The use of conditional inference to reduce prediction error—A mismatch negativity (MMN) study

Neuropsychologia 48 (2010) 3009–3018

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The use of conditional inference to reduce prediction error—A mismatch negativity (MMN) study Juanita Todd a,b,c,∗ , Jennifer Robinson a a b c

School of Psychology, University of Newcastle, Australia Centre for Brain and Mental Health Research, University of Newcastle, Australia Schizophrenia Research Institute, Darlinghurst, Australia

a r t i c l e

i n f o

Article history: Received 14 January 2010 Received in revised form 11 May 2010 Accepted 5 June 2010 Available online 12 June 2010 Keywords: Auditory Event-related potentials

a b s t r a c t The brain uses regularities in the sound environment to build inference models predicting the most likely attributes of subsequent sounds. When the inference model fails, a prediction-error signal (the mismatch negativity or MMN) is generated. This study is designed to explore the capacity to use information about when a deviant sound will occur to switch between inference models in memory. We measured MMN generated to rare frequency, duration, intensity and spatial deviant sounds randomly occurring in a stream of identical repeating “standard” sounds. We then measured MMN to the same deviants in a linked sequence where deviants were paired—duration deviants followed an intensity change and spatial deviants followed a frequency change. To minimise prediction error, the brain should use the occurrence of the intensity and frequency deviant to prompt a change in the dominant inference (“expectthe-standard”) to anticipate the characteristics of the linked deviant. Anticipation was quantified as the proportion decline in duration and spatial MMN in the linked versus random sequence. We report three main outcomes on a sample of 23 healthy adults: (1) a significant reduction in duration MMN amplitude in linked versus random sequence; (2) a subgroup of participants exhibited significant reduction in spatial MMN amplitude in linked versus random sequence; and (3) the capacity to anticipate a linked deviant (reduce MMN) was a related to performance on the Continuous Performance Task-Identical Pairs. The results are discussed with respect to a possible co-reliance of CPT-IP and inference models on the inferior frontal gyrus. © 2010 Elsevier Ltd. All rights reserved.

The mismatch negativity (MMN) component of the auditory event-related potential (ERP) is considered a simple example of a “prediction error” coding process that occurs in the brain (Garrido, Kilner, Stephan, & Friston, 2009). Prediction error coding allows the brain to integrate actual incoming sensory information with a bias in response that reflects a prediction about the likely causes of sensory input. We build up these biases or prediction models based on past experience and a prediction-error signal is generated when the actual state of the world differs from what the brain had anticipated. The error signal is a prompt for the brain to update its model to ensure more accurate future predictions. This process of prediction, error detection and correction enables the brain to operate efficiently by ensuring that highly predictable events are ignored and attention can be reserved for events that violate predictions and promote new learning (Friston, 2005).

∗ Corresponding author at: School of Psychology, University of Newcastle, University Drive, Callaghan, NSW 2308, Australia. Tel.: +61 2 49215977; fax: +61 2 49216980. E-mail address: [email protected] (J. Todd). 0028-3932/$ – see front matter © 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.neuropsychologia.2010.06.009

The auditory MMN occurs when sounds deviate from what the brain expects based on regularities in recent sound information (Naatanen & Alho, 1997). The MMN is a particularly useful tool to study the prediction coding process because it occurs automatically (even during sleep) and is generally recorded whilst participants direct their attention towards another task (Naatanen, Paavilainen, Tiitinen, Jiang, & Alho, 1993). The present study is the second in a series designed to explore how well the brain can use sound associations in an unattended sound sequence to change a prediction model in order to minimise prediction error. In simple MMN sequences, a repeating identical standard sound is interrupted occasionally by a deviant sound (different in some physical feature such as pitch or duration). The brain monitors probability information about the sound characteristics and if a regularity surpasses some probabilistic certainty (around 70%) a MMN will be elicited to sounds that do not match the regularity (Picton, Alain, Otten, Ritter, & Achim, 2000). In simple MMN sequences, the prediction model is driven by the probability of repetitive sound features. The prediction model is static (i.e., expect the properties of the repeating standard) and the MMN updates the brain about violations of one or more features. In a previous study we developed

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a MMN recording sequence that included a dominant simple MMN rule (i.e., expect the properties of the repeating standard), but in a separate sequence included linkages between deviants to present a context or cue for when this prediction model should be changed (Todd, Myers, Pirillo, & Drysdale, 2010). In this study we recorded MMN to physically deviant tones in a simple MMN sequence where the occurrence of four rare deviant sounds (6% each) was random (Random sequence). We then recorded the MMN to these same sounds in a linked sequence where longer deviant sounds always followed loud deviant sounds and frequency deviant sounds always followed soft deviant sounds (Linked sequence). We hypothesized that the brain would be able to use learned associations to reduce the MMN to a linked deviant sound (similar to Sussman & Winkler, 2001). We also hypothesized that this ability would be a function of prefrontal cortex (PFC) integrity since the PFC is essential to associative learning (Moghaddam & Homayoun, 2008; Passingham, 1993). To suppress the MMN to duration and frequency deviants in the Linked sequence, the brain has to use an intensity change (a loud or soft sound) as the context cue to override the dominant prediction model and switch the bias in auditory cortex response to expect a sound that is longer or higher pitch, respectively. Our previous study produced two main findings: (1) the MMN to the duration but not the frequency change was significantly suppressed in the Linked sequence; and (2) the proportion of MMN suppression was significantly correlated with performance on the Continuous Performance Task-Identical Pairs version (CPT-IP, Cornblatt et al., 1998). Both findings were interpreted by us to be consistent with PFC involvement in the process of adjusting the prediction model. Firstly, the PFC input to auditory cortex is restricted to association areas (Pandya & Yeterian, 1990; Pasternak & Greenlee, 2005). This may explain why duration MMN (which derives from association areas of auditory cortex) but not frequency MMN was suppressed because the latter has more prominent sources in primary auditory cortex (Molholm, Martinez, Ritter, Javitt, & Foxe, 2005) to which PFC has no direct input. Secondly, performance on the CPT-IP tasks is very sensitive to PFC integrity including the inferior frontal gyrus (IFG, Salgado-Pineda et al., 2003). The IFG is a location repeatedly implicated in studies identifying the frontal lobe contribution to the MMN signal (Doeller et al., 2003; Molholm et al., 2005; Rinne, Degerman, & Alho, 2005; Schall, Johnston, Todd, Ward, & Michie, 2003; Tse & Penney, 2008). The MMN signal begins in the region of auditory cortex that encodes the sound feature/s that violate the prediction model and it is generally accepted that the auditory cortical source reflects the initial error registration or change detection (Naatanen & Winkler, 1999; but see Shalgi & Deouell, 2007). However, there are other contributions to the MMN that vary depending on the nature and size of the sound change. The process reflected by the IFG source is not yet clear but appears to involve an evaluation of the importance of the error for subsequent processing and determining whether a switch in attention or preparation for action is required (Rinne et al., 2005). However, it is noteworthy that the IFG itself could also be more actively involved in learning complex structures in sound (Fadiga et al., 2009) and controlling recollection of contextual details about past events (Badre & Wagner, 2007). In the present study, we expand upon our previous study by investigating whether the MMN generated to a spatially deviant tone could be suppressed in a linked relative to random sequence structure. Like duration MMN, the MMN to a spatially deviant tone derives from association regions of the auditory cortex (Deouell, Parnes, Pickard, & Knight, 2006). If direct PFC connections to auditory cortex are responsible for why duration but not frequency MMN can be suppressed, then the brain should be able to reduce the MMN to a cued spatial deviant. We also investigated whether the relationship between MMN suppression and CPT-IP performance

