Mechanisms for Robust Cognition

Walsh, Matthew M.; Gluck, Kevin A.

doi:10.1111/cogs.12192

Cited by 10 publications

(5 citation statements)

References 125 publications

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“…The idea that participants’ prior experience with benefiting from external normalization contributes to the decision to try to offload raises an important issue. Specifically, as noted above, models of strategy selection that incorporate such an influence (Lovett & Anderson, ; Rieskamp & Otto, ; Walsh & Gluck, ) tend to be based on evidence that participants are influenced by their experience with strategy use within the experiment. However, we did not find similar evidence here.…”

Section: Discussionmentioning

confidence: 99%

“…This history would arguably be absent for the RW‐UF and UW‐RF displays, despite the fact that external normalization does benefit performance in the former as much as it does in the case of the RW‐RF display. This kind of experienced‐based strategy selection is an important part of at least some models of strategy selection (e.g., Lovett & Anderson, ; Rieskamp & Otto, ; Walsh & Gluck, ), but it has rarely been considered in the cognitive offloading domain, which has focused more on online performance/effort in driving the behavior. The former theories feature a kind of learning mechanism that associates a stimulus/task context with a strategy (still based on performance), and this learning biases strategy selection.…”

Section: Summary Of Experiments 1–4mentioning

confidence: 99%

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Toward a Metacognitive Account of Cognitive Offloading

2015

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Individuals frequently make use of the body and environment when engaged in a cognitive task. For example, individuals will often spontaneously physically rotate when faced with rotated objects, such as an array of words, to putatively offload the performance costs associated with stimulus rotation. We looked to further examine this idea by independently manipulating the costs associated with both word rotation and array frame rotation. Surprisingly, we found that individuals' patterns of spontaneous physical rotations did not follow patterns of performance costs or benefits associated with being physically rotated, findings difficult to reconcile with existing theories of strategy selection involving external resources. Individuals' subjective ratings of perceived benefits, rather, provided an excellent match to the patterns of physical rotations, suggesting that the critical variable when deciding on-the-fly whether to incorporate an external resource is the participant's metacognitive beliefs regarding expected performance or the effort required for each approach (i.e., internal vs. internal + external). Implications for metacognition's future in theories of cognitive offloading are discussed.

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Section: Discussionmentioning

confidence: 99%

Section: Summary Of Experiments 1–4mentioning

confidence: 99%

Toward a Metacognitive Account of Cognitive Offloading

2015

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“…Which heuristic is ecologically rational to use hence depends on the specific properties of the decision environment. In addition, heuristics can be more robust to variations in environments than optimization approaches, as Walsh, Einstein, and Gluck (2013) recently demonstrated in a series of computer simulations (see also Gluck, 2015 andGluck et al, 2012 on the notion of robustness).…”

Section: The Decision Environmentmentioning

confidence: 99%

Applied decision making with fast-and-frugal heuristics.

Hafenbrädl¹,

Waeger²,

Marewski³

et al. 2016

Journal of Applied Research in Memory and Cognition

110

112

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In applied settings, such as aviation, medicine, and finance, individuals make decisions under various degrees of uncertainty, that is, when not all risks are known or can be calculated. In such situations, decisions can be made using fast-and-frugal heuristics. These are simple strategies that ignore part of the available information. In this article, we propose that the conceptual lens of fast-and-frugal heuristics is useful not only for describing but also for improving applied decision making. By exploiting features of the environment and capabilities of the decision makers, heuristics can be simple without trading off accuracy. Because decision aids based on heuristics build on how individuals make decisions, they can be adopted intuitively and used effectively. Beyond enabling accurate decisions, heuristics possess characteristics that facilitate their adaptation to varied settings. These characteristics include accessibility, speed, transparency, and cost effectiveness. Altogether, the article offers an overview of the literature on fast-and-frugal heuristics and their usefulness in diverse applied settings.

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“…Non-optimal human performance A simple answer to the first question is that people do not behave formally optimal in many decision situations [ 36 ] in general, and in the Sugar Factory in particular [ 14 , 15 ]. The structure of the human cognitive system seems to be geared towards robust information processing in typical human environments with incomplete or uncertain information [ 37 , 38 ], rather than formal optimization given strict assumptions. Another possibility is that the model of human cognition used in this study is not valid to start with.…”

Section: Properties Of the Model And Choice Of Optimization Methodsmentioning

confidence: 99%

Applying Mathematical Optimization Methods to an ACT-R Instance-Based Learning Model

et al. 2016

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Computational models of cognition provide an interface to connect advanced mathematical tools and methods to empirically supported theories of behavior in psychology, cognitive science, and neuroscience. In this article, we consider a computational model of instance-based learning, implemented in the ACT-R cognitive architecture. We propose an approach for obtaining mathematical reformulations of such cognitive models that improve their computational tractability. For the well-established Sugar Factory dynamic decision making task, we conduct a simulation study to analyze central model parameters. We show how mathematical optimization techniques can be applied to efficiently identify optimal parameter values with respect to different optimization goals. Beyond these methodological contributions, our analysis reveals the sensitivity of this particular task with respect to initial settings and yields new insights into how average human performance deviates from potential optimal performance. We conclude by discussing possible extensions of our approach as well as future steps towards applying more powerful derivative-based optimization methods.

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Mechanisms for Robust Cognition

Cited by 10 publications

References 125 publications

Toward a Metacognitive Account of Cognitive Offloading

Toward a Metacognitive Account of Cognitive Offloading

Applied decision making with fast-and-frugal heuristics.

Applying Mathematical Optimization Methods to an ACT-R Instance-Based Learning Model

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