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TEMPORAL DYNAMICS OF DECISION-MAKING DURING MOTION PERCEPTION
IN THE VISUAL CORTEX
(2008) Vision Research, 48, 1345-1373
Praveen K. Pilly Stephen Grossberg
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Decision-Making?
Cognitive decision-making
Perceptual decision-making
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Motivation
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Main Questions
How does the brain make perceptual decisions?
How do we decide the direction of a moving object embedded in clutter?
How does the brain perform a direction discrimination task in a context-appropriate manner?
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Motion Direction Discrimination Experiments
VALUABLE PARADIGM
Train monkeys to
discriminate the direction of a random dot motion stimulus
report the judgment via a choice saccade
Record behavior and area LIP neuronal responses
Shadlen & Newsome, 2001 Roitman & Shadlen, 2002
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Random Dot Motion Stimulus
Interleaving of 3 uncorrelated random dot sequences
Coherence level: the fraction of dots moving non-randomly
60 Hz frame rate
Signal dots move from
frame n to frame n+3,
frame n+3 to frame n+6,
and so on
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3.2% MORE AMBIGUITY
Two-Alternative Forced Choice Task
Right or Left?
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51.2%LESS
AMBIGUITY
Two-Alternative Forced Choice Task
Right or Left?
9Two Experimental Contexts
REACTION TIME (RT) FIXED DURATION (FD)
Unlimited viewing duration before saccade in the
judged direction
Fixed viewing duration before saccade in the
judged direction
Shadlen and Newsome, 2001 Roitman and Shadlen, 2002
Roitman and Shadlen, 2002
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Data from the Experiments
Accuracy of decisions in both FD and RT tasks as a function of
coherence
Reaction time of decisions in the RT task as a function of coherence
for correct and error trials
Area LIP neuronal responses during correct and error trials in both FD
and RT tasks for various coherences
Correlation between the temporal dynamics of LIP responses and
saccadic behavior (accuracy, reaction time of decisions)
Differences between sensory MT/MST and decision LIP responses
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Existing Proposals / Models
‘BAYESIAN INFERENCE’ IN THE BRAIN
Beck et al., 2008; Gold & Shadlen, 2001, 2007; Jazayeri & Movshon, 2006; Ma et al., 2006; Pouget et al., 2003; Rao, 2004
NEURAL MODELS
Ditterich, 2006a, 2006b; Mazurek et al., 2003; Wang, 2002
Abstract; Non-neural; Propose explicit Bayesian decoders in brain areas
Do not clarify important computations that need to occur between the motion stimulus and saccadic response
Have a number of issues that need to addressed
Rev. Thomas Bayes
Treatise on Man(Rene Descartes)
12MOtion DEcision (MODE) Model
MOTION BCS: Grossberg et al., 2001 Berzhanskaya et al., 2007
Contextual gating of response
Choice of saccadic response
Winning direction chosen and fed back to MT
Pool signals over multiple orientations, opposite contrast- polarities, both eyes, multiple depths, and a larger spatial range
FT signals are strengthened, ambiguous signals weakened
Evidence accumulation amplifies feature tracking (FT)
signals
Local directional signals
Random dot motion input
Non-directional signals
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Motion Processing from Retina to Area MST
Geometric aperture problem
BARBERPOLE ILLUSION
Feature tracking signals
Percept
Ambiguous signals
How do sparse feature tracking signals capture so many ambiguous signals to determine the global motion direction?
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Local Directional Signals
Fried et al., 2002, 2005
Null direction inhibition model
Barlow & Levick, 1965
Grossberg et al., 2001
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Short-Range Motion Signals
Local directional processes can be fooled by
low coherence
multiple dots
interleaving of uncorrelated dot sequences
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Do Random Dot Motion Stimuli pose an Aperture Problem?
