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The statistical nature of geometric reasoning

View ORCID ProfileYuval Hart, View ORCID ProfileMoira R. Dillon, Andrew Marantan, Anna L. Cardenas, Elizabeth Spelke, L. Mahadevan
doi: https://doi.org/10.1101/183152
Yuval Hart
1Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA 02138
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Moira R. Dillon
2Department of Psychology, Harvard University, Cambridge, MA, USA 02138
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Andrew Marantan
3Department of Physics, Harvard University, Cambridge, MA, USA 02138
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Anna L. Cardenas
1Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA 02138
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Elizabeth Spelke
2Department of Psychology, Harvard University, Cambridge, MA, USA 02138
4Center for Brain Science, Harvard University, Cambridge, MA, USA 02138
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L. Mahadevan
1Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA 02138
3Department of Physics, Harvard University, Cambridge, MA, USA 02138
4Center for Brain Science, Harvard University, Cambridge, MA, USA 02138
5Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, USA 02138
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Abstract

Geometric reasoning has an inherent dissonance: its abstract axioms and propositions refer to infinitesimal points and infinite straight lines while our perception of the physical world deals with fuzzy dots and curved stripes. How we use these disparate mechanisms to make geometric judgments remains unresolved. Here, we deploy a classically used cognitive geometric task - planar triangle completion - to study the statistics of errors in the location of the missing vertex. Our results show that the mean location has an error proportional to the side of the triangle, the standard deviation is sub-linearly dependent on the side length, and has a negative skewness. These scale-dependent responses directly contradict the conclusions of recent cognitive studies that innate Euclidean rules drive our geometric judgments. To explain our observations, we turn to a perceptual basis for geometric reasoning that balances the competing effects of local smoothness and global orientation of extrapolated trajectories. The resulting mathematical framework captures our observations and further predicts the statistics of the missing angle in a second triangle completion task. To go beyond purely perceptual geometric tasks, we carry out a categorical version of triangle completion that asks about the change in the missing angle after a change in triangle shape. The observed responses show a systematic scale-dependent discrepancy at odds with rule-based Euclidean reasoning, but one that is completely consistent with our framework. All together, our findings point to the use of statistical dynamic models of the noisy perceived physical world, rather than on the abstract rules of Euclid in determining how we reason geometrically.

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Posted August 31, 2017.
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The statistical nature of geometric reasoning
Yuval Hart, Moira R. Dillon, Andrew Marantan, Anna L. Cardenas, Elizabeth Spelke, L. Mahadevan
bioRxiv 183152; doi: https://doi.org/10.1101/183152
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The statistical nature of geometric reasoning
Yuval Hart, Moira R. Dillon, Andrew Marantan, Anna L. Cardenas, Elizabeth Spelke, L. Mahadevan
bioRxiv 183152; doi: https://doi.org/10.1101/183152

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