The Visual System's Measurement of Invariants Need Not Itself Be Invariant

Wagemans, Johan; Gool, Luc Van; Lamote, Christian

doi:10.1111/j.1467-9280.1996.tb00365.x

Cited by 41 publications

(38 citation statements)

References 18 publications

(12 reference statements)

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“…Different single-system accounts posit that object representations are relatively abstract (see, e.g., Biederman, 1987;Biederman & Bar, 1999;E. E. Cooper et al, 1992;Hayworth & Biederman, 2006;Hummel & Stankiewicz, 1996;Wagemans et al, 1996), relatively specific (see, e.g., Bülthoff & Edelman, 1992;Gauthier et al, 2002;Poggio & Edelman, 1990;Tarr, 1995;Ullman, 1996), or both abstract and specific at different points on a continuum in a single system (see, e.g., cessing high spatial frequency information, and RH advantages are found for processing low spatial frequency information (see, e.g., Christman, Kitterle, & Hellige, 1991;Kitterle, Christman, & Hellige, 1990;Kitterle & Selig, 1991). Similarly, and in line with our previous research (Marsolek, 1999;Marsolek & Hudson, 1999), the results presented here indicate that even when the same visual stimuli and similar visual working memory tasks are used, different patterns of hemispheric asymmetries can be observed.…”

Section: Discussionmentioning

confidence: 99%

“…Some theories posit that such representations are relatively abstract, in that a common representation may be activated by multiple exemplars or by multiple views of the same object exemplar (see, e.g., Biederman, 1987;Biederman & Bar, 1999;E. E. Cooper, Biederman, & Hummel, 1992;Hayworth & Biederman, 2006;Hummel & Stankiewicz, 1996;Wagemans, Van Gool, & Lamote, 1996). Other theories p posit that object representations are relatively specific, in that different representations are activated by different exemplars or by different views of the same object exemp plar (see, e.g., Bülthoff & Edelman, 1992;Gauthier et al, 2002;Poggio & Edelman, 1990;Tarr, 1995;Tarr, Williams, Hayward, & Gauthier, 1998;Ullman, 1996).…”

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confidence: 99%

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Dissociable neural subsystems underlie visual working memory for abstract categories and specific exemplars

Marsolek

Burgund

2008

Cognitive, Affective, & Behavioral Neuroscience

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The nature of representations of visual objects remains a topic of debate. Some theories posit that such representations are relatively abstract, in that a common representation may be activated by multiple exemplars or by multiple views of the same object exemplar (see, e.g., Biederman, 1987;Biederman & Bar, 1999;E. E. Cooper, Biederman, & Hummel, 1992;Hayworth & Biederman, 2006;Hummel & Stankiewicz, 1996;Wagemans, Van Gool, & Lamote, 1996). Other theories p posit that object representations are relatively specific, in that different representations are activated by different exemplars or by different views of the same object exemp plar (see, e.g., Bülthoff & Edelman, 1992;Gauthier et al., 2002;Poggio & Edelman, 1990;Tarr, 1995;Tarr, Williams, Hayward, & Gauthier, 1998;Ullman, 1996). Alternatively, both abstract and specific representations may exist along different points on a continuum in a single, unified processing system (see, e.g., Farah, 1992;Hayward & Williams, 2000;Tarr & Bülthoff, 1995). Finally, abstract and specific object representations may exist in separate and dissociable neural subsystems, with an abstract subsystem operating effectively in the left hemisphere (LH) and a specific subsystem operating effectively in the right hemisphere (RH) (see, e.g., Burgund & Marsolek, 2000;Marsolek, 1995Marsolek, , 1999 Marsolek & g , , ) Burgund, 1997, 2003.The bulk of the evidence for the dissociable neural subr systems theory comes from experiments in which familiar shapes were used. This may be cause for concern, as Curby, Hayward, and Gauthier (2004) recently suggested, since abstract effects in the LH could reflect semantic processing of postvisual information that is associated with the visualshape information, rather than processing of (abstract) visual shapes only, as posited by the dissociable neural subsystems theory. A related concern is that abstract effects in the LH could reflect linguistic processing of names associated with the visual shapes, rather than processing of abstract visual-shape information. In addition, when objects are presented multiple times in an experiment, abstract ef-f f fects in the LH could reflect episodic memory for previous trials in the experiment. In this article, we report results that indicate, in line with the dissociable neural subsystems theory, that abstract processing of visual shapes in the LH does not require semantic, linguistic, or episodic information. Abstract processing of visual shapes can occur effecd tively in the LH when the stimuli are novel objects viewed for the first and only time.According to the dissociable neural subsystems theory, an abstract category subsystem and a specific exemplar subsystem operate relatively independently and in parallel. An abstract category subsystem encodes the visual category to p g(g , p p ) which an input stimulus belongs (e.g., pen vs. cup). It uses Rice University, Houston, TexasAn ongoing debate concerns whether visual object representations are relatively abstract, relatively specific, b both abstract an...

