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Quantifying cognitive complexity: evidence from a reasoning task
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AI
DOI：10.1016/S0191-8869(02)00243-X.png)
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摘要
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There are some doubts about the nature of cognitive complexity. It has been proposed that the loadings on the first un-rotated factor can be taken as a way to quantify the cognitive complexity of a given task. However, the evidence is sparse. The present study tests 1968 participants in a computerized task that comprises linear syllogisms or three-term series problems. The correlation matrix is submitted to a factor analysis. The first un-rotated factor is taken as the vector of cognitive complexity. The vector of task difficulty was obtained after the proportion of participants that failed each syllogism. In addition to task empirical difficulty, three information processing models are taken as predictors of cognitive complexity. Then, regression analyses were carried out to predict cognitive complexity from the information processing (IP) models and task difficulty. Results show that the IP models and task difficulty predict cognitive complexity defined by the loadings on the first un-rotated factor. Therefore, it is concluded that those loadings can be taken as a way to quantify cognitive complexity. (C) 2003 Elsevier Ltd. All rights reserved.
Keyword:
reasoning
linear syllogisms
information processing models
cognitive complexity
factor loadings
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