bioRxiv ScienceSearch

Biology subjects

Hart, Y.

Publications and source records attributed to Hart, Y..

2 recordsLinked to original sources

The statistical nature of geometric reasoning

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.

animal behavior and cognition

Neuronal components of evaluating the human 1 origin of abstract shapes

Communication through visual symbols is a key aspect of human culture. However, to what extent can people distinguish between human-origin and artificial symbols, and the neuronal mechanisms underlying this process are not clear. Using fMRI we contrasted brain activity during presentation of human-created abstract shapes and random-algorithm created shapes, both sharing similar low level features.\n\nWe found that participants correctly identified most shapes as human or random. The lateral occipital complex (LOC) was the main brain region showing preference to human-made shapes, independently of task. Furthermore, LOC activity was parametrically correlated to beauty and familiarity scores of the shapes (rated following the scan). Finally, a model classifier based only on LOC activity showed human level accuracy at discriminating between human-made and randomly-made shapes.\n\nOur results highlight the sensitivity of the human brain to social and cultural cues, and point to high-order object areas as central nodes underlying this capacity.

neuroscience