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Cortical plasticity as a mechanism for storing Bayesian priors in sensory perception.

Köver H, Bao S - PLoS ONE (2010)

Bottom Line: Human perception of ambiguous sensory signals is biased by prior experiences.It is not known how such prior information is encoded, retrieved and combined with sensory information by neurons.For the case of auditory perception, we use a computational model to show that prior information about sound frequency distributions may be stored in the size of primary auditory cortex frequency representations, read-out by elevated baseline activity in all neurons and combined with sensory-evoked activity to generate a perception that conforms to Bayesian integration theory.

View Article: PubMed Central - PubMed

Affiliation: Helen Wills Neuroscience Institute, University of California, Berkeley, California, United States of America.

ABSTRACT
Human perception of ambiguous sensory signals is biased by prior experiences. It is not known how such prior information is encoded, retrieved and combined with sensory information by neurons. Previous authors have suggested dynamic encoding mechanisms for prior information, whereby top-down modulation of firing patterns on a trial-by-trial basis creates short-term representations of priors. Although such a mechanism may well account for perceptual bias arising in the short-term, it does not account for the often irreversible and robust changes in perception that result from long-term, developmental experience. Based on the finding that more frequently experienced stimuli gain greater representations in sensory cortices during development, we reasoned that prior information could be stored in the size of cortical sensory representations. For the case of auditory perception, we use a computational model to show that prior information about sound frequency distributions may be stored in the size of primary auditory cortex frequency representations, read-out by elevated baseline activity in all neurons and combined with sensory-evoked activity to generate a perception that conforms to Bayesian integration theory. Our results suggest an alternative neural mechanism for experience-induced long-term perceptual bias in the context of auditory perception. They make the testable prediction that the extent of such perceptual prior bias is modulated by both the degree of cortical reorganization and the magnitude of spontaneous activity in primary auditory cortex. Given that cortical over-representation of frequently experienced stimuli, as well as perceptual bias towards such stimuli is a common phenomenon across sensory modalities, our model may generalize to sensory perception, rather than being specific to auditory perception.

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Related in: MedlinePlus

Decoded frequency as a function of input frequency.Both the naïve model AI (a and c) and 7-kHz-over-represented AI (b and d) were examined with (c and d) and without (a and b) elevated baseline activity. In addition, standard deviation of the decoded frequencies (red) was used to measure the output variability. When baseline activity was elevated in the 7-kHz-over-represented AI, the decoded frequencies show shifts characteristic of Bayesian prior bias (d). The pink line shows the slope of the input-output curve at the over-represented frequency. The slope is a measure of the prior bias.
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pone-0010497-g003: Decoded frequency as a function of input frequency.Both the naïve model AI (a and c) and 7-kHz-over-represented AI (b and d) were examined with (c and d) and without (a and b) elevated baseline activity. In addition, standard deviation of the decoded frequencies (red) was used to measure the output variability. When baseline activity was elevated in the 7-kHz-over-represented AI, the decoded frequencies show shifts characteristic of Bayesian prior bias (d). The pink line shows the slope of the input-output curve at the over-represented frequency. The slope is a measure of the prior bias.

Mentions: We first examined model auditory perception with normal levels of baseline activity for both the naïve and 7kHz-over-represented model AIs. The maximum likelihood estimate or ‘percept’ converged at the input frequency for both naïve and 7kHz-over-represented model AIs (Fig. 2a, Fig. 3a–b), even for the under-represented frequencies that no neurons were tuned to. This is not surprising because primary auditory cortical neurons are broadly tuned, and responsive to those frequencies. Thus, the maximum-likelihood estimate of sensory input from population responses is insensitive to inhomogeneity of sensory representations, and always converges on the input stimulus.


Cortical plasticity as a mechanism for storing Bayesian priors in sensory perception.

Köver H, Bao S - PLoS ONE (2010)

Decoded frequency as a function of input frequency.Both the naïve model AI (a and c) and 7-kHz-over-represented AI (b and d) were examined with (c and d) and without (a and b) elevated baseline activity. In addition, standard deviation of the decoded frequencies (red) was used to measure the output variability. When baseline activity was elevated in the 7-kHz-over-represented AI, the decoded frequencies show shifts characteristic of Bayesian prior bias (d). The pink line shows the slope of the input-output curve at the over-represented frequency. The slope is a measure of the prior bias.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2864750&req=5

pone-0010497-g003: Decoded frequency as a function of input frequency.Both the naïve model AI (a and c) and 7-kHz-over-represented AI (b and d) were examined with (c and d) and without (a and b) elevated baseline activity. In addition, standard deviation of the decoded frequencies (red) was used to measure the output variability. When baseline activity was elevated in the 7-kHz-over-represented AI, the decoded frequencies show shifts characteristic of Bayesian prior bias (d). The pink line shows the slope of the input-output curve at the over-represented frequency. The slope is a measure of the prior bias.
Mentions: We first examined model auditory perception with normal levels of baseline activity for both the naïve and 7kHz-over-represented model AIs. The maximum likelihood estimate or ‘percept’ converged at the input frequency for both naïve and 7kHz-over-represented model AIs (Fig. 2a, Fig. 3a–b), even for the under-represented frequencies that no neurons were tuned to. This is not surprising because primary auditory cortical neurons are broadly tuned, and responsive to those frequencies. Thus, the maximum-likelihood estimate of sensory input from population responses is insensitive to inhomogeneity of sensory representations, and always converges on the input stimulus.

Bottom Line: Human perception of ambiguous sensory signals is biased by prior experiences.It is not known how such prior information is encoded, retrieved and combined with sensory information by neurons.For the case of auditory perception, we use a computational model to show that prior information about sound frequency distributions may be stored in the size of primary auditory cortex frequency representations, read-out by elevated baseline activity in all neurons and combined with sensory-evoked activity to generate a perception that conforms to Bayesian integration theory.

View Article: PubMed Central - PubMed

Affiliation: Helen Wills Neuroscience Institute, University of California, Berkeley, California, United States of America.

ABSTRACT
Human perception of ambiguous sensory signals is biased by prior experiences. It is not known how such prior information is encoded, retrieved and combined with sensory information by neurons. Previous authors have suggested dynamic encoding mechanisms for prior information, whereby top-down modulation of firing patterns on a trial-by-trial basis creates short-term representations of priors. Although such a mechanism may well account for perceptual bias arising in the short-term, it does not account for the often irreversible and robust changes in perception that result from long-term, developmental experience. Based on the finding that more frequently experienced stimuli gain greater representations in sensory cortices during development, we reasoned that prior information could be stored in the size of cortical sensory representations. For the case of auditory perception, we use a computational model to show that prior information about sound frequency distributions may be stored in the size of primary auditory cortex frequency representations, read-out by elevated baseline activity in all neurons and combined with sensory-evoked activity to generate a perception that conforms to Bayesian integration theory. Our results suggest an alternative neural mechanism for experience-induced long-term perceptual bias in the context of auditory perception. They make the testable prediction that the extent of such perceptual prior bias is modulated by both the degree of cortical reorganization and the magnitude of spontaneous activity in primary auditory cortex. Given that cortical over-representation of frequently experienced stimuli, as well as perceptual bias towards such stimuli is a common phenomenon across sensory modalities, our model may generalize to sensory perception, rather than being specific to auditory perception.

Show MeSH
Related in: MedlinePlus