YNIMG-11340; No. of pages: 8; 4C: 4, 5, 6 NeuroImage xxx (2014) xxx–xxx

Contents lists available at ScienceDirect

NeuroImage journal homepage: www.elsevier.com/locate/ynimg

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Yi-Fang Hsu a,b,⁎, Jarmo A. Hämäläinen c, Florian Waszak a,b a

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Article history: Accepted 30 April 2014 Available online xxxx

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Keywords: Repetition suppression Adaptation Prediction Attention Electroencephalography (EEG)

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Université Paris Descartes, Sorbonne Paris Cité, 75006 Paris, France CNRS (Laboratoire Psychologie de la Perception, UMR 8158), 75006 Paris, France Department of Psychology, University of Jyväskylä, 40014 Jyväskylä, Finland

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Repetition suppression, a robust phenomenon of reduction in neural responses to stimulus repetition, is suggested to consist of a combination of bottom-up adaptation and top-down prediction effects. However, there is little consensus on how repetition suppression is related to attention in functional magnetic resonance imaging (fMRI) studies. It is probably because fMRI integrates neural activity related to adaptation and prediction effects, which are respectively attention-independent and attention-dependent. Here we orthogonally manipulated repetition and attention in a target detection task while participants' electroencephalography (EEG) was recorded. In Experiment 1, we found a significant repetition effect on N1 amplitude regardless of attention, whereas the repetition effect on P2 amplitude was attention-dependent. In Experiment 2 where the attentional manipulation was more stringent than that in Experiment 1, we replicated a significant repetition effect on N1 amplitude regardless of attention, whereas the repetition effect on P2 amplitude was eliminated. The results show that repetition suppression comprises both attention-independent and attention-dependent components. © 2014 Published by Elsevier Inc.

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Introduction

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Repetition suppression (RS) is a robust phenomenon of reduction in neural responses to stimulus repetition. It can be demonstrated with a variety of neuroimaging measures (Grill-Spector et al., 2006; Henson and Rugg, 2003), mostly with haemodynamic responses in functional magnetic resonance imaging (fMRI) but also with electrophysiological responses in single-cell recording and non-invasive magnetoencephalography (MEG) and electroencephalography (EEG). Initially, RS was believed to be an automatic consequence of stimulus repetition, probably reflecting the fatigue (Grill-Spector and Malach, 2001; Kaliukhovich and Vogels, 2011), sharpening (Desimone, 1996; Kok et al., 2012a; Wiggs and Martin, 1998), and facilitation (Henson and Rugg, 2003; James and Gauthier, 2006) of the neural activation in the sensory cortices. Therefore, RS was considered as an indicator for the nature of representations across different stages of a processing stream (Grill-Spector et al., 2006). This idea was later challenged by findings that RS was attenuated when stimulus repetition was improbable in the visual (Summerfield et al., 2008, 2011) and auditory (Todorovic et al., 2011) domains. The pattern of results suggested RS to reflect the reduction in prediction error (Friston, 2005). These

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Repetition suppression comprises both attention-independent and attention-dependent processes

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⁎ Corresponding author at: Laboratoire Psychologie de la Perception, 45, rue des Saints-Pères, 75006 Paris, France. Fax: +33 1 42 86 33 22. E-mail addresses: [email protected] (Y.-F. Hsu), jarmo.a.hamalainen@jyu.fi (J.A. Hämäläinen), [email protected] (F. Waszak).

