effect are reported in combination with that the overall performance was significantly different the main effects of condition. Bonferroni post hoc tests from chance for all groups [high learners: t(9) = 24.9, were used to assess specific differences. The word onset p < .0001; middle learners: t(8) = 26.22, p < .0001; low effect on the N100 and N400 amplitudes was subjected learners: t(8) = 4.9, p = .001]. The mean score of the to a separate ANOVA with group (high, middle, and low choice test for participants exposed to Language 1 (L1) learners) and session (each 6.6-min session) as between- was 27.14 out of 36 (n = 14; 75.4%). The mean score for subject factors. participants exposed to Language 2 (L2) was 26.43 (n =

Abla, Katahira, and Okanoya 955 Downloaded from http://www.mitpressjournals.org/doi/pdf/10.1162/jocn.2008.20058 by guest on 27 September 2021 14; 73.41%). A single-sample t test revealed that the over- words (word onset) than for the middle or final tones all performance was significantly different from chance (Figure 1A). The topographic distribution of N400 and for both L1 and L2 [L1: t(13) = 6.77, p <.0001;L2: N100 peak amplitudes for initial tones in the first session t(13) = 6.06, p < .0001]. There was no difference be- also revealed larger negativity for foci at middle frontal tween L1 and L2. Because each language served as a and central scalp locations (Figures 2A and 3A). Figure 4 control for the other (words from L1 were nonwords for shows the difference in N400 amplitude between least- L2 and vice versa), the lack of significant differences predictable tones (initial tones) and most-predictable between the language groups suggests that these results tones (final tones) after subtraction (initial À final tone). reflect learning of the statistical structure of the tone These figures show that the larger N400 and N100 am-

sequences presented during exposure. plitudes of the high learners in the first session de- Downloaded from http://mitprc.silverchair.com/jocn/article-pdf/20/6/952/1759514/jocn.2008.20058.pdf by guest on 18 May 2021 creased in the second and third sessions (Figures 1A–C and 4A). ANOVA for the mean N400 amplitudes of the high ERP Data learners detected a significant main effect of stimulus Figure 1 presents the grand-averaged ERPs elicited at position [F(2, 27) = 6.38, p = .024, h2 = .42, pw = .84] FCz for the initial, middle, and final tones of the tone and electrode site [F(6, 54) = 8.28, p = .001, h2 = .48, words for each session and each group. pw = .97] in the first session. The post hoc test for this session indicated significant differences between the ini- tial and middle tones [t(209) = À10.31, p < .0001] and High Learners between the initial and final tones [t(209) = À8.26, p < ERPs of the high learners (n = 10) listening to the tone .0001]. There was no significant difference between mid- sequence in the first 6.6-min session had larger N100 dle and final tones. Additional ANOVA for stimulus posi- and N400 amplitudes for the initial tones of the tone tion revealed a significant N400 word-onset effect in the

Figure 1. Grand-averaged ERPs of each group elicited at FCz for the initial, middle, and final tones of tone words presented in a continuous sequence. (A) First, (B) second, and (C) third sessions of high learners. (D) First, (E) second, and (F) third sessions of middle learners. (G) First, (H) second, and (I) third sessions of low learners.

956 Journal of Cognitive Neuroscience Volume 20, Number 6 Downloaded from http://www.mitpressjournals.org/doi/pdf/10.1162/jocn.2008.20058 by guest on 27 September 2021 Figure 2. Topographic distribution of the N400 peak amplitudes at 400 msec after stimuli onset for initial tones in each group and session. The distribution is seen from the top, looking down on the head. White dots on the topographies indicate the channel positions. The small circle shows the position of the FCz channel. (A) First, (B) Downloaded from http://mitprc.silverchair.com/jocn/article-pdf/20/6/952/1759514/jocn.2008.20058.pdf by guest on 18 May 2021 second, and (C) third sessions of high learners. (D) First, (E) second, and (F) third sessions of middle learners. (G) First, (H) second, and (I) third sessions of low learners.