could be replicated in a second sample and further investigate what might explain the shared variance on these tasks. Two abilities highly relevant to performance on the CPT-IP are working memory capacity and inhibition. In the CPT-IP, four digit numbers are presented visually on screen and the participant has to press a button whenever two identical four digit numbers are presented consecutively. Each trial sets a current relevant stimulus, a subsequent match indicates that a response is required and a mismatch indicates no-response. To identify the targets correctly the participant must retain information about four digits which is considered a higher working memory load than some other CPT tasks (Michie et al., 2000). The CPT-IP also includes a small number of catch trials in which the number is almost identical to the last. To have low false-alarm rates and high target sensitivity, the participant must correctly inhibit a primed response to the catch trials. It is possible that shared variance in the CPT-IP and MMN suppression could reflect a common demand on neural inhibition and/or the higher demands on memory. The suppression of MMN to the linked deviant is likely to involve a context-based inhibition in the response properties of neurons coding the deviant sound features which could perhaps share variance with the context-based inhibition required to withhold a response to catch trials. There is evidence that placing a higher demand on working memory (as in the case of CPT-IP) can improve the sensitivity of the measure to individual differences (Bachman, Kim, & Yee, 2009; Seidman, Thermenos, & Koch, 2007). Developing a prediction model based on the sparsely repeated sound associations (repeated on average every 6 s) in the linked MMN sequence places higher demand on memory than the prediction model for the dominant rule which is repeated, on average, every 0.45 s. Although MMN is considered to depend on sensory or short-term memory capacity (Naatanen & Alho, 1997), working memory and short-term memory abilities are correlated (Jarrold & Towse, 2006, Kane et al., 2004) so it is possible that the shared variance in MMN suppression and CPT-IP is a function of both being sensitive indices of individual differences in underlying memory capacities. To assess these possibilities further we included additional cognitive measures sensitive to memory load and inhibition. 1. Method 1.1. Participants Thirty-two undergraduate Psychology students were offered course credit for participation in two testing sessions (aged between 18 and 27 years, mean = 20 years, SD = 2.6 years). A screening interview was conducted to exclude participants who had a mental illness, a first degree relative diagnosed with schizophrenia, were regular users of recreational drugs, a history of serious head injury and alcohol abuse or dependence. Hearing was tested using a portable audiometer to ensure volunteers hearing acuity was less than 20 dB SPL across 750–4000 Hz. Seven volunteers did not meet the exclusion criteria and five ERP recordings were lost due to technical difficulties. This resulted in 25 sets of neurocognitive data and 23 usable MMN recordings. 1.2. MMN sequences Two sound sequences (Random and Linked) were presented binaurally over headphones at a constant 400 ms stimulus onset asynchrony. The order of sequence presentation was counter-balanced across participants. Each sequence comprised 2200 sounds and was delivered in two blocks of 1100 sounds. The majority (p = 0.76) were standard sounds presented at 1000 Hz frequency, 80 dB SPL intensity and were 50 ms in duration. Four types of deviant sounds were each presented at a probability of 0.06: a 1200 Hz frequency deviant, a 100 ms duration deviant, 90 dB SPL loud intensity deviant and a 90◦ to the right spatial deviant. The spatial deviant was created by inducing an interaural loudness difference cue (Matthews, Todd, Budd, Cooper, & Michie, 2007). The sound was presented at 86 dB SPL in the right ear and 70 dB SPL in the left ear as opposed to the 80 dB SPL in both ears for the standard. Fig. 1 features a diagrammatic representation of the Random and Linked sequences. In the Random sequence, deviants occurred randomly (p = 0.24) with the exception that there were always at least two standard sounds between deviants. In the Linked sequence, all duration deviants occurred immediately after a loud deviant, and all spatial deviants occurred immediately after a frequency deviant. A

J. Todd, J. Robinson / Neuropsychologia 48 (2010) 3009–3018

Fig. 1. Schematic representation of the sequence structure for Random and Linked sequences.

run of 600 repetitions of deviants was presented before each sequence. These were presented in the following configuration: 300 repetitions of the duration deviant followed by 300 repetitions of the frequency deviant at the beginning of the Random sequence and 300 repetitions of loud deviant followed by 300 repetitions of the spatial deviant at the beginning of the Linked sequence. These repeated deviants were used to create an approximation of the ERP of the deviant-as-standard for computation of the MMN, controlling for some of the known exogenous effects on the ERP (Jacobsen & Schroger, 2003).

1.3. EEG recordings Electroencephalographic data were collected from twelve sites on the scalp (F2, F3, F4, FC2, FC3, FC4, CZ, C3, C4, PZ, the right and left mastoids referenced to an electrode on the nose and horizontal and vertical electro-oculograms. The data was collected continuously at a sampling rate of 500 Hz with a band pass filter of 0.1–50 Hz. During the recording, participants watched a movie with subtitles and were asked to ignore the sounds presented over headphones. The recording of MMN is optimised when the attention of the participant is diverted away from the sound sequence (Naatanen et al., 1993).

