### Abstract

Language | English |
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Number of pages | 123 |

DOIs | |

Publication status | Published - Mar 2010 |

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*The physics of decision making: stochastic differential equations as models for neural dynamics and evidence accumulation in cortical circuits*. https://doi.org/10.1142/9789814304634_0006

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*The physics of decision making: stochastic differential equations as models for neural dynamics and evidence accumulation in cortical circuits*. https://doi.org/10.1142/9789814304634_0006

**The physics of decision making: stochastic differential equations as models for neural dynamics and evidence accumulation in cortical circuits.** / Holmes, Philip; Eckhoff, Philip; Wong-Lin, KongFatt; Bogacz, Rafal; Zackenhouse, Miriam; Cohen, Jonathan.

Research output: Book/Report › Book

TY - BOOK

T1 - The physics of decision making: stochastic differential equations as models for neural dynamics and evidence accumulation in cortical circuits

AU - Holmes, Philip

AU - Eckhoff, Philip

AU - Wong-Lin, KongFatt

AU - Bogacz, Rafal

AU - Zackenhouse, Miriam

AU - Cohen, Jonathan

N1 - Plenary lecture at the XVIth International Congress on Mathematical Physics, Prague, Czech Republic, Aug 2-8, 2009

PY - 2010/3

Y1 - 2010/3

N2 - 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.

AB - 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.

U2 - 10.1142/9789814304634_0006

DO - 10.1142/9789814304634_0006

M3 - Book

SN - 978-981-4304-63-4

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

ER -