Paper Info
Title
Visual Decisions in the Presence of Measurement and Stimulus Correlations
Abstract
Humans and other animals base their decisions on noisy sensory input. Much work has been devoted to understanding the computations that underlie such decisions. The problem has been studied in a variety of tasks and with stimuli of differing complexity. However, how the statistical structure of stimuli, along with perceptual measurement noise, affects perceptual judgments is not well understood. Here we examine how correlations between the components of a stimulus-stimulus correlations-together with correlations in sensory noise, affect decision making. As an example, we consider the task of detecting the presence of a single or multiple targets among distractors. We assume that both the distractors and the observer's measurements of the stimuli are correlated. The computations of an optimal observer in this task are nontrivial yet can be analyzed and understood intuitively. We find that when distractors are strongly correlated, measurement correlations can have a strong impact on performance. When distractor correlations are weak, measurement correlations have little impact unless the number of stimuli is large. Correlations in neural responses to structured stimuli can therefore have a strong impact on perceptual judgments.
Year
DOI
Venue
2015
10.1162/NECO_a_00778
Neural Computation
Field
DocType
Volume
Computer vision,Cognitive psychology,Psychology,Artificial intelligence,Stimulus (physiology),Sensory system,Observer (quantum physics),Perception,Machine learning,Visual perception
Journal
27
Issue
ISSN
Citations 
11
0899-7667
0
PageRank 
References 
Authors
0.34
10
4
Name
Order
Citations
PageRank
Manisha Bhardwaj100.68
Sam Carroll210.69
Wei Ji Ma3106.25
Kresimir Josic412316.63