Name
Abstract | ||
|---|---|---|
Visual perception involves the grouping of individual elements into coherent patterns, such as object representations, that reduce the descriptive complexity of a visual scene. The computational and physiological bases of this perceptual remain poorly understood. We discuss recent fMRI evidence from our laboratory where we measured activity in a higher object processing area (LOC), and in primary visual cortex (V1) in response to visual elements that were either grouped into objects or randomly arranged. We observed significant activity increases in the LOC and concurrent reductions of activity in V1 when elements formed coherent shapes, suggesting that activity in early visual areas is reduced as a result of grouping processes performed in higher areas. In light of these results we review related empirical findings of context-dependent changes in activity, recent neurophysiology research related to cortical feedback, and computational models that incorporate feedback operations. We suggest that feedback from high-level visual areas reduces activity in lower areas in order to simplify the description of a visual image--consistent with both predictive coding models of perception and probabilistic notions of 'explaining away.' |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1016/j.neunet.2004.03.010 | Neural Networks |
Keywords | Field | DocType |
bayesian,computer model,feedback,context dependent,probabilistic model,visual perception,group process | Cognitive psychology,Artificial intelligence,Probabilistic logic,Artificial neural network,Visual perception,Computer vision,Object-oriented programming,Visual cortex,Neurophysiology,Computational model,Perception,Mathematics,Machine learning | Journal |
Volume | Issue | ISSN |
17 | 5-6 | 0893-6080 |
Citations | PageRank | References |
20 | 1.91 | 6 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Scott O. Murray | 1 | 20 | 2.25 |
| Paul R. Schrater | 2 | 141 | 22.71 |
| Daniel Kersten | 3 | 53 | 11.19 |