The importance of age-related differences in prospective memory: Evidence from diffusion model analyses

Abstract

Event-based prospective memory (PM) refers to relying on environmental cues to trigger retrieval of a deferred action plan from long-term memory. Considerable research has demonstrated PM declines with increased age. Despite efforts to better characterize the attentional processes that underlie these decrements, the majority of research has relied on measures of central tendency to inform theoretical accounts of PM that may not entirely capture the underlying dynamics involved in allocating attention to intention-relevant information. The purpose of the current study was to examine the utility of the diffusion model to better understand the cognitive processes underlying age-related differences in PM. Results showed that emphasizing the importance of the PM intention increased cue detection selectively for older adults. Standard cost analyses revealed that PM importance increased mean response times and accuracy, but not differentially for young and older adults. Consistent with this finding, diffusion model analyses demonstrated that PM importance increased response caution as evidenced by increased boundary separation. However, the selective benefit in cue detection for older adults may reflect peripheral target-checking processes as indicated by changes in non-decision time. These findings highlight the use of modeling techniques to better characterize the processes underlying the relations among aging, attention, and PM.

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

“…Unlike previous models, which did not predict PM accuracy (Heathcote, Loft, & Remington, 2015;Horn & Bayen, 2015;Strickland et al, 2017;Ball & Aschenbrenner, 2017), PMDC quantitatively estimates the extent to which proactive control over ongoing task decisions benefits PM accuracy. For both experiments, we found that conditions with higher ongoing task thresholds (non-focal and PM-important), did have higher PM accuracy as a result of those thresholds.…”

“…Total RT is determined by accumulation time plus non-decision time. non-PM trials using both the LBA and DDM (Ball & Aschenbrenner, 2017;Heathcote, Loft, & Remington, 2015;Horn & Bayen, 2015;Strickland et al, 2017). The modeling revealed that PM cost is largely due to increases in response threshold, and is not due to changes in ongoing task evidence accumulation.…”

“…On the basis of the reviewed effects the multiprocess view distinguishes focal and non-focal PM mechanisms, claiming that non-focal PM is reliant on PM monitoring that shares capacity with the ongoing task, whereas focal PM is not reliant on sharing ongoing task capacity (and hence instead reliant on spontaneous, cue-driven PM processing) (e.g., Einstein et al, 2005;Scullin, McDaniel, Shelton, & Lee, 2010). However, as reviewed, the recent PM cost modeling suggests that non-focal cost resulted from increased response thresholds, rather than decreased ongoing task accumulation rates, over a range of non-focal tasks (Ball & Aschenbrenner, 2017;Heathcote, Loft, & Remington, 2015;Horn & Bayen, 2015;Strickland et al, 2017). Given that, even with this lack of evidence for capacity sharing, non-focal PM accuracy was relatively high in the modeled data sets (>50%), it may be unnecessary to distinguish focal and non-focal PM tasks by their demand for ongoing task capacity.…”

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

“…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.…”

Systematic Parameter Reviews in Cognitive Modeling: Towards a Robust and Cumulative Characterization of Psychological Processes in the Diffusion Decision Model

“…In these models, evidence is first extracted from the stimuli and then accumulated over time until a decision boundary is reached and a response initiated. Among the many evidence-accumulation models, the DDM is the most widely applied, not only in psychology, but also in economics and neuroscience, accounting for experiments ranging from decision making under time-pressure (Dutilh, Krypotos, & Wagenmakers, 2011;Leite, Ratcliff, Lette, & Ratcliff, 2010;Voss, Rothermund, & Brandtstädter, 2008), prospective memory (Ball & Aschenbrenner, 2018;Horn, Bayen, & Smith, 2011) to cognitive control (Gomez, Ratcliff, & Perea, 2007;Schmitz & Voss, 2012). (2) the separation of the two response boundaries (a), representing response caution; (3) the mean starting point of evidence accumulation (z), representing response bias; and (4) mean non-decision time (T er ), which is the sum of times for stimulus encoding and response execution.…”