Figure 3. Topographic distribution of the N100 peak amplitudes at 122 msec after stimuli onset for initial tones in each group and session. The distribution is seen from the top, looking down on the head. White dots on the topographies indicate the channel positions. The small circle indicates the position of the FCz channel. (A) First, (B) second, and (C) third sessions of high learners. (D) First, (E) second, and (F) third sessions of middle learners. (G) First, (H) second, and (I) third sessions of low learners.

Abla, Katahira, and Okanoya 957 Downloaded from http://www.mitpressjournals.org/doi/pdf/10.1162/jocn.2008.20058 by guest on 27 September 2021 Downloaded from http://mitprc.silverchair.com/jocn/article-pdf/20/6/952/1759514/jocn.2008.20058.pdf by guest on 18 May 2021

Figure 4. The ERP amplitude for the final tone was subtracted from that for the initial tone. The amplitude difference is plotted for N400 (top row) and N100 (bottom row) of each group.

middle anterior [F(2, 27) = 4.90, p < .05], middle cen- Middle Learners tral [F(2, 27) = 11.31, p < .001], left anterior [F(2, 27) = 5.42, p < .05], and right anterior [F(2, 27) = 5.80, p < The grand-averaged ERPs of middle learners (n =9) .01] regions. There were no significant N400 effects in listening to the tone sequence in the third 6.6-min ses- the posterior regions. No laterality (left–right) effect was sion revealed the largest N100 and N400 for word on- found for the N400 word-onset effect. However, it could set, compared to other positions of a word (Figure 1F). not be determined whether the lateral difference in fact The topographic distribution of ERP amplitudes for did not exist or whether the difference disappeared be- initial tones revealed a larger N400 effect for foci at cause the reference electrodes of earlobes were linked. middle frontal and central scalp locations in the third There was no significant main effect of stimulus position session (Figure 2F) as well as an N100 effect at middle in the second or third session. However, there was a frontal and central scalp locations in the third session significant effect of session on N400 amplitudes of word (Figure 3F). The N100 and N400 amplitudes of middle onset [F(2, 27) = 4.45, p = .021, h2 = .25, pw = .72]. learners for initial tones gradually increased in the sec- Combined, these results indicate the appearance of an ond and third sessions (first session < second session < N400 amplitude, word-onset effect during the early learn- third session) (Figures 1D–F and 4B, E). ing session in high learners. For the mean N400 amplitudes of middle learners, a The ANOVA of the mean N100 amplitudes of the high significant main effect was found for stimulus position learners also detected a significant main effect of stim- [F(2, 24) = 12.01, p < .001, h2 = .73, pw = .99] and ulus position [F(2, 27) = 5.69, p = .037, h2 = .38, pw = electrode site [F(6, 48) = 5.76, p = .019, h2 = .42, pw = .59] and electrode site [F(6, 54) = 7.2, p = .002, h2 = .73] in the third session. The post hoc test for this ses- .44, pw = .93] in the first session. The post hoc test for sion indicated that significant differences occurred be- this session indicated significant differences between the tween the initial and middle tones [t(188) = À11.92, p < initial and middle tones [t(209) = À6.11, p < .0001] and .0001] and between the initial and final tones [t(188) = between the initial and final tones [t(209) = À4.48, À12.36, p < .0001]. However, there was no significant p < .0001]. There was no significant difference between difference between the middle and final tones. Addition- middle and final tones. Additional ANOVA for stimulus al ANOVA of stimulus position in the third session re- position indicated a significant N100 effect only for the vealed a significant N400 word-onset effect on the middle middle central region [F(6, 27) = 6.31, p < .01]. There anterior [F(2, 24) = 3.91, p < .05], middle central [F(2, was no significant main effect of stimulus position in the 24) = 7.37, p < .01], left anterior [F(2, 24) = 5.81, p < second or third session. There was no significant effect .01], and right anterior [F(2, 24) = 6.77, p <.01]regions. of session for the N100 amplitudes of word onset. There was no significant main effect of stimulus position

