GATSBY COMPUTATIONAL NEUROSCIENCE UNIT
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Roger Ratcliff

Department of Psychology, The Ohio State University, USA

 

Wednesday 29 April 2009

16.00

Seminar Room B10 (Basement)

Alexandra House, 17 Queen Square, London, WC1N 3AR

 

Insights from Physiological Measures into Modeling Simple Decision Processes

The diffusion model for simple decision making can decompose a group of response times (RTs) and their accuracy into components of processing that reflect the quality of evidence used in the decision, the amount of evidence required to make a decision, and the duration of stimulus processing and response production, along with the variability in these components across trials. Research using single and multi-unit recordings in primates and neuroimaging studies in humans have recently begun a quest to identify where and how the relevant neural computations are carried out.  In the first study we show how a measure derived from single-trial analysis (Sajda and Philiastides) of the EEG can index the quality of evidence used in the decision process even within a class of nominally identical stimuli.  The second study examines simultaneous recordings in neurons corresponding to two response targets from opposite sides of the superior colliculus in rhesus monkeys in a simple two-choice brightness discrimination task.  We sorted trials based on whether there was a high versus low firing rate in neurons corresponding to the decision made.  We then examined firing rates in neurons corresponding to the other target.  If there were inhibition between the two pools of neurons, when the firing rate was high in the target neuron prior to the decision, the firing rate should be low in neurons corresponding to the competing decision.  Two models with racing diffusion processes were fit to the behavioral data and the same analysis was carried out on simulated paths in the diffusion processes which have found to represent firing rate. Results showed no evidence for inhibition across the colliculi in either the models or the neural firing rate data.