Strategic attention and decision control support prospective memory in a complex dual-task environment

Abstract

Human performance in complex multiple-task environments depends critically on the interplay between cognitive control and cognitive capacity. In this paper we propose a tractable computational model of how cognitive control and capacity influence the speed and accuracy of decisions made in the event-based prospective memory (PM) paradigm, and in doing so test a new quantitative formulation that measures two distinct components of cognitive capacity (gain and focus) that apply generally to choices among two or more options. Consistent with prior work, individuals used proactive control (increased ongoing task thresholds under PM load) and reactive control (inhibited ongoing task accumulation rates to PM items) to support PM performance. Individuals used cognitive gain to increase the amount of resources allocated to the ongoing task under time pressure and PM load. However, when demands exceeded the capacity limit, resources were reallocated (shared) between ongoing task and PM processes. Extending previous work, individuals used cognitive focus to control the quality of processing for the ongoing and PM tasks based on the particular demand and payoff structure of the environment (e.g., higher focus for higher priority tasks; lower focus under high time pressure and with PM load). Our model provides the first detailed quantitative understanding of cognitive gain and focus as they apply to evidence accumulation models, which-along with cognitive control mechanisms-support decision-making in complex multiple-task environments.

A new model of decision processing in instrumental learning tasks

“…Increases in drift rates in a race model could indicate an urgency signal, implemented by drift gain modulation, with qualitatively similar effects to collapsing thresholds over the course of a decision (Cisek et al, 2009; Hawkins et al, 2015; Miletić, 2016; Miletić and Van Maanen, 2019; Murphy et al, 2016; Thura and Cisek, 2016; Trueblood et al, 2020; van Maanen et al, 2019). In cognitively demanding tasks, it has been shown that two distinct components of evidence accumulation (quality and quantity of evidence) are affected by SAT manipulations, with quantity of evidence being analogous to an urgency signal (Boag et al, 2019b, 2019a). Recent evidence suggests that different SAT manipulations can affect different psychological processes: cue-based manipulations that instruct participants to be fast or accurate, lead to overall threshold adjustments, whereas deadline-based manipulations lead to a collapse of thresholds (Katsimpokis et al, 2020).…”

Spatial Attention and Saccade Preparation Both Independently Contribute to the Discrimination of Oblique Orientations

“…Specifically, by taking the difference in drift rate between an accumulator for the correct response (i.e., the rate at which evidence accumulates for the response that matches the stimulus or the “true” accumulator) and the incorrect response (i.e., the rate at which evidence accumulates for the response that mismatches the stimulus or the “false” accumulator), we can quantify the quality of information in the system. That is, larger differences in drift rate between the true and false accumulator represent a higher quality of information accumulating from the stimulus ( Boag et al, 2019 ). The drift rate parameter allowed us to quantify the relative contributions of saccade preparation and covert spatial attention to performance and separate this influence from other factors in the dual-task design that may influence performance ( Lewandowsky & Oberauer, 2018 ).…”

Correlated racing evidence accumulator models

“…Positive correlations can result if trial-to-trial fluctuations in attention or arousal scale evidence-accumulation rates by the same amount in all accumulators. In the cognitive literature the idea that attention has a variable "sensitivity" or "gain" is part of both older theories, such as the spotlight theory of visual attention (e.g., Posner & Boies, 1971) and more recent work, such the Prospective Memory Decision Control theory of dual-task costs in prospective-memory paradigms (Boag et al, 2019). The normalization framework that is prominent within the neurosciences (Carandini & Heeger, 2011) also features the idea of a broadly-tuned gain or signalboost mechanism.…”