Strategy selection as rational metareasoning.

Lieder, Falk; Griffiths, Thomas L.

doi:10.1037/rev0000075

Cited by 134 publications

(139 citation statements)

References 81 publications

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“…Thus, even within the experimental context in which there is not the opportunity to make multiple interventions, children's use of a more confirmatory strategy may actually reflect a rational use of cognitive resources (Lieder & Griffiths, 2017). Future studies could isolate changes in ability from changes in effort allocation, by raising the cost of making an uninformative intervention or forcing all participants to spend a long time deliberating prior to allowing them to perform their intervention.…”

Section: Discussionmentioning

confidence: 99%

Causal information-seeking strategies change across childhood and adolescence

Nussenbaum¹,

Cohen²,

Davis³

et al. 2019

Preprint

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Intervening on causal systems can illuminate their underlying structures. Past work has shown that, relative to adults, young children often make intervention decisions that appear to confirm a single hypothesis rather than those that optimally discriminate alternative hypotheses. Here, we investigated how the ability to make informative causal interventions changes across development. Ninety participants between the ages of 7 and 25 completed 40 different puzzles in which they had to intervene on various causal systems to determine their underlying structures. Each puzzle comprised a three- or four-node computer chip with hidden wires. On each trial, participants viewed two possible arrangements of the chip’s hidden wires and had to select a single node to activate. After observing whether their intervention caused other nodes to turn on, participants selected a wire configuration, and rated their confidence in their selection. We characterized participant choices with a Bayesian measurement model that indexed the extent to which participants selected nodes that would best disambiguate the two possible causal structures vs. those that elicited evidence in favor of one of the two causal hypotheses, but did not necessarily discriminate between them. Our model estimates revealed that the use of discriminatory strategies increased through early adolescence. Further, developmental improvements in intervention strategy were related to changes in the ability to accurately judge the strength of evidence that their interventions revealed, as indexed by participants’ confidence in their selections. Our results suggest that improvements in causal information-seeking extend into adolescence and may be driven by metacognitive sensitivity to the efficacy of previous interventions in discriminating competing ideas.

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Section: Discussionmentioning

confidence: 99%

Causal information-seeking strategies change across childhood and adolescence

Nussenbaum¹,

Cohen²,

Davis³

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…However, we deem it likely that both mechanisms contributed simultaneously, which has already been proposed for situations in which problem solvers can select between internal and external strategies (Gilbert, 2015;Risko & Gilbert, 2016) and in which they can select between different internal strategies (for a review, see Lieder & Griffiths, 2017). However, we deem it likely that both mechanisms contributed simultaneously, which has already been proposed for situations in which problem solvers can select between internal and external strategies (Gilbert, 2015;Risko & Gilbert, 2016) and in which they can select between different internal strategies (for a review, see Lieder & Griffiths, 2017).…”

Section: How Do Problem Solvers Establish a Goal-driven Recruitment Omentioning

confidence: 99%

Problem Solvers Adjust Cognitive Offloading Based on Performance Goals

Weis

Wiese

2019

Cognitive Science

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When incorporating the environment into mental processing (cf., cognitive offloading), one creates novel cognitive strategies that have the potential to improve task performance. Improved performance can, for example, mean faster problem solving, more accurate solutions, or even higher grades at university. 1 Although cognitive offloading has frequently been associated with improved performance, it is yet unclear how flexible problem solvers are at matching their offloading habits with their current performance goals (can people improve goal-related instead of generic performance, e.g., when being in a hurry and aiming for a "quick and dirty" solution?). Here, we asked participants to solve a cognitive task, provided them with different goals -maximizing speed (SPD) or accuracy (ACC), respectively-and measured how frequently (Experiment 1) and how proficiently (Experiment 2) they made use of a novel external resource to support their cognitive processing. Experiment 1 showed that offloading behavior varied with goals: Participants offloaded less in the SPD than in the ACC condition. Experiment 2 showed that this differential offloading behavior was associated with high goal-related performance: fast answers in the SPD, accurate answers in the ACC condition. Simultaneously, goal-unrelated performance was sacrificed: inaccurate answers in the SPD, slow answers in the ACC condition. The findings support the notion of humans as canny offloaders who are able to successfully incorporate their environment in pursuit of their current cognitive goals. Future efforts should be focused on the finding's generalizability, for example, to settings without feedback or with high mental workload.

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“…Our approach falls into the class of rational process models, that explain biases as the result of the algorithm used to perform inference (Griffiths, Vul, & Sanborn, 2012;Sanborn & Chater, 2016;Sanborn et al, 2010). Recently, this approach has been extended to derive biases from a rational use of time or limited cognitive resources (Griffiths, Lieder, & Goodman, 2015;Lieder & Griffiths, 2017). The Bayesian sampling model is in the same spirit of the resource-rational framework as it aims to produce the best possible adjustment given a limited number of samples.…”

Section: A Bayesian Sampling Model Of Conservatism In Probability Judmentioning

confidence: 99%

The Bayesian Sampler: Generic Bayesian Inference Causes Incoherence in Human Probability Judgments

Zhu¹,

Sanborn²,

Chater³

2018

Preprint

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Human probability judgments are systematically biased, in apparent tension with Bayesian models of cognition. But perhaps the brain does not represent probabilities explicitly, but approximates probabilistic calculations through a process of sampling, as used in computational probabilistic models in statistics. Naïve probability estimates can be obtained by calculating the relative frequency of an event within a sample, but these estimates tend to be extreme when the sample size is small. We propose instead that people use a generic prior to improve the accuracy of their probability estimates based on samples, and we call this model the Bayesian sampler. The Bayesian sampler trades off the coherence of probabilistic judgments for improved accuracy, and provides a single framework for explaining phenomena associated with diverse biases and heuristics such as conservatism and the conjunction fallacy. The approach turns out to provide a rational reinterpretation of “noise” in an important recent model of probability judgment, the probability theory plus noise model (Costello & Watts, 2014, 2016a, 2017, 2019; Costello, Watts, & Fisher, 2018), making equivalent average predictions for simple events, conjunctions, and disjunctions. The Bayesian sampler does, however, make distinct predictions for conditional probabilities, and we show in a new experiment that this model better captures these judgments both qualitatively and quantitatively.

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Strategy selection as rational metareasoning.

Cited by 134 publications

References 81 publications

Causal information-seeking strategies change across childhood and adolescence

Causal information-seeking strategies change across childhood and adolescence

Problem Solvers Adjust Cognitive Offloading Based on Performance Goals

The Bayesian Sampler: Generic Bayesian Inference Causes Incoherence in Human Probability Judgments

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