958 Journal of Cognitive Neuroscience Volume 20, Number 6 Downloaded from http://www.mitpressjournals.org/doi/pdf/10.1162/jocn.2008.20058 by guest on 27 September 2021 in the first or second session. There was a significant with the TPs in the first session for high learners (r = .44, effect of session, however, for N400 amplitudes of word p = .003) and in the third session for middle learners onset [F(2, 24) = 3.79, p =.037,h2 =.24,pw =.63]. (r = .39, p = .007; Figure 5A, F). There was no signifi- Combined, the N400 amplitude in middle learners grad- cant correlation between N400 amplitudes and TPs in ually increased as the learning sessions progressed and other sessions for high (Figure 5B, C) and middle learn- reached a maximum in the third session. ers (Figure 5D, E) or in any sessions for low learners ANOVA of the mean N100 amplitudes of middle learn- (Figure 5G–I). There were significant correlations be- ers showed significant differences in stimulus position tween N100 amplitudes and TPs in the first session for [F(2, 24) = 17.57, p <.001,h2 =.68,pw = .99] and elec- high learners (r = .32, p = .037) and in the third session 2 trode site [F(6, 48) = 5.27, p =.018,h =.39,pw =.75] for middle learners (r = .41, p = .005; Figure 6A, F). Downloaded from http://mitprc.silverchair.com/jocn/article-pdf/20/6/952/1759514/jocn.2008.20058.pdf by guest on 18 May 2021 in the third session. The post hoc test for this session There was no significant correlation between N100 am- indicated that there were significant differences between plitudes and TPs in other sessions for high (Figure 6B, the initial and middle tones [t(188) = À10.04, p < .0001] C) and middle learners (Figure 6D, E) or in any session and between the initial and final tones [t(188) = À8.29, for low learners (Figure 6G–I). p < .0001]. There was no significant difference between the middle and final tones. Additional ANOVA of stimulus position in the third session revealed a significant N100 DISCUSSION effect only in the middle central region [F(6, 24) = 6.90, p < .01]. No main effects were observed over lateral and Our study revealed an interesting phenomenon: Word- posterior regions. There was no significant main effect of onset effects on the amplitudes of ERPs differed greatly stimulus position in the first or second session, and there in each group in each 6.6-min session. Specifically, the was no significant effect of session for the N100 ampli- N100 and N400 amplitudes were larger in the early tudes of word onset. learning session among the high learners and in a later session in the middle learners; however, these effects were not elicited in low learners. The N100 and N400 Low Learners amplitudes in the early session for high learners and in None of the sessions showed word-onset effects for N100 the later session for middle learners were correlated and N400 amplitudes among low learners (n =9;Fig- with TPs in the tone streams. The N400 significantly dif- ures 1G–I, 2G–I, 3G–I, 4C, F), nor could we detect a sig- fered between the three sessions in high- and middle- nificant main effect of stimulus position on either N100 or learner groups and between three learner groups in early N400 in any of the sessions. There was no significant and later sessions, whereas the N100 did not show dif- effect of session for either the N100 or N400 amplitudes ferences among groups and sessions. of word onset in this group. The Cue of Word Segmentation— Transitional Probabilities Differences in the Groups The tone words used in this study were not based on ANOVA of groups for the N400 amplitudes of word onset musical composition. In addition, the stimuli did not showed significant differences among the three learner resemble any paradigmatic melodic fragments. The only groups in the first [F(2, 25) = 4.15, p = .028, h2 = .25, consistent cues for the beginnings and ends of the tone pw = .68] and third [F(2, 25) = 4.83, p = .017, h2 = .28, words were the TPs between tones. Participants suc- pw = .75] sessions, whereas there were no significant ceeded in learning the tone words, which consisted differences in the second session [F(2, 25) = 2.52, p = of higher TPs surrounded by lower TPs. Adult partic- .10]. The N400 amplitudes revealed a significant Group  ipants in this experiment showed performance levels in Session interaction [F(4, 75) = 3.07, p = .021, h2 = .14, the tone-segmentation task equivalent to those of par- pw = .78] for word onset. The ANOVA of groups for the ticipants in the analogous segmentation task studied by N100 amplitudes of word onset showed no significant Saffran et al. (1999). differences among the three learner groups in either In this study, word onset (initial tones) elicited larger session. There was no Group  Session interaction for N100 and N400 amplitudes than the middle and final N100 amplitudes. tones embedded in continuous streams. Because words in continuous tone stimuli can elicit word-onset compo- nents, it seems reasonable that these word-onset com- ponents might index word segmentation. This result Correlations between ERP and TPs agrees with those of previous studies on word segmen- Figures 5 and 6 show the correlations between ERPs tation based on linguistic syllable streams (Cunillera et al., amplitudes (N400 and N100) and TPs for each session 2006; Sanders et al., 2002). Some ERP studies showed and group. The mean N400 amplitudes were correlated that low-frequency and less predictable words elicited

