Connectionists: temporal dynamics of decision-making during motion perception in the visual cortex

[Stephen Grossberg]

The following article is now available at 
<http://www.cns.bu.edu/Profiles/Grossberg>http://www.cns.bu.edu/Profiles/Grossberg 
:

Grossberg, S. and Pilly, P.
Temporal dynamics of decision-making during motion perception in the 
visual cortex.
Vision Research, in press.

ABSTRACT
How does the brain make decisions? Speed and accuracy of perceptual 
decisions covary with certainty in the input, and correlate with the 
rate of evidence accumulation in parietal and frontal cortical 
"decision neurons." A biophysically realistic model of interactions 
within and between Retina/LGN and cortical areas V1, MT, MST, and 
LIP, gated by basal ganglia, simulates dynamic properties of 
decision-making in response to ambiguous visual motion stimuli used 
by Newsome, Shadlen, and colleagues in their neurophysiological 
experiments. The model clarifies how brain circuits that solve the 
aperture problem interact with a recurrent competitive network with 
self-normalizing choice properties to carry out probabilistic 
decisions in real time. Some scientists claim that perception and 
decision-making can be described using Bayesian inference or related 
general statistical ideas, that estimate the optimal interpretation 
of the stimulus given priors and likelihoods. However, such concepts 
do not propose the neocortical mechanisms that enable perception, and 
make decisions. The present model explains behavioral and 
neurophysiological decision-making data without an appeal to Bayesian 
concepts and, unlike other existing models of these data, generates 
perceptual representations and choice dynamics in response to the 
experimental visual stimuli. Quantitative model simulations include 
the time course of LIP neuronal dynamics, as well as behavioral 
accuracy and reaction time properties, during both correct and error 
trials at different levels of input ambiguity in both fixed duration 
and reaction time tasks. Model MT/MST interactions compute the global 
direction of random dot motion stimuli, while model LIP computes the 
stochastic perceptual decision that leads to a saccadic eye movement.

Key words: motion perception; direction discrimination; 
decision-making; visual cortex; aperture problem; noise-saturation 
dilemma; recurrent competitive field; Bayesian inference; MT; MST; LIP