1.4. Cognitive tasks The four cognitive tasks reported here were administered in a larger battery of cognitive testing and were all computerised.

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1) Continuous Performance Task-Identical Pairs version (CPT-IP, Cornblatt, Risch, Faris, Friedman, & Erlenmeyer-Kimling, 1988) was used to determine whether associations with MMN suppression could be replicated. A sequence of four digit numbers was presented on a computer screen for 50 ms at 1 s intervals and participants were asked to respond with a button press when two identical four-digit numbers appeared consecutively. Two blocks of 120 trials were presented. In each block, 10 were target trials which required a response; 10 were catch trials where 3 digits were identical to target trials and one digit was altered and the remaining stimuli (100) were randomly distributed fillers. Outcome measures included: proportion of correct target detections (hits), false alarms to catch trial, false alarms to other trials, correct rejections and misses with associated reaction times. In addition sensitivity (d ) and bias was computed in accordance with methods used by Cornblatt. 2) N-Back Task—(modelled on methods described in Owen, McMillan, Laird, & Bullmore, 2005) was used to measure working memory capability with increasing load demands. A letter of the alphabet was presented on the screen every 2.5 s for 500 ms. Participants were instructed to respond with a button press when the current letter was the same as a letter presented N stimuli before (where N = 1, 2 or 3 in separate blocks). Five blocks of 60 items were presented: one of 1-Back (12 targets total) and two each of 2-Back (20 targets total) and 3-Back (20 targets total). Outcome measures for each level of N-Back included proportion of hits, false alarms, correct rejections and misses. Sensitivity (Zhits-Zfalse-alarms) was also computed. 3) A Stroop task with a negative-priming component (modelled on Little & Hartley, 2000) was used to measure cognitive inhibition. Words in red, blue, green and yellow were presented against a black background on a computer monitor. On each trial, the participant’s task was to identify the colour of the stimulus using a response pad with four colour labelled buttons. They were instructed to press the correct colour button as quickly and accurately as possible using their left and right index fingers. A practice block consisting of 36 trials was performed prior to recorded trials. The task began with 36 trials presented in a random order comprising 12 congruent trials (the print colour matched the colour word name—red, blue, green, or yellow), 12 incongruent trials (colour did not match colour word name) and 12 neutral trials (a sequence of X’s balanced to match the word lengths of the colour word names) presented in one of the four colours. Following the 36 random trials, 60 incongruent trials were presented. On half of these trials the incongruent colour word name on the current trial became the target colour on the next trial (negative-priming trials). On the other half of trials there was no relation between the previous word and the current trial (no-relation trials) . Negative-priming and no-relation trials were randomly intermixed. The 60 incongruent trials were followed by another 36 trials that randomly mixed congruent, incongruent and neutral trials. Outcome measures were reaction times for correct response on each trial type (congruent, incongruent, negatively primed and neutral). Reaction times were used to compute Total (Incongruent minus Congruent trials), Interference (Incongruent minus Neutral trials), Facilitation (Congruent minus Neutral trials) and Priming (Negatively primed minus Incongruent no-relation trials) effects. 4) Stop-Signal Reaction Time (SSRT) is an estimate of the time taken to stop a primed motor response and was used to measure motor inhibition. The participant was presented with a series of X and O stimuli at the centre of a computer screen (presented in Courier New font at 24 pt for 100 ms). The participant was asked to respond to each trial with a button press to identify whether stimulus was an X (left index finger response) or an O (right index finger response). The participant was additionally asked to inhibit the response to the X or O if a tone (the stop signal) was presented. The tone was a 1000 Hz, 50 ms tone generated by the computer and presented via headphones. The Stop signal was presented randomly on 25% of trials. One practice block and five blocks of 56 trials per block were presented with equal numbers of Xs and Os in each block. Each there were two separate staircases—one for the left hand stop trials and one for the right hand stop trials. Responses were tracked to adjust the stop-signal delay (that is, the time between the appearance of the visual stimulus and the onset of the sound) for each hand so participants were successfully inhibiting 50% of the time. This was achieved by adjusting stop-signal delay depending on whether the participant could successfully inhibit the response on the previous stop-signal trial. The stop-signal delay was initially set at 300 ms and was lengthened by 50 ms when the participant successfully inhibited and shortened by 50 ms with unsuccessful inhibitions. Setting the stop signal in this manner controls for individual variability in primary reaction time and stop-signal reaction time and is therefore considered a more accurate and robust measure of SSRT (Band, van der Molen, & Logan, 2003). Participants were instructed that speed and accuracy on the primary Go task was of equal importance to correct inhibition on the Stop task. They were told that the test design would result in a number of inhibition errors but they were not to slow reaction time to increase correct inhibitions. Additionally, a low proportion of stop stimuli (25%) is recommended by Logan, Schachar, and Tannock (1997) to discourage such a strategy. Participants who produced staircases that did not converge on 50% inhibition (n = 2) were removed from further analysis. SSRT averaged over left and right hand stops was estimated for each participant by subtracting SSD from median Go RT.

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1.5. Procedure The ERP recording and cognitive assessment took place on separate days varying from one day to 27 days apart with an average of 7 days apart. The completion of the computerised cognitive tasks was supervised by author JR to ensure the participant fully understood the requirements of the tasks and completed the tasks according to instructions. As the tasks may have required different levels of concentration and difficulty, the order of the tasks was counterbalanced across participants to control for the effects of fatigue or boredom.

