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