Rapid instructed task learning (but not automatic effects of instructions) is influenced by working memory load

Pereg, Maayan; Meiran, Nachshon

doi:10.1371/journal.pone.0217681

Cited by 13 publications

(23 citation statements)

References 36 publications

(80 reference statements)

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“…We used the short version of Raven's Advanced Progressive Matrices to assess one's reasoning and problem solving skill (70,71), which is referred to as fluid intelligence (72). We selected one practice question from Set I (i.e., Problem 1) and 12 questions from Set II (i.e., Problems 1,4,8,11,15,18,21,23,25,30,31,and 35). These problems were presented sequentially from easy to difficult.…”

Section: Studymentioning

confidence: 99%

Working Memory Capacity Predicts Individual Differences in Social Distancing Compliance during the COVID-19 Pandemic in the U.S.

Campbell¹,

Zhang²

2020

Preprint

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Noncompliance with social distancing during the early stage of the COVID-19 pandemic poses a great challenge to the public health system. These noncompliance behaviors partly reflect people’s concerns for the inherent costs of social distancing while discounting its public health benefits. We propose that this oversight may be associated with the limitation in one’s mental capacity to simultaneously retain multiple pieces of information in working memory (WM) for rational decision making that leads to social distancing compliance. We tested this hypothesis in 850 U.S. residents during the first 2 weeks following the presidential declaration of national emergency because of the COVID-19 pandemic. We found that participants’ social distancing compliance at this initial stage could be predicted by individual differences in WM capacity, partly due to increased awareness of benefits over costs of social distancing among higher WM capacity individuals. Critically, the unique contribution of WM capacity to the individual differences in social distancing compliance could not be explained by other psychological and socioeconomic factors (e.g., moods, personality, education, and income levels). Furthermore, the critical role of WM capacity in social distancing compliance can be generalized to the compliance with another set of rules for social interactions, namely the fairness norm, in Western cultures. Collectively, our data reveal novel contributions of a core cognitive process underlying social distancing compliance during the early stage of the COVID-19 pandemic, highlighting a potential cognitive venue for developing strategies to mitigate a public health crisis.

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Section: Studymentioning

confidence: 99%

Working Memory Capacity Predicts Individual Differences in Social Distancing Compliance during the COVID-19 Pandemic in the U.S.

Campbell¹,

Zhang²

2020

Preprint

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“…Although SLD's model suggests that task-switching involves many aspects of control, it does not suggest an involvement of the key control element of working memory. This conclusion seems to be in line with the fact that following novel instructions is impaired by working memory load (Pereg & Meiran, 2019). However, working memory seems to be relatively minimally involved in the task-switching task, in which the same instructions are executed multiple times (e.g., Kessler & Meiran, 2009;Rubin & Meiran, 2005;van 't Wout, Lavric, & Monsell, 2013).…”

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confidence: 65%

Simple Control

Meiran

2020

Journal of Cognition

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Schmidt, Liefooghe and De Houwer (2020, henceforth, SLD) present an impressive theoretical work, which suggests a novel perspective on task-switching behavior and also shows its unique contribution relative to other models. In a nutshell, SLD show that switching-cost, believed to be an empirical marker of cognitive control, can be explained in terms of simple episodic binding. Models serve for model-based estimation of latent variables (e.g., Signal Detection Theory, McMillan, 2004, enabling the estimation of sensitivity and bias) and as proofs of concept (e.g., SLD's model, showing that episodic binding can explain switching cost), thereby clarifying the necessary assumptions in a scientific explanation. I address these two aspects in turn. Estimation of latent variables is widespread in cognitive psychology, with subtraction (also employed to compute switch cost) being probably the most widely used model. As usual, the estimate (e.g., switch cost) is valid as long as the underlying model is approximately valid. To appreciate this point, consider a hypothetical model assuming that task-switch trials entail reconfiguration, a switch-unique proactive processing stage that precedes response selection. Under this model, switch-cost = RT switch-RT non-switch provides an estimate for the duration of the (latent) reconfiguration processing stage. We however already know this model to be inaccurate because (a) residual switch cost, i.e. switch cost observed after ample advance task-preparation, is often observed, suggesting the involvement of additional processes beyond reconfiguration; and (b), the slope describing the reduction in switch cost as a function of task preparation time is far shallower than-1 (see Pashler, 1994). This finding suggests that reconfiguration, at minimum, is very slow and inefficient when performed ahead of the imperative stimulus, a fact that seems to argue against the proactivity hypothesis in general. SLD provide an alternative account of switch costs but seem to also suggest that switch costs do not represent cognitive control but represent what they describe as simpler memory mechanisms. I doubt this conclusion and will suggest one challenging fact: the increased switch cost observed in attention deficits. This finding that has been replicated several times, including a related finding of "normalization" of switch costs under methylphenidate treatment (

