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Intelligent problem-solvers externalize cognitive operations


Humans are nature’s most intelligent and prolific users of external props and aids (such as written texts, slide-rules and software packages). Here we introduce a method for investigating how people make active use of their task environment during problem-solving and apply this approach to the non-verbal Raven Advanced Progressive Matrices test for fluid intelligence. We designed a click-and-drag version of the Raven test in which participants could create different external spatial configurations while solving the puzzles. In our first study, we observed that the click-and-drag test was better than the conventional static test at predicting academic achievement of university students. This pattern of results was partially replicated in a novel sample. Importantly, environment-altering actions were clustered in between periods of apparent inactivity, suggesting that problem-solvers were delicately balancing the execution of internal and external cognitive operations. We observed a systematic relationship between this critical phasic temporal signature and improved test performance. Our approach is widely applicable and offers an opportunity to quantitatively assess a powerful, although understudied, feature of human intelligence: our ability to use external objects, props and aids to solve complex problems.

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Code availability

The routines/code that were used to perform the statistical analyses in this study are available from the corresponding author upon request. For the routine/code that was used for simulating the dual-mode and single-mode problem-solvers see Supplementary Code.

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The data that support the findings of this study are available from the corresponding author upon request.

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The authors received no specific funding for this work. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

B.R.B., F.H.P. and B.F. designed the experiments. B.R.B. carried out the experiments, simulations and statistical analyses. B.R.B., F.H.P., B.F. and A.C. wrote the paper.

Competing interests

The authors declare no competing interests.

Correspondence to Bruno R. Bocanegra.

Supplementary information

Supplementary Information

Supplementary Methods, Supplementary Results, Supplementary Tables 1 and 2, Supplementary Note, Supplementary Figs 1–10, and Supplementary References

Reporting Summary

SI Guide

An explanation of how the Supplementary Code can be run.

Supplementary Code

An excel file that allows simulations of the data, noted in the SI and described in the SI Guide.

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Fig. 1: Predicting academic achievement using the conventional and the adapted click-and-drag Raven Advanced Progressive Matrices test in Experiments 1a and 1b (n = 495).
Fig. 2: Simulated data for the dual-mode (green) and single-mode model (blue) and empirical data for experimental participants (black) in Experiment 2 (n = 70).
Fig. 3: Shape parameters, scale parameters, partial autocorrelations as a function of Raven IQ test performance in Experiment 2 (n = 70).
Fig. 4: Variance of intermovement intervals, total number of movements, total time spent on task as a function of Raven IQ test performance in Experiment 2 (n = 70).