Abla, Katahira, and Okanoya 959 Downloaded from http://www.mitpressjournals.org/doi/pdf/10.1162/jocn.2008.20058 by guest on 27 September 2021 Downloaded from http://mitprc.silverchair.com/jocn/article-pdf/20/6/952/1759514/jocn.2008.20058.pdf by guest on 18 May 2021

Figure 5. Correlation between the ERP amplitudes for N400 and TPs in each session and group. (A) First, (B) second, and (C) third sessions of high learners. (D) First, (E) second, and (F) third sessions of middle learners. (G) First, (H) second, and (I) third sessions of low learners. Vertical bars show the N400 mean amplitudes elicited at FCz. Horizontal bars indicate the TPs across the continuous tone stream. Each dot indicates the N400 mean amplitude of the grand-averaged ERP for averaged TP in the subinterval.

larger N400 amplitudes than high-frequency and more participants to predict the next tone within a word but predictable words (Kim, Kim, & Kwon, 2001; Kutas & difficult for them to predict the next tone after a word. Iragui, 1998; Petten & Kutas, 1990) and that N400 ampli- The word-onset effect (N100 and N400) were larger in tudes decreased upon repeated presentations of words the less-predictable positions (low TPs, boundaries of the (Phillips, Klein, Mercier, & de Boysson, 2006). In our tone words) than in the more-predictable positions (high study, the TPs between tone words were lower than with- TPs, middle and final tones within words) in the higher- in tones of words. Given these TPs, it is easy for the score groups (Figures 4, 5, and 6).

960 Journal of Cognitive Neuroscience Volume 20, Number 6 Downloaded from http://www.mitpressjournals.org/doi/pdf/10.1162/jocn.2008.20058 by guest on 27 September 2021 Downloaded from http://mitprc.silverchair.com/jocn/article-pdf/20/6/952/1759514/jocn.2008.20058.pdf by guest on 18 May 2021

Figure 6. Correlation between the ERP amplitudes for N100 and TPs in each session and group. (A) First, (B) second, and (C) third sessions of high learners. (D) First, (E) second, and (F) third sessions of middle learners. (G) First, (H) second, and (I) third sessions of low learners. Vertical bars show the N100 mean amplitudes elicited at FCz. Horizontal bars indicate the TPs across the continuous tone stream. Each dot indicates the N100 mean amplitude of the grand-averaged ERP for averaged TP in the subinterval.

Presumably, when the stream begins, it is heard as to- strong ERP word-onset effect, as shown in Figures 1A tally unstructured and all tones are equally novel. As the and 4A for high learners. Even if some participants in the statistical structure emerges with additional exposure, high-learner group did not have ERPs showing differen- the adjacent tones with high TPs stand out as coherent tiation of the first tone and last tone during the initial units. Although the tones within words are easy to portion of the first session, subsequent buildup of the predict, the initial tone (word onset) of other tone words differential effects may have occurred in the following with low TPs occurs relatively infrequently and elicits a portion of the session, eventually overshadowing such

