Task-independent metrics of computational hardness predict human cognitive performance
| Autor: | Nitin Yadav, Carsten Murawski, Peter Bossaerts, Juan Pablo Franco, Karlo Doroc |
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| Přispěvatelé: | Apollo - University of Cambridge Repository |
| Rok vydání: | 2022 |
| Předmět: |
Elementary cognitive task
Multidisciplinary Relation (database) Computational complexity theory business.industry Computer science Cognition Machine learning computer.software_genre Task (project management) Benchmarking Hardness Face (geometry) Bounded function Task Performance and Analysis Humans Artificial intelligence Set (psychology) business computer |
| Zdroj: | Scientific Reports. 12 |
| ISSN: | 2045-2322 |
| DOI: | 10.1038/s41598-022-16565-w |
| Popis: | Funder: University of Melbourne; doi: http://dx.doi.org/10.13039/501100001782 The survival of human organisms depends on our ability to solve complex tasks in the face of limited cognitive resources. However, little is known about the factors that drive the complexity of those tasks. Here, building on insights from computational complexity theory, we quantify the computational hardness of cognitive tasks using a set of task-independent metrics related to the computational resource requirements of individual instances of a task. We then examine the relation between those metrics and human behavior and find that they predict both time spent on a task as well as accuracy in three canonical cognitive tasks. Our findings demonstrate that performance in cognitive tasks can be predicted based on generic metrics of their inherent computational hardness. |
| Databáze: | OpenAIRE |
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