Cognitive flexibility and adaptability to environmental changes in dynamic complex problem-solving tasks

Cañas, José J.; Quesada, José F.; Antolí, Adoración; Fajardo, Inmaculada

doi:10.1080/0014013031000061640

Cited by 312 publications

(201 citation statements)

References 21 publications

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“…On one hand, it may prevent inadequate changes in correct procedures in the face of misleading information, as reflected by a small number of Lossshift errors in this group. On the other hand, this tendency may be accompanied by less cognitive flexibility to recognize important cues demanding new behaviors, compromising them to react quickly to potential Bthreats^and to potentially commit perseverative mistakes more often (Cañas, Quesada, Antoli, & Fajardo, 2003;Norman & Shallice, 1986;Rougier, Noelle, Braver, Cohen, & O'Relly, 2005;Spiro, Coulson, Feltovich, & Andersib, 1988). The other way around might be the case for Low ET subjects who, being faster in reacting to cues demanding rule-based adaptive changes, are more prone to change ongoing correct actions following spurious information.…”

Section: Discussionmentioning

confidence: 99%

Individual differences in error tolerance in humans: Neurophysiological evidences

Padrão

Mallorquí²,

Cucurell

et al. 2015

Cogn Affect Behav Neurosci

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When interacting in error-prone environments, humans display different tolerances to changing their decisions when faced with erroneous feedback information. Here, we investigated whether these individual differences in error tolerance (ET) were reflected in neurophysiological mechanisms indexing specific motivational states related to feedback monitoring. To explore differences in ET, we examined the performance of 80 participants in a probabilistic reversal-learning task. We then compared eventrelated brain responses (ERPs) of two extreme groups of participants (High ET and Low ET), which showed radical differences in their propensity to maintain newly learned rules after receiving spurious negative feedback. We observed that High ET participants showed reduced anticipatory activity prior to the presentation of incoming feedback, informing them of the correctness of their performance. This was evidenced by measuring the amplitude of the stimulus-preceding negativity (SPN), an ERP component indexing attention and motivational engagement of incoming informative feedback. Postfeedback processing ERP components (the so-called Feedback-Related Negativity and the P300) also showed reduced amplitude in this group (High ET). The general decreased responsiveness of the High ET group to external feedback suggests a higher proneness to favor internal(rule)-based strategies, reducing attention to external cues and the consequent impact of negative evaluations on decision making. We believe that the present findings support the existence of specific cognitive and motivational processes underlying individual differences on error-tolerance among humans, contributing to the ongoing research focused on understanding the mental processes behind human fallibility in error-prone scenarios.Keywords Error tolerance . Decision-making . Action-monitoring . ERPs . Individual differences Humans have different tolerance to negative feedback about their performance and changing their ongoing behaviors based on external environmental cues. As these individual differences are likely to influence the way their decisions are acted upon, an important challenge for cognitive psychologists and neuroscientists is not only to understand the nature of the brain mechanisms and inherent cognitive and motivational processes underlying these differences but also to explain how these differences are related to (in)adequate decisional processes carried out by human agents in their interaction with real-world and error-prone environments.The study of human error processing has advanced enormously during the last 2 decades, mostly due to the discovery of specific event-related brain components (ERPs) and neural networks that respond selectively to error monitoring, error correction, and compensation processes (Carter et al., 1998;

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Section: Discussionmentioning

confidence: 99%

Individual differences in error tolerance in humans: Neurophysiological evidences

Padrão

Mallorquí²,

Cucurell

et al. 2015

Cogn Affect Behav Neurosci

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“…Their popularity and range make them ideal for studying the acquisition and transfer of skill-based knowledge in a variety of complex interactive environments (Campbell, 1988;Cañas, Quesada, Antoli, & Fajardo, 2003;Funke, 2001). A typical CDCT includes several inputs connected to several outputs via a complex causal structure or rule (See Figure 1).…”

Section: Complex Dynamic Control Tasks (Cdcts)mentioning

confidence: 99%

Positive transfer and negative transfer/antilearning of problem-solving skills.