1.6. Data analysis Neuroscan Software was used to identify and eliminate major movement artifacts and remove eye blink artifacts (Semlitsch, Anderer, Schuster, & Presslich, 1986). The continuous file was epoched from 50 ms pre-stimulus to 400 ms poststimulus. Epochs were averaged according to the stimulus type. These averages were then baseline corrected over the pre-stimulus interval and lowpass filtered at 30 Hz, 12 dB/oct. The MMN was derived by subtracting the averaged response to the repeated deviant-as-standard that preceded runs (see above) from the averaged response to each deviant. Four MMNs were computed from each sequence (lowpass filtered at 20 Hz, 12 dB/oct). The MMNs were re-referenced to the mastoids to maximise signal to noise ratio (Kujala, Tervaniemi, & Schroger, 2007). The peak latency and peak amplitude for the eight resultant MMNs were extracted from a search window of 50–250 ms post-stimulus. MMN peak amplitude and latency at Fz (where MMN is maximal, Kujala et al., 2007) were analysed in repeated measures ANOVAs to identify any sequence main effects and interactions with type of deviant with within subjects factors of sequence (Random, Linked) and deviant type (Duration, Frequency, Loud, Spatial). Where epsilon is ≤0.7, the Greenhouse Geisser correction is used. The effect of sequence

structure on MMN amplitude was calculated using the proportion change in MMN amplitude at Fz from the Random to the Linked sequence in accordance with the following equation: (MMN in Random Sequence − MMN in Linked sequence)/MMN in Random sequence. Given that MMN is a negative potential, large positive values in the proportion change index equate to larger decline in MMN amplitude from the Random to the Linked sequence. This proportion change index at Fz was used to examine the relationship between the degree of change in MMN and performance on the cognitive tasks using Spearman Rank correlation coefficients to minimise the effect of outliers and control for non-normal distribution. Tests were one-tailed for analysis of associations with proportion change in duration and spatial MMN due to an a priori hypothesis that larger change would be associated with better cognitive performance and two-tailed elsewhere.

2. Results 2.1. Sequence effects on MMN The MMN generated to each of the four deviant sounds in both the Random and Linked sequence is presented in the top rows of Fig. 2. The third row of graphs demonstrates the standard and deviant ERPs from which the MMNs were derived. The final row overlays the deviant-as-standard with the regular standard in the Random and Linked sequence. The visible differences between regular standard and deviant-as-standard ERPs support the use of the latter in computing the MMNs to remove exogenous effects associated with the physical properties of these sounds.

Fig. 2. The mastoid-referenced (top) and nose-referenced (second row) MMNs elicited to the duration, frequency, loud and spatial deviant sounds in the Random and Linked sequences. The polarity inversion of the MMN at the right mastoid is also plotted in the second row. The nose-referenced ERP to each of the deviant sounds is plotted against the each deviant-as-standard ERP in the third row to show the waveforms from which the MMNs were derived. The nose-referenced ERP to the deviant-as-standard stimuli is plotted against the standards for the Linked and Random sequences in the bottom row.

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Table 1 Descriptive statistics for performance on the continuous performance task-identical pairs version (CPT-IP), the N-back tasks (1, 2 and 3), the Stroop reaction time measures and the stop-signal reaction time task (SSRT). Descriptive statistics N

Minimum

Maximum

CPT-IP

Hits False Alarms Sensitivity (d )

0.60 0.00 2.07

1.00 0.50 7.43

0.91 0.19 4.43

0.11 0.15 1.72

1-BACK

Hits False Alarms Sensitivity (d )

0.83 0.00 −3.19

1.00 0.02 1.15

0.96 0.00 0.00

0.05 0.01 1.60

2-BACK

Hits False Alarms Sensitivity (d )

0.60 0.00 −4.23

0.98 0.05 2.93

0.84 0.01 0.00

0.13 0.01 1.61

3-BACK

Hits False Alarms Sensitivity (d )

0.35 0.00 −3.62

0.98 0.09 2.99

0.64 0.02 0.00

0.17 0.02 1.67

STROOP

Neutral (ms) Congruent (ms) Incongruent (ms) Negative Prime (ms) Total (ms) Interference (ms) Priming Effect (ms)

473.36 499.14 513.55 543.97 −54.11 −49.88 −83.80

793.71 796.79 918.90 858.70 137.08 125.19 102.32

630.93 643.08 661.49 677.74 15.97 30.56 18.97

81.09 87.08 92.86 89.99 51.44 54.62 42.90

SSRT

Average (ms)

98.69

350.54

232.29

69.31

The repeated measures ANOVA on peak MMN amplitudes revealed a significant Deviant main effect (F(3,20) = 16.59, p < .001) and a significant Deviant by Sequence interaction (ε = .878, F(3,66) = 10.90, p < .001). The effect of sequence on MMN amplitude was significant for duration MMN (F(1,22) = 17.19, p < .001) and loud MMN (F(1,22) = 7.89, p < .010). From Fig. 2 it is clear that the duration MMN was significantly reduced in the Linked versus Random sequence with the opposite true for the loud MMN. Order of sequence presentation was not a significant covariate of the repeated measures ANOVA (p = .77). Independent t-tests were conducted between those who received the Random sequence first and second and revealed no difference in proportion suppression of duration and spatial MMN at Fz (p > .80). Repeated measures ANOVA on peak MMN latency revealed no significant main effects or interactions with sequence.

Mean

Std. deviation

2.3. Correlates of MMN suppression The only cognitive performance variable to correlate significantly with the proportion decline in MMN from the Random to Linked sequence was CPT-IP. Higher hit rates and higher sensitivity were associated with a larger proportion decline in the spatial MMN (rs = .42, p < .05 and rs = .37, p < .05, respectively). The proportion decline in spatial MMN was also highly correlated with the proportion decline in duration MMN (rs = .61, p < .005). Plots of the proportion change in duration and spatial MMN are presented in Fig. 3. From Fig. 3 it is clear that while the majority of participants produced a positive proportion change for duration MMN (smaller MMN in Linked versus Random sequence), the Table 2 Spearman Rank correlation coefficients between performance indices on the CPT-IP and other cognitive tasks. CPT-IP Hits

2.2. Cognitive measures Descriptive statistics for performance on the cognitive tasks are shown in Table 1. Participants performed at a high level on the CPT-IP and 1-Back tasks. Hit rates on the N-Back decreased with increasing task difficulty. False-alarm rates on all the N-Back tasks were very low compared to catch trial false alarms on the CPTIP. A negative-priming effect occurred in the Stroop test with this condition of the task having the slowest reaction time of all the Stroop conditions. Although a facilitation effect is usually present (congruent faster than neutral response), there was no such effect in this data set and so facilitation is not mentioned any further. Table 2 shows the relationships between performance on the CPT-IP and other cognitive tasks. Higher false-alarm rates on to catch trials on the CPT-IP were associated with higher false-alarm rates on the 1-Back task and faster reaction times to congruent and neutral trials of the Stroop. Higher hit rates on the CPT-IP were associated with faster reaction time on the negative-priming trials of the Stroop and a smaller interference effect on the Stroop. Higher sensitivity on the CPT-IP was associated with lower interference (better cognitive inhibition) on the Stroop.