opposing views were reconciled in recent research proposing that RS consists of a combination of bottom-up adaptation and top-down prediction mechanisms (Ewbank et al., 2011; Larsson and Smith, 2012). Interestingly, there is little consensus on how RS is related to attention. Some research suggested that RS is independent of attention. For example, significant RS was found for both attended and unattended faces on inferior temporo-occipital cortex (Bentley et al., 2003) and objects on bilateral anterior fusiform areas (Vuilleumier et al., 2005). The attention-independent RS was believed to be the neural substrate for repetition priming effects which can arise regardless of attention. Some research, on the other hand, suggested that attention is necessary to induce RS. RS was observed in face-responsive (Henson and Mouchlianitis, 2007; Yi et al., 2006), object-responsive (Eger et al., 2004), and scene-responsive (Moore et al., 2013; Yi and Chun, 2005) areas only for stimuli attended both at the initial presentation and subsequent presentation. Is the discrepancy due to different manipulations of attention? A recent behavioural study suggested that the effects of stimulus repetition can be modulated by spatial attention but not dimensional attention (Zäske et al., 2013). However, just as spatial attention was found to have either null effects (Bentley et al., 2003) or significant effects (Eger et al., 2004; Henson and Mouchlianitis, 2007) on RS, dimensional attention was found to have either null effects (Vuilleumier et al., 2005) or significant effects (Moore et al., 2013; Yi and Chun, 2005; Yi et al., 2006) on RS. Therefore, it is unlikely that the discrepancy is due to different manipulations of attention.

http://dx.doi.org/10.1016/j.neuroimage.2014.04.084 1053-8119/© 2014 Published by Elsevier Inc.

Please cite this article as: Hsu, Y.-F., et al., Repetition suppression comprises both attention-independent and attention-dependent processes, NeuroImage (2014), http://dx.doi.org/10.1016/j.neuroimage.2014.04.084

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Stimuli

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community. Seventeen healthy volunteers (average age 25; five males; fourteen right-handed) with no history of neurological, psychiatric, or hearing impairments as indicated by self-report participated in the experiment. Five participants were excluded from behavioural and EEG data analysis as their hit rate was lower than 0.50 and/or their false alarm rate was higher than 0.50, leaving twelve participants in the behavioural data sample (average age 25; five males; eleven right-handed). One additional participant was excluded from EEG data analysis for having less than 50 artefact-free trials in any of the conditions, leaving eleven participants in the EEG data sample (average age 25; five males; ten right-handed). All participants gave written informed consent and were paid for participation. Ethical approval was granted by the CPP (Comité de Protection des Personnes) Ile de France II.

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Materials and methods

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Participants

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Experiment 1 Participants were recruited from the Université Paris Descartes and surrounding community. Sixteen healthy volunteers (average age 29; six males; all right-handed) with no history of neurological, psychiatric, or hearing impairments as indicated by self-report participated in the experiment. Three participants were excluded from EEG data analysis for having less than 100 artefact-free trials in any of the conditions, leaving thirteen participants in the EEG data sample (average age 30; five males; all right-handed).

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Experiment 2 We replicated Experiment 1 (with one exception, see below) in a different lab to warrant reliability of the new findings. Participants were recruited from the University of Jyväskylä and surrounding

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Procedures

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A total of eight blocks of 200 tones were presented, including 100 tones from the repeated stimulus stream and 100 tones from the random stimulus stream in each block. Participants were instructed to pay attention to the low-pitched/high-pitched stimulus streams in different blocks, so that their attention was directed to the repeated/ random stimulus streams in 50% of the blocks respectively. In

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Sinusoidal tones with a loudness of 80 phons (i.e. 80 dB for tones of 1000 Hz) were generated using Matlab. The duration of each tone was 50 ms (including 5 ms rise/fall times). The frequency of each tone was within the range of 261.626–493.883 Hz and 2093.000–3951.070 Hz, matching the absolute frequency of two sets of seven natural keys on a modern piano (low-pitched set: C4 D4 E4 F4 G4 A4 B4; high-pitched set: C7 D7 E7 F7 G7 A7 B7). Within each frequency set, a repeated stimulus stream and a random stimulus stream were created. In the repeated stimulus stream, the frequency of each tone was repeated once before the next random selection was done. In the random stimulus stream, the frequency of each tone was determined by a random selection with equal probability. Continuous series of tones were presented via headphones with alternate tones from repeated/random stimulus streams from different frequency sets to allow for the efficient manipulation of attention on the two stimulus streams (Fig. 1). Note that we interleaved one repeated stimulus stream and one random stimulus stream instead of two repeated stimulus streams, given that the latter can create a regular sequence of four consecutive tones across the two stimulus streams and, therefore, introduce higher order regularity. A stimulus onset asynchrony (SOA) of 500 ms was used. A total of 800 tones were selected for the repeated/random stimulus stream, respectively. Stimulation was randomised individually for each participant. All stimuli were presented binaurally so there was no spatial confound in the current study.

