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An Integrated Model of Decision Making and Visual
Attention
Philip L. Smith
University of Melbourne
Collaborators: Roger Ratcliff, Bradley Wolfgang
Attention and Decision Making
● Psychophysical “front end” provides input to decision mechanisms
● Visual search (saccade-to-target) task is attentional task
● Areas implicated in decision making (LIP, FEF, SC) also implicated in
attentional control (e.g., LIP as a “salience map”)
● Visual signal detection: close coupling of attention and decision mechanisms
Attentional Cuing Effects in Visual Signal Detection
● Posner paradigm, 180 ms cue-target interval
● Orthogonal discrimination (proxy for detection)
● Do attentional cues enhance detectability of luminance targets?
● Historically controversial
Attentional Cuing Effects in Visual Signal Detection
● Depends on:
– Dependent variable:
● RT or accuracy
– How you limit detectability:
● with or without backward
masks
Smith, Ratcliff & Wolfgang (2004)
● Detection sensitivity increased by
cues only with masked stimuli
(mask-dependent cuing)
● RT decreased by cues for both
masked and unmasked stimuli
● Interaction between attention and
decisions mechanisms
● Smith (2000), Smith & Wolfgang
(2004), Smith, Wolfgang & Sinclair
(2004), Smith & Wolfgang (2005),
Gould, Smith & Wolfgang (in prep.)
A Model of Decision Making and Visual Attention
● Link visual encoding, masking, spatial attention, visual short term memory and
decision making
A Model of Decision Making and Visual Attention
● Link visual encoding, masking, spatial attention, visual short term memory and
decision making
Visual Encoding and Masking
● Stimuli encoded by low-pass filters
● Masks limit visual persistence of
stimuli
● Unmasked: slow iconic decay
● Masked: Rapid suppression by mask
(interruption masking)
● Smith & Wolfgang (2004, 2005)
Attention and Visual Short Term Memory
VSTM Shunting Equation
● Trace strength modeled by
shunting equation (Grossberg,
Hodgkin-Huxley)
● Preserve STM activity after
stimulus offset
● Opponent-channel coding
prevents saturation (bounded
between -b and +b)
● Recodes luminances as
contrasts
Attentional Dynamics
I. Gain Model. Affects rate of uptake into VSTM:
II. Orienting Model. Affects time of entry into VSTM:
Attentional Dynamics
I. Gain Model. Affects rate of uptake into VSTM:
II. Orienting Model. Affects time of entry into VSTM:
Decision Model
I. (Wiener) Diffusion Model (Ratcliff, 1978)
● VSTM trace strength determines
(nonstationary) drift
● Orientation determines sign of
drift
● Contrast determines size of drift
● Within-trial decision noise
determines diffusion coefficient
● Between-trial encoding noise
determines drift variability
II. Dual Diffusion (Smith, 2000; Ratcliff & Smith 2004)
● Information for competing responses
accumulated in separate totals
● Parallel Ornstein-Uhlenbeck
diffusion processes (accumulation
with decay)
● Symmetrical stimulus representation
● (equal and opposite drifts)
Attentional Dynamics (Gain Model)
● Gain interacts with masking to determine VSTM trace
strength via shunting equation
Gain Model + Diffusion
● Quantile probability plot: RT
quantiles {.1,.3,.5,.7,.9} vs.
probability
● Quantile averaged data
● Correct and error RT
● Drift amplitude is Naka-Rushton
function of contrast (c):
Gain Model + Diffusion
● 220 data degrees of freedom
● 14 parameters:
– 3 Naka-Rushton drift parameters
– 3 encoding filter parameters
– 2 attentional gains
– 2 drift variability parameters
– 2 decision criteria
– 2 post-decision parameters
Model Summary
Model Parameters G2 df BICDiffusion, Gain 14 175.9 206 301.7Diffusion, Orienting 14 247.6 206 373.4Dual Diffusion, Gain 15 169.9 205 304.7Dual Diffusion, Orienting 15 183.3 205 318.1
Dual diffusion has same parameters as single diffusion plus additional
OU decay parameter
Conclusions
● Need model linking visual encoding, masking, VSTM, attention, decision
making
● Stochastic dynamic framework with sequential sampling decision models
● Predicts shapes of entire RT distributions for correct responses and errors,
choice probabilities
● Possible neural substrate? Behavioral diffusion from Poisson shot noise
● Accumulated information modeled as integrated OU diffusion; closely
approximates Wiener diffusion