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Perceptual learning as improved probabilistic inference in early sensory areas.

ABSTRACT: Extensive training on simple tasks such as fine orientation discrimination results in large improvements in performance, a form of learning known as perceptual learning. Previous models have argued that perceptual learning is due to either sharpening and amplification of tuning curves in early visual areas or to improved probabilistic inference in later visual areas (at the decision stage). However, early theories are inconsistent with the conclusions of psychophysical experiments manipulating external noise, whereas late theories cannot explain the changes in neural responses that have been reported in cortical areas V1 and V4. Here we show that we can capture both the neurophysiological and behavioral aspects of perceptual learning by altering only the feedforward connectivity in a recurrent network of spiking neurons so as to improve probabilistic inference in early visual areas. The resulting network shows modest changes in tuning curves, in line with neurophysiological reports, along with a marked reduction in the amplitude of pairwise noise correlations.

SUBMITTER: Bejjanki VR

PROVIDER: S-EPMC3329121 | biostudies-literature | 2011 May

REPOSITORIES: biostudies-literature

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Perceptual learning as improved probabilistic inference in early sensory areas.

Bejjanki Vikranth R VR Beck Jeffrey M JM Lu Zhong-Lin ZL Pouget Alexandre A

Nature neuroscience 20110403 5

Extensive training on simple tasks such as fine orientation discrimination results in large improvements in performance, a form of learning known as perceptual learning. Previous models have argued that perceptual learning is due to either sharpening and amplification of tuning curves in early visual areas or to improved probabilistic inference in later visual areas (at the decision stage). However, early theories are inconsistent with the conclusions of psychophysical experiments manipulating e ...[more]

PMID: 21460833

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Project description:The brain uses its intrinsic dynamics to actively predict observed sensory inputs, especially under perceptual ambiguity. However, it remains unclear how this inference process is neurally implemented in biasing perception of ambiguous inputs towards the predicted percepts. The process of perceptual inference can be well illustrated by the phenomenon of bistable apparent motion in the Ternus display, in which subjective perception spontaneously alternates between element motion (EM) and group motion (GM) percepts depending on whether two consecutively presented frames are grouped over time or not. The frequency of alpha-band oscillations has long been hypothesized to gate the temporal window of perceptual grouping over time. Under this hypothesis, variation in the intrinsic alpha frequency should predict perceptual outcome of the bistable Ternus display. Moreover, we hypothesize that the perception system employs this prior knowledge on intrinsic alpha frequency to resolve perceptual ambiguity, by shifting perceptual inference towards the predicted percepts. Using electroencephalography and intracranial recordings, we showed that both between and within subjects, lower prestimulus alpha frequencies (PAFs) predicted the EM percepts since the two frames fell in the same alpha cycle and got temporally integrated, while higher PAFs predicted the GM percepts since the two frames fell in different alpha cycles. Multivariate decoding analysis between the EM percepts with lower PAFs and the GM percepts with higher PAFs further revealed a representation of the subsequently reported bistable percept in the neural signals shortly before the actual appearance of the second frame. Therefore, perceptual inference, based on variation in intrinsic PAFs, biases poststimulus neural representations by inducing preactivation of the predicted percepts. In addition, enhanced prestimulus blood-oxygen-level-dependent (BOLD) signals and network dynamics in the frontoparietal network, together with reduced prestimulus alpha power, upon perceiving the EM percepts suggest that temporal grouping is an attention-demanding process.

Dataset Information

Perceptual learning as improved probabilistic inference in early sensory areas.

Publications

Perceptual learning as improved probabilistic inference in early sensory areas.

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