Proceeding with care for successful prospective memory: Do we delay ongoing responding or actively monitor for cues?

Anderson, Francis; Rummel, Jan; McDaniel, Mark A.

doi:10.1037/xlm0000504

Cited by 21 publications

(31 citation statements)

References 53 publications

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“…To date, every evidence accumulation modeling study that has compared ongoing task performance between control and PM blocks has found elevated thresholds in the latter, consistent with proactive control (Anderson et al, 2018;Ball & Aschenbrenner, 2017;Boag et al, 2019;Heathcote, Loft & Remington, 2015;Horn & Bayen, 2015;Horn, Bayen, & Smith, 2011Strickland et al, 2017Strickland et al, , 2018. In Strickland et al (2018), participants set even higher ongoing task thresholds when instructed that the PM task was important.…”

Section: Proactive and Reactive Controlmentioning

confidence: 78%

“…Since our design minimized any potential differences in the motor movement required to make each response (i.e., participants kept their fingers positioned above the response keys), one non-decision time (ter) parameter was estimated for each participant. Moreover, previous research has suggested that non-decision time does not play a role in PM cost for the LBA (e.g., Anderson et al, 2018;Heathcote et al, 2015;Strickland et al, 2017Strickland et al, , 2018. Owing to very low numbers of PM false alarms (PM responses to non-PM stimuli in PM blocks), estimates of both accumulation rate and variance (v and sv) were pooled across time pressure and PM blocks to give one PM rate for PM responses to non-PM items and one corresponding sv parameter (which was used as a fixed scaling parameter as mentioned above) for each importance group.…”

Section: Model Analysismentioning

confidence: 99%

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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: Proactive and Reactive Controlmentioning

confidence: 78%

Section: Model Analysismentioning

confidence: 99%

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

et al. 2019

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“…Our results leave uncertain what processes might be reflected by the older adults’ PM costs, costs that were equivalent to those of younger adults (e.g., in the Nonfocal condition). Perhaps, older adults were attempting to engage monitoring, but did so ineffectively; or perhaps the PM costs reflect other processes unrelated to monitoring (e.g., older adults became more conservative in their decision thresholds for the ongoing task in the presence of a PM demand (Anderson, Rummel, & McDaniel, 2018; see Heathcote et al, 2015).…”

Section: Discussionmentioning

confidence: 99%

Age-related changes in neural mechanisms of prospective memory

Lamichhane

McDaniel

Waldum

et al. 2018

Cogn Affect Behav Neurosci

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The capability to remember and execute intentions in the future - termed prospective memory (PM) - may be of special significance for older adults to enable successful completion of important activities of daily living. Despite the importance of this cognitive function, mixed findings have been obtained regarding age-related decline in PM, and, currently, there is limited understanding of potential contributing mechanisms. In the current study, older (N=41) and younger adults (N=47) underwent task-functional MRI during performance of PM conditions that encouraged either spontaneous retrieval (Focal) or sustained attentional monitoring (Non-focal) to detect PM targets. Older adults exhibited a reduction in PM-related sustained activity within the anterior prefrontal cortex (aPFC) and associated dorsal frontoparietal cognitive control network, due to an increase in non-specific sustained activation in (no-PM) control blocks (i.e., an age-related compensatory shift). Transient PM-trial specific activity was observed in both age groups within a ventral parietal memory network that included the precuneus. However, within a left posterior inferior parietal node of this network, transient PM-related activity was selectively reduced in older adults during the non-focal condition. These age differences in sustained and transient brain activity statistically mediated age-related declines in PM performance, and were potentially linked via age-related changes in functional connectivity between the aPFC and precuneus. Together, they support an account consistent with the Dual Mechanisms of Control framework, in which age-related PM declines are due to neural mechanisms that support proactive cognitive control processes, such as sustained attentional monitoring, while leaving reactive control mechanisms relatively spared.

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“…Every evidence accumulation modeling study to date that has compared ongoing task performance between control and PM blocks has found elevated thresholds in the latter (Anderson, Rummel & McDaniel, 2018;Ball & Aschenbrenner, 2017;Heathcote et al, 2015;Horn & Bayen, 2015;Horn et al, 2011Horn et al, , 2013Strickland et al, 2017Strickland et al, , 2018, consistent with proactive control. Further implicating control, Strickland et al (2018) showed that participants increased their ongoing task thresholds further when instructed that the PM task was important.…”

Section: Prospective Memory Decision Controlmentioning

confidence: 93%

“…The majority of studies modeling PM costs have reported no change in either the quality or quantity of evidence accumulation to non-PM items across PM and control blocks, suggesting no change in the allocation or availability of resources as a result of PM (Ball & Aschenbrenner, 2017;Heathcote et al, 2015;Horn & Bayen, 2015;Strickland et al, 2017Strickland et al, , 2018. In contrast, one recent study did find some evidence of reduced processing quality under PM load (Anderson et al, 2018). However, this experiment did not counterbalance PM and control conditions, so it is possible that the evidence of reduced capacity was due to order effects rather than PM.…”

Section: Prospective Memory Decision Controlmentioning

confidence: 99%

Cognitive control and capacity for prospective memory in complex dynamic environments.

Boag¹,

Strickland²,

Heathcote³

et al. 2019

Journal of Experimental Psychology: General

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Performing deferred actions in the future relies upon Prospective Memory (PM). Often, PM demands arise in complex dynamic tasks. Not only can PM be challenging in such environments, the processes required for PM may affect the performance of other tasks. To adapt to PM demands in such environments, humans may use a range of strategies, including flexible allocation of cognitive resources and cognitive control mechanisms. We sought to understand such mechanisms by using the Prospective Memory Decision Control (Strickland et al., 2018) model to provide a comprehensive, quantitative account of dual task performance in a complex dynamic environment (a simulated air traffic control conflict detection task). We found that PM demands encouraged proactive control over ongoing task decisions, but that this control was reduced at high time pressure to facilitate fast responding. We found reactive inhibitory control over ongoing task processes when PM targets were encountered, and that time pressure and PM demand both affect the attentional system, increasing the amount of cognitive resources available. However, as demands exceeded the capacity limit of the cognitive system, resources were reallocated (shared) between the ongoing and PM tasks. As the ongoing task used more resources to compensate for additional time pressure demands, it drained resources that would have otherwise been available for PM task processing. This study provides the first detailed quantitative understanding of how attentional resources and cognitive control mechanisms support PM and ongoing task performance in complex dynamic environments.

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Proceeding with care for successful prospective memory: Do we delay ongoing responding or actively monitor for cues?

Cited by 21 publications

References 53 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

Age-related changes in neural mechanisms of prospective memory

Cognitive control and capacity for prospective memory in complex dynamic environments.

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