Visual perception as retrospective Bayesian decoding from high- to low-level features
Abstract
The nature of perceptual decoding remains an open, fundamental question. Many studies assume that decoding follows the same low- to high-level hierarchy of encoding, yet this assumption was never rigorously tested. We performed such a test, which refutes the assumption to the extent that absolute and relative/ordinal orientations are features of different levels. Additionally, the backward aftereffect we discovered cannot be explained by the efficient-coding theories of adaptation. Finally, we proposed a new theory that explains our data as retrospective Bayesian decoding from high to low levels in working memory. This decoding hierarchy is justified by considering memory stability/distortion of high/low-level features. Thus, our work rejects the currently dominant decoding scheme and offers a framework that integrates perceptual decoding and working memory.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Oct 09, 2017
- Source ID
- 10.1073/pnas.1706906114
Entities
People
- Christopher J. Cueva
- Misha Tsodyks
- Ning Qian
- Stephanie Ding
Organizations
- Air Force Office of Scientific Research
- Columbia College
- Columbia University
- Weizmann Institute of Science