Abla, Katahira, and Okanoya 961 Downloaded from http://www.mitpressjournals.org/doi/pdf/10.1162/jocn.2008.20058 by guest on 27 September 2021 initial refractoriness. However, we did not have enough that the ERP results, rather than the behavioral test, EEG epochs to enable such fine-grain analysis. seem to most closely reflect the on-line statistical learn- As learning progressed and all six of the tone words ing process. emerged, there should be no novelty effect because In their study, Sanders et al. (2002) interpreted the each tone word was equally frequent and less prediction word effect on the N400 component as a possible lexical is needed; therefore, the word-onset effect disappeared, search process triggered by segmented words. Note, as shown in Figure 1B and C for high learners. We can however, that the scalp distribution of the N400 com- also assume that the high learners generated implicit ponent found by Cunillera et al. (2006) and by our study hypotheses about where tone-triad boundaries lay based shows a maximum at middle frontal and central sites, a

on less reliable information than participants in medium- finding that does not agree with the more posterior dis- Downloaded from http://mitprc.silverchair.com/jocn/article-pdf/20/6/952/1759514/jocn.2008.20058.pdf by guest on 18 May 2021 or low-score groups. If high learners generated hypoth- tribution of the N400 component described by Sanders eses early, there would be a greater possibility of their et al. Differences in the scalp distribution of the N400 confirming their hypotheses and subsequently obtaining component are probably due to differences in the para- higher scores. The middle learners were simply delayed digms used in these studies. Sanders et al. trained par- in projecting hypotheses and thus the emergence of the ticipants to recognize six nonsense words presented in word-onset effect was also delayed (see Figures 1D–F, a continuous auditory stream. Possible nonsense words 2D–F). For the low learners, at least as assessed by the were compared before and after training. In contrast, behavioral task, all tone sequences remained novel and Cunillera et al.’s and our paradigm focused directly unstructured. Although the TPs across streams were on on-line segmentation because nonsense words were equal for all groups, the behavioral performances dif- discovered during exposure to the continuous auditory fered considerably among the participants. A significant streams without any previous training. The N400 com- correlation between TPs and ERP components was only ponent was elicited in both high- and low-learner groups seen in the first session in the high learners and in the by Sanders et al. but was not elicited in our low-learner last session in the middle learners. Another set of ex- group. These differences between the studies might be periments would be necessary to identify what caused attributable to differences in the strategy used to seg- such individual variations in statistical learning. A previ- ment the auditory stream: that is, lexical recognition of ous study (Sanders et al., 2002) also found variations previously learned nonsense words (as in Sanders et al., in learning performances of participants after a 20-min 2002) or use of statistical properties of tone streams that training protocol. had never been heard before. In the study of Sanders et al., although the overall performances of participants increased after training with the six nonsense words for 20 min, performances on tests given before and after ERP Word-onset Effect and Learning Differentials 14 min of continuous tone exposure still did not differ, According to our behavioral test, the performances of all indicating that the listeners did not learn anything from participants after being exposed to 19.8 min of the con- the continuous sequence. Therefore, although the ERP tinuous tone task indicated that the tone sequence was components in their study were elicited during word seg- segmented and that the participants had learned the mentation using previous lexical knowledge, they might tone words statistically. In the ERP recordings, however, not reflect brain processes that occurred during on-line the significant word-onset effect was elicited only in the statistical learning. high- and middle-learner groups. The ERP amplitudes In our study, larger N100 amplitudes were detected in changed dramatically as the sequence progressed. The sessions with larger N400 (Figures 1A and F, 3A and F), word-onset effect decreased in the high-learner group showing a maximum at middle central sites, but N100 and increased in the middle-learner group as learning amplitudes of word onset showed no significant differ- progressed, whereas it did not change in the low-learner ences between the three sessions in any of the groups or group. In our study, the low learners also performed between the three learner groups. We assumed that the better than chance in the behavioral test. However, they word boundaries with low TPs elicited an enhanced N100, did not show the word-onset effect in ERPs. The absence with a probable overlap by N400 and possibly bearing of an ERP word-onset effect across all three blocks of a similarity to the enhanced N1 and mismatch negativ- learning might simply be due to poor measurement sen- ity (MMN) elicited by infrequent changes in repetitive sitivity. However, it is also possible that, in forced-choice acoustic stimulation (Korzyukov et al., 1999; Escera, Alho, tests, participants can choose the one out of the two Winkler, & Na¨a¨ta¨nen, 1998; Na¨a¨ta¨nen, 1992). Another pos- tritone sequences that contains familiar combinations of sible interpretation is that the higher-score groups may tones, even if they cannot find ‘‘true’’ word boundaries have allocated greater attention to word onsets (Sanders during continuous exposure. As such, the participants et al., 2002). Because speech segmentation is basically might have used the frequency of co-occurrence, rather a learning task, attention has been shown to play a than TPs, to select the familiar item in the behavioral role in successful segmentation (Toro, Sinnett, & Soto- test (Toro & Trobalo´n, 2005). Therefore, we can infer Faraco, 2005) and may be critical in the neurophysiologic