*

False Alarms

Sensitivity (d )

.13 .51* −.23

.02 −.15 .25

1-BACK Hits False Alarms Sensitivity (d )

.21 .24 .23

2-BACK Hits False Alarms Sensitivity (d )

.25 −.32 .18

−.16 −.22 .09

.24 −.06 −.16

3-BACK Hits False Alarms Sensitivity (d )

.14 −.09 −.26

.00 −.31 .15

.08 .12 −.28

Stroop Neutral Congruent Incongruent Negative Priming Total Effect Interference Priming Effect SSRT

−.27 −.35 −.30 −.41* −.32 −.55* .01 .05

−.51* −.43* −.38 −.31 .07 .06 .14 −.05

.13 .05 .03 −.10 −.32 −.41* −.02 .11

p < .05, ** p < .01, *** p < .001.

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Fig. 3. The proportion change in duration MMN (A) and spatial MMN (B) from the Random to the Linked sequence for each participant in order of magnitude. The zero point marks no change with positive values indicating larger MMNs in the Random sequence. Table 3 Comparison of suppression and no-suppression groups indicating variables on which they differed significantly using between-groups t-tests. Means and standard deviations (SD) are reported for duration (Dur) and spatial (Spa) MMN amplitude in Linked and Random sequences, the proportion and actual change in MMN amplitude between the Linked and Random sequences and the performance on the Continuous Performance Task – Identical Pairs version (CPT-IP). Random MMN (␮V)

Linked MMN (␮V)

Proportion Change

Actual Change (␮V)

CPT-IP

Dur

Spa

Dur

Spa

Dur

Spa

Dur

Hits

False Alarms

d

No Suppression Mean SD

−3.89 2.61

−2.79 1.53

−3.63 2.70

−3.96 1.47

0.43 0.88

−0.67 0.82

−0.25 1.64

1.17 1.07

0.91 0.12

0.25 0.12

3.76 1.04

Suppression Mean SD

−4.69 2.07

−2.86 1.50

−2.45 2.10

−1.54 1.21

0.53 0.48

0.47 0.34

−2.24 1.81

−1.32 1.05

0.95 0.10

0.14 0.13

5.07 1.68

t-test t21 value Significance

−0.57 0.57

−0.03 0.97

1.03 0.31

1.76 0.09

3.66 0.004

0.98 0.34

2.1 0.05

2.44 0.016

4.00 0.001

2.31 0.03

Spa

5.03 0.001

Fig. 4. The mastoid re-referenced duration and spatial MMN produced at Fz by the suppression and no-suppression subgroups in the Random and Linked sequences.

results for spatial MMN are bimodal. To explore this further, the participants were divided into a “Suppression” (n = 13) and “No-Suppression” (n = 10) group based on proportion change in spatial MMN and cognitive and MMN variables were explored to determine how these groups differed. Variables differing significantly between these subgroups are presented in Table 3 and the MMN produced in both sequences by these subgroups is presented in Fig. 4. The only cognitive measures on which the subgroups differed significantly were false-alarm rates and sensitivity on the CPT-IP (Table 3). The subgroups did not differ in the MMN produced to duration and spatial deviants in the Random sequence, only in the effect of sequence on these MMNs. A repeated measures ANOVA on MMN amplitude revealed a significant group by sequence interaction for both duration (F(1,21) = 5.34, p < .031) and spatial MMN

(F(1,21) = 25.32, p < .001). Paired t-test within the “Suppression” group indicated that they produced a significantly smaller MMN to both the duration (t12 = 4.28, p < .001) and spatial (t12 = 4.07, p < .002) deviants in the Linked versus Random sequence. In the “No-Suppression” group however, paired t-test revealed a nonsignificant change in duration MMN and a significantly larger spatial MMN in the Linked versus Random sequence (t9 = 3.10, p < .013). The subgroup differences are clearly apparent in Table 3 and Fig. 4. 3. Discussion In this study we compared the MMN elicited to a randomly occurring duration and spatial deviant sound with that produced to the same deviant sounds when their occurrence could be antic-

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ipated on the basis of conditional linkage with the identity of the prior sound. The results replicate a previous finding (Todd et al., 2010) demonstrating that the MMN to a duration deviant sound is significantly smaller (see Fig. 2) when its occurrence in a Linked sequence is cued by the presence of a prior loud (or soft) deviant sound than when its occurrence is random (i.e., in the Random sequence). Fig. 3A shows that the majority of participants in this study exhibited a positive proportion change in duration MMN (smaller MMN in the Linked relative to Random sequence) with the mean reduction in amplitude of approximately 40%. The results partially support our hypothesis that participants would also be able to suppress the MMN produced to a linked/cued spatial deviant tone. In our previous study, the MMN produced to a frequency deviant sound was not significantly smaller in the Linked versus Random sequences. We proposed that this may be due to the location at which the MMN is generated. We hypothesized that the PFC may influence the predictive model used by the auditory cortex to minimise prediction error and that the cues in the Linked sequence would prompt a change in the prediction model to anticipate the properties of the subsequent deviant (see Garrido et al., 2008). We argued that the suppression of duration but not frequency MMN could be explained by direct projections from PFC to auditory cortex being restricted to association areas (Pandya & Yeterian, 1990; Pasternak & Greenlee, 2005). In the present study, the group as a whole showed no significant sequence effect on the spatial MMN (see Fig. 2) which appears to contradict our hypothesis. However the positive correlation between the proportion change in duration and spatial MMN indicates that those exhibiting a reduction in the Linked duration MMN were also more inclined to exhibit a positive proportion change in the spatial MMN. The histogram in Fig. 3B indicates a bimodal distribution of the effect of sequence on spatial MMN with approximately half the group exhibiting significant suppression of the spatial MMN in the Linked sequence and the other half actually showing augmentation of the spatial MMN. The results could therefore be coarsely summarized as demonstrating that whilst most participants could use the occurrence of the loud deviant to reduce the MMN response to the duration deviant, only some participants could use the occurrence of the frequency deviant sound to reduce their response to the spatial deviant. Results in the present study did not replicate our previous finding (Todd et al., 2010) showing a significant (and high) correlation between the proportion change in duration MMN catch trial falsealarm rates and sensitivity indices on the CPT-IP task. This result is puzzling but we believe this may be due to a difference in the range of proportion change in duration MMN. In our previous study the proportion change in duration MMN varied from −1.2 to 0.9 with a mean of 15% decline in amplitude while in the present study the range was −.2 to 1.7 with a much larger average of 40% decline in amplitude. Though the range was about 2 in both studies, it represented a considerably stronger decline in the present study with the vast majority of participants showing suppression. The stronger duration MMN suppression in this study may have obscured the relationship with CPT-IP previously seen by creating a ceiling effect in MMN suppression. This argument is supported by the fact that false-alarm rates and sensitivity on the CPT-IP were the only cognitive indices to differentiate the “suppressors” from “non-suppressors” for the spatial MMN. The range of proportion change in spatial MMN −2.8 to 1.0 is more similar to that observed for duration MMN in our first study as it includes a number of participants showing no change or in fact negative proportion change. In summary, the results are consistent with this being a second study to show an association between the ability to suppress MMN to a cued deviant and the ability to perform the CPT-IP. As outlined in the introduction, performance on the CPT-IP is influenced by a number of abilities including working memory

