Same same but different? Modeling N-1 switch cost and N-2 repetition cost with the diffusion model and the linear ballistic accumulator model

Hartmann, Eva-Maria; Rey-Mermet, Alodie; Gade, Miriam

doi:10.1016/j.actpsy.2019.05.010

Cited by 5 publications

(7 citation statements)

References 55 publications

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“…However, we note that since gate opening and closing both (necessarily) involve switching between trial types, these measures may include effects related to task switching, such as task-set reconfiguration and proactive interference from previously active sets (we note that our updating and comparison measures were based only on no-switch trials and so were not contaminated by potential switching effects [4,60,61]). This would go some way toward explaining why both gating measures (and switching) were associated with lower drift rates, since interference from previously active sets and noise in the retrieved set would be expected to reduce the quality of decision processing on switch trials [62]. We also note that the larger costs associated with closing versus opening the gate (i.e., when switching to maintenance mode) provide further support for the PBWM's assumption that WM sits in maintenance (gate-closed) mode by default.…”

Section: Discussionmentioning

confidence: 99%

“…Future work may thus profit from integrating a PBWM-like learning mechanism into our evidence accumulation framework to obtain finer control over the temporal dynamics of referenceback performance (e.g., by having threshold and/or reactive control settings vary from trial to trial as a function of learning; see [65,66] for examples of such an approach in the domain of instrumental learning). In addition, we speculate that some of the minor misfits (e.g., to empirical switching and comparison costs) of our model were likely due to certain sequential or 'carry-over' effects that are unaccounted for in the current framework, such as proactive interference, priming, task-set inertia/reconfiguration, and Gratton effects arising from previously encountered stimuli and responses [11,42,43,62,[67][68][69][70][71][72]. Due to our limited number of trials per subject, we were unable to conduct a thorough model-based analysis of sequential effects in the reference-back task.…”

Section: Discussionmentioning

confidence: 99%

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Cognitive Control of Working Memory: A Model-Based Approach

et al. 2021

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Working memory (WM)-based decision making depends on a number of cognitive control processes that control the flow of information into and out of WM and ensure that only relevant information is held active in WM’s limited-capacity store. Although necessary for successful decision making, recent work has shown that these control processes impose performance costs on both the speed and accuracy of WM-based decisions. Using the reference-back task as a benchmark measure of WM control, we conducted evidence accumulation modeling to test several competing explanations for six benchmark empirical performance costs. Costs were driven by a combination of processes, running outside of the decision stage (longer non-decision time) and showing the inhibition of the prepotent response (lower drift rates) in trials requiring WM control. Individuals also set more cautious response thresholds when expecting to update WM with new information versus maintain existing information. We discuss the promise of this approach for understanding cognitive control in WM-based decision making.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Cognitive Control of Working Memory: A Model-Based Approach

et al. 2021

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“…To discuss the implications of these concerns, it is important to distinguish between studies that employ mean performance measures of cognitive control (i.e., performance in a specific condition or averaged across different conditions in an experimental task) and studies that employ difference score measures of cognitive control (i.e., difference scores or slope estimates between at least two experimental conditions). Both mean performance and difference score measures are typically calculated based on the average number of correct responses and/or averaged response times (RTs) across experimental trials (Draheim, Mashburn, Martin, & Engle, 2019), but they can in principle also be calculated on the basis of other performance measures such as model-based estimates of information-processing (Frischkorn & Schubert, 2018; Hartmann, Rey-Mermet, & Gade, 2019).…”

Section: Problems In the Measurement Of Cognitive Controlmentioning

confidence: 99%

“…calculated based on the average number of correct responses and/or averaged response times (RTs) across experimental trials , but they can in principle also be calculated on the basis of other performance measures such as model-based estimates of informationprocessing Hartmann, Rey-Mermet, & Gade, 2019). Mean performance measures of cognitive control typically show high reliabilities but questionable validities.…”

Section: Limitations Of Behavioral Measures Of Cognitive Controlmentioning

confidence: 99%

A chronometric model of the relationship between frontal midline theta functional connectivity and human intelligence.

Schubert

Hagemann

Löffler

et al. 2021

Journal of Experimental Psychology: General

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Individual differences in cognitive control have been suggested to act as a domain-general bottleneck constraining performance in a variety of cognitive ability measures including but not limited to fluid intelligence, working memory capacity, and processing speed. However, due to psychometric problems associated with the measurement of individual differences in cognitive control, it has been challenging to empirically test the assumption that individual differences in cognitive control underlie individual differences in cognitive abilities. In the present study, we addressed these issues by analyzing the chronometry of intelligence-related differences in midfrontal global theta connectivity, which has been shown to reflect cognitive control functions. We demonstrate in a sample of 98 adults, who completed a cognitive control task while their EEG was recorded, that individual differences in mid-frontal global theta connectivity during stages of higher-order information-processing explained 65 percent of the variance in fluid intelligence. In comparison, task-evoked theta connectivity during earlier stages of information processing was not related to fluid intelligence. These results suggest that more intelligent individuals benefit from an adaptive modulation of theta-band synchronization during the time-course of information processing. Moreover, they emphasize the role of interregional goal-directed informationprocessing for cognitive control processes in human intelligence and support theoretical accounts of intelligence which propose that individual differences in cognitive control processes give rise to individual differences in cognitive abilities.

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“…However, to infer stronger causal relationships between variables under study, future research is strongly recommended to employ experimental and longitudinal designs. Furthermore, we assessed n − 1 switch and n − 2 repetition cost in a single task-switching session, which may reduce the involvement of inhibitory processes ( Hartmann et al, 2019 ). Moreover, our study comprised a convenience and small sample of university students.…”

Section: Discussionmentioning

confidence: 99%

Insomnia Symptoms Moderate the Relationship Between Perseverative Cognition and Backward Inhibition in the Task-Switching Paradigm

et al. 2020

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Perseverative cognition (PC), that is, the continuous cognitive representation of uncontrollable threats, is known to dampen executive control processes in experimental paradigms. Similarly, PC has been shown to impair sleep and to be implicated in the exacerbation of insomnia, which may in turn contribute to the disruption of executive functions. The interactions between PC and insomnia in influencing executive functions, however, have never been tested to date. In the present study, we explored whether insomnia symptoms may moderate the associations between PC and disrupted executive functions, with the hypothesis to find a stronger relationship between these variables at increasing levels of insomnia. Fifty participants completed measures of trait PC and insomnia severity in the previous month and also completed a computerized task-switching paradigm assessing backward inhibition, switch cost, and accuracy. Prior to the task switching, participants completed a measure of state rumination in order to control for the effects of state PC on cognitive performance. Results show that trait PC was significantly correlated with higher insomnia symptoms and state rumination and marginally correlated with lower backward inhibition and longer switch cost. Moreover, insomnia severity moderated the relationship between trait PC and backward inhibition after controlling for the effects of state rumination; that is, the relationship between PC and inhibitory deficits was stronger in those with higher versus lower levels of insomnia symptoms. Findings suggest the need to better elucidate the associations between PC, insomnia, and executive functioning in clinical samples and longitudinal designs.

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Same same but different? Modeling N-1 switch cost and N-2 repetition cost with the diffusion model and the linear ballistic accumulator model

Cited by 5 publications

References 55 publications

Cognitive Control of Working Memory: A Model-Based Approach

Cognitive Control of Working Memory: A Model-Based Approach

A chronometric model of the relationship between frontal midline theta functional connectivity and human intelligence.

Insomnia Symptoms Moderate the Relationship Between Perseverative Cognition and Backward Inhibition in the Task-Switching Paradigm

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