Racing to remember: A theory of decision control in event-based prospective memory.

Abstract

Event-based prospective memory (PM) requires remembering to perform intended deferred actions when particular stimuli or events are encountered in the future. We propose a detailed process theory within Braver's (2012) proactive and reactive framework of the way control is maintained over the competing demands of prospective memory decisions and decisions associated with ongoing task activities. The theory is instantiated in a quantitative "Prospective Memory Decision Control" (PMDC) architecture, which uses linear ballistic evidence accumulation (Brown & Heathcote, 2008) to model both PM and ongoing decision processes. Prospective control is exerted via decision thresholds, as in Heathcote, Loft, and Remington's (2015) "Delay Theory" of the impact of PM demands on ongoing-task decisions. However, PMDC goes beyond Delay Theory by simultaneously accounting for both PM task decisions and ongoing task decisions. We use Bayesian estimation to apply PMDC to experiments manipulating PM target focality (i.e., the extent to which the ongoing task directs attention to the features of PM targets processed at encoding) and the relative importance of the PM task. As well as confirming Delay Theory's proactive control of ongoing task thresholds, the comprehensive account provided by PMDC allowed us to detect both proactive control of the PM accumulator threshold and reactive control of the relative rates of the PM and ongoing-task evidence accumulation processes. We discuss potential extensions of PMDC to account for other factors that may be prevalent in real-world PM, such as failures of memory retrieval. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

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

“…For example, quantitative modeling can provide a unified account of disparate performance data, characterize the latent cognitive mechanisms underlying performance, and provide insights via simulation, for example by simulating decision making within systems when human in-the-loop testing is not feasible. Recently, a quantitative model of event-based PM, "Prospective Memory Decision Control" (PMDC; Strickland, Loft, Remington, & Heathcote, 2018), was shown to provide a cohesive and informative account of the cognitive processes underpinning event-based PM in a basic laboratory paradigm.…”

Computing Bayes factors for evidence-accumulation models using Warp-III bridge sampling

“…Despite recent advances in the Bayesian estimation of evidence-accumulation models, model comparison continues to rely on suboptimal procedures, such as posterior parameter inference based on complex models where separate model parameters are estimated for each experimental condition. In this approach, differences between parameters are often evaluated using posterior p values (e.g., Klauer, 2010;Matzke, Dolan, Batchelder, & Wagenmakers, 2015;Matzke, Hughes, Badcock, Michie, & Heathcote, 2017;Matzke, Boehm, & Vandekerckhove, 2018;Smith & Batchelder, 2010;Strickland et al, 2018;Tilman, Osth, van Ravenzwaaij, & Heathcote, 2017;Tilman, Strayer, Eidels, & Heathcote, 2017;Osth, Jansson, Dennis, & Heathcote, 2018). Posterior parameter inference has at least three limitations.…”

“…Continuing the example from Strickland et al (2018), suppose that M 1 refers to the model in which only rates are affected by prospective-memory load and M 2 refers to the model in which only thresholds are affected. Different researchers may start with different prior beliefs about the relative plausibility of the two competing psychological explanations of the prospective-memory load effect.…”

Adapting to workload in complex environments: Insights from evidence accumulation models of decision making

“…Our results emphasize how using behavioural data alone to infer resource tradeoffs could result in erroneous or oversimplified conclusions. Recently, other researches have showed that traditional assumptions of prospective memory, which were inferred from behavioural data, do not hold when submitted to formal modeling (Strickland, Loft, Remington, & Heathcote, 2018). Similarly, in this paper we show the long-held assumption of resource reallocation does not completely account for dual-task performance.…”

“…The current findings are consistent with a growing body of literature which suggests that people manage demands on competing tasks by strategically adjusting their response thresholds for the different tasks. For example, Strickland, Loft, Remington and Heathcote's (2018) prospective memory decision control theory includes a proactive control framework, which suggests that individuals increase their response threshold for the primary task when a prospective memory task is present. The reason for raising the primary task threshold is to provide sufficient time for the prospective memory response to be made.…”