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Another possible explanation for the discrepancy is that most studies examining the relationship of RS and attention used fMRI to assess 81 cortical activation. The limited temporal resolution of fMRI, which 82 measures the integration of several seconds of neural activity, makes 83 it difficult to tell in detail how RS is modulated by attention. Considering 84 that RS consists of a combination of bottom-up adaptation and top85 Q14 down prediction mechanisms (Ewbank et al., 2011; Larsson and 86 Smith, 2012), it may be that different parts of the mechanisms underly87 ing RS are differently affected by attention. Stimulus-specific adaptation 88 Q15 has been described in the cortex of the anesthetised animal (Brosch and 89 Q16 Schreiner, 1997; Pienkowski and Eggermont, 2009; Ulanovsky et al., 90 2003, 2004), suggesting that the bottom-up adaptation mechanism is 91 attention-independent. The top-down prediction mechanism, on the 92 other hand, has been reported to interact with attention (Chennu et al., 93 2013; Kok et al., 2012b). Therefore, whether attention-independent or 94 attention-dependent RS is found might depend on the particular com95 position of the RS in the given experiment. Obviously, to address this 96 issue a method of higher temporal resolution such as EEG needs to be 97 used. Only then we might be capable of teasing apart different compo98 nents of RS that are differently affected by attention. 99 In the current study we used EEG in two experiments to test the 100 hypothesis that different parts of the mechanisms underlying RS are dif101 ferently affected by attention. We orthogonally manipulated stimulus 102 repetition (to assess different components of RS) and attention (to 103 assess its effect on RS) in a target detection task. Participants were 104 instructed to pay attention to one of the two interleaved stimulus 105 streams of repeated/random tone frequency. To confirm that partici106 pants followed the instructions correctly, in Experiment 1, we intro107 duced target tones in the attended stimulus streams which participants 108 should respond to. Using EEG, we quantified N1 and P2 as neural corre109 lates of RS on the basis of the findings that repetition of auditory stimuli 110 is associated with attenuated N1 and P2 responses due to changes in au111 ditory cortical neurons (Kuriki et al., 2007; Zäske et al., 2009). We found 112 a significant repetition effect on N1 amplitude regardless of attention. On 113 the other hand, the P2 repetition effect was attention-dependent. Exper114 iment 2 was meant to replicate the independency of the repetition 115 effects and attention on N1 observed in Experiment 1. Moreover, we 116 intended to further control for participants' attention to corroborate 117 the attention-dependency of the P2 repetition effect. Therefore, we intro118 duced target tones in the attended stimulus streams which participants 119 should respond to and distractor tones in the unattended stimulus 120 streams which participants should not respond to. Such attentional ma121 nipulation engaged participants in a difficult target detection task. Exper122 iment 2 fully replicated the N1 effect of Experiment 1. The P2 repetition 123 effect, however, was eliminated. The pattern of the results suggests that 124 RS comprises both attention-independent and attention-dependent 125 components.

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Fig. 1. Experimental design.

Please cite this article as: Hsu, Y.-F., et al., Repetition suppression comprises both attention-independent and attention-dependent processes, NeuroImage (2014), http://dx.doi.org/10.1016/j.neuroimage.2014.04.084

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Experiment 1. EEG was recorded with 64-channel active electrode caps (Brain Products GmbH, Germany) with the sampling rate of 500 Hz. No online filter was used. The Cz served as the reference electrode. The data was recomputed to average reference offline.

