The Experts below are selected from a list of 27270 Experts worldwide ranked by ideXlab platform
Thomas L Griffiths - One of the best experts on this subject based on the ideXlab platform.
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advancing rational analysis to the algorithmic level
Behavioral and Brain Sciences, 2020Co-Authors: Falk Lieder, Thomas L GriffithsAbstract:: The commentaries raised questions about normativity, Human Rationality, cognitive architectures, cognitive constraints, and the scope or resource rational analysis (RRA). We respond to these questions and clarify that RRA is a methodological advance that extends the scope of rational modeling to understanding cognitive processes, why they differ between people, why they change over time, and how they could be improved.
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resource rational analysis understanding Human cognition as the optimal use of limited computational resources
Behavioral and Brain Sciences, 2020Co-Authors: Falk Lieder, Thomas L GriffithsAbstract:Modeling Human cognition is challenging because there are infinitely many mechanisms that can generate any given observation. Some researchers address this by constraining the hypothesis space through assumptions about what the Human mind can and cannot do, while others constrain it through principles of Rationality and adaptation. Recent work in economics, psychology, neuroscience, and linguistics has begun to integrate both approaches by augmenting rational models with cognitive constraints, incorporating rational principles into cognitive architectures, and applying optimality principles to understanding neural representations. We identify the rational use of limited resources as a unifying principle underlying these diverse approaches, expressing it in a new cognitive modeling paradigm called resource-rational analysis . The integration of rational principles with realistic cognitive constraints makes resource-rational analysis a promising framework for reverse-engineering cognitive mechanisms and representations. It has already shed new light on the debate about Human Rationality and can be leveraged to revisit classic questions of cognitive psychology within a principled computational framework. We demonstrate that resource-rational models can reconcile the mind's most impressive cognitive skills with people's ostensive irRationality. Resource-rational analysis also provides a new way to connect psychological theory more deeply with artificial intelligence, economics, neuroscience, and linguistics.
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strategy selection as rational metareasoning
Neural Information Processing Systems, 2017Co-Authors: Falk Lieder, Thomas L GriffithsAbstract:: Many contemporary accounts of Human reasoning assume that the mind is equipped with multiple heuristics that could be deployed to perform a given task. This raises the question of how the mind determines when to use which heuristic. To answer this question, we developed a rational model of strategy selection, based on the theory of rational metareasoning developed in the artificial intelligence literature. According to our model people learn to efficiently choose the strategy with the best cost-benefit tradeoff by learning a predictive model of each strategy's performance. We found that our model can provide a unifying explanation for classic findings from domains ranging from decision-making to arithmetic by capturing the variability of people's strategy choices, their dependence on task and context, and their development over time. Systematic model comparisons supported our theory, and 4 new experiments confirmed its distinctive predictions. Our findings suggest that people gradually learn to make increasingly more rational use of fallible heuristics. This perspective reconciles the 2 poles of the debate about Human Rationality by integrating heuristics and biases with learning and Rationality. (PsycINFO Database Record
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when to use which heuristic a rational solution to the strategy selection problem
Cognitive Science, 2015Co-Authors: Falk Lieder, Thomas L GriffithsAbstract:The Human mind appears to be equipped with a toolbox full of cognitive strategies, but how do people decide when to use which strategy? We leverage rational metareasoning to derive a rational solution to this problem and apply it to decision making under uncertainty. The resulting theory reconciles the two poles of the debate about Human Rationality by proposing that people gradually learn to make rational use of fallible heuristics. We evaluate this theory against empirical data and existing accounts of strategy selection (i.e. SSL and RELACS). Our results suggest that while SSL and RELACS can explain people’s ability to adapt to homogeneous environments in which all decision problems are of the same type, rational metareasoning can additionally explain people’s ability to adapt to heterogeneous environments and flexibly switch strategies from one decision to the next.
Falk Lieder - One of the best experts on this subject based on the ideXlab platform.
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advancing rational analysis to the algorithmic level
Behavioral and Brain Sciences, 2020Co-Authors: Falk Lieder, Thomas L GriffithsAbstract:: The commentaries raised questions about normativity, Human Rationality, cognitive architectures, cognitive constraints, and the scope or resource rational analysis (RRA). We respond to these questions and clarify that RRA is a methodological advance that extends the scope of rational modeling to understanding cognitive processes, why they differ between people, why they change over time, and how they could be improved.
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resource rational analysis understanding Human cognition as the optimal use of limited computational resources
Behavioral and Brain Sciences, 2020Co-Authors: Falk Lieder, Thomas L GriffithsAbstract:Modeling Human cognition is challenging because there are infinitely many mechanisms that can generate any given observation. Some researchers address this by constraining the hypothesis space through assumptions about what the Human mind can and cannot do, while others constrain it through principles of Rationality and adaptation. Recent work in economics, psychology, neuroscience, and linguistics has begun to integrate both approaches by augmenting rational models with cognitive constraints, incorporating rational principles into cognitive architectures, and applying optimality principles to understanding neural representations. We identify the rational use of limited resources as a unifying principle underlying these diverse approaches, expressing it in a new cognitive modeling paradigm called resource-rational analysis . The integration of rational principles with realistic cognitive constraints makes resource-rational analysis a promising framework for reverse-engineering cognitive mechanisms and representations. It has already shed new light on the debate about Human Rationality and can be leveraged to revisit classic questions of cognitive psychology within a principled computational framework. We demonstrate that resource-rational models can reconcile the mind's most impressive cognitive skills with people's ostensive irRationality. Resource-rational analysis also provides a new way to connect psychological theory more deeply with artificial intelligence, economics, neuroscience, and linguistics.
