Coarse Blobs or Fine Edges? Evidence That Information Diagnosticity Changes the Perception of Complex Visual Stimuli

Oliva, Aude; Schyns, Philippe G.

doi:10.1006/cogp.1997.0667

Cited by 366 publications

(311 citation statements)

References 95 publications

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“…Hence, we do not expect perfect agreement between model-predicted salience and human eye position. In particular, our bottom-up model as used here does not yet account, among others, for how the rapid identification of the gist (semantic category) of a scene may provide contextual priors to more efficiently guide attention towards target objects of interest (Biederman, Teitelbaum, & Mezzanotte, 1983;Friedman, 1979;Hollingworth & Henderson, 1998;Oliva & Schyns, 1997;Potter & Levy, 1969;Torralba, 2003); how search for a specific target might be guided top-down, for example by boosting visual neurons tuned to the attributes of the target (Ito & Gilbert, 1999;Moran & Desimone, 1985;Motter, 1994;Müller, Reimann, & Krummenacher, 2003;Reynolds, Pasternak, & Desimone, 2000;Treue & Maunsell, 1996;Treue & Trujillo, 1999;Wolfe, 1994Wolfe, , 1998Wolfe, Cave, & Franzel, 1989;Yeshurun & Carrasco, 1998); or how task, expertise, and internal scene models may influence eye movements (Henderson & Hollingworth, 2003;Moreno, Reina, Luis, & Sabido, 2002;Nodine & Krupinski, 1998;Noton & Stark, 1971;Peebles & Cheng, 2003;Savelsbergh, Williams, van der Kamp, & Ward, 2002;Tanenhaus, Spivey-Knowlton, Eberhard, & Sedivy, 1995;Yarbus, 1967). Nevertheless, our hypothesis for this study is that a more realistic simulation framework might yield better agreement between human and model than a less realistic one.…”

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confidence: 99%

Quantitative modelling of perceptual salience at human eye position

Itti

2006

Visual Cognition

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We investigate the extent to which a simple model of bottom-up attention and salience may be embedded within a broader computational framework, and compared with human eye movement data. We focus on quantifying whether increased simulation realism significantly affects quantitative measures of how well the model may predict where in video clips humans direct their gaze. We hence compare three variants of the model, tested with 15 video clips of natural scenes shown to three observers. We measure model-predicted salience at the locations gazed to by the observers, compared to random locations. The first variant simply processes the raw video clips. The second adds a gaze-contingent foveation filter. The third further attempts to realistically simulate dynamic human vision by embedding the video frames within a larger background, and shifting them to eye position. Our main finding is that increasing simulation realism significantly improves the predictive ability of the model. Better emulating the details of how a visual stimulus is captured by a constantly rotating retina during active vision has a significant positive impact onto quantitative comparisons between model and human behaviour.

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confidence: 99%

Quantitative modelling of perceptual salience at human eye position

Itti

2006

Visual Cognition

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“…In this field, the concept of diagnosticity had been coined to single out the kind of attributes that are crucial for recognizing a certain object (see Skowronski and Carlston, 1986;Deane, 1992;Oliva and Schijns, 1997). In general, an entity X is considered a part of some object O if X is an attribute of O, but not (necessarily) predictable by means of O.…”

Section: (8)mentioning

confidence: 99%

Can pictures say no or not? Negation and denial in the visual mode

Oversteegen

Schilperoord

2014

Journal of Pragmatics

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In principle, verbal and image languages have different ways of coding conceptual content. Moreover, there is no reason to believe that both modes, the linguistic and the visual, can convey identical contents, and indeed, linguists have claimed that images are not suited for expressing the meaning of certain linguistic categories, like negation. As the linguistic literature argues convincingly, in natural language a distinction between negation and denial is justified. Employing insights in visual communication and cognition science, this paper explores the possibilities for visually expressing negation and/or denial. At the hand of both the analysis and an empirical pilot study of a set of advertisements, we come up with a positive answer to the title question: yes, pictures can say 'no'.

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“…Hybrids were used in several recognition experiments (Oliva & Schyns 1997;Schyns & Oliva 1994; which made two distinct points. The first one is that different categorization tasks can flexibly use the cues associated with a different spatial scale.…”

Section: The Case For Cognitive Penetrabilitymentioning

confidence: 99%

An ecological approach to cognitive (im)penetrability

Withagen¹,

Michaels²

1999

Behav Brain Sci

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We offer an ecological (Gibsonian) alternative to cognitive (im)penetrability. Whereas Pylyshyn explains cognitive (im)penetrability by focusing solely on computations carried out by the nervous system, according to the ecological approach the perceiver as a knowing agent influences the entire animal-environmental system: in the determination of what constitutes the environment (affordances), what constitutes information, what information is detected and, thus, what is perceived.

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Coarse Blobs or Fine Edges? Evidence That Information Diagnosticity Changes the Perception of Complex Visual Stimuli

Cited by 366 publications

References 95 publications

Quantitative modelling of perceptual salience at human eye position

Quantitative modelling of perceptual salience at human eye position

Can pictures say no or not? Negation and denial in the visual mode

An ecological approach to cognitive (im)penetrability

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