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Experiment 2. EEG was recorded with 129-channel HydroCel electrode nets (Electrical Geodesics Inc., USA) with the sampling rate of 500 Hz. An online bandpass filter of 0.1–200 Hz was used. The Cz served as 215 the reference electrode. The data was notch filtered at 45–55 Hz to 216 remove electrical noise and recomputed to average reference offline. 217 For both experiments, the target stimuli and the first stimuli follow218 ing target stimuli were removed from analyses. Epochs extended from 219 −1500 ms to 1500 ms relative to stimulus onset, using a 100 ms pre220 stimulus baseline. Ocular artefact correction was conducted with inde221 Q18 pendent component analysis in EEGlab (Delorme and Makeig, 2004). 222 Epochs containing voltage deviations exceeding +/− 100 μV relative 223 to baseline at any of the electrodes were rejected. The trial numbers 224 after artefact rejection in each condition are listed in Table 1. Note that 225 all conditions (first stimuli, attended stimuli; second stimuli, attended 226 stimuli; first stimuli, unattended stimuli; second stimuli, unattended 227 stimuli) were extracted from the repeated stimulus stream because it 228 was where the first/second stimuli constitute the stimulus repetition. 229 A comparison between the repeated/random stimulus streams was 230 avoided as a comparison between the two stimulus streams would be 231 Q19 a comparison of conditions of different entropies.

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EEG analysis The time-domain analysis was conducted with a temporal principal 234 component analysis (PCA) in SPSS 20 to provide objective data-driven 235 Q20 ERP component measures (Dien, 2012; Kayser and Tenke, 2003). The 236 data used for component extraction included time points ranging from Table 1 Mean and range of trial numbers after artefact rejection in each condition. Second attended

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Overall, participant's behavioural performance in the target detection task was close to ceiling (hit: mean = 0.92, SD = 0.04; false alarm: mean = 0.03, SD = 0.03; RT: mean = 555.40, SD = 70.16). There was no difference between participant's behavioural performance when they attended to repeated/random stimulus stream (hit: t(15) = − 0.65, p = 0.53; false alarm: t(15) b 0.01, p = 1.00; RT: t(15) = 1.96, p = 0.07), suggesting that task difficulty was equivalent across blocks.

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Fig. 2A shows the grand average ERPs on nine representative electrodes (F3, Fz, F4, C3, Cz, C4, P3, Pz, P4), where N1 with a frontocentral distribution and P2 with a central distribution are evident. Fig. 2B shows the component loadings of 108 components in the temporal PCA yielding components clearly corresponding to N1 and P2. Fig. 2C shows the N1 and P2 component scores averaged across three electrodes showing the largest responses in each condition (N1: F2, FC2, C4; P2: FC1, C1, C2).

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− 100 ms to 500 ms relative to stimulus onset within the 3000 ms long epoch from all electrodes. Covariance matrix and Promax rotation were used. All components accounting for a total of 99% of the variance (maximum iterations for convergence = 500) were included in the rotation (Promax Kappa = 4). The temporal decomposition provided a set of component loadings reflecting the contribution of each time point on certain temporal components. The component loadings were used to derive component scores representing the magnitude of neural activity within the time windows of interest. We identified N1 and P2 by selecting components showing typical N1 and P2 latency and topography. The component scores of N1 and P2 were averaged across three electrodes showing the largest responses to serve as inputs for a 2 (first/second stimuli) × 2 (attended/unattended stimuli) repeated measures analysis of variance (ANOVA). To provide a comprehensive representation of EEG, we also performed a frequency-domain analysis by calculating the inter-trial coherence (ITC), a measure representing the strength of phase coherence (Makeig et al., 2004). It takes a value between 0 and 1, in which a value of 0 represents no phase coherence and a value of 1 represents maximal phase coherence. The ITC was computed with fast Fourier transform in EEGlab (Delorme and Makeig, 2004) on Cz, where the signal-to-noise ratio is typically high. A sliding window of 512 ms was applied 200 times, providing output latency from around − 1244 ms to 1242 ms and output frequency from around 0.24 Hz to 30.03 Hz. Significance of the ITC was assessed with the bootstrap method (p b 0.01) which randomly permutes the single-trial spectral estimates from different latency windows in the baseline period to construct a surrogate baseline amplitude distribution. Given a clear peak of the ITC in the frequency range of theta (4–8 Hz) after stimulus onset in the datasets, the ITC in the frequency range of theta was averaged across the time windows of N1 (90–110 ms) and P2 (190–210 ms) to serve as inputs for a 2 (first/second stimuli) × 2 (attended/unattended stimuli) ANOVA.

