Prospective memory in the red zone: Cognitive control and capacity sharing in a complex, multi-stimulus task.

Strickland, Luke; Elliott, David; Wilson, Michael David; Loft, Shayne; Neal, Alan C.; Heathcote, Andrew

doi:10.1037/xap0000224

Cited by 19 publications

(34 citation statements)

References 69 publications

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“…Although there was a clear role for capacity sharing and capacity mobilization mechanisms, we also replicated previous work using an ongoing conflict detection task and other demanding ongoing tasks (Boag et al, 2019;Strickland et al, 2019), and less demanding ongoing tasks such as lexical decision (Ball & Aschenbrenner, 2017;Heathcote et al, 2015;Horn & Bayen, 2015;Strickland et al, 2017Strickland et al, , 2018, in finding that proactive and reactive control processes explained the majority of variation in performance (however, as will be discussed, cognitive gain and focus play critical roles in explaining certain effects). Hence, we first summarize our findings with respect to cognitive control effects in order to provide an appropriate context for a more detailed consideration of capacity-related effects.…”

Section: Pm Decisionsupporting

confidence: 83%

“…However, as we discuss next, recent work has demonstrated that PM-induced costs to ongoing task information processing can occur in paradigms where the ongoing task is sufficiently demanding so as to fully occupy cognitive resources (Boag et al, 2019;Strickland, Elliott, Wilson, Loft, Neal, & Heathcote, 2019). These findings motivate the current article, in which we develop a formal framework for understanding cognitive capacity in PMDC.…”

Section: Capacity For Pm In Basic Paradigmsmentioning

confidence: 74%

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

et al. 2019

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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.

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Section: Pm Decisionsupporting

confidence: 83%

Section: Capacity For Pm In Basic Paradigmsmentioning

confidence: 74%

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

et al. 2019

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“…Similarly, the “non-word” accumulation rate corresponds to “match” accumulation on non-word trials and “mismatch” accumulation on word trials. Evidence accumulation models generally indicate that PM demands do not cost ongoing-task capacity in standard paradigms (e.g., Heathcote et al, 2015; Horn & Bayen, 2015; Strickland et al, 2017), but that capacity sharing can occur in more demanding paradigms, such as in simulations of air traffic control (Boag, Strickland, Heathcote, et al, 2019; Boag, Strickland, Loft, & Heathcote, 2019) and maritime surveillance (Strickland et al, 2019).…”

Section: Pmdcmentioning

confidence: 99%

Investigating the effects of ongoing-task bias on prospective memory

Strickland

Loft

Heathcote

2020

Quarterly Journal of Experimental Psychology

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Event-based prospective memory (PM) refers to the cognitive processes required to perform a planned action upon encountering a future event. Event-based PM studies engage participants in an ongoing task (e.g., lexical decision-making) with an instruction to make an alternative PM response to certain items (e.g., items containing “tor”). The Prospective Memory Decision Control (PMDC) model, which provides a quantitative process account of ongoing-task and PM decisions, proposes that PM and ongoing-task processes compete in a race to threshold. We use PMDC to test whether, as proposed by the Delay Theory of PM costs, PM can be improved by biasing decision-making against a specific ongoing-task choice, so that the PM process is more likely to win the race. We manipulated bias in a lexical decision task with an accompanying PM intention. In one condition, a bias was induced against deciding items were words, and in another, a bias was induced against deciding items were non-words. The bias manipulation had little effect on PM accuracy but did affect the types of ongoing-task responses made on missed PM trials. PMDC fit the observed data well and verified that the bias manipulation had the intended effect on ongoing-task processes. Furthermore, although simulations from PMDC could produce an improvement in PM accuracy due to ongoing-task bias, this required implausible parameter values. These results illustrate the importance of understanding event-based PM in terms of a comprehensive model of the processes that interact to determine all aspects of task performance.

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“…There is recent evidence that the LBA can be used to account for decisions in tasks that are inspired by applied domains and produce extended mean response times (~2.5s; e.g., Palada et al, 2018). For example, Strickland et al (2019) and Boag et al (2019) used the LBA to understand the processes underlying prospective memory in a maritime surveillance task and air traffic control task, respectively. The LBA has also been used to understand how individuals adapt to time pressure in an applied multi-stimulus environment (Palada et al, 2018), and in a dual-task environment with multiattribute stimuli (Palada et al, 2016;Palada, Neal, Strayer, Ballard & Heathcote, 2019).…”

Section: Evidence Accumulation Modelsmentioning

confidence: 99%

An evidence accumulation model of perceptual discrimination with naturalistic stimuli

Palada¹,

Searston²,

Persson³

et al. 2018

Preprint

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Evidence accumulation models have been used to describe the cognitive processes underlying performance in tasks involving two-choice decisions about unidimensional stimuli, such as motion or orientation. Given the multidimensionality of natural stimuli, however, we might expect qualitatively different patterns of evidence accumulation in more applied perceptual tasks. One domain that relies heavily on human decisions about complex natural stimuli is fingerprint discrimination. We know little about the ability of evidence accumulation models to account for the dynamic decision process of a fingerprint examiner resolving if two different prints belong to the same finger or not. Here, we apply a dynamic decision-making model -the linear ballistic accumulator (LBA) -to fingerprint discrimination decisions in order to gain insight into the cognitive processes underlying these complex perceptual judgments. Across three experiments, we show that the LBA provides an accurate description of the fingerprint discrimination decision process with manipulations in visual noise, speedaccuracy emphasis, and training. Our results demonstrate that the LBA is a promising model for furthering our understanding of applied decision-making with naturally varying visual stimuli.

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Prospective memory in the red zone: Cognitive control and capacity sharing in a complex, multi-stimulus task.

Abstract: This research was funded by the Australian Government through the Australian Research Council (DP160101891). We thank Daniel White for programming the maritime surveillance task and for providing excellent technical support.

Cited by 19 publications

References 69 publications

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

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

Investigating the effects of ongoing-task bias on prospective memory

An evidence accumulation model of perceptual discrimination with naturalistic stimuli

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