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capacity and inhibition. We used the N-Back task to determine whether the relationship between CPT-IP and proportion change in MMN in linked sequences could be accounted for by working memory load. There was some relationship between performance on the CPT-IP and the 1-back task (see Table 2) which is not surprising since both are essentially 1-back tasks. However, there were no correlations between N-Back performance and proportion change in MMN and the “suppressor” and “non-suppressor” groups for spatial MMN did not differ on these indices. Cognitive and motor inhibition are recognized to represent two independent abilities (Harnishfeger, 1995; Nigg, 2000; Rubia, Smith, & Taylor, 2007) and only cognitive inhibition was related to CPT-IP performance in this study (better inhibition, better CPT-IP performance). However, neither cognitive nor motor inhibition correlated with proportion change in MMN and the “suppressor” and “non-suppressor” groups for spatial MMN did not differ on these indices. Combined, the results of our previous and the present study indicate that performance on the CPT-IP seems particularly related to our index of MMN suppression (to the exclusion of a number of other cognitive measures). This study cannot provide evidence of PFC involvement in MMN suppression but we find the associations between CPT-IP and MMN suppression intriguing with respect to the position that the brain will use whatever means it has to reduce prediction error and that regions of the PFC could be engaged (particularly with higher demands on memory) to optimize predictions via top-down connections to the auditory cortex (Garrido et al., 2009). To guide our behaviour, the PFC uses contextual information to determine the most appropriate action, particularly if that appropriate action is a non-dominant or less frequent one (Miller & Cohen, 2001). Two important contributions of the PFC are to control memory retrieval (Tomita., Ohbayashi, Nakahara, Hasegawa, & Mlyashlta, 1999) and to provide a top-down enhancement of memory capacity (Edin et al., 2009). In the CPT-IP, the participant must actively compare memory contents with a current number to determine whether to make a motor response (less frequent action) or not (more frequent action). The IFG region of the PFC is likely to be involved in CPT-IP performance in a number of ways. The right IFG is crucial to the ability to inhibit a motor response (Aron & Poldrack, 2005) which is particularly relevant to inhibiting a response to catch trials. However, the same ability is also critical to SSRT which was not related to MMN suppression. The left IFG on the other hand plays an important role in processing and learning sequences in both language and non-language domains (Friederici, 2000). In a functional imaging study, the left IFG was the primary region showing an in increased activation over trials in learning and representing an ordered letter sequence (Van Opstal, Fias, Peigneux, & Verguts, 2009). Learning and recall of the correct serial order of digits is of course critical to performance on the CPT-IP. In the Linked MMN sequence, we encounter the possibility of two analogous roles for IFG in MMN suppression. Firstly, like recalling the serial order of digits in the CPT-IP, suppressing an MMN requires the brain to learn the sequential relationship between deviants occurring sparsely throughout the Linked sequence. There are 132 repetitions of these associations (ample for learning associations: see Bendixen, Prinz, Horvath, Trujillo-Barreto, & Schroger, 2008), repeated at 6 s intervals on average. It is possible that the IFG plays an active role in learning these sound pairings. Secondly, on most occasions the appropriate command to the auditory cortex (the dominant action) is to anticipate the properties of the standard. However, this command should be adjusted if the current sound matches the identity of a cue (louder for the duration deviant, higher frequency for the spatial deviant) which alerts the brain that the appropriate neural response is to alter its prediction model to a non-dominant rule—to anticipate the linked deviant. The similarity

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in these demands may help to explain the shared variance between MMN suppression and CPT-IP performance. There are a number of caveats to interpreting the results from this type of study design. The first consideration is whether the MMN suppression we see here could be accounted for by deviancerepetition effects. A repeated deviant will elicit a smaller MMN due to the process of model adjustment (Winkler, Karmos, & Naatanen, 1996). According to the model-adjustment hypothesis, the deviance-repetition effect occurs because the MMN elicited to a deviant sound prompts or is in fact evidence of the brain adjusting its prediction model in favour of the characteristics of that deviant—hence a subsequent sound that deviates in the same feature would be perceived as less deviant from this new model and elicit a smaller MMN. However, of critical importance to the present design is a study by Nousak, Deacon, Ritter, and Vaughan (1996) exploring what happens to the MMN to successive deviants if they differ in different features from the forgoing standard. The results of experiment II indicate that “when two or more deviants occur in a row, each of which differ from the standard on a different feature, the MMN elicited by the second deviant is comparable to the amplitude of the MMN elicited to the same deviant when it is not proceeded by a deviant” (p. 314). It is our impression that these findings argue strongly to the independent storage of features in sequences like the ones used here, an assumption which of course contributes to the rationale for approaches like the “optimal recording paradigm” (Naatanen, Pakarinen, Rinne, & Takegata, 2004). Our design may not be optimal to study MMN suppression as although our duration and loud intensity deviants represent changes in different physical dimensions from the standard, they are not entirely perceptually independent. In addition to sounding longer, the duration deviant will sound slightly louder due to the summation of energy during the persistence of the sound (Zwislocki, 1969). An important question is whether the average 40% suppression of the linked duration MMN in our study could be accounted for by a “deviance repetition”-related suppression of the contribution of the perceived loudness increment to the duration MMN. We offer four points weighted against this interpretation of our data. (1) We also see a reduction in duration MMN in analogous linked sequences when it follows a frequency MMN. The frequency MMN has no “loudness” deviance so the reduction in the subsequent duration MMN cannot be explained by repetition in this quality. These data are part of an ongoing study and not available to present here. (2) It is our understanding that the “model adjustment” in this case this means that the MMN elicited to the loud deviant sound prompts or is in fact evidence of the brain adjusting its prediction model in favour of louder sounds—hence, a subsequent louder sound would be perceived as less deviant (and elicit a smaller MMN). In our previous study (Todd et al., 2010) we had a Partial Link condition in which half the time the duration deviant followed a loud deviant (intensity increment) and half the time it followed a soft deviant (intensity decrement). The duration MMN was equally suppressed in the Partial and Full Link sequences (when it always followed a loud deviant). This seems incompatible with the suppression of the duration MMN being due to an effect of perceived loudness increment since the perceived loudness in the duration deviant following a soft deviant should represent a larger change from the adjusted model and conceivable counteract the MMN suppression if loudness cues are contributing substantially to the duration MMN response. (3) In a previous study (Todd & Michie, 2000) we estimated (using method of adjustment) the perceived loudness increment of a 125 ms tone over a 50 ms tone to be, on average, 1.3 dB SPL. When the 125 ms duration deviant tone was experimentally adjusted to each individuals equivalent loudness point (i.e., 50 ms sounds equally loud to 125 ms), there was no significant decline in the size of the duration MMN compared