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Experiment 1, 10% of the tones were of attenuated loudness by 20 dB in the attended stimulus stream to serve as targets. In Experiment 2, 10% of the tones were of attenuated loudness by 20 dB in the attended stimulus stream to serve as targets and 10% of the tones were of attenuated loudness by 20 dB in the unattended stimulus stream to serve as distractors. In both experiments, participants were required to press a key when they detect a softer tone in the attended stimulus stream which randomly occurred 10 times in each block, ignoring the stimulation in the unattended stimulus stream. When participants focused on the repeated stimulus stream and ignored the random stimulus stream, data for the conditions of “first stimuli, attended stimuli” and “second stimuli, attended stimuli” was obtained. When participants focused on the random stimulus stream and ignored the repeated stimulus stream, data for the conditions of “first stimuli, unattended stimuli” and “second stimuli, unattended stimuli” was obtained. Block order was counterbalanced across participants, as half of the participants were required to pay attention to the repeated stimulus stream in the first four blocks and half of the participants were required to pay attention to the random stimulus stream in the first four blocks.

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The repetition × attention interaction was not significant (F(1,12) b 0.001, p = 0.98). There was a significant main effect of repetition (F(1,12) = 9.11, p b 0.05) where repetition suppressed the N1 amplitude and a significant main effect of attention (F(1,12) = 9.12, p b 0.05) where attention enhanced the N1 amplitude.

Please cite this article as: Hsu, Y.-F., et al., Repetition suppression comprises both attention-independent and attention-dependent processes, NeuroImage (2014), http://dx.doi.org/10.1016/j.neuroimage.2014.04.084

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Fig. 2. (A) Grand average ERPs on nine representative electrodes (marked as white dots: F3, Fz, F4, C3, Cz, C4, P3, Pz, P4). (B) Component loadings of 108 components in the temporal PCA. (C) The N1 and P2 topographical distribution and component scores averaged across three electrodes showing the largest responses (marked as white dots) in each condition.

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The 2 × 2 repeated measures ANOVA revealed a significant repetition × attention interaction (F(1,12) = 19.24, p b 0.01). Post hoc comparisons showed that repetition suppressed the P2 amplitude for attended stimuli (t(12) = 5.03, p b 0.001) but not for unattended stimuli (t(12) = −1.68, p = 0.12).

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Fig. 3 shows the ITC on Cz in each condition in Experiment 1. There was a clear peak of the ITC in the frequency range of theta (4–8 Hz) after stimulus onset.

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N1 time window There was no interaction between repetition and attention (F(1,12) = 3.70, p = 0.08). On the other hand, there was a significant

main effect of repetition (F(1,12) = 5.01, p b 0.05) where repetition 307 suppressed the ITC and a significant main effect of attention (F(1,12) = 308 14.83, p b 0.01) where attention enhanced the ITC. 309 P2 time window There was a significant interaction between repetition and attention (F(1,12) = 12.99, p b 0.01). Post hoc comparisons showed that repetition suppressed the ITC for attended stimuli (t(12) = 5.37, p b 0.001) but not for unattended stimuli (t(12) = −0.74, p = 0.47).