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Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

Dissociable neural subsystems underlie visual working memory for abstract categories and specific exemplars

Marsolek

Burgund

2008

Cognitive, Affective, & Behavioral Neuroscience

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“…In invariant-property models, formless mathematical properties are defined that are invariant to certain spatial transformations (e.g., Cassirer, 1944; J. J. Gibson, 1950;Pitts & McCulloch, 1947;Van Gool, Moons, Pauwels, & Wagemans, 1994;Wagemans, Van Gool, & Lamote, 1996). Examples for invariant properties are the aspect ratio or the cross ratio.…”

Section: Models Based On Abstract (View-independent) Representationsmentioning

confidence: 99%

Coordinate transformations in object recognition.

Graf¹

2006

Psychological Bulletin

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A basic problem of visual perception is how human beings recognize objects after spatial transformations. Three central classes of findings have to be accounted for: (a) Recognition performance varies systematically with orientation, size, and position; (b) recognition latencies are sequentially additive, suggesting analogue transformation processes; and (c) orientation and size congruency effects indicate that recognition involves the adjustment of a reference frame. All 3 classes of findings can be explained by a transformational framework of recognition: Recognition is achieved by an analogue transformation of a perceptual coordinate system that aligns memory and input representations. Coordinate transformations can be implemented neurocomputationally by gain (amplitude) modulation and may be regarded as a general processing principle of the visual cortex.Keywords: alignment, coordinate transformations, gain modulation, object recognition, reference frames How can we recognize objects regardless of spatial transformations such as plane and depth rotation, size scaling, and position changes? This ability is often discussed under the label object constancy or shape constancy. Even young children recognize objects so immediately and effortlessly that it seems to be a rather ordinary and simple task. However, changes in the spatial relation between observer and object lead to large changes of the image that is projected onto the retina. Hence, to recognize objects regardless of orientation, size, and position is not a trivial problem. No computational system proposed so far can successfully recognize objects over wide ranges of object categories and contexts.Several different approaches have been proposed over the years (for reviews, see Palmeri & Gauthier, 2004;Ullman, 1996). A number of models rely on abstract object representations, which predict that recognition performance is typically invariant regarding spatial transformations (e.g., structural description models; see Hummel & Biederman, 1992;Marr & Nishihara, 1978). In contrast, image-based or view-based models propose that object representations are close to the format of the perceptual input and therefore depend systematically on image transformations (e.g.,

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“…Therefore, although these metric aspects are irrelevant to the invariants themselves, they might affect the calculation of the invariants by the visual system. In fact, that is exactly what was demonstrated in a study in which observers were explicitly instructed to give affine-invariant coordinates; hence, mental transformations were excluded because no comparisons had to be made (Wagemans, Van Gool, & Lamote, 1996).…”

Section: Numerical Invariantsmentioning

confidence: 99%

Minimal information to determine affine shape equivalence.

Wagemans¹,

Gool²,

Troy³

et al. 2000

Journal of Experimental Psychology: Human Perception and Perfor

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Participants judged the affine equivalence of 2 simultaneously presented 4-point patterns. Performance level (d') varied between 1.5 and 2.7, depending on the information available for solving the correspondence problem (insufficient in Experiment 1a, superfluous in Experiment 1b, and minimal in Experiments 1c, 2a, 2b) and on the exposure time (unlimited in Experiments 1 and 2a and 500 ms in Experiment 2b), but it did not vary much with the complexity of the affine transformation (rotation and slant in Experiment 1 and same plus tilt in Experiment 2). Performance in Experiment 3 was lower with 3-point patterns than with 4-point patterns, whereas blocking the trials according to the affine transformation parameters had little effect. Determining affine shape equivalence with minimal-information displays is based on a fast assessment of qualitatively or quasi-invariant properties such as convexity/ concavity, parallelism, and collinearity.

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The Visual System's Measurement of Invariants Need Not Itself Be Invariant

Abstract: Abstract-When

Cited by 41 publications

References 18 publications

Dissociable neural subsystems underlie visual working memory for abstract categories and specific exemplars

Dissociable neural subsystems underlie visual working memory for abstract categories and specific exemplars

Coordinate transformations in object recognition.

Minimal information to determine affine shape equivalence.

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