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What Property of the Contour of a Deforming Region Biases Percepts toward Liquid?

ABSTRACT: Human observers can perceive the existence of a transparent surface from dynamic image deformation. They can also easily discriminate a transparent solid material such as plastic and glass from a transparent fluid one such as water and shampoo just by viewing them. However, the image information required for material discrimination of this sort is still unclear. A liquid changes its contour shape non-rigidly. We therefore examined whether additional properties of the contour of a deformation-defined region, which indicated contour non-rigidity, biased percepts of the region toward liquid materials. Our stimuli had a translating circular region wherein a natural texture image was deformed at the spatiotemporal deformation frequency that was optimal for the perception of a transparent layer. In Experiment 1, we dynamically deformed the contour of the circular region and found that large deformation of the contour biased the percept toward liquid. In Experiment 2, we manipulated the blurriness of the contour and observed that a strongly blurred contour biased percepts toward liquid. Taken together, the results suggest that a deforming region lacking a discrete contour biases percepts toward liquid.

SUBMITTER: Kawabe T

PROVIDER: S-EPMC5471326 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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What Property of the Contour of a Deforming Region Biases Percepts toward Liquid?

Kawabe Takahiro T

Frontiers in psychology 20170615

Human observers can perceive the existence of a transparent surface from dynamic image deformation. They can also easily discriminate a transparent solid material such as plastic and glass from a transparent fluid one such as water and shampoo just by viewing them. However, the image information required for material discrimination of this sort is still unclear. A liquid changes its contour shape non-rigidly. We therefore examined whether additional properties of the contour of a deformation-def ...[more]

PMID: 28663735

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Project description:In real life, we often have to make judgements under uncertainty. One such judgement task is estimating the probability of a given event based on uncertain evidence for the event, such as estimating the chances of actual fire when the fire alarm goes off. On the one hand, previous studies have shown that human subjects often significantly misestimate the probability in such cases. On the other hand, these studies have offered divergent explanations as to the exact causes of these judgment errors (or, synonymously, biases). For instance, different studies have attributed the errors to the neglect (or underweighting) of the prevalence (or base rate) of the given event, or the overweighting of the evidence for the individual event (‘individuating information’), etc. However, whether or to what extent any such explanation can fully account for the observed errors remains unclear. To help fill this gap, we studied the probability estimation performance of non-professional subjects under four different real-world problem scenarios: (i) Estimating the probability of cancer in a mammogram given the relevant evidence from a computer-aided cancer detection system, (ii) estimating the probability of drunkenness based on breathalyzer evidence, and (iii & iv) estimating the probability of an enemy sniper based on two different sets of evidence from a drone reconnaissance system. In each case, we quantitatively characterized the contributions of the various potential explanatory variables to the subjects’ probability judgements. We found that while the various explanatory variables together accounted for about 30 to 45% of the overall variance of the subjects’ responses depending on the problem scenario, no single factor was sufficient to account for more than 53% of the explainable variance (or about 16 to 24% of the overall variance), let alone all of it. Further analyses of the explained variance revealed the surprising fact that no single factor accounted for significantly more than its ‘fair share’ of the variance. Taken together, our results demonstrate quantitatively that it is statistically untenable to attribute the errors of probabilistic judgement to any single cause, including base rate neglect. A more nuanced and unifying explanation would be that the actual biases reflect a weighted combination of multiple contributing factors, the exact mix of which depends on the particular problem scenario.

Dataset Information

What Property of the Contour of a Deforming Region Biases Percepts toward Liquid?

Publications

What Property of the Contour of a Deforming Region Biases Percepts toward Liquid?

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