Connectionists: Gain modulation by an urgency signal controls the speed-accuracy trade-off in a network model of a cortical decision circuit

[Dominic Standage]

Apologies for cross posting. A new paper on the modulation of decision
circuitry by the encoding of elapsed time is available at

 

http://www.frontiersin.org/computational_neuroscience/10.3389/fncom.2011.000
07/abstract

 

Standage D, You H, Wang D and Dorris MC (2011). Gain modulation by an
urgency signal controls the speed-accuracy trade-off in a network model of a
cortical decision circuit. Front. Comput. Neurosci. 5:7. doi:
10.3389/fncom.2011.00007

 

Abstract:

 

The speed-accuracy trade-off (SAT) is ubiquitous in decision tasks. While
the neural mechanisms underlying decisions are generally well characterized,
the application of decision-theoretic methods to the SAT has been difficult
to reconcile with experimental data suggesting that decision thresholds are
inflexible. Using a network model of a cortical decision circuit, we
demonstrate the SAT in a manner consistent with neural and behavioural data
and with mathematical models that optimize speed and accuracy with respect
to one another. In simulations of a reaction time task, we modulate the gain
of the network with a signal encoding the urgency to respond. As the urgency
signal builds up, the network progresses through a series of processing
stages supporting noise filtering, integration of evidence, amplification of
integrated evidence, and choice selection. Analysis of the network's
dynamics formally characterizes this progression. Slower buildup of urgency
increases accuracy by slowing down the progression. Faster buildup has the
opposite effect. Because the network always progresses through the same
stages, decision-selective firing rates are stereotyped at decision time.

 

Dominic Standage

Postdoctoral Research Fellow

Centre for Neuroscience Studies

Queen's University, Botterell Hall, Room 453

Kingston, Ontario, Canada K7L 3N6

Tel: 613 533-3256 Fax: 613 533-6880

Email: standage at biomed.queensu.ca

 

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