962 Journal of Cognitive Neuroscience Volume 20, Number 6 Downloaded from http://www.mitpressjournals.org/doi/pdf/10.1162/jocn.2008.20058 by guest on 27 September 2021 changes observed during speech segmentation (Cunillera and Scholl (2005), Fiser and Aslin (2002), and Kirkham, et al., 2006). Slemmer, and Johnson (2002), in their behavioral studies, It is important to note that the continuous tone was have also shown that adults and infants (2-month-olds to presented for 19.8 min in three 6.6-min sessions in our 9-month-olds) can perform statistical learning for a study. Consequently, we were able to record the ERPs stream of shapes as visual input, with an equivalent ability that most closely reflected the on-line segmentation, to use the statistical consistencies among adjacent shapes and we were able to observe the ERPs in each 6.6-min to group them into shape pairs. These studies suggested time session separately. The word-onset effects on the that learners readily group sequences of auditory and vi- ERP amplitudes differed greatly in each group in each sual events in the same manner, regardless of whether

6.6-min session. Specifically, the N400 word-onset effect the input is linguistic (syllables) or nonlinguistic (tones Downloaded from http://mitprc.silverchair.com/jocn/article-pdf/20/6/952/1759514/jocn.2008.20058.pdf by guest on 18 May 2021 was larger in the early learning session in high learners and shapes). These results are consistent with the exis- and in a later session in middle learners but was not tence of a domain-general statistical learning device and elicited in low learners. The N400 amplitudes signifi- indicate that statistical learning is a process independent cantly differed between the three learning sessions in of linguistic faculty. Further evidence for this general- the high- and middle-learner groups and between the ization comes from experiments in nonhuman primates three groups in early and later sessions. The results sug- (cotton-top tamarins, a species of New World monkey) gest that individual segmentation ability could be de- (Hauser, Newport, & Aslin, 2001) and rodents (Pons, tected in the brain potentials using the on-line sequence 2006; Toro & Trobalo´n, 2005). In the Hauser et al. study, learning paradigm. We suggest that the N400 effect indi- after exposure to the same set of auditory stimuli used by cates not only word segmentation but also the degree Saffran, Aslin, et al. (1996), adult monkeys showed reliably of on-line statistical learning. Regarding the learning- greater interest in nonwords than in familiar words, sug- related changes to the N400 component, our findings gesting that they were able to extract the statistical infor- agree with those of McLaughlin, Osterhout, and Kim mation defining word boundaries in the artificial speech, (2004). Their study showed that the N400 amplitude in a manner similar to human infants. These experiments elicited by pseudowords (word-like letters) in a prime– and our results imply that statistical learning is a common target task gradually increased across continuing instruc- learning device. These results also support the hypothesis tion sessions in a second language for adult monolingual that statistical learning, as a domain-general mechanism, subjects; in contrast, the N400 amplitude elicited by re- is used in language acquisition (Gomez & Gerken, 2000). lated words (predictable words) decreased across learn- In humans, the ability to deal with a complex sequential ing sessions. The N400 waveforms elicited in our study structure is perhaps most evident in language acquisition are very similar to their results. and processing. In the high-learner group, the ERP word-onset effect was greatest in the first session and decreased as learn- Conclusion ing progressed. In the middle-learner group, the word- onset effect increased as learning progressed, reaching Our results suggest that ERPs accurately reflect the process a maximum in the third session. The total sequence of on-line word segmentation and statistical learning and exposure time was 19.8 min in this study. If we had ex- provide insight into the neural mechanisms underlying the tended the exposure time, the N400 word-onset effect of on-line statistical learning. This study also determined middle learners might have disappeared after the third that the ERP word-onset effect can be used as an on-line session. We discovered these phenomena during the measure of speech segmentation and is suitable for data analysis, after all of the experiments had been com- studying the brain mechanisms of sequence prediction. pleted, and we could not redo the experiments for these participants using extended sequences. Nevertheless, Acknowledgments we can infer that, in statistical learning, the N400 word- onset effect is largest during the discovery phase of the This work was supported by a grant from PRESTO, the Japan statistical structure within the continuous stream. Be- Science and Technology Agency (JST), and by a Grant-in-Aid for Scientific Research (#16011208) on Priority Areas (Infor- fore and after the discovery phase, the word-onset effect matics) of the Japanese Government to K. O. would be much smaller. Reprint requests should be sent to Kazuo Okanoya, Laboratory for Biolinguistics, Brain Science Institute, RIKEN, 2-1 Hirosawa, Common Learning Device for General Wako, Saitama 351-0198, Japan, or via e-mail: okanoya@brain. and Special Stimuli riken.jp. In our study, we used nonsense tone words, rather than the pronounceable syllables used by Cunillera et al. REFERENCES (2006) and Sanders et al. (2002). Despite this difference, Aslin, R. N., Saffran, J. R., & Newport, E. L. (1998). Computation both their studies and ours showed that ERP is related to of conditional probability statistics by 8-month-old infants. the process of word segmentation. Turk-Browne, Junge, Psychological Science, 9, 321–324.

