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Strategies for memory-based decision making: Modeling behavioral and neural signatures within a cognitive architecture

Fechner, H. B., Pachur, T., Schooler, L. J., Mehlhorn, K., Battal, C., Volz, K. G. & Borst, J. P., Dec-2016, In : Cognition. 157, p. 77-99 23 p.

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  • Strategies for memory-based decision making

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DOI

How do people use memories to make inferences about real-world objects? We tested three strategies based on predicted patterns of response times and blood-oxygen-level-dependent (BOLD) responses: one strategy that relies solely on recognition memory, a second that retrieves additional knowledge, and a third, lexicographic (i.e., sequential) strategy, that considers knowledge conditionally on the evidence obtained from recognition memory. We implemented the strategies as computational models within the Adaptive Control of Thought-Rational (ACT-R) cognitive architecture, which allowed us to derive behavioral and neural predictions that we then compared to the results of a functional magnetic resonance imaging (fMRI) study in which participants inferred which of two cities is larger. Overall, versions of the lexicographic strategy, according to which knowledge about many but not all alternatives is searched, provided the best account of the joint patterns of response times and BOLD responses. These results provide insights into the interplay between recognition and additional knowledge in memory, hinting at an adaptive use of these two sources of information in decision making. The results highlight the usefulness of implementing models of decision making within a cognitive architecture to derive predictions on the behavioral and neural level.
Original languageEnglish
Pages (from-to)77-99
Number of pages23
JournalCognition
Volume157
Publication statusPublished - Dec-2016

    Keywords

  • Decision making, Memory, Recognition heuristic, ACT-R, Computational modeling, Neuroimaging, PROBABILISTIC MENTAL MODELS, MULTIPLE-CUE JUDGMENT, FMRI ANALYSIS, RECOGNITION, RETRIEVAL, TIME, CONFIDENCE, INFERENCE, FRUGAL, REPRESENTATION

ID: 35279937