A diffusion model analysis of task interference effects in prospective memory

Abstract

Holding an intention often interferes with other ongoing activities, indicating that resource-demanding processes are involved in maintaining the intention and noticing the appropriate event to fulfill it. Little is known, however, about the nature of the processes underlying this task interference effect. The goal of the present research was to decompose the processes contributing to the task interference effect by applying the diffusion model (Ratcliff, Psychological Review 85:59-108, 1978) to an event-based prospective memory task. In the first experiment, we validated the interpretation of the response criterion parameter (a) of the diffusion model as reflecting strategies to cope with the anticipated demands of a prospective memory task in the context of the ongoing task. The second experiment served to investigate which underlying processes contribute to the task interference often found with prospective memory tasks. Diffusion model analyses revealed that the task interference effect was due to (1) less efficient processing in the more demanding than in the less demanding prospective memory task and (2) a more conservative response criterion. We suggest that the anticipated demands and the additional processing demands of the prospective memory task jointly contribute to the task interference effect.

Performance predictions affect attentional processes of event-based prospective memory

“…The parameter z (i.e., the starting point of the diffusion process) was set to a/2 (cf. Boywitt & Rummel, 2012). Individual Kolmogorov-Smirnov goodness-of-fit tests were all non-significant, indicating that the drift diffusion model fit the present data (Voss & Voss, 2008).…”

“…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.…”

“…Albinski, Sedek, & Kliegel, 2012). Providing bogus information that the PM target is unlikely to occur (while holding factual task demands constant), on the other hand, has been shown to result in less cautious OT responding as reflected by a less conservative response criterion (i.e., the parameter a) of the drift diffusion model (Boywitt & Rummel, 2012, Experiment 1). Boywitt and Rummel conclude that subjective expectations about PM-task demands are selectively reflected by the parameter a.…”

“…Next, the three core parameters of the model as well as their inter-trial variability parameters were estimated. As these core parameters have been shown to reflect different attentional processes of PM ( Boywitt andHorn et al, 2011) we computed separate one-way ANOVAs with the between-subjects variable prediction condition (PMprediction, PM/OT-prediction, No-prediction control) for all three parameters (see Table 1). The ANOVAs did not yield significance for the non-decisional t0 parameter, F < 1, or the processing parameter v, F(2, 135) = 2.31, p = .103, .…”

“…Then, trimmed RTs and accuracy rates for words that were not PM targets were submitted to the drift diffusion model (cf. Boywitt & Rummel, 2012) using the software Fast-dm (Voss & Voss, 2007). The upper threshold of the model was associated with a correct word classification, whereas the lower threshold was associated with an incorrect word classification (i.e., classifying a word as a nonword).…”