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Participant's behavioural performance in the target detection task was 317 as follows (hit: mean = 0.70, SD = 0.11; false alarm: mean = 0.11, 318 SD = 0.10; RT: mean = 500.23, SD = 59.71). There was no difference 319

Please cite this article as: Hsu, Y.-F., et al., Repetition suppression comprises both attention-independent and attention-dependent processes, NeuroImage (2014), http://dx.doi.org/10.1016/j.neuroimage.2014.04.084

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EEG data: time-domain analysis

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Fig. 4A shows the grand average ERPs on nine representative elec- 325 trodes (E24, E11, E124, E36, E129, E104, E60, E62, E85), where N1 326

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between participant's behavioural performance when they attended to repeated/random stimulus stream (hit: t(11) = 0.34, p = 0.74; false alarm: t(11) = 1.20, p = 0.25; RT: t(11) = −0.10, p = 0.92), suggesting that task difficulty was equivalent across blocks.

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Fig. 3. ITC on Cz in each condition in Experiment 1.

Fig. 4. (A) Grand average ERPs on nine representative electrodes (marked as white dots: E24, E11, E124, E36, E129, E104, E60, E62, E85). (B) Component loadings of 147 components in the temporal PCA. (C) The N1 and P2 topographical distribution and component scores averaged across three electrodes showing the largest responses (marked as white dots) in each condition.

Please cite this article as: Hsu, Y.-F., et al., Repetition suppression comprises both attention-independent and attention-dependent processes, NeuroImage (2014), http://dx.doi.org/10.1016/j.neuroimage.2014.04.084

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The repetition × attention interaction was not significant (F(1,10) = 0.09, p = 0.77). There was a significant main effect of repetition (F(1,10) = 5.87, p b 0.05) where repetition suppressed the N1 amplitude and a significant main effect of attention (F(1,10) = 5.86, p b 0.05) where attention enhanced the N1 amplitude.

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The repetition x attention interaction was not significant (F(1,10) = 0.10, p = 0.76). The main effect of repetition was not significant (F(1,10) = 1.24, p = 0.29) but the main effect of attention was significant (F(1,10) = 9.43, p b 0.05) where attention suppressed the P2 amplitude.

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Fig. 5 shows the ITC on Cz in each condition in Experiment 2. Similar to Experiment 1, a clear peak of the ITC in the frequency range of theta (4–8 Hz) after stimulus onset can be seen.

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N1 time window There was no interaction between repetition and attention (F(1,10) = 0.50, p = 0.50). Neither the main effect of repetition (F(1,10) = 0.99, p = 0.34) nor the main effect of attention (F(1,10) = 4.74, p = 0.06) reached significance.

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P2 time window There was no interaction between repetition and attention (F(1,10) = 0.17, p = 0.69). Neither the main effect of repetition (F(1,10) = 0.46, p = 0.51) nor the main effect of attention (F(1,10) = 2.60, p = 0.14) reached significance.

