Motion discrimination under uncertainty and ambiguity.
Publication year
2011Source
Journal of Vision, 11, 1, (2011), pp. 20, article 20ISSN
Publication type
Article / Letter to editor
Display more detailsDisplay less details
Organization
Cognitive Neuroscience
Journal title
Journal of Vision
Volume
vol. 11
Issue
iss. 1
Page start
p. 20
Page end
p. 20
Subject
DCN 1: Perception and Action; DCN 2: Functional NeurogenomicsAbstract
Speed and accuracy of visual motion discrimination depend systematically on motion strength. This behavior is traditionally explained by diffusion models that assume accumulation of sensory evidence over time to a decision bound. However, how does the brain decide when sensory evidence is ambiguous, such as in binocular rivalry? Theories on bistable vision propose that such a conflict is resolved through competitive interactions between adapting units encoding the alternative stimulus interpretations. Thus, distinctly different theoretical frameworks have been proposed for deciding under uncertainty and ambiguity; a discrepancy overlooked so far. Here, we studied motion discrimination at stimulus onset under both conditions. In Experiment 1, speed and accuracy were similar when observers viewed noisy, unambiguous motion patterns in which signal dots were either at identical or at different, uncorrelated locations for the two eyes. This result is compatible with a race between two monocular discrimination processes. However, Experiments 2 and 3 showed that reaction times increase under rivalry conditions and that this increase cannot be explained by motion transparency. The data thus reveal competitive rivalry interactions. We discuss a model that can account for the accuracy and latencies observed under both ambiguous and unambiguous conditions, by combining key elements from diffusion and rivalry models.
This item appears in the following Collection(s)
- Academic publications [243984]
- Electronic publications [130695]
- Faculty of Medical Sciences [92811]
- Open Access publications [104973]
Upload full text
Use your RU credentials (u/z-number and password) to log in with SURFconext to upload a file for processing by the repository team.