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• W2767526654 abstract "A Bayesian Antidote Against Strategy Sprawl Benjamin Scheibehenne (benjamin.scheibehenne@unibas.ch) University of Basel, Missionsstrasse 62a 4055 Basel, Switzerland Jorg Rieskamp (joerg.rieskamp@unibas.ch) University of Basel, Missionsstrasse 62a 4055 Basel, Switzerland Abstract Many theories in cognitive science assume that people possess a repertoire of strategies or a toolbox from which they choose depending on the situation. This approach suffers from the problem that the number of assumed strategies is often not constrained and may be extended post-hoc to improve the fit to the data. This makes it difficult to rigorously test and compare strategy repertoire models. To prevent this strategy sprawl , a criterion is necessary to decide how many strategies a toolbox should include. Here, Bayesian statistics provide a powerful tool to evaluate toolboxes of different sizes based on their marginal likelihoods. The present work illustrates how such a Bayesian approach can be implemented and demonstrates its applicability by means of parameter recovery studies. Our approach also makes the novel contribution of showing how Bayesian statistics allow testing the strategy repertoire theory against alternative decision theories. Keywords: Strategy repertoire theories, Bayes factor, model selection, simulation, Bugs. but not necessarily to greater insight. For example, by assuming a specific tool for each possible task, the toolbox should provide a good description of observed behavior due to its great flexibility. Along the same lines, Dougherty, Thomas, and Franco-Watkins (2008) criticized that in a situation in which no strategy out of a set is able to describe a person’s choices, an unconstrained toolbox could be enlarged by a new strategy to describe the data. On the other hand, if the toolbox is restricted to only a few or to a single strategy it would lose its ability to describe different cognitive processes. In the following, we outline a possible solution to the question of how many tools a toolbox should contain based on a Bayesian approach. Having a criterion for determining how many strategies to include keeps strategy sprawl at bay and is also a necessary pre-condition for rigorously comparing different toolbox models with competing cognitive theories that do not assume different strategies but rely on the idea of an all-purpose process (Newell, 2005). Example of a cognitive toolbox The problem of strategy sprawl A common assumption within many research areas in cognitive science is that people possess a repertoire of cognitive strategies to solve the problems they face. For example, people use different strategies for making consumer decisions (Payne, Bettman, and Johnson, 1993), for organizational memory tasks (Coyle, Read, Gaultney, & Bjorklund, 1998), for estimations of frequencies (Brown, 1995), for categorization problems (Patalano, Smith, Jonides, & Koeppe, 2001), for the development of mathematical skills (Siegler, 1991), or for inference problems (Gigerenzer, Todd, and the ABC Research Group, 1999). The strategy repertoire approach provides a fruitful way to explain intra- and inter-individual differences in cognitive processes. This approach has also been described by the metaphor of an adaptive toolbox according to which individual decision makers select between different cognitive strategies to solve specific tasks just as a craftsman selects tools from a toolbox. Despite its undisputed success in explaining a wide range of human behavior, the idea of a toolbox raises the question of how many different strategies the mental toolbox should contain in the first place. A larger number of possible strategies will always lead to a better description of the data As an illustrative example of a cognitive toolbox, imagine a situation in which a person tries to determine which of two used cars is a better deal. To make this decision, a person could use different pieces of information (i.e., cues) such as mileage, number of previous owners, or accident history of the cars. In such a situation, each single cue provides a hint of which car might be better, but none of the cues provide an indisputable prediction, because it could be that a car with many previous owners still turns out to be superior overall. In other words, the cues are probabilistically related to the criterion, so that even an object with positive cue values for all cues could sometimes be inferior compared to an object with negative cue values. Probabilistic inferences can be complicated because it is not always clear which information is relevant and how and whether the different pieces of information should be combined. To make probabilistic inferences, such as choosing the better of two cars, people may choose from a variety of cognitive strategies, that is, from their adaptive toolbox (Gigerenzer, Todd & the ABC Research Group, 1999). For instance, when choosing between two options, people could use a simple non-compensatory decision strategy called take the best (TTB) that only focuses on the single most important or valid cue that discriminates between the two" @default.
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• W2767526654 title "A Bayesian Antidote Against Strategy Sprawl" @default.
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