Accession Number:

AD1105497

Title:

Stochastic Resonance and Perceptual Decision Making Inattention

Descriptive Note:

Technical Report,01 Apr 2015,31 Oct 2019

Corporate Author:

University of California Los Angeles Los Angeles United States

Personal Author(s):

• Lau,Hakwan
• Odegaard,Brian
• Peters,Megan
• Knotts,J. D.

Report Date:

2020-01-14

Pagination or Media Count:

32.0

Abstract:

In this study, we empirically investigated to what extent the phenomenon of stochastic resonance under lack of attention generalizes to naturalistic stimuli and different contexts, and may work under different mechanismsSpecific Objectives 1 and 2. We also tried to understand the neurobiological mechanism behind this intriguing phenomenon, by means of computational modeling Specific Objectives 3. We found that people tend to confirm what they expect whenever they dont attend and that participants subjective sense of the visual surround is inflated. We also developed a simple leaky competing accumulator neural network model incorporating a known property of sensory neurons tuned normalization. We demonstrate that this biologically plausible model can account for several counterintuitive findings reported in the literature, where confidence and decision accuracy were shown to dissociate -- and that the differential contribution a neuron makes to decisions versus confidence judgments based on its normalization tuning is vital to capturing some of these effects.

Descriptors:

• psychology
• artificial neural networks
• reliability
• signal detection
• accumulators
• false alarms
• bayesian networks
• detection
• brain
• neurons
• neurosciences
• computational modeling
• visual perception
• cognitive science
• decision making

Subject Categories:

• Psychology

Distribution Statement:

APPROVED FOR PUBLIC RELEASE