2011
DOI: 10.1037/a0022808
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What can the diffusion model tell us about prospective memory?

Abstract: Cognitive process models, such as Ratcliff’s (1978) diffusion model, are useful tools for examining cost- or interference effects in event-based prospective memory (PM). The diffusion model includes several parameters that provide insight into how and why ongoing-task performance may be affected by a PM task and is ideally suited to analyze performance because both reaction time and accuracy are taken into account. Separate analyses of these measures can easily yield misleading interpretations in cases of spee… Show more

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Cited by 40 publications
(103 citation statements)
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“…No other main effects or interactions reached significance for these two parameters, all Fs < 1. These results suggest that the presence of a PM intention resulted in the engagement of additional processing while performing the OT replicating Horn et al (2011). As the main purpose of the block without a PM intention was to reduce error variance in terms of a priori differences in response speed, however, we did not counterbalance the order of blocks.…”
Section: Reactive Effects Of Making Predictions On Ot Performancementioning
confidence: 89%
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“…No other main effects or interactions reached significance for these two parameters, all Fs < 1. These results suggest that the presence of a PM intention resulted in the engagement of additional processing while performing the OT replicating Horn et al (2011). As the main purpose of the block without a PM intention was to reduce error variance in terms of a priori differences in response speed, however, we did not counterbalance the order of blocks.…”
Section: Reactive Effects Of Making Predictions On Ot Performancementioning
confidence: 89%
“…Last, the non-decisional component captures time spent for processing unrelated to the decision such as perception of the target stimulus or execution of the response but also stimulus encoding time (Voss, Voss, & Klauer, 2010). Past applications of the drift diffusion model to PM data with a lexical-decision task as OT have found variations of factual PM task demands to be reflected by changes in the drift rate (i.e., the parameter v) and sometimes in the non-decisional (t0) component ( Boywitt andHorn et al, 2011). This implies that these parameters represent the (dis)engagement in additional resource-demanding PM processing (e.g., monitoring of a different quality, cf.…”
Section: The Present Studymentioning
confidence: 99%
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“…As a potential solution, researchers have often turned to the analysis of ongoing-task performance to draw inferences about the processes involved in PM-task performance (e.g., Hicks et al, 2005;Marsh, Hicks, & Cook, 2006b;Marsh et al, 2003). Following this rationale, Horn, Bayen, and Smith (2011) first suggested applying the diffusion model (Ratcliff, 1978) to the data of ongoing-task performance in order to draw more elaborate inferences about the processes involved in PM task performance. While RTs and ongoingtask accuracy have traditionally been analyzed separately, thereby making use of only a subset of the available data, the diffusion model simultaneously considers the whole RT distribution, as well as accuracy data.…”
Section: Task Interference From Event-based Intentionsmentioning
confidence: 99%
“…In the first application of the diffusion model to reanalyze data from a standard event-based PM task, Horn et al (2011) showed that the addition of a nonfocal and nonsalient PM task, as compared with a condition without a PM task, reduced the efficiency of information uptake (parameter v). Furthermore, participants in the condition with a PM task adopted a more conservative response criterion (parameter a) in the ongoing task, as compared with participants who did not hold an intention.…”
Section: The Diffusion Model and Its Application To Prospective Memorymentioning
confidence: 99%