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Implicit learning of predictable sound sequences modulates human brain responses at different levels of the auditory hierarchy.

Lecaignard F, Bertrand O, Gimenez G, Mattout J, Caclin A - Front Hum Neurosci (2015)

Bottom Line: We observed a decrease of the MMN with predictability and interestingly, a similar effect at earlier latencies, within 70 ms after deviance onset.Following these pre-attentive responses, a reduced P3a was measured in the case of predictable deviants.We conclude that early and late deviance responses reflect prediction errors, triggering belief updating within the auditory hierarchy.

View Article: PubMed Central - PubMed

Affiliation: Lyon Neuroscience Research Center, CRNL, INSERM, U1028 - CNRS, UMR5292, Brain Dynamics and Cognition Team Lyon, France ; University Lyon 1 Lyon, France ; MEG Department, CERMEP Imaging Center Lyon, France.

ABSTRACT
Deviant stimuli, violating regularities in a sensory environment, elicit the mismatch negativity (MMN), largely described in the Event-Related Potential literature. While it is widely accepted that the MMN reflects more than basic change detection, a comprehensive description of mental processes modulating this response is still lacking. Within the framework of predictive coding, deviance processing is part of an inference process where prediction errors (the mismatch between incoming sensations and predictions established through experience) are minimized. In this view, the MMN is a measure of prediction error, which yields specific expectations regarding its modulations by various experimental factors. In particular, it predicts that the MMN should decrease as the occurrence of a deviance becomes more predictable. We conducted a passive oddball EEG study and manipulated the predictability of sound sequences by means of different temporal structures. Importantly, our design allows comparing mismatch responses elicited by predictable and unpredictable violations of a simple repetition rule and therefore departs from previous studies that investigate violations of different time-scale regularities. We observed a decrease of the MMN with predictability and interestingly, a similar effect at earlier latencies, within 70 ms after deviance onset. Following these pre-attentive responses, a reduced P3a was measured in the case of predictable deviants. We conclude that early and late deviance responses reflect prediction errors, triggering belief updating within the auditory hierarchy. Beside, in this passive study, such perceptual inference appears to be modulated by higher-level implicit learning of sequence statistical structures. Our findings argue for a hierarchical model of auditory processing where predictive coding enables implicit extraction of environmental regularities.

No MeSH data available.


Related in: MedlinePlus

Early responses. Traces at electrode Fz in the time interval [-50, 100] ms, with original [-200, 0] ms baseline correction. Fast components, bandwidth 15–45 Hz (top row). Grand-average ERPs elicited by standards just preceding a deviant (solid line) and deviants (dotted line) for condition UF (left column) and PF (middle column). Data were re-referenced to the average of both mastoids to facilitate the identification of Middle Latency Responses (MLR) components. Fast MLR components are indicated for condition UF with corresponding scalp topographies (from standard ERPs, with original nose reference allowing for the visibility of temporal polarity inversion) at the latencies 13, 26, 36, 50, and 68 ms for P0, Na, Pa, Nb, and Pb, respectively. Right column: Grand-average ERPs corresponding to difference responses (bold lines) for condition UF and PF. Slow components, bandwidth 2–15 Hz (bottom row).
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Figure 3: Early responses. Traces at electrode Fz in the time interval [-50, 100] ms, with original [-200, 0] ms baseline correction. Fast components, bandwidth 15–45 Hz (top row). Grand-average ERPs elicited by standards just preceding a deviant (solid line) and deviants (dotted line) for condition UF (left column) and PF (middle column). Data were re-referenced to the average of both mastoids to facilitate the identification of Middle Latency Responses (MLR) components. Fast MLR components are indicated for condition UF with corresponding scalp topographies (from standard ERPs, with original nose reference allowing for the visibility of temporal polarity inversion) at the latencies 13, 26, 36, 50, and 68 ms for P0, Na, Pa, Nb, and Pb, respectively. Right column: Grand-average ERPs corresponding to difference responses (bold lines) for condition UF and PF. Slow components, bandwidth 2–15 Hz (bottom row).

Mentions: At early latencies, larger responses were elicited with deviants in condition UF compared to standards, leading to a positive difference response spanning from about 10 to 90 ms over the frontal and central areas. It was confirmed statistically significant from 11 to 28 ms at six adjacent electrodes located in left fronto-central area (-0.2 and 0.6 μV at Fz at 20 ms for standards and deviants, respectively). In condition UI, the deviant response was very similar to the one in UF, thus leading to very similar difference responses (deviant – standard) in those two conditions. Statistical analysis for UI revealed a significant interval occurring from 16 to 38 ms on left frontal and fronto-central areas. On the contrary, in condition PF, no significant effect was found at this early latency range. Because at this early latency there is an overlap of slow components (such as the P50) and fast Middle Latency Responses (MLR), we ran a complementary analysis with two different filtering (2–15 and 15–45 Hz) to further characterize this deviance effect. As shown on Figure 3, statistical analysis in the bandwidth 2–15 Hz confirmed the significant early deviance effect measured in UF (from 13 to 58 ms) whereas statistical tests in the bandwidth 15–45 Hz did not reveal any significant effect. A similar pattern was observed for condition UI (data not shown). Altogether, these results suggest that early deviance effects measured here in UF and UI pertain to a slow component at the latency of the P50 and do not concern the peaks of the MLR per se.


