Against the likelihood principle in visual form perception.

Leeuwenberg, Emanuel; Boselie, Frans

doi:10.1037/0033-295x.95.4.485

Cited by 60 publications

(59 citation statements)

References 21 publications

(27 reference statements)

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“…But from the point of view of perceptual theory, one may be more attractive than the other. Leeuwenberg and Boselie (1988) give an important argument against the likelihood principle and in favor of the simplicity principle: that the likelihood principle presupposes that patterns are interpreted, rather than explaining the interpretation of those patterns. This is because the likelihood principle holds that the structure in the world explains the structure in perceptual organization; but the theorist has no independent way of accessing structure in the world, aside from relying on the results of the principles of perceptual organization.…”

Section: Residual Debates Between Likelihood and Simplicitymentioning

confidence: 99%

“…Leeuwenberg and Boselie (1988) show a schematic drawing of a symmetrical, two-headed horse. The more likely interpretation, also consistent with the drawing, is that there are two horses, one occluding the other.…”

Section: Putative Evidence For Simplicitymentioning

confidence: 99%

“…It has also been advanced as a general framework for understanding perceptual organization (e.g., Attneave & Frost, 1969;Leeuwenberg, 1971;Leeuwenberg & Boselie, 1988).…”

Section: Calls Pattern Languagesmentioning

confidence: 99%

“…Suppose, to borrow an example from Leeuwenberg and Boselie (1988), that the stimulus consists of a sequence of letters:…”

Section: H(a) < L(a)mentioning

confidence: 99%

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Reconciling simplicity and likelihood principles in perceptual organization.

Chater¹

1996

Psychological Review

196

191

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Two principles of perceptual organization have been proposed. The likelihood principle, following H. L. F. von Helmholtz (1910 /1962), proposes that perceptual organization is chosen to correspond to the most likely distal layout. The simplicity principle, following Gestalt psychology, suggests that perceptual organization is chosen to be as simple as possible. The debate between these two views has been a central topic in the study of perceptual organization. Drawing on mathematical results in A. N. Kolmogorov's (1965) complexity theory, the author argues that simplicity and likelihood are not in competition, but are identical. Various implications for the theory of perceptual organization and psychology more generally are outlined.

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Section: Residual Debates Between Likelihood and Simplicitymentioning

confidence: 99%

Section: Putative Evidence For Simplicitymentioning

confidence: 99%

“…It has also been advanced as a general framework for understanding perceptual organization (e.g., Attneave & Frost, 1969;Leeuwenberg, 1971;Leeuwenberg & Boselie, 1988).…”

Section: Calls Pattern Languagesmentioning

confidence: 99%

“…Suppose, to borrow an example from Leeuwenberg and Boselie (1988), that the stimulus consists of a sequence of letters:…”

Section: H(a) < L(a)mentioning

confidence: 99%

See 2 more Smart Citations

Reconciling simplicity and likelihood principles in perceptual organization.

Chater¹

1996

Psychological Review

196

191

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“…arguments against these approaches see (Leeuwenberg & Boselie, 1988) and (Jepson, 1996). 'Note that, we are implicitly assuming the prior probabiiity of every setting of the lower priority assertions is les than the prior probability of any interpretation with a larger set of higher pnority assertions.…”

Section: ~O Rmentioning

confidence: 99%

The Computational Perception of Scene Dynamics

Mann

Jepson

Siskind

1997

Computer Vision and Image Understanding

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CanadaThe author has ganted a nonexclusive licence allowing the National Library of Canada to reproduce, loan, distribute or selI copies of this thesis in microform, paper or electronic formats.The author retains ownership of the copyright in this thesis. Neither the thesis nor substantial extracts fkom it may be printed or othelurise reproduced without the author's permission.L'auteur a accordé une licence non exclusive permettant à la BibIiotheque nationale du Canada de reproduire, prêter' distribuer ou vendre des copies de cette thèse sous la forme de microfiche/nlm, de reproduction sur papier ou sur format électronique.L'auteur conserve la propriété du droit d'auteur qui protège cette thèse. Ni la thèse ni des extraits substantiels de celle-ci ne doivent être imprimés -ou autrement reproduits sans son autorisation. AbstractThe Computational Perception of Scene Dynamics Richard Mann Doctor of Philosophy Graduate Depart ment of Computer ScienceUniversity of Toronto 1998Understanding observations of image sequences requires one to reason about qualitative scene dynamics. For example, on observing a hand lifting a cup, we may infer that an 'active' hand is applying an upwards force (by graspingj on a 'passive' cup. In order to perform such reasoning, we require an ontology that describes object properties and the generation and transfer of forces in the scene. Such an ontology should include, for example: the presence of gravity, the presence of a ground plane, whether objects are 'active' or 'passive', whether objects are contacting and/or attached to other objects, and so on. In this work we make these ideas precise by presenting an implemented computational system that derives symbolic force-dynamic descriptions fÎom video sequences.Our approach to scene dynamics i s based on an analysis of the Newtonian mechanics of a simpmed scene model. The critical requirement is that, @en image sequences, we can obtain estimates for the shape and motion of the objects in the scene. To do this, we assume that the objects can be approximated by a two-dimensional 'layered' scene model. The input to our system consists of a set of polygonal outlines dong with estimates for their velocities and acceIerations, obtained kom a view-based tracker. Given such input, we prment a system t hat ext rac ts force-dynamic descriptions for the image sequence. We provide computat ional examples to demonstrate that our ontology is sufiiciently rich to describe a wide variety of image sequences. aThis work rnakes three central contributions. First, we provide an ontology suitable for describing object properties and the generation and transfer of forces in the scene. Second. we provide a computational procedure to test the feasibiLity of such interpretations by reducing the problem to a feasibility test in linear progrnmming. Finally, we provide a theory of preference orderhg between muitipIe interpretations dong with an efficient computational procedure to determine maximal element s in such orderings AcknowledgementsStudying in the Compute...

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The simplicity principle in perception and cognition

Feldman

2016

WIRES Cognitive Science

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The simplicity principle, traditionally referred to as Occam’s razor, is the idea that simpler explanations of observations should be preferred to more complex ones. In recent decades the principle has been clarified via the incorporation of modern notions of computation and probability, allowing a more precise understanding of how exactly complexity minimization facilitates inference. The simplicity principle has found many applications in modern cognitive science, in contexts as diverse as perception, categorization, reasoning, and neuroscience. In all these areas, the common idea is that the mind seeks the simplest available interpretation of observations— or, more precisely, that it balances a bias towards simplicity with a somewhat opposed constraint to choose models consistent with perceptual or cognitive observations. This brief tutorial surveys some of the uses of the simplicity principle across cognitive science, emphasizing how complexity minimization in a number of forms has been incorporated into probabilistic models of inference.

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Against the likelihood principle in visual form perception.

Cited by 60 publications

References 21 publications

Reconciling simplicity and likelihood principles in perceptual organization.

Reconciling simplicity and likelihood principles in perceptual organization.

The Computational Perception of Scene Dynamics

The simplicity principle in perception and cognition

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