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  1. Bayesian reverse-engineering considered as a research strategy for cognitive science.Carlos Zednik & Frank Jäkel - 2016 - Synthese 193 (12):3951-3985.
    Bayesian reverse-engineering is a research strategy for developing three-level explanations of behavior and cognition. Starting from a computational-level analysis of behavior and cognition as optimal probabilistic inference, Bayesian reverse-engineers apply numerous tweaks and heuristics to formulate testable hypotheses at the algorithmic and implementational levels. In so doing, they exploit recent technological advances in Bayesian artificial intelligence, machine learning, and statistics, but also consider established principles from cognitive psychology and neuroscience. Although these tweaks and heuristics are highly pragmatic in character and (...)
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  2.  22
    Flexible use of tactics in Sudoku.Thea Behrens, Max Räuker, Michelle Kalbfleisch & Frank Jäkel - 2023 - Thinking and Reasoning 29 (4):488-530.
    When we solve problems there are usually a number of different strategies that we can use. It is a hallmark of intelligence that we, as humans, are able to flexibly choose between them. Siegler and...
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  3.  14
    Solving Bongard Problems With a Visual Language and Pragmatic Constraints.Stefan Depeweg, Contantin A. Rothkopf & Frank Jäkel - 2024 - Cognitive Science 48 (5):e13432.
    More than 50 years ago, Bongard introduced 100 visual concept learning problems as a challenge for artificial vision systems. These problems are now known as Bongard problems. Although they are well known in cognitive science and artificial intelligence, only very little progress has been made toward building systems that can solve a substantial subset of them. In the system presented here, visual features are extracted through image processing and then translated into a symbolic visual vocabulary. We introduce a formal language (...)
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    Descending Marr's levels: Standard observers are no panacea.Carlos Zednik & Frank Jäkel - 2018 - Behavioral and Brain Sciences 41:e249.
    According to Marr, explanations of perceptual behavior should address multiple levels of analysis. Rahnev & Denison (R&D) are perhaps overly dismissive of optimality considerations at the computational level. Also, an exclusive reliance on standard observer models may cause neglect of many other plausible hypotheses at the algorithmic level. Therefore, as far as explanation goes, standard observer modeling is no panacea.
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    Towards a taxonomy of tasks for human sequential decision-making.Claire Ott, Inga Ibs, Constantin A. Rothkopf & Frank Jäkel - forthcoming - Thinking and Reasoning.
    People face a vast range of different cognitive tasks in their lives. Classic problem-solving theories do not fully capture the impact of the task structure on sequential decision-making. Here, we argue that it is important to consider task features that determine the search space structure of a task as it presents itself to the problem solver because the search space determines which algorithms are appropriate to solve the problem optimally. Knowledge of these optimal algorithms provides a base for cognitive modelling (...)
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