Proactive and reactive metacontrol in task switching

Kang, Moon Sun; Chiu, Yu-Chin

doi:10.3758/s13421-021-01189-8

Cited by 20 publications

(36 citation statements)

References 64 publications

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“…To further examine the effect of CSI, we combined data in Experiment 3 with data from Kang and Chiu (2021) Experiment 2 ( N = 101) and conducted an across-experiment ANOVA on RT with CSI as a between-subjects variable. However, to make it easier to interpret, we conducted two separate 2 (CSI: 0 vs. 100) × 2 (Switch Probability: high, low) × 2 (Transition: switch, repeat) mixed-effect ANOVAs—one to examine the LWSP effect and the other to examine the ISSP effect.…”

Section: Resultsmentioning

confidence: 99%

“…Despite the similarities between the LWSP and the ISSP behavioral effects, they are thought to reflect different forms of regulation of cognitive control, or metacontrol driven by experience (Chiu & Egner, 2019; Gonthier et al, 2016; Kang & Chiu, 2021). Namely, the LWSP effect reflects regulations of cognitive control across blocks of trials (or lists).…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, the present study is interested in the differences between the LWSP and the ISSP effects in the presence of task foreknowledge. Because, based on the proactive versus reactive distinction made for the LWSP versus the ISSP effects (Kang & Chiu, 2021), there might exist differences in the relationship (collaboration vs. competition) between retrieval of top-down task set and retrieval of context-control associations that are learned through experiences.…”

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confidence: 99%

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Task foreknowledge swallows item-specific but not list-wide control learning effects.

Chiu¹

2023

Journal of Experimental Psychology: Learning, Memory, and Cogni

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Recent context-control learning studies have shown that switch costs are reduced in a particular context predicting a high probability of switching as compared to another context predicting a low probability of switching. These context-specific switch probability effects suggest that control of task sets, through experience, can become associated with a particular context cue and be retrieved subsequently to modulate task-switching efficiency. However, advanced task foreknowledge, mediated by top-down retrieval of a specific task set, also modulates switch costs, as shown in the task-switching literature. Here, we examined how the retrieval of task sets interacts with the retrieval of task control. In Experiments 1 and 2, we provided the task foreknowledge midway through the experiment and examined how it might alter the item-specific switch probability (ISSP) effect and the list-wide switch probability effect (LWSP). Both ISSP and LWSP effects were replicated with no task foreknowledge, demonstrating modulations of switch costs due to context-control learning. These modulations, however, were swallowed once task foreknowledge became available, likely due to the certainty of task foreknowledge overpowering learned probabilistic information. Experiment 3 provided task foreknowledge throughout and compared how it affects the list-versus the item-based control learning. In this case, we detected the LWSP but not the ISSP effect. Experiment 3's findings suggest that, with task foreknowledge, list-based contextcontrol learning does modulate switch costs-likely because the switch probability associations are retrieved proactively. Together, these data suggest that task-switching performance benefits from multiple paths to retrieve information in memory, best if done proactively.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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confidence: 99%

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Task foreknowledge swallows item-specific but not list-wide control learning effects.

Chiu¹

2023

Journal of Experimental Psychology: Learning, Memory, and Cogni

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“…In addition, trial frequencies have often been varied across conditions to explore a variety of cognitive processes by investigating their interactions with probability (e.g., Broadbent & Gregory, 1965;Den Heyer, Briand, & Dannenbring, 1983;Miller & Pachella, 1973;Sanders, 1970;Theios, Smith, Haviland, Traupmann, & Moy, 1973). Currently, trial frequencies are commonly varied in studies of spatial and temporal statistical learning (e.g., Flowers, Palitsky, Sullivan, & Peterson, 2021;Gibson, Pauszek, Trost, & Wenger, 2021;Liesefeld & Müller, 2021;Vadillo, Giménez-Fernández, Beesley, Shanks, & Luque, 2021), the modulation of attentional control processes by environmental contingencies (e.g., Cochrane, Simmering, & Green, 2021;Huang, Theeuwes, & Donk, 2021;Kang & Chiu, 2021), action-outcome contingency learning (e.g., Gao & Gozli, 2021), adaptation to the frequency of congruent versus incongruent information (e.g., Bausenhart, Ulrich, & Miller, 2021;Ivanov & Theeuwes, 2021;Thomson, Simone, & Watter, 2021), and between-task resource sharing (e.g., Miller & Tang, 2021), to name just a few areas. Unfortunately, median bias is still sometimes overlooked and may contaminate published comparisons of conditions with different POWER OF MEDIAN RTS 8 trial frequencies (e.g., Bulger, Shinn-Cunningham, & Noyce, 2021).…”

