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 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