Effects of aging in a task-switch paradigm with the diffusion decision model.

Abstract

We investigated aging effects in a task-switch paradigm with degraded stimuli administered to college students, 61–74 year olds, and 75–89 year olds. We studied switch costs (the performance difference between task-repeat and task-switch trials) in terms of accuracy and mean reaction times (RTs). Previous aging research focused on switch costs in terms of mean RTs (with accuracy at ceiling). Our results emphasize the importance of distinguishing between switch costs indexed by accuracy and by RTs because these measures lead to different interpretations. We used the Diffusion Decision Model (DDM; Ratcliff, 1978) to study the cognitive components contributing to switch costs. The DDM decomposed the cognitive process of task switching into multiple components. Two parameters of the model, the quality of evidence on which decisions were based (drift rate) and the duration of processes outside the decision process (nondecision time component), indexed different sources of switch costs. We found that older participants had larger switch costs indexed by nondecision time component than younger participants. This result suggests age-related deficits in preparatory cognitive processes. We also found group differences in switch costs indexed by drift rate for switch trials with high stimulus interference (stimuli with features relevant for both tasks). This result suggests that older participants have less effective cognitive processes involved in resolving interference. Our findings show that age-related effects in separate components of switch costs can be studied with the DDM. Our results demonstrate the utility of using discrimination tasks with degraded stimuli in conjunction with model-based analyses.

Improving neurocognitive testing using computational psychiatry—A systematic review for ADHD.

“…Similar results were also obtained in other research that involved adults (McLean et al, 2004; White & Shah, 2006). This contrasts with most research in cognitive psychology, which also found local switch costs in terms of both accuracy and mean RTs (Ging-Jehli & Ratcliff, 2020; Rogers & Monsell, 1995; Rubinstein et al, 2001; Schmitz & Voss, 2012). The absence of local switch costs in Hung et al’s study could indicate that task-switching in their paradigm was undemanding because the two tasks used in their task-switch paradigm were similar (Table A1).…”

“…These results suggest impairments in information integration and in the activation of early- and late-stage control processes. Ging-Jehli and Ratcliff (2020) recently illustrated with a DDM application to a task-switch paradigm that DDM parameters can be used to index distinct control processes. They showed that the nondecision time component can index early-stage control processes (involved in reconfiguring the cognitive system for task switches), whereas the drift rate can index late-stage control processes (involved in resolving any interference that arises from performing multiple tasks).…”

“…Evidence from cognitive psychology should have more impact in the design of cognitive tests. For instance, future research may consider the following findings from cognitive psychology when designing task-switch paradigms (Ging-Jehli & Ratcliff, 2020; Madden et al, 2009; Rogers & Monsell, 1995; Rubinstein et al, 2001; Schmitz & Voss, 2012; Schneider & Logan, 2005). First, the longer the CSI, the more likely are participants able to compensate for their deficits in cognitive flexibility.…”

“…First, the models have to adequately fit the experimental data (accuracy and correct and error RT distributions) before it can be legitimately used to interpret the experimental data. This means that applications should have plots of fits to data (cf., Ging-Jehli & Ratcliff, 2020; Ratcliff & Childers, 2015) presented in the text, and appendix, or at least a supplement. Second, the models need to be adequately parametrized.…”

Characterizing Underlying Cognitive Components of ADHD Presentations and Co-morbid Diagnoses: A Diffusion Decision Model Analysis

“…Nondecision time represents the latency of processes outside the decision process such as task preparation, perceptual encoding of cue, and response execution (i.e., the latency from having reached a decision to pressing the response key associated with that decision). Nondecision time is longer by the introduction of task switches (Ging-Jehli & Ratcliff, 2020;Schmitz and Voss, 2012), by aging (Cohen-Gilbert et al, 2014;Ratcliff, Thapar, & McKoon, 2006), and poor individual allocation of attention at the beginning of a trial (Nunez, 2015).…”