Visual perception of the physical stability of asymmetric three-dimensional objects.

Visual estimation of object stability is an ecologically important judgment that allows observers to predict the physical behavior of objects. A natural method that has been used in previous work to measure perceived object stability is the estimation of perceived "critical angle"--the angle at which an object appears equally likely to fall over versus return to its upright stable position. For an asymmetric object, however, the critical angle is not a single value, but varies with the direction in which the object is tilted. The current study addressed two questions: (a) Can observers reliably track the change in critical angle as a function of tilt direction? (b) How do they visually estimate the overall stability of an object, given the different critical angles in various directions? To address these questions, we employed two experimental tasks using simple asymmetric 3D objects (skewed conical frustums): settings of critical angle in different directions relative to the intrinsic skew of the 3D object (Experiment 1), and stability matching across 3D objects with different shapes (Experiments 2 and 3). Our results showed that (a) observers can perceptually track the varying critical angle in different directions quite well; and (b) their estimates of overall object stability are strongly biased toward the minimum critical angle (i.e., the critical angle in the least stable direction). Moreover, the fact that observers can reliably match perceived object stability across 3D objects with different shapes suggests that perceived stability is likely to be represented along a single dimension.

[1]  I. Biederman Recognition-by-components: a theory of human image understanding. , 1987, Psychological review.

[2]  F. Lacquaniti,et al.  Visual perception and interception of falling objects: a review of evidence for an internal model of gravity , 2005, Journal of neural engineering.

[3]  Lawrence Birnbaum,et al.  Causal Scene Understanding , 1995, Comput. Vis. Image Underst..

[4]  Glyn W. Humphreys,et al.  View-specific effects of depth rotation and foreshortening on the initial recognition and priming of familiar objects , 1998, Perception & psychophysics.

[5]  A. Caramazza,et al.  Curvilinear motion in the absence of external forces: naive beliefs about the motion of objects. , 1980, Science.

[6]  S. McKee,et al.  Predicting future motion. , 2002, Journal of vision.

[7]  Heinrich H. Bülthoff,et al.  Perceived Object Stability Depends on Multisensory Estimates of Gravity , 2011, PloS one.

[8]  G K Humphrey,et al.  An Examination of the Effects of Axis Foreshortening, Monocular Depth Cues, and Visual Field on Object Identification , 1993, The Quarterly journal of experimental psychology. A, Human experimental psychology.

[9]  D. Proffitt,et al.  Understanding natural dynamics. , 1989, Journal of experimental psychology. Human perception and performance.

[10]  R. Baillargeon,et al.  Is the Top Object Adequately Supported by the Bottom Object? Young Infants' Understanding of Support Relations , 1990 .

[11]  R. Baillargeon,et al.  The Development of Young Infants' Intuitions about Support , 1992 .

[12]  D G Pelli,et al.  The VideoToolbox software for visual psychophysics: transforming numbers into movies. , 1997, Spatial vision.

[13]  D. Proffitt,et al.  Judgments of natural and anomalous trajectories in the presence and absence of motion. , 1985, Journal of experimental psychology. Learning, memory, and cognition.

[14]  D. Marr,et al.  Representation and recognition of the spatial organization of three-dimensional shapes , 1978, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[15]  H. Hecht,et al.  Influence of animation on dynamical judgments. , 1992, Journal of experimental psychology. Human perception and performance.

[16]  Steven A. Cholewiak,et al.  The perception of physical stability of 3D objects: The role of parts , 2010 .

[17]  Jessica B. Hamrick Internal physics models guide probabilistic judgments about object dynamics , 2011 .

[18]  Jacob Feldman,et al.  Perceived Causality Can Alter the Perceived Trajectory of Apparent Motion , 2013, Psychological science.

[19]  Denis G. Pelli,et al.  ECVP '07 Abstracts , 2007, Perception.

[20]  Dirk Kerzel,et al.  Is this object balanced or unbalanced? Judgments are on the safe side. , 2011, Journal of experimental psychology. Human perception and performance.

[21]  Brian J. Scholl,et al.  The origins of causal perception: Evidence from postdictive processing in infancy , 2008, Cognitive Psychology.

[22]  M. Pavel,et al.  Extrapolation of linear motion , 1992, Vision Research.

[23]  J T Todd,et al.  Visual Perception of Relative Mass in Dynamic Events , 1982, Perception.

[24]  D H Brainard,et al.  The Psychophysics Toolbox. , 1997, Spatial vision.

[25]  Martin Faint,et al.  Does the brain model newton’s laws? , 2001 .

[26]  A. Fuchs,et al.  Prediction in the oculomotor system: smooth pursuit during transient disappearance of a visual target , 2004, Experimental Brain Research.

[27]  Z. Pylyshyn,et al.  Tracking Multiple Items Through Occlusion: Clues to Visual Objecthood , 1999, Cognitive Psychology.

[28]  L. Maloney,et al.  Explicit estimation of visual uncertainty in human motion processing , 2005, Vision Research.