Noise in decision-making: theory meets experiments

May 30, 2013

Mike Shadlen, me and Encarni Marcos

Mike Shadlen, me and Encarni Marcos

I gave a talk this week at a European Science Foundation workshop about noise in decision-making. It took place at Sant Fruitos des Bages in Catalonia, Spain. The workshop was notable in a number of respects. First, for someone like me who thinks a lot about neural variability, it was great to be among so many like-minded folks (I wore my VarCE t-shirt with pride). Second, the organizers of the meeting did an unusually good job bringing together some great women who work on this topic: Hendrikje Nienborg , Laura Busse, Sophie Deneve, Tania Pasternak, Catherine Tallon-Baudry, Satu Palva, Petra Ritter, Georgia Gregoiou, and a postdoc from Marlene Cohen‘s lab. One of the most interesting talks from my point of view was presented by Encarni Marcos from Paul Verschure’s lab at the UPF in Barcelona. She described work from a recent publication of hers in which they calculated a quantity called the Variance of the Conditional Expectation (also known as the VarCE). Mike Shadlen and I (along with some co-authors) defined this quantity in our 2011 paper about variance in parietal cortex.
A figure from Encarni's Neuron paper showing the VarCE growing over time.

A figure from Encarni’s Neuron paper showing the VarCE growing over time.

Encarni used the VarCE in the service of a different goal: she tested whether a signature of a known trial history effect might be evident in the VarCE. To this end, she calculated the VarCE of dorsal premotor neurons recorded during a countermanding task where subjects sometimes have to cancel a planned movement. Her main finding was that trials that were just after a “stop” trial were highly variable: some had a much higher-than-average firing rate, and some a much lower-than-average firing rate. Encarni’s dataset is particularly intriguing because the two conditions she compared had nearly identical firing rate means. By examining VarCE, she was able to uncover a neural mechanism that would have been invisible using traditional analyses.

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