Solving motion coherence with biologically plausible agents

Keywords

No Thumbnail Available

Issue Date

2017-08-30

Language

en

Document type

Journal Title

Journal ISSN

Volume Title

Publisher

Title

ISSN

Volume

Issue

Startpage

Endpage

DOI

Abstract

Artificial Neural Networks (ANNs) have been increasingly used in attempts to mechanis- tically explain the inner workings of human cognition. Motion coherence is a a well-known paradigm whose computation requires closing the Perception-Action cycle. Here, artificial agents with short-term memory and simulated vision learn how to solve the task through reinforcement learning. The agents are able to solve the task in a way comparable to human participants. The results are consistent with behavioral and psychological measures, and bear electrophysiological similarities. The results suggest that the proposed framework is a robust model choice for solving tasks that require closing the Perception-Action cycle.

Description

Citation

Faculty

Faculteit der Sociale Wetenschappen