INFORMATIONAL APERTURE PROBLEM
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MT-MST Circuit: Motion Capture
Inter-directional competition across space in area MST
Directionally-asymmetricfeedback inhibition from area MST to area MT across space
18MT and MST Responses during Stimulus Viewing
MODEL SIMULATIONS
MT MST
Britten et al., 1993
MTpref
null
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Informational Aperture Problem
Directional short-range filters (V1)
51.2% coherence
Rightwardmotion
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Informational Aperture Problem Resolution
Area MST
51.2% coherence
Rightwardmotion
Effectiveness of the motion capture process is limited by coherence level
and also viewing duration
21LIP Recurrent Competitive Field (RCF)
Grossberg, 1973+
Self-normalizes total activitylike computing real-time probabilities
Recurrent on-center off-surround shunting network
RCFs have also been used to model reach decisions in dorsal premotor cortex
Cisek, 2006
Noise-saturation problem
22Stochastic LIP RCF
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RT Task SimulationsSample Correct Trials
RT Task
24LIP Responses during RT Task Correct Trials
SimulationsRoitman & Shadlen, 2002
More coherence in preferred direction causes:Faster cell activation
More coherence in opposite direction causes:Faster cell inhibition
Coherence stops playing a role in the final stages of LIP firing for preferred choices
25FD Task SimulationsSample Correct Trials
FD Task
The “gain of the LIP response is greater in the RT version of the task” when compared to the FD task
Roitman & Shadlen, 2002
26LIP Responses during FD Task Correct Trials
More coherence in preferred direction causes:Faster cell activationHigher maximal cell activation
More coherence in opposite direction causes:Faster cell inhibitionLower minimal cell activation
SimulationsRoitman & Shadlen, 2002
27Accuracy of Decisions
More coherence in the motion causes more accurate decisions
SimulationsMazurek et al., 2003
Roitman & Shadlen, 2002
RT task accuracy is slightly better than FD task accuracy at lower coherences (< 25%)
50 50
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Effect of Viewing Duration on Accuracy in FD Task
Gold & Shadlen, 2003 Simulations
29LIP Responses in the RT Task during
Correct and Error Trials
Roitman & Shadlen, 2002 Simulations
LIP encodes the perceptual decision regardless of the direction and strength of the dots, unlike sensory MT/MST neurons
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LIP Response Dynamics correlate with Reaction Time
Roitman & Shadlen, 2002 Simulations
6.4%
31Speed of Decisions (RT Task)
Correct (-) and Error (- -) Trials
More coherence in the motion causes faster reaction time
RTs on error trials are greater than those on correct trials
SimulationsRoitman & Shadlen, 2002
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Slower Error Trial RTs?
At low coherences, the LIP cell dynamics are controlled more by cellular noise processes
As time passes, the likelihood of a wrong LIP cell being chosen increases
Slower RT indirectly explains slower rate of change in LIP responses on error trials
Brownian motion process
33Is Motion Direction Discrimination an example of
Bayesian Decision-Making?
“ logarithm of the likelihood ratio (logLR) provides a natural currency for trading off sensory information, prior probability and expected value to form a perceptual decision ”
Gold & Shadlen, 2001
S1: direction d
S2: opposite direction D
I: spatio-temporal input
logLR is proposed to be equivalent to opponent motion read-out
How does this explain decision-making properties in response to a variety of perceptual stimuli and task conditions?
)()/(
)()/(
22
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SpSIp
SpSIpLR
)()/(ln)()/(lnln 2211 SpSIpSpSIpLR
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Bayesian Inference is a Popular Hypothesis
This approach does provides an intuitive framework
Does it disclose brain mechanisms underlying perception and decision-making?
Probabilistic nature of decision-making in response to uncertainty
Neuronal variabilityBayesian inference in the brain?
Gold & Shadlen, 2001, 2007 Knill & Pouget, 2004Pouget et al., 2003; etc.
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Brain without Bayes
“… We question the popular wisdom that the brain operates as an information-processing device that performs probabilistic inference …”
Shadlen et al., 2008
“… a categorical decision is readout by a Bayesian decoder ... Our work suggests that explicit representation of probability densities by neurons might not be necessary …”
Furman & Wang, 2008
Grossberg & Pilly, 2008
“… This generality is part of its [Bayes’ rule] broad appeal, but is also its weakness in not proving enough constraints to discover models of any particular science …”
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Wang, 2002; Wong & Wang, 2006; Mazurek et al., 2003; Ditterich, 2006a, 2006b
Comparison to other Neural Models
Our model goes beyond alternative models:
Uses the real-time perceptual stimuli used in the experiments
Does not make many of the specialized assumptions of previous models
Clarifies the different roles of sensory MT/MST and decision LIP cells
Simulates the effect of viewing duration on the psychometric function
Incorporates the difference in LIP responsiveness to the two task conditions
Considers the visual contribution to LIP response due to choice target
Simulates the entire time course of LIP responses during both tasks on both correct and error trials
Highlights the important role of BG in contextually gating the saccadic response
…
37MODE Model Predictions
Gradual resolution of the informational aperture problem in area MT Pack & Born, 2001
Explanation for the lack of coherence-independent initial transient pause in LIP activity in the FD task, unlike the RT task
Lower LIP activity, before motion onset, in multiple-choice tasks
Volitional top-down mechanism to make ‘forced choices’
Churchland et al., 2007
Stimulus manipulations such as:higher dot densitymore interleaved sequencesbriefer signal dots
should:decrease accuracy increase reaction times have influences on MT, MST, and LIP responses similar to those that
occur due to lowering motion coherence