Format

Send to

Choose Destination
J Vis. 2016 Oct 1;16(13):6. doi: 10.1167/16.13.6.

Perceptual suppression of predicted natural images.

Author information

1
Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, US,rachel.denison@nyu.eduwww.racheldenison.com.
2
College of Letters and Science, University of California, Berkeley, Berkeley, CA, USAjacob.sheynin@mail.mcgill.ca.
3
Helen Wills Neuroscience Institute, Vision Science Graduate Group, and School of Optometry, University of California, Berkeley, Berkeley, CA, USAmasilver@berkeley.eduhttp://argentum.ucbso.berkeley.edu.

Abstract

Perception is shaped not only by current sensory inputs but also by expectations generated from past sensory experience. Humans viewing ambiguous stimuli in a stable visual environment are generally more likely to see the perceptual interpretation that matches their expectations, but it is less clear how expectations affect perception when the environment is changing predictably. We used statistical learning to teach observers arbitrary sequences of natural images and employed binocular rivalry to measure perceptual selection as a function of predictive context. In contrast to previous demonstrations of preferential selection of predicted images for conscious awareness, we found that recently acquired sequence predictions biased perceptual selection toward unexpected natural images and image categories. These perceptual biases were not associated with explicit recall of the learned image sequences. Our results show that exposure to arbitrary sequential structure in the environment impacts subsequent visual perceptual selection and awareness. Specifically, for natural image sequences, the visual system prioritizes what is surprising, or statistically informative, over what is expected, or statistically likely.

PMID:
27802512
PMCID:
PMC5098454
DOI:
10.1167/16.13.6
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Silverchair Information Systems Icon for PubMed Central
Loading ...
Support Center