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Yi and Chun, 2005; Yi et al., 2006). In Experiment 2 where the attentional manipulation involves introducing target tones in the attended stimulus streams and distractor tones in the unattended stimulus streams, the P2 repetition effect was eliminated. Our findings, thus, suggest that the effect of RS is based on two mechanisms, one being attentionindependent and the other being strongly influenced by the attentional demands of the experiment. In auditory studies, the N1 response is known to be sensitive to stimulus repetition (Näätänen and Picton, 1987). The N1 showed marked decrease in amplitude after the first presentation of a sound. This reduction in amplitude was suggested to be due to the refractory properties of neural populations (Budd et al., 1998). We found that the repetitionrelated reduction on the N1 amplitude is independent of attention. The result indicates that RS on the N1 is a rather automatic phenomenon. It speaks against the hypothesis that the repetition-related reduction in the N1 amplitudes is due to attention switching (Alcaini et al., 1994; Ponton et al., 2001). Rather, it suggests that the repetition effect on the N1 amplitude reflects the spontaneous detection of novel sensory inputs (Näätänen and Picton, 1987). Our results also fit well with recent findings of significant RS regardless of perceptual load on ERP correlates of face representation (Neumann and Schweinberger, 2008; Neumann et al., 2011), which suggested the existence of a facespecific processing system independent of attention. On the other hand, it was suggested that the P2 may reflect a neural process of the comparison between sensory inputs and the internal prediction (Costa-Faidella et al., 2011; Evans and Federmeier, 2007), a process likely to be affected by attention. The P2 data of the current study is admittedly less straightforward than the N1 data, such that our conclusions remain to a considerable extend speculative. However, it is noteworthy that the same attentional manipulations that have a very clear effect on N1 amplitude have a very different and taskdependent effect on P2 amplitude. We do not have a full account of all the boundary conditions necessary to observe RS on P2 amplitude, but the pattern of results observed in the two experiments suggests the following tentative description of how attention might influence RS. The P2 repetition effect is conspicuous when there is a moderate level of attention (i.e., in the attended condition in Experiment 1). On the other hand, it is not the case when too little or too much attention is involved. When attention is directed away from the stimuli (i.e., in the unattended condition in Experiment 1 and Experiment 2), the P2 repetition effect may be absent altogether. When attention is intensely focused on the stimuli (i.e., in the attended condition in Experiment 2 where the attentional manipulation was more stringent than that in Experiment 1), the P2 repetition effect in the attended condition may be masked by the floor effect of attention. Note that this attentional attenuation of the P2 observed in Experiment 2 is in accordance with previous reports showing decreasing P2 amplitude with increasing attentiveness (Crowley and Colrain, 2004; Okita, 1979). As said before, our interpretation of how attention modulates the P2 repetition effect is rather speculative. However, the scenario can be tested in future research manipulating attention at multiple levels. For example, within a single

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with a frontocentral distribution and P2 with a central distribution are evident. Fig. 4B shows the component loadings of 147 components in the temporal PCA yielding components clearly corresponding to N1 and P2. Fig. 4C shows the N1 and P2 component scores averaged across three electrodes showing the largest responses in each condition (N1: E106, E31, E129; P2: E55, E80, E129).

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The current study orthogonally manipulated repetition and attention in a target detection task. We quantified N1 and P2 as neural correlates of 362 the RS. In both experiments reported above, we found a significant 363 repetition effect on N1 amplitude regardless of attention. On the other 364 hand, the repetition effect on P2 amplitude was rather variable. In Exper365 iment 1 where the attentional manipulation involves introducing target 366 tones in the attended stimulus streams, the P2 repetition effect was 367 attention-dependent, which is in accordance with previous studies dem368 onstrating attentional modulation of RS on haemodynamic responses 369 Q22 (Eger et al., 2004; Henson and Mouchlianitis, 2007; Moore et al., 2013;

Fig. 5. ITC on Cz in each condition in Experiment 2.

Please cite this article as: Hsu, Y.-F., et al., Repetition suppression comprises both attention-independent and attention-dependent processes, NeuroImage (2014), http://dx.doi.org/10.1016/j.neuroimage.2014.04.084

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This research received funding from the European Research Council (ERC) under the European Union's Seventh Framework Programme (FP7/2007-2013)/ERC grant agreement no 263067. We thank Trevor Agus for help on stimulus calibration and the Paris Descartes Platform for Sensorimotor Studies (Université Paris Descartes, CNRS, INSERM, Région Ile-de-France) for supporting the experimental work presented here.

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suggests that RS can survive interruptions, at least when interruptions can be segregated from the stimulus stream of interest. Overall, our results suggest that different parts of the mechanisms underlying RS are differently affected by attention, at least in the auditory system. This is consistent with previous studies in the visual system demonstrating that the underlying mechanisms of RS can be region dependent (Weiner et al., 2010) and, more relevant to the current study, time dependent (Verhoef et al., 2008). Nevertheless, future research is needed to determine whether the dynamic influence of attention on RS is equivalent across different sensory systems. The findings of the current study have important implications for the interpretation of RS. While it was proposed in recent research that RS may reflect a composite of bottom-up adaptation and top-down prediction mechanisms (Ewbank et al., 2011; Larsson and Smith, 2012), it is difficult to separate the contribution of the two effects in fMRI given that haemodynamic responses represent the integration of several seconds of neural activity. Our EEG results suggest that the attentiondependency of RS may be used to identify whether the measure mainly reflects the bottom-up adaptation mechanism or the top-down prediction mechanism.