Section: Type I Error Ratesmentioning

confidence: 99%

Another Warning about Median Reaction Time --- Version of 19 Jan 2022

Miller¹

2020

Preprint

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Contrary to the warning of Miller (1988), Rousselet and Wilcox (2020) argued that it is better to summarize each participant’s single-trial reaction times (RTs) in a given condition with the median than with the mean when comparing the central tendencies of RT distributions across experimental conditions. They acknowledged that median RTs can produce inflated Type I error rates when conditions differ in the number of trials tested, consistent with Miller’s warning, but they showed that the bias responsible for this error rate inflation could be eliminated with a bootstrap bias correction technique. The present simulations extend their analysis by examining the power of bias-corrected medians to detect true experimental effects and by comparing this power with the power of analyses using means and regular medians. Unfortunately, although bias-corrected medians solve the problem of inflated Type I error rates, their power is lower than that of means or regular medians in many realistic situations. In addition, even when conditions do not differ in the number of trials tested, the power of tests (e.g., t-tests) is generally lower using medians rather than means as the summary measures. Thus, the present simulations demonstrate that summary means will often provide the most powerful test for differences between conditions, and they show what aspects of the RT distributions determine the size of the power advantage for means.

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“…In these conflict or task switching studies, specific stimuli were presented multiple times with specific proportions of task switches or conflict (incongruent mappings). For stimuli with experienced high probabilities of task switches/conflict, the effects of task switches/conflict decreased compared to stimuli with low probability of task switches/conflict (e.g., item-specific proportion congruency effect: e.g., Bugg et al, 2008 ; Jacoby et al, 2003 ; Schmidt & Besner, 2008 ); or item-specific switch probability effect (e.g., Chiu & Egner, 2017 ; Chiu et al, 2020 ; Kang & Chiu, 2021 ). These effects of experienced probabilities can be explained by associative learning mechanisms.…”

Section: Introductionmentioning

confidence: 99%

Instructing item-specific switch probability: expectations modulate stimulus–action priming

Jargow

Wolfensteller

Pfeuffer

et al. 2022

Psychological Research

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Both active response execution and passive listening to verbal codes (a form of instruction) in single prime trials lead to item-specific repetition priming effects when stimuli re-occur in single probe trials. This holds for task-specific classification (stimulus–classification, SC priming, e.g., apple–small) and action (stimulus–action, SA priming, e.g., apple–right key press). To address the influence of expectation on item-specific SC and SA associations, we tested if item-specific SC and SA priming effects were modulated by the instructed probability of re-encountering individual SC or SA mappings (25% vs. 75% instructed switch probability). Importantly, the experienced item-specific switch probability was always 50%. In Experiment 1 (N = 78), item-specific SA/SC switch expectations affected SA, but not SC priming effects exclusively following active response execution. Experiment 2 (N = 40) was designed to emphasize SA priming by only including item-specific SC repetitions. This yielded stronger SA priming for 25% vs. 75% expected switch probability, both following response execution as in Experiment 1 and also following verbally coded SA associations. Together, these results suggest that SA priming effects, that is, the encoding and retrieval of SA associations, is modulated by item-specific switch expectation. Importantly, this expectation effect cannot be explained by item-specific associative learning mechanisms, as stimuli were primed and probed only once and participants experienced item-specific repetitions/switches equally often across stimuli independent of instructed switch probabilities. This corroborates and extends previous results by showing that SA priming effects are modulated by expectation not only based on experienced item-specific switch probabilities, but also on mere instruction.

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Proactive and reactive metacontrol in task switching

Cited by 20 publications

References 64 publications

Task foreknowledge swallows item-specific but not list-wide control learning effects.

Task foreknowledge swallows item-specific but not list-wide control learning effects.

Another Warning about Median Reaction Time --- Version of 19 Jan 2022

Instructing item-specific switch probability: expectations modulate stimulus–action priming

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