to when both were presented at 80 dB SPL suggesting no significant impact of perceived loudness on this type of duration MMN. The duration increment in this study (50 ms) is smaller than that in our prior study (75 ms) and the perceived loudness increment is therefore probably slightly less. It seems unlikely that a deviance repetition related suppression of perceived loudness contributions to duration MMN could account for the suppression of duration MMNs in the linked sequence which is complete (MMN abolished) in some cases (see Fig. 3). (4) Deviance repetition cannot explain why about half of our sample (those that show the largest duration MMN suppression) also show suppression of the spatial deviant that follows a frequency deviant. There is no spatial or intensity (the spatial deviant is created by using intensity cues) deviance associated with the frequency deviant so there is no repetition of the deviance in the same feature to account for this decline. Despite these four points, we cannot exclude the possibility that the perceived loudness increment associated with the duration deviant could affect the findings of this study in at least some cases. Our ongoing research in which duration deviants follow other feature deviants will be important to validating our paradigm. The results of Sussman and Winkler (2001) are important to the interpretations of our paper. In this study, the second of two successive (in their case both frequency) deviants will cease to elicit an MMN when the brain learns that it provides no new information. The adjustment is rapid but still requires a period of learned association which is subsequently unlearned if the double deviant is nested in a block with single deviants also. Our paradigm is of course very similar but takes the two extremes (no relation and 100% pairing) in two sequences. In our paradigm, by pairing deviants in different features, the second deviant always provides new information with respect to the dominant rule because there is a change in features away from the standard but with respect to the global sequence structure, the pairings replicate the less frequent associative or conditional rule. The capacity to simultaneously extract and implement global and local rules has been demonstrated elsewhere (Horvath, Czigler, Sussman, & Winkler, 2001). We find it particularly interesting that our “Suppressor” and “Non-suppressor” groups do not differ significantly in the size of the MMN generated to duration and spatial deviants in the Random sequence which would be influenced by such factors as discrimination ability and sensory memory (Naatanen & Alho, 1997). Instead, they differ only in the effect of sequence structure on these deviants. One might suppose that this is akin to stating that they do not differ in the capacity to establish the dominant rule (used in both sequences) but do differ in the establishment or use of the nondominant conditional rule. A second consideration is whether the pairing of deviants in this paradigm also has implications for other components of the ERP, and more specifically, whether the effects on other components could contribute to the suppression we observe in the duration and in some cases spatial MMN. There are detailed suggestions for how to maximize the likelihood that the measures you are taking reflect the memory-based comparison MMN-proper as opposed to a combination of MMN and exogenous effects on other ERP components such as the N1 (Jacobsen & Schroger, 2003; Jacobsen, Schroger, Horenkamp, & Winkler, 2003). Our repeated-deviant ERPs in Fig. 2 show the influence of some of these exogenous effects and computing the MMNs using these as the standard offers a partial control. The repetitious deviant-as-standard sequences we have used are only partial controls and could leave additional effects of probability unaccounted for (see Jacobsen et al., 2003). However, the action of comparing the same MMN in the Random and Linked sequence may also go some way to controlling for these effects (and of course the act of subtracting one from the other in computing the proportion decline). What we cannot control for here is any differential sequence effects—more specifically, the possi-