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experiment one may monitor participants' allocation of attention and use it as a continuous variable to see how the P2 repetition effect may change accordingly. We would also like to point out that our results suggest that the attentional manipulation worked in both experiments, as we observed interactions as well as main effects involving this factor. The pattern of the results (i.e., attention-independent repetition effects on N1 amplitude and attention-dependent effects on P2 amplitude) suggests that the processing of stimulus repetition in the auditory cortices involves a succession of cognitive events. Stimulus repetition at first triggers suppressed neural activity as a consequence of the refractory properties of neural populations. The attention-independent RS is then regulated by attention which “silences” the repetition effect for unattended stimuli. Such mechanism has strong adaptive value. While the attentionindependent RS allows for the spontaneous detection of novel sensory inputs, it is redundant to further retain such discrimination in the unattended condition. This is in line with a recent finding that the repetition effects on N1 and P2 had distinct RS profiles to changes in temporal properties of stimulus repetition (Lanting et al., 2013). It was thus suggested that RS on the N1 might be more related to a stable bottom-up process involving stimulus-specific thalamocortical activation, whereas RS on the P2 might be more related to a variation-prone process involving a combination of non-specific thalamocortical and widespread cortico-cortical responses. The idea is in line with the literature on the sources of the N1 and P2 responses. The sources of the N1 response are well known to be in the auditory cortices near planum temporale, which project their electrical fields toward the frontocentral areas with reversed polarity near the mastoid areas (Picton et al., 2000; Ponton et al., 2002). In contrast, the P2 response seems to be generated by more widespread sources in the planum temporale and the auditory association cortex (Crowley and Colrain, 2004; Godey et al., 2001). Therefore, it is possible that the N1 repetition effect is more related to bottom-up adaptation mechanism. On the other hand, the P2 repetition effect might be more related to top-down prediction mechanism that is susceptible to influences of the current attentional set (Feldman and Friston, 2010; Friston, 2009; Rao, 2005; Summerfield and Egner, 2009), although underlying processes are less clear here. Importantly, the interpretation does not mean to say that there is a one-to-one correspondence between the two ERP components and the two cognitive processes. Indeed, it has been documented that topdown prediction effect can be seen at around 100 ms (Todorovic et al., 2011; Wacongne et al., 2011) and bottom-up adaptation effect can be seen at around 200 ms (Altmann et al., 2008) after stimulus onset. Rather, what we intend to suggest here is that the attention-dependency of RS may index how much each ERP repetition effect reflects bottom-up adaptation and top-down prediction mechanisms. The ITC analysis further suggests that the ERP effects can be attributed to the changes in the strength of theta-band phase coherence. Specifically, we demonstrated that RS in the ERPs was associated with suppressed theta-band phase coherence in Experiment 1. While it is not possible to distinguish in the current context whether the thetaband phase coherence reflects the frequency representation of the ERPs or the endogenous oscillation in the theta range, theta synchronisation was reported to act as a windowing mechanism for long-term potentiation which is crucial for stimulus encoding (Klimesch, 1999; Rizzuto et al., 2003). Therefore, the suppressed theta synchronisation likely represents the reduced need to encode new information upon stimulus repetition. However, the association between the RS in the ERPs and the suppressed theta-band phase coherence was not replicated in Experiment 2, probably due to the smaller trial number. Therefore, its reliability remains to be tested. It is noteworthy that, unlike typical research on RS, stimulus repetition in the current study was always interrupted by a tone from a stimulus stream of different frequency sets. This might resemble the finding of repetition priming effects when the intervals between stimulus repetitions were long (Henson et al., 2004). The presence of RS here

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