T19_Decision Making

Solid lines are best tting functions based on a model

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Unformatted text preview: time. Solid lines are best-fitting functions based on a model for the decision using signal detection theory. The best fits suggest that the internal signals are proportional to motion strength and longer performance increased with time (overviewing than 200 600 time. N = 45,511 trials from 32 experiments in two monkeys. (Reprinted with permission from J. I. Gold and M. N. Shadlen, The influence of behavioral context on the representation of a percepSuggests an “evidence accumulator” tual decision in developing oculomotor commands. J. Neurosci. 500 23:632–651. © 2003 by the Society for Neuroscience.) msec) Two variants of the dot direction task B Variable Viewing Duration experimenter controls viewing time Motion Saccade Response Time monkey controls viewing time Targets RT A Saccade Motion Targets Fixation Fixation e Tim F 88.1 Two versions of the motion task used to study decision making in monkeys. This task was formerly used to study the relationship between the properties of neurons in the visual cortex and the limits of perception (see Parker and Newsome, 1998). The monkey decides whether the net direction of random-dot motion is in one of two directions—here, right or left. The ease and difficulty of the task can be controlled by varying the percentage of dots that are moving coherently in one of the two directions. The remaining dots merely appear and disappear at random locations. The experimenter determines viewing time Tim e motion, to the left for leftward motion, and so on. The monkey is also trained to handle stimuli with different directions, speeds, and locations in the visual field. To study sensory processing, the random-dot stimulus is placed in the receptive field of a directionselective neuron. To study decision making, one of the targets that signals the monkey’s commitment to a particular choice is placed in a neuron’s response field. (A) In the variable-duration version of the task, the viewing time is a random value drawn from an expo...
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This note was uploaded on 09/08/2013 for the course BBB 217 taught by Professor Nicolerust during the Spring '12 term at UPenn.

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