Academic journal article Canadian Journal of Experimental Psychology

What Can the Diffusion Model Tell Us about Prospective Memory?

Academic journal article Canadian Journal of Experimental Psychology

What Can the Diffusion Model Tell Us about Prospective Memory?

Article excerpt

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 analyse performance because both reaction time and accuracy are taken into account. Separate analyses of these measures can easily yield misleading interpretations in cases of speed-accuracy trade-offs. The diffusion model allows us to measure possible criterion shifts and is thus an important methodological improvement over standard analyses. Performance in an ongoing lexical-decision task was analysed with the diffusion model. The results suggest that criterion shifts play an important role when a PM task is added, but do not fully explain the cost effect on reaction time.

Keywords: prospective memory, diffusion model, monitoring and attentional resources, response time models

Mots-clés : mémoire prospective, modèle de diffusion, surveillance et ressources attentionnelles, modèles de temps de réponses

Event-based prospective memory (PM) tasks involve remembering to perform intended actions after a delay, when a specific target event occurs. Such tasks often occur in the midst of other activities that must be interrupted to perform the intended action. To capture this aspect of real world PM, the PM task is embedded in an ongoing task in the typical laboratory paradigm (Einstein & McDaniel, 1990). For instance, participants may be busily engaged in an ongoing lexical-decision task, and at the same time must remember to interrupt their decisions to carry out another action (i.e., press a certain key on a computer keyboard) when a particular target occurs (e.g., the word tiger appears on the screen).

Much theoretical and experimental work on PM has focused on the processes involved in retrieving such intentions (e.g., Einstein & McDaniel, 1996; Smith, Hunt, McVay, & McConnell, 2007; West, 2007). Particularly, researchers have examined whether successful PM always requires resource-demanding preparatory attentional processes (Smith, 2003, 2008, 2010), or whether spontaneous retrieval of the intention occurs under specific circumstances (McDaniel & Einstein, 2000, 2007). The empirical approach toward addressing this question rests on the analysis of ongoing-task performance in the presence versus absence of a PM task. The cost or interference effect of PM refers to the finding that reaction time (RT) on non-PM-target trials in the ongoing task can be increased by the need to remember the PM task, and can covary with PM performance (Smith, 2003). It is assumed that RT can increase as PM absorbs attentional resources that would otherwise be devoted to the ongoing task. In the last decade, numerous studies have examined cost effects (see Smith et al., 2007, for an overview) and their relationship with characteristics of the PM targets, such as salience and focality (Einstein et al., 2005), individuals' resource allocation (Marsh, Hicks, & Cook, 2005), and potential boundary conditions to demonstrations of cost effects (Cohen, Jaudas, & Gollwitzer, 2008; but see Smith, 2010). However, to date surprisingly little is known about the specific processes that lead to the slowing when cost effects occur. Why and how does processing change in the ongoing task with an additional requirement to remember an intention?

In this article, we argue that cognitive process models, such as the diffusion model (e.g., Ratcliff, 1978), are useful tools for addressing these questions through the measurement of latent variables assumed to underlie performance in ongoing tasks. We will first describe the diffusion model in more detail and point out the importance of considering speed-accuracy trade-offs in task performance. We will then present a model-based reanalysis of data from Smith (2003, Experiment 1) to demonstrate how additional insight into cost effects can be gained. …

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