Ulster University Logo

Ulster Institutional Repository

The physics of decision making: stochastic differential equations as models for neural dynamics and evidence accumulation in cortical circuits

Biomedical Sciences Research Institute Computer Science Research Institute Environmental Sciences Research Institute Nanotechnology & Advanced Materials Research Institute

Holmes, Philip, Eckhoff, Philip, Wong-Lin, KongFatt, Bogacz, Rafal, Zackenhouse, Miriam and Cohen, Jonathan (2010) The physics of decision making: stochastic differential equations as models for neural dynamics and evidence accumulation in cortical circuits. World Scientific. 123 pp ISBN 978-981-4304-63-4 [Book (authored)]

Full text not available from this repository.

URL: http://eproceedings.worldscinet.com/9789814304634/9789814304634_0006.html

DOI: 10.1142/9789814304634_0006

Abstract

We describe how drift-diffusion (DD) processes – systems familiar in physics – can be used to model evidence accumulation and decision-making in two-alternative, forced choice tasks. We sketch the derivation of these stochastic differential equations from biophysically-detailed models of spiking neurons. DD processes are also continuum limits of the sequential probability ratio test and are therefore optimal in the sense that they deliver decisions of specified accuracy in the shortest possible time. This leaves open the critical balance of accuracy and speed. Using the DD model, we derive a speed-accuracy tradeoff that optimizes reward rate for a simple perceptual decision task, compare human performance with this benchmark, and discuss possible reasons for prevalent sub-optimality, focussing on the question of uncertain estimates of key parameters. We present an alternative theory of robust decisions that allows for uncertainty, and show that its predictions provide better fits to experimental data than a more prevalent account that emphasises a commitment to accuracy. The article illustrates how mathematical models can illuminate the neural basis of cognitive processes.

Item Type:Book (authored)
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Intelligent Systems
Research Institutes and Groups:Computer Science Research Institute
Computer Science Research Institute > Intelligent Systems Research Centre
ID Code:21362
Deposited By:Dr Kongfatt Wong-Lin
Deposited On:13 Mar 2012 12:22
Last Modified:13 Mar 2012 12:22

Repository Staff Only: item control page