Implicit learning of predictable sound sequences modulates human brain responses at different levels of the auditory hierarchy.

Lecaignard F, Bertrand O, Gimenez G, Mattout J, Caclin A - Front Hum Neurosci (2015)

Early responses. Traces at electrode Fz in the time interval [-50, 100] ms, with original [-200, 0] ms baseline correction. Fast components, bandwidth 15–45 Hz (top row). Grand-average ERPs elicited by standards just preceding a deviant (solid line) and deviants (dotted line) for condition UF (left column) and PF (middle column). Data were re-referenced to the average of both mastoids to facilitate the identification of Middle Latency Responses (MLR) components. Fast MLR components are indicated for condition UF with corresponding scalp topographies (from standard ERPs, with original nose reference allowing for the visibility of temporal polarity inversion) at the latencies 13, 26, 36, 50, and 68 ms for P0, Na, Pa, Nb, and Pb, respectively. Right column: Grand-average ERPs corresponding to difference responses (bold lines) for condition UF and PF. Slow components, bandwidth 2–15 Hz (bottom row).
© Copyright Policy
Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC4584941&req=5

Figure 3: Early responses. Traces at electrode Fz in the time interval [-50, 100] ms, with original [-200, 0] ms baseline correction. Fast components, bandwidth 15–45 Hz (top row). Grand-average ERPs elicited by standards just preceding a deviant (solid line) and deviants (dotted line) for condition UF (left column) and PF (middle column). Data were re-referenced to the average of both mastoids to facilitate the identification of Middle Latency Responses (MLR) components. Fast MLR components are indicated for condition UF with corresponding scalp topographies (from standard ERPs, with original nose reference allowing for the visibility of temporal polarity inversion) at the latencies 13, 26, 36, 50, and 68 ms for P0, Na, Pa, Nb, and Pb, respectively. Right column: Grand-average ERPs corresponding to difference responses (bold lines) for condition UF and PF. Slow components, bandwidth 2–15 Hz (bottom row).
Mentions: At early latencies, larger responses were elicited with deviants in condition UF compared to standards, leading to a positive difference response spanning from about 10 to 90 ms over the frontal and central areas. It was confirmed statistically significant from 11 to 28 ms at six adjacent electrodes located in left fronto-central area (-0.2 and 0.6 μV at Fz at 20 ms for standards and deviants, respectively). In condition UI, the deviant response was very similar to the one in UF, thus leading to very similar difference responses (deviant – standard) in those two conditions. Statistical analysis for UI revealed a significant interval occurring from 16 to 38 ms on left frontal and fronto-central areas. On the contrary, in condition PF, no significant effect was found at this early latency range. Because at this early latency there is an overlap of slow components (such as the P50) and fast Middle Latency Responses (MLR), we ran a complementary analysis with two different filtering (2–15 and 15–45 Hz) to further characterize this deviance effect. As shown on Figure 3, statistical analysis in the bandwidth 2–15 Hz confirmed the significant early deviance effect measured in UF (from 13 to 58 ms) whereas statistical tests in the bandwidth 15–45 Hz did not reveal any significant effect. A similar pattern was observed for condition UI (data not shown). Altogether, these results suggest that early deviance effects measured here in UF and UI pertain to a slow component at the latency of the P50 and do not concern the peaks of the MLR per se.

Bottom Line: We observed a decrease of the MMN with predictability and interestingly, a similar effect at earlier latencies, within 70 ms after deviance onset.Following these pre-attentive responses, a reduced P3a was measured in the case of predictable deviants.We conclude that early and late deviance responses reflect prediction errors, triggering belief updating within the auditory hierarchy.

View Article: PubMed Central - PubMed

Affiliation: Lyon Neuroscience Research Center, CRNL, INSERM, U1028 - CNRS, UMR5292, Brain Dynamics and Cognition Team Lyon, France ; University Lyon 1 Lyon, France ; MEG Department, CERMEP Imaging Center Lyon, France.

ABSTRACT
Deviant stimuli, violating regularities in a sensory environment, elicit the mismatch negativity (MMN), largely described in the Event-Related Potential literature. While it is widely accepted that the MMN reflects more than basic change detection, a comprehensive description of mental processes modulating this response is still lacking. Within the framework of predictive coding, deviance processing is part of an inference process where prediction errors (the mismatch between incoming sensations and predictions established through experience) are minimized. In this view, the MMN is a measure of prediction error, which yields specific expectations regarding its modulations by various experimental factors. In particular, it predicts that the MMN should decrease as the occurrence of a deviance becomes more predictable. We conducted a passive oddball EEG study and manipulated the predictability of sound sequences by means of different temporal structures. Importantly, our design allows comparing mismatch responses elicited by predictable and unpredictable violations of a simple repetition rule and therefore departs from previous studies that investigate violations of different time-scale regularities. We observed a decrease of the MMN with predictability and interestingly, a similar effect at earlier latencies, within 70 ms after deviance onset. Following these pre-attentive responses, a reduced P3a was measured in the case of predictable deviants. We conclude that early and late deviance responses reflect prediction errors, triggering belief updating within the auditory hierarchy. Beside, in this passive study, such perceptual inference appears to be modulated by higher-level implicit learning of sequence statistical structures. Our findings argue for a hierarchical model of auditory processing where predictive coding enables implicit extraction of environmental regularities.

No MeSH data available.


Related in: MedlinePlus