48 research outputs found

    The Flushtration Count Illusion: Attribute substitution tricks our interpretation of a simple visual event sequence

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    When faced with a difficult question, people sometimes work out an answer to a related, easier question without realizing that a substitution has taken place (e.g., Kahneman; 2011). In two experiments, we investigated whether this attribute substitution effect can also affect the interpretation of a simple visual event sequence. We used a magic trick called the "Flushtration Count Illusion," which involves a technique used by magicians to give the illusion of having seen multiple cards with identical backs, when in fact only the back of one card (the bottom card) is repeatedly shown. In Experiment 1, we demonstrated that most participants are susceptible to the illusion, even if they have the visual and analytical reasoning capacity to correctly process the sequence. In Experiment 2, we demonstrated that participants construct a biased and simplified representation of the Flushtration Count by substituting some attributes of the event sequence. We discussed of the psychological processes underlying this attribute substitution effect

    Dynamic competition between contour integration and contour segmentation probed with moving stimuli

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    AbstractLine-ends, corners and junctions are important singularities for form analysis, object recognition, depth ordering or motion processing. In this study, we investigate the extent to which processing the motion of line ends depends on the spatial configuration of their immediate surround. To that aim, we used two vertical collinear line segments, translating clockwise or anti-clockwise along a circular path, together with a direction discrimination task. Direction discrimination was measured independently for outer line-ends––at both segments extremities––and inner line-ends––in between collinear segments––using line segments partially occluded by invisible masks such that the direction of either inner or outer line-ends’ motion was restricted to a sinusoidal translation along a horizontal axis, and thus irrelevant for the motion task. Under these conditions, access to the direction of inner line-ends is longer and more difficult than it is for outer line-ends. Subsequent experiments show that these effects depend on the degree of collinearity between line segments. Similar experiments were performed after volunteers took a dose of Lorazepam, a benzodiazepine that facilitates the fixation of GABA on GABAa receptors. The results show that the differences between the processing of inner and outer line-ends is reduced, suggesting that the effect of the surround is modulated by inhibitory mechanisms. Using a simple model, we propose that this effect can be explained by a competition between a segmentation process based on surround suppression and contour integration through long-range horizontal connections, at or prior to motion processing stages

    Global Probabilistic Reinforcement of Straight Segments

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    Internal surface representations approximated by reverse correlation

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    We presented two naı¨ve observers with 20,000 random-dot stereograms. On each trial, the observers had to indicate the presence or absence of a complex 3D pattern (a large ‘+’ sign in relief). However, unbeknownst to them, the stereograms did not contain any signal, but only disparity noise. Responses and verbal reports indicate that the observers ‘saw’ the suggested 3D surface configuration in roughly half the trials even though structured local low-level signal was never presented. Using reverse correlation, we derived an approximation of the internal surface-based representations, or templates, that best accounted for the observers’ responses. These templates were shown to be spatially well defined and temporally stable. We propose that the 3D surface-based representations that we derived are the first approximations and depictions of the intermediary process that allows the visual system to successfully link degraded, bottom–up signal and high-level, top–down object recognition
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