Abla, Katahira, and Okanoya 963 Downloaded from http://www.mitpressjournals.org/doi/pdf/10.1162/jocn.2008.20058 by guest on 27 September 2021 Brent, M. R. (1999). Speech segmentation and word McLaughlin, J., Osterhout, L., & Kim, A. (2004). Neural discovery: A computational perspective. Trends in correlates of second-language word learning: Minimal Cognitive Sciences, 3, 294–301. instruction produces rapid change. Nature Neuroscience, Brent, M. R., & Cartwright, T. A. (1996). Distributional 7, 703–704. regularity and phonotactic constraints are useful for Na¨a¨ta¨nen, R. (1992). Attention and brain function. Hillsdale, segmentation. Cognition, 61, 93–125. NJ: Erlbaum. Christiansen, M. H., Allen, J., & Seidenberg, M. S. (1998). Oldfield, R. C. (1971). The assessment and analysis of Learning to segment speech using multiple cues: A handedness: The Edinburgh Inventory. Neuropsychologia, connectionist model. Language and Cognitive Processes, 9, 97–113. 13, 221–268. Pena, M., Bonatti, L. L., Nespor, M., & Mehler, J. (2002). Christophe, A., Dupoux, E., Bertoncini, J., & Mehler, J. Signal-driven computations in speech processing. Science,