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strategy selection as rational metareasoning
Neural Information Processing Systems, 2017Co-Authors: Falk Lieder, Thomas L GriffithsAbstract:: Many contemporary accounts of Human reasoning assume that the mind is equipped with multiple heuristics that could be deployed to perform a given task. This raises the question of how the mind determines when to use which heuristic. To answer this question, we developed a rational model of strategy selection, based on the theory of rational metareasoning developed in the artificial intelligence literature. According to our model people learn to efficiently choose the strategy with the best cost-benefit tradeoff by learning a predictive model of each strategy's performance. We found that our model can provide a unifying explanation for classic findings from domains ranging from decision-making to arithmetic by capturing the variability of people's strategy choices, their dependence on task and context, and their development over time. Systematic model comparisons supported our theory, and 4 new experiments confirmed its distinctive predictions. Our findings suggest that people gradually learn to make increasingly more rational use of fallible heuristics. This perspective reconciles the 2 poles of the debate about Human Rationality by integrating heuristics and biases with learning and Rationality. (PsycINFO Database Record
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when to use which heuristic a rational solution to the strategy selection problem
Cognitive Science, 2015Co-Authors: Falk Lieder, Thomas L GriffithsAbstract:The Human mind appears to be equipped with a toolbox full of cognitive strategies, but how do people decide when to use which strategy? We leverage rational metareasoning to derive a rational solution to this problem and apply it to decision making under uncertainty. The resulting theory reconciles the two poles of the debate about Human Rationality by proposing that people gradually learn to make rational use of fallible heuristics. We evaluate this theory against empirical data and existing accounts of strategy selection (i.e. SSL and RELACS). Our results suggest that while SSL and RELACS can explain people’s ability to adapt to homogeneous environments in which all decision problems are of the same type, rational metareasoning can additionally explain people’s ability to adapt to heterogeneous environments and flexibly switch strategies from one decision to the next.
X U Yingji - One of the best experts on this subject based on the ideXlab platform.
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why does evolution favor Human cognitive architecture tending to commit conjunction fallacy
Fudan Journal, 2014Co-Authors: X U YingjiAbstract:The so-called "conjunction fallacy"occurs when it is theoretically assumed that the conjunction of two conditions is more probable than a single general one. Since a high ratio of subjects are tending to commit this fallacy in psychological experiments,it may be tempting to assume that Human-beings are by nature irrational in the sense that most of them cannot avoid violating simple rules of probability theory in practical reasoning. But perhaps philosophers can still defend Human Rationality by re-interpreting conjunction fallacy. More specifically,three arguments will be provided in this article for rationalizing Human's preference of conjunctions rather than conjuncts. First,when certain background information is given,conjunctions partially confirmed by it should be more favorable than conjuncts entirely irrelevant to it. Second,conjunctions are more favorable than conjuncts also from the evolutionary perspective in the sense that the former is routinely more valuable in guiding and forming agents' actions leading to their survival and reproduction. Last,the risk of betting on a conjunction( with a low intrinsic probability value) would sometimes be offset by the higher reward of doing it,whereas the reward of betting on a conjunct is usually lower.
Lael J. Schooler - One of the best experts on this subject based on the ideXlab platform.
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ecological Rationality a framework for understanding and aiding the aging decision maker
Frontiers in Neuroscience, 2012Co-Authors: Rui Mata, Ralph Hertwig, Thorsten Pachur, Bettina Von Helversen, Jörg Rieskamp, Lael J. SchoolerAbstract:Ecological Rationality sees Human Rationality as the result of the adaptive fit between the Human mind and the environment. The concept of ecological Rationality focuses the study of cognition on two key questions: First, what are the environmental regularities to which people’s decision strategies are matched, and how frequently do these regularities occur in natural environments? Second, how well can people adapt their use of specific strategies to particular environments? Research on aging suggests a number of changes in cognitive function, for instance, deficits in learning and memory that may impact decision-making skills. However, it has been shown that simple strategies can work well in many natural environments, which suggests that age-related deficits in strategy use may not necessarily translate into diminished decision performance. Consequently, we argue that predictions about the impact of aging on decision performance depend not only on how aging affects decision-relevant capacities but also on the decision ecology in which decisions are made. In sum, we propose that the concept of the ecological Rationality is crucial to understanding and aiding the aging decision maker.
Bradley C. Love - One of the best experts on this subject based on the ideXlab platform.
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Limits in decision making arise from limits in memory retrieval
Proceedings of the National Academy of Sciences of the United States of America, 2013Co-Authors: Gyslain Giguère, Bradley C. LoveAbstract:Some decisions, such as predicting the winner of a baseball game, are challenging in part because outcomes are probabilistic. When making such decisions, one view is that Humans stochastically and selectively retrieve a small set of relevant memories that provides evidence for competing options. We show that optimal performance at test is impossible when retrieving information in this fashion, no matter how extensive training is, because limited retrieval introduces noise into the decision process that cannot be overcome. One implication is that people should be more accurate in predicting future events when trained on idealized rather than on the actual distributions of items. In other words, we predict the best way to convey information to people is to present it in a distorted, idealized form. Idealization of training distributions is predicted to reduce the harmful noise induced by immutable bottlenecks in people’s memory retrieval processes. In contrast, machine learning systems that selectively weight (i.e., retrieve) all training examples at test should not benefit from idealization. These conjectures are strongly supported by several studies and supporting analyses. Unlike machine systems, people’s test performance on a target distribution is higher when they are trained on an idealized version of the distribution rather than on the actual target distribution. Optimal machine classifiers modified to selectively and stochastically sample from memory match the pattern of Human performance. These results suggest firm limits on Human Rationality and have broad implications for how to train Humans tasked with important classification decisions, such as radiologists, baggage screeners, intelligence analysts, and gamblers.