Osman¹

2008

Journal of Experimental Psychology: General

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The present study examined how self-regulatory processes influenced problem solving behavior under conditions in which people were either re-exposed to their prior learning, or experienced someone else's. In a series of four experiments, participants solved two complex control tasks that were identical in structure but varied in presentation format. Participants either learnt to solve the second task, based on their original learning phase from the first task, or learnt to solve the second task based on another participant's learning phase. Experiment 1 showed that, under conditions in which participants' learning phase was experienced twice, performance deteriorated in the second task compared to the first. In contrast, when the learning phases in the first and second task differed, performance improved in the second task.Experiment 2 introduced instructional manipulations that induced the same response patterns as Experiment 1, and Experiment 3 showed that participants differentially weighted the learning phase in the second task. In Experiment 4, judgments of selfefficacy were shown to track control performance. The implications of these findings for induction, self-regulation, and insight are discussed. examples share a precise goal that involves accurately and reliably controlling a complex dynamic control task (CDCT-i.e., the aircraft). Control behavior in this, and other types of CDCTs (e.g., air-traffic control, naval mine sweeping, car driving) depends on the development of expertise (e.g., Brehmer, 1992;Funke, 2001; Gonzalez, Lerch, & Lebiere, 2003;Kerstholt, 1996;Randel & Pugh, 1996;Sweller, 1988;.Expertise involves both monitoring-through self-evaluation of goal-directed actions (e.g., Bandura & Locke, 2003; Ericsson & Lehman, 1996;Karoly, 1993;Lerch & Harter, 2001;Rossano, 2003;VanLehn, 1996)-and control, or the generation and selection of goal-directed actions (e.g., Lerch & Harter, 2001; Locke & Latham, 2002;Rossano, 2003;Sweller, 1988;VanLehn, 1996;Vollmeyer, Burns, & Holyoak, 1996). Both behaviors are also interrelated (e.g., Bandura & Locke, 2003; Ericsson & Lehman, 1996;Karoly, 1993;Lerch & Harter, 2001;Rossano, 2003;VanLehn, 1996). However, studies of skill learning in CDCTs have overlooked this relationship, particularly how it influences the transferability of expertise (e.g., Berry, 1991;Berry & Broadbent, 1988;Burns & Vollmeyer, 2002). Without an Anti-Learning 5 understanding of how individuals monitor their behavior, little can be said about how evaluative processes are employed when transferring learnt skills to achieve unpracticed goals.For instance, the critical difference between the examples above is that, in the first case, training (i.e., control performance) and assessment (i.e., monitoring) are based on self-generated behavior; whereas, in the second, assessment is based on comparing self-and other-generated training behavior. These types of training procedures are often used in educational (Pintrich & De Groot, 1990;Pressley, & Ghatala, 1990;Zimmerman, 1990), clin...

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“…Baer et al 2008;Bishop et al 2004;Kabat-zinn 1990). In this context, cognitive flexibility is defined as the ability to adapt cognitive strategies in response to new and unexpected conditions, and as intimately related to the (re-)investment of attention (Cañas et al 2003).…”

Section: Mindfulness Meditation Cognitive Flexibility and Mental Healthmentioning

confidence: 99%

Meditation, Cognitive Flexibility and Well-Being

Hartkamp

Thornton

2017

J Cogn Enhanc

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A number of previous studies have suggested that mindfulness meditation can enhance cognitive performance. Although both western empirical findings and Buddhist psychological theory have emphasised a role for cognitive flexibility in the development and maintenance of mental health, few studies have specifically focused on flexibility in relation to mindfulness. The present study used a range of objective, behavioural measures to assess cognitive flexibility in 41 individuals before and after a 6-day intensive Vipassana (mindfulness) retreat. Subjective assessments of mindfulness, positive functioning and well-being were also taken. A comparably sized control group of students and government employees was given the same pre-and post-test measures. In contrast to our expectations and to previous reports, there were no significant changes in cognitive performance, over and above practice-related improvements also shown by the control group. Retreat participants did, however, register positive improvements on the subjective measures. We discuss possible limitations with our experimental design and highlight important issues that may assist future studies aiming to assess effects of mindfulness in the cognitive domain.

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Cognitive flexibility and adaptability to environmental changes in dynamic complex problem-solving tasks

Cited by 312 publications

References 21 publications

Individual differences in error tolerance in humans: Neurophysiological evidences

Individual differences in error tolerance in humans: Neurophysiological evidences

Positive transfer and negative transfer/antilearning of problem-solving skills.

Meditation, Cognitive Flexibility and Well-Being

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