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Towards a taxonomy of tasks for human sequential decision-making.

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Title: Towards a taxonomy of tasks for human sequential decision-making.
Authors: Ott, Claire (AUTHOR), Ibs, Inga (AUTHOR), Rothkopf, Constantin A. (AUTHOR), Jäkel, Frank (AUTHOR)
Source: Thinking & Reasoning. Nov2025, Vol. 31 Issue 4, p439-473. 35p.
Subjects: Decision making, Human behavior, Cognition, Optimization algorithms, Task analysis, Human behavior models
Abstract: 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 of sequential decision-making. We propose a new task taxonomy by identifying ten features in four categories related to search space properties. We describe how these constrain the space of optimal algorithms. Furthermore, we categorise and compare several sequential decision-making tasks used in studies on human behaviour with our taxonomy and illustrate how the proposed task features can inform the creation of new tasks and computational modelling of behaviour. [ABSTRACT FROM AUTHOR]
Copyright of Thinking & Reasoning is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: Towards a taxonomy of tasks for human sequential decision-making.
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  Data: <searchLink fieldCode="JN" term="%22Thinking+%26+Reasoning%22">Thinking & Reasoning</searchLink>. Nov2025, Vol. 31 Issue 4, p439-473. 35p.
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  Data: 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 of sequential decision-making. We propose a new task taxonomy by identifying ten features in four categories related to search space properties. We describe how these constrain the space of optimal algorithms. Furthermore, we categorise and compare several sequential decision-making tasks used in studies on human behaviour with our taxonomy and illustrate how the proposed task features can inform the creation of new tasks and computational modelling of behaviour. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Thinking & Reasoning is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1080/13546783.2025.2453151
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      – SubjectFull: Cognition
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      – SubjectFull: Optimization algorithms
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              Text: Nov2025
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