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bility that the physical properties of the cue deviant may have effects on non-MMN components of the response to the subsequent linked deviant. Expressed more concretely, it is possible that nonMMN components of the ERP to the duration deviant tone could be affected by the change in intensity of the prior sound. We suspect that the impact of such effects would be minor relative to the more dominant MMN effects but ideally, it would be preferable to compare the ERPs to the second of two successive deviants when this deviant can be predicted with the ERPs to the second of two successive deviants when this deviant cannot be predicted. Not being able to do so limits the conclusions that can be drawn from the MMN suppression we see here. The MMNs showing an increase in the Linked compared to Random sequences require explanation also. The group as a whole produced a larger MMN to the loud deviant in the Linked versus Random sequence and the “non-suppressor” subgroup produced a larger MMN to the spatial deviant in the Linked versus Random sequence. These increases in MMN appear counter-intuitive but may be understood with reference to the effects of the global sequence structure on MMN amplitude. In general, the MMN to a deviant event is always largest with the least variance in a sequence and the MMN can be influenced by global as well as local sequence characteristics (Horvath et al., 2001; Winkler, Schroger, & Cowan, 2001). Although the stimulus probabilities (and indeed numbers) remain the same in our two sequences, the pairing of the deviants means that the Linked sequence contains considerably less variance than the Random sequence in its global structure. At a feature level, the average number of repetitions of a standard feature (e.g., 50 ms duration) between deviants is equivalent between sequences at around 16–17 (only 6 violations every 100 tones). However, the actual average number of “standard” repetitions (standard in every feature) between loud and frequency deviants in the Linked sequence is 8.3 (12 deviant pairs in every 100 sounds) relative to 4.2 (24 deviants in every 100 sounds) in the Random sequence due to the pairing of deviants. This global structure difference could have the effect of increasing the size of the MMN to the unanticipated deviants in the Linked sequence. Evidence of this can be found in both this and our previous study. In this study the MMN to the loud cue MMN was larger in the Linked than in the Random sequence and in our previous study, you can also see a slightly larger MMN to loud and soft cue MMNs in the Linked versus Random sequences. It is possible that our “non-suppressors”, who are unable to use the frequency cue MMN to suppress the spatial MMN, show an augmentation of the spatial MMN due to these same global structure effects on MMN amplitude. A similar pattern is also evident in our previous study where the frequency MMN (which was not significantly suppressed) is in fact visibly slightly larger in the Linked sequences. These global sequence structure or essentially probability effects would presumably have the most impact on smaller MMNs as MMN amplitude does plateau once it reaches a certain amplitude (Javitt, Grochowski, Shelley, & Ritter, 1998). This is consistent with the loud but not the frequency MMN showing an increase in the present study and the frequency and soft MMNs being slightly larger in the Linked sequences in our prior study. An attractive alternative interpretation of the increase in cueMMN size is that that an inverse relationship between loudness MMN and duration MMN (and frequency and spatial MMNs) could develop due to the predictive value of a cue increasing and therefore carrying more ‘weight’ in terms of signaling the subsequent deviant. Post hoc correlational analyses do not provide support for this interpretation. In the Random sequence, there is one significant correlation (between loud and spatial MMN only rs = 0.64, p < 0.001). In the Linked sequence, duration MMN does not correlate with any other MMN, the association

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between spatial and loud MMN remains (rs = 0.76, p < 0.001) and the frequency MMN becomes positively correlated with both spatial and loud MMN (rs = 0.64, p < 0.001 and rs = 0.47, p < 0.023, respectively). The proportion or literal change in duration and spatial MMN were not correlated with the cue sound MMNs. Larger proportion change in duration MMN was associated with smaller duration MMN in the Linked sequence (rs = .85, p < .001) and larger proportion change in spatial MMN was associated with both smaller duration and spatial MMN in the Linked sequence (rs = .51, p < .012 and rs = .64, p < .001, respectively). The proportion change therefore seems most clearly related to the sequence effects only. In summary, this is the second study to demonstrate that the MMN generated to a duration deviant sound is suppressed when it is reliably cued by an intensity deviant sound versus when it occurs randomly in a sequence. The results indicate that some individuals also appear able use associations to reduce the MMN to a cued spatially deviant sound. Combined with Todd et al. (2010), the results reveal a relationship between performance on the CPTIP and the ability to suppress the MMN to a cued deviant sound. Of course the sample size of these studies is small and the analysis exploratory so these observations need to be considered preliminary and require additional replications. However, we consider a possible co-reliance of the CPT-IP and MMN suppression on the IFG as a parsimonious explanation for the correlation, whether it be in the process of sequence learning and/or determining the relevance of events for subsequent response. Acknowledgements This research was supported by the School of Psychology, University of Newcastle. We wish to gratefully acknowledge the contributions of Gavin Cooper and Tony Kemp for programming support and Professor Pat Michie for assistance with editing. We thank the many participants for their time and co-operation. References Aron, A. R., & Poldrack, R. A. (2005). The cognitive neuroscience of response inhibition: Relevance for genetic research in attention-deficit/hyperactivity disorder. Biological Psychiatry, 57, 1285–1292. Bachman, P., Kim, J., Yee, C. M., et al. (2009). Efficiency of working memory encoding in twins discordant for schizophrenia. Psychiatry Research, 174, 97–104. Badre, D., & Wagner, A. D. (2007). Left ventrolateral prefrontal cortex and the cognitive control of memory. Neuropsychologia, 45, 2883–2901. Band, G. P. H., van der Molen, M. W., & Logan, G. D. (2003). Horse-race model simulations of the stop-signal procedure. Acta Psychologica, 112, 105–142. Bendixen, A., Prinz, W., Horvath, J., Trujillo-Barreto, N. J., & Schroger, E. (2008). Rapid extraction of auditory feature contingencies. Neuroimage, 41, 1111–1119. Cornblatt, B. A., Risch, N. J., Faris, G., Friedman, D., & Erlenmeyer-Kimling, L. (1988). The Continuous Performance Test, identical pairs version (CPT-IP): I. New findings about sustained attention in normal families. Psychiatry Research, 26, 223–238. Deouell, L. Y., Parnes, A., Pickard, N., & Knight, R. T. (2006). Spatial location is accurately tracked by human auditory sensory memory: Evidence from the mismatch negativity. European Journal of Neuroscience, 24, 1488–1494. Doeller, C. F., Opitz, B., Mecklinger, A., Krick, C., Reith, W., & Schroger, E. (2003). Prefrontal cortex involvement in preattentive auditory deviance detection: Neuroimaging and electrophysiological evidence. Neuroimage, 20, 1270–1282. Edin, F., Klingberg, T., Johansson, P., McNab, F., Tegner, J., & Compte, A. (2009). Mechanism for top-down control of working memory capacity. Proceedings of the National Academy of Science, 106, 6802–6807. Fadiga, L., Craighero, L., & D’Ausilio, A. (2009). Broca’s area in language, action, and music. Annals of the New York Academy of Sciences, 1169, 448–458. Friederici, A. D. (2000). The developmental cognitive neuroscience of language: a new research domain. Brain & Language, 71, 65–68. Friston, K. (2005). A theory of cortical responses. Philosophical Transactions of the Royal Society of London - Series B: Biological Sciences, 360, 815–836. Garrido, M. I., Friston, K. J., Kiebel, S. J., Stephan, K. E., Baldeweg, T., & Kilner, J. M. (2008). The functional anatomy of the MMN: a DCM study of the roving paradigm. Neuroimage, 42, 936–944. Garrido, M. I., Kilner, J. M., Stephan, K. E., & Friston, K. J. (2009). The mismatch negativity: A review of underlying mechanisms. Clinical Neurophysiology, 120, 453–463.

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