(1994). Do infants perceive word boundaries? An empirical 298, 604–607. Downloaded from http://mitprc.silverchair.com/jocn/article-pdf/20/6/952/1759514/jocn.2008.20058.pdf by guest on 18 May 2021 study of the bootstrapping of lexical acquisition. Journal Petten, C. V., & Kutas, M. (1990). Interaction between sentence of the Acoustical Society of America, 95, 1570–1580. context and word frequency in event-related potentials. Cunillera, T., Toro, J. M., Sebastian-Galles, N., & Memory & Cognition, 18, 380–393. Rodriguez-Fornells, A. (2006). The effects of stress and Phillips, N. A., Klein, D., Mercier, J., & de Boysson, C. statistical cues on continuous speech segmentation: (2006). ERP measures of auditory word repetition and An event-related brain potential study. Brain Research, translation priming in bilinguals. Brain Research, 1125, 1123, 168–178. 116–131. Escera, C., Alho, K., Winkler, I., & Na¨a¨ta¨nen, R. (1998). Pons, F. (2006). The effects of distributional learning on Neural mechanisms of involuntary attention to acoustic rats’ sensitivity to phonetic information. Journal of novelty and change. Journal of Cognitive Neuroscience, Experimental Psychology: Animal Behavior Processes, 10, 590–604. 32, 97–101. Fiser, J., & Aslin, R. N. (2002). Statistical learning of Saffran, J. R., Aslin, R. N., & Newport, E. L. (1996). Statistical higher-order temporal structure from visual shape learning by 8-month-old infants. Science, 274, 1926–1928. sequences. Journal of Experimental Psychology: Saffran, J. R., Johnson, E. K., Aslin, R. N., & Newport, E. L. Learning, Memory, and Cognition, 28, 458–467. (1999). Statistical learning of tone sequences by human Gomez, R. L., & Gerken, L. (2000). Infant artificial language infants and adults. Cognition, 70, 27–52. learning and language acquisition. Trends in Cognitive Saffran, J. R., Newport, E. L., & Aslin, R. N. (1996). Word Sciences, 4, 178–186. segmentation: The role of distributional cues. Journal Goodsitt, J. V., Morgan, J. L., & Kuhl, P. K. (1993). Perceptual of Memory and Language, 35, 606–621. strategies in speech segmentation. Journal of Child Sanders, L. D., & Neville, H. J. (2003a). An ERP study of Language, 20, 229–252. continuous speech processing. I. Segmentation, semantics, Hauser, M. D., Newport, E. L., & Aslin, R. N. (2001). and syntax in native speakers. Cognitive Brain Research, Segmentation of the speech stream in a non-human 15, 228–240. primate: Statistical learning in cotton-top tamarins. Sanders, L. D., & Neville, H. J. (2003b). An ERP study of Cognition, 78, 53–64. continuous speech processing: II. Segmentation, semantics, Jusczyk, P. W., & Aslin, R. N. (1995). Infants’ detection of and syntax in non-native speakers. Cognitive Brain the sound patterns of words influent speech. Cognitive Research, 15, 214–227. Psychology, 29, 1–23. Sanders, L. D., Newport, E. L., & Neville, H. J. (2002). Kim, M. S., Kim, J. J., & Kwon, J. S. (2001). The effect of Segmentation nonsense: An event-related potential index immediate and delayed word repetition on event-related of perceived onsets in continuous speech. Nature potential in a continuous recognition task. Cognitive Neuroscience, 5, 700–703. Brain Research, 11, 387–396. Seidenberg, M. S., MacDonald, M. C., & Saffran, J. R. (2002). Kirkham, N. Z., Slemmer, J. A., & Johnson, S. P. (2002). Does grammar start where statistics stop? Science, 298, Visual statistical learning in infancy: Evidence for a domain 553–554. general learning mechanism. Cognition, 83, 35–42. Toro, J. M., Sinnett, S., & Soto-Faraco, S. (2005). Speech Kooijman, V., Hagoort, P., & Cutler, A. (2005). segmentation by statistical learning depends on attention. Electrophysiological evidence for prelinguistic infants’ Cognition, 97, B25–B34. word recognition in continuous speech. Cognitive Brain Toro, J. M., & Trobalo´n, J. B. (2005). Statistical computations Research, 24, 109–116. over a speech stream in a rodent. Perception & Korzyukov, O., Alho, K., Kujala, A., Gumenyuk, V., Ilmoniemi, Psychophysics, 67, 867–875. R. J., Virtanen, J., et al. (1999). Electromagnetic responses Turk-Browne, N. B., Junge, J., & Scholl, B. J. (2005). The of the human auditory cortex generated by sensory-memory automaticity of visual statistical learning. Journal of based processing of tone–frequency changes. Neuroscience Experimental Psychology: General, 134, 552–564. Letters, 276, 169–172. Vasey, M. W., & Thayer, J. F. (1987). The continuing Kutas, M., & Iragui, V. (1998). The N400 in a semantic problem of false positives in repeated measures ANOVA categorization task across 6 decades. Electroencephalography in psychophysiology: A multivariate resolution. and Clinical Neurophysiology, 108, 456–471. Psychophysiology, 24, 479–486.

964 Journal of Cognitive Neuroscience Volume 20, Number 6 Downloaded from http://www.mitpressjournals.org/doi/pdf/10.1162/jocn.2008.20058 by guest on 27 September 2021