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“…Specifically, the Interference phase in the I-D paradigm requires remembering and choosing the correct response from a set of two rules (if italic press right; if upright press left). Differently, in the NEXT paradigm, participants are not required to make any choice but are instead asked to "simply advance the screen using a constant NEXT response" (for example, see Pereg & Meiran, 2019 ). One may suggest that this dissimilarity causes different WM loads between the two paradigms, with a higher WM load in I-D than in NEXT.…”

Section: Differences Between the Paradigmsmentioning

confidence: 99%

“…Two main behavioral markers indicate that implementing new instructions is highly efficient and autonomous: (1) High accuracy rate from first execution (around 90%, for few examples, see Cole, 2009 ; Pereg & Meiran, 2019 ; Theeuwes et al, 2015 ). This marker indicates that the instructions had been learned (to a substantial degree) without any prior practice.…”

Section: Introductionmentioning

confidence: 99%

“…When examining AEI in the I-D paradigm, it is usually about 20 ms (Everaert et al, 2014 ; Liefooghe et al, 2012 , 2013 ; Theeuwes et al, 2015 ). In contrast, in the NEXT paradigm, AEI ranges from about 40 to 100 ms (Meiran, et al, 2015 , 2016 ; Pereg & Meiran, 2019 ). Do these differences tell us something about the conditions that promote the automaticity of instructions?…”

Section: Introductionmentioning

confidence: 99%

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Automatic effects of instructions: a tale of two paradigms

Amir

Peleg

Meiran

2021

Psychological Research

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When examining rapid instructed task learning behaviorally, one out of two paradigms is usually used, the Inducer-Diagnostic (I-D) and the NEXT paradigm. Even though both paradigms are supposed to examine the same phenomenon of Automatic Effect of Instructions (AEI), there are some meaningful differences between them, notably in the size of the AEI. In the current work, we examined, in two pre-registered studies, the potential reasons for these differences in AEI size. Study 1 examined the influence of the data-analytic approach by comparing two existing relatively large data-sets, one from each paradigm (Braem et al., in Mem Cogn 47:1582–1591, 2019; Meiran et al., in Neuropsychologia 90:180–189, 2016). Study 2 focused on the influence of instruction type (concrete, as in NEXT, and abstract, as in I-D) and choice complexity of the task in which AEI-interference is assessed. We did that while using variants of the NEXT paradigm, some with modifications that approximated it to the I-D paradigm. Results from Study 1 indicate that the data-analytic approach partially explains the differences between the paradigms in terms of AEI size. Still, the paradigms remained different with respect to individual differences and with respect to AEI size in the first step following the instructions. Results from Study 2 indicate that Instruction type and the choice complexity in the phase in which AEI is assessed do not influence AEI size, or at least not in the expected direction. Theoretical and study-design implications are discussed. Supplementary Information The online version contains supplementary material available at 10.1007/s00426-021-01596-1.

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Rapid instructed task learning (but not automatic effects of instructions) is influenced by working memory load

Cited by 13 publications

References 36 publications

Working Memory Capacity Predicts Individual Differences in Social Distancing Compliance during the COVID-19 Pandemic in the U.S.

Working Memory Capacity Predicts Individual Differences in Social Distancing Compliance during the COVID-19 Pandemic in the U.S.

Simple Control

Automatic effects of instructions: a tale of two paradigms

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