An Integrated Model of Decision Making and Visual Attention

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

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:

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 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

An Integrated Model of Decision Making and Visual Attention