Cognitive and Computational Complexity: Considerations from Mathematical Problem Solving.

Following Marr's famous three-level distinction between explanations in cognitive science, it is often accepted that focus on modeling cognitive tasks should be on the computational level rather than the algorithmic level. When it comes to mathematical problem solving, this approach suggests that th...

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Publicado en:Erkenntnis Vol. 86; no. 4; pp. 961 - 998
Autor principal: Pantsar, Markus
Formato: Artículo
Publicado: Springer Nature Aug2021
Materias:
Acceso en línea:Ver este registro en EBSCOhost
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        atl: Cognitive and Computational Complexity: Considerations from Mathematical Problem Solving.
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        au: Pantsar, Markus
        affil: Department of Philosophy, History and Art, University of Helsinki, Unioninkatu 40A, 00014, Helsinki, Finland
      su:
        Computational complexity
        Problem solving
        Cognitive science
        Socialization
      sug:
        subj:
          Computational complexity
          Problem solving
          Cognitive science
          Socialization
      ab: Following Marr's famous three-level distinction between explanations in cognitive science, it is often accepted that focus on modeling cognitive tasks should be on the computational level rather than the algorithmic level. When it comes to mathematical problem solving, this approach suggests that the complexity of the task of solving a problem can be characterized by the computational complexity of that problem. In this paper, I argue that human cognizers use heuristic and didactic tools and thus engage in cognitive processes that make their problem solving algorithms computationally suboptimal, in contrast with the optimal algorithms studied in the computational approach. Therefore, in order to accurately model the human cognitive tasks involved in mathematical problem solving, we need to expand our methodology to also include aspects relevant to the algorithmic level. This allows us to study algorithms that are cognitively optimal for human problem solvers. Since problem solving methods are not universal, I propose that they should be studied in the framework of enculturation, which can explain the expected cultural variance in the humanly optimal algorithms. While mathematical problem solving is used as the case study, the considerations in this paper concern modeling of cognitive tasks in general.
      pubtype: Academic Journal
      doctype: Article
      src: R
    language: English
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