Subordinate-level categorization relies on high spatial frequencies to a greater degree than basic-level categorization

Collin, Charles A.; McMullen, Patricia A.

doi:10.3758/bf03206498

Cited by 77 publications

(89 citation statements)

References 47 publications

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“…As an example, a snare drum can be identified as a musical instrument, a drum, or a snare drum, where Rosch's taxonomy respectively assigns these descriptions to the superordinate, basic, and subordinate levels of abstraction. The object recognition studies demonstrate that humans can reliably recognize an object at the basic level using only low-frequency content, whereas subordinate-level recognition requires more highfrequency content [116,117]. Thus, humans only perceive an object's basic-level details but not its subordinate-level details in a low-pass filtered distorted image, and this result is consistent with low-pass filtering leading to a decrease in perceived utility as subordinate-level object details disappear.…”

Section: Comparing Vif and Nice: Estimates Of Image Contour Degradationsupporting

confidence: 70%

Estimating the usefulness of distorted natural images using an image contour degradation measure

et al. 2011

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Quality estimators aspire to quantify the perceptual resemblance, but not the usefulness, of a distorted image when compared to a reference natural image. However, humans can successfully accomplish tasks (e.g., object identification) using visibly distorted images that are not necessarily of high quality. A suite of novel subjective experiments reveals that quality does not accurately predict utility (i.e., usefulness). Thus, even accurate quality estimators cannot accurately estimate utility. In the absence of utility estimators, leading quality estimators are assessed as both quality and utility estimators and dismantled to understand those image characteristics that distinguish utility from quality. A newly proposed utility estimator demonstrates that a measure of contour degradation is sufficient to accurately estimate utility and is argued to be compatible with shape-based theories of object perception.

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Section: Comparing Vif and Nice: Estimates Of Image Contour Degradationsupporting

confidence: 70%

Estimating the usefulness of distorted natural images using an image contour degradation measure

et al. 2011

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“…Although the prominent role of LSF information for rapid scene categorization has been extensively investigated and documented Schyns & Oliva, 1994; for a review see Hegdé, 2008;Kauffmann et al, 2014), there is also considerable experimental evidence of a predominant processing of HSF information even for very short stimuli exposure duration (Campagne et al, 2016;Harel & Bentin, 2009;Morrison & Schyns, 2001;Rotshtein, Schofield, Funes, & Humphreys, 2010;Schyns, 1998;Schyns & Oliva, 1999). Furthermore, many studies have shown that the use of spatial frequency information during the processing of complex stimuli such as scenes is highly flexible and depends on many factors such as stimulus exposure duration (Schyns & Oliva, 1994), category (e.g., Awasthi, Sowman, Friedman, & Williams, 2013;Collin & McMullen, 2005;Rotshtein et al, 2010;Vannucci, Viggiano, & Argenti, 2001), or task constraints (Abrams, Barbot, & Carrasco, 2010;Campagne et al, 2016;Caplette, West, Gomot, Gosselin, & Wicker, 2014;Fradcourt, Peyrin, Baciu, & Campagne, 2013;Morrison & Schyns, 2001;Ozgen, Payne, Sowden, & Schyns, 2006;Schyns & Oliva, 1999;Sowden, Özgen, Schyns, & Daoutis, 2003). For example, different spatial frequency bands would be used according to their diagnosticity to categorize a specific visual stimulus.…”

Section: Introductionmentioning

confidence: 99%

How does information from low and high spatial frequencies interact during scene categorization?

et al. 2017

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Current models of visual perception suggest that, during scene categorization, low spatial frequencies (LSF) are rapidly processed and activate plausible interpretations of visual input. This coarse analysis would be used to guide subsequent processing of high spatial frequencies (HSF). The present study aimed to further examine how information from LSF and HSF interact and influence each other during scene categorization. In a first experimental session, participants had to categorize LSF and HSF filtered scenes belonging to two different semantic categories (artificial vs. natural). In a second experimental session, we used hybrid scenes as stimuli made by combining LSF and HSF from two different scenes which were semantically similar or dissimilar. Half of the participants categorized LSF scenes in hybrids, and the other half categorized HSF scenes in hybrids. Stimuli were presented for 30 or 100 ms. Session 1 results showed better performance for LSF than HSF scene categorization. Session 2 scene categorization was faster when participants attended and categorized LSF than HSF scene in hybrids. The semantic interference of a semantically dissimilar HSF scene on LSF scene categorization was greater than the semantic interference of a semantically dissimilar LSF scene on HSF scene categorization, irrespective of exposure duration. These results suggest a LSF advantage for scene categorization, and highlight the prominent role of HSF information when there is uncertainty about the visual stimulus, in order to disentangle between alternative interpretations. ARTICLE HISTORY

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“…Archambault et al (2000) speculated that the reason basic-level categorizations were more robust to changes in the size of the stimuli was because perceptual cues conveyed by either low or high spatial frequencies were equally diagnostic for basic-level identifications. A recent study by Collin and McMullen (2005) showed that, indeed, subordinate-level identifications rely on high spatial frequencies much more than basic-level identifications do. The differences in priming effects for basic-level object discriminations could reflect the flexibility of basic-level categorization processes in the usage of perceptual cues.…”

Section: Discussionmentioning

confidence: 99%

Hierarchical attention in discriminating objects at different levels of specificity

Large

McMullen

2006

Perception & Psychophysics

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Since Navon's (1977) seminal article, there have been numerous research papers on the topic of global and local attention. In the bulk of this research, artificial stimuli consisting of large digits or letters made up of smaller digits or letters (hierarchical figures) have been used. An underlying assumption is that effects of global and local attention observed with hierarchical figures reflect global and local attentional allocation during object and scene recognition in the real world. It is true that objects and scenes can be conceptualized as having levels of structure that are ordered hierarchically, where global levels encompass local structural components. A forest scene can be broken down into separate trees, bushes, and other forest flora and fauna. However, the hierarchical figures traditionally employed for research into global and local attention have been artificial stimuli that have no place outside the laboratory (Navon, 2003). How can we know that observations based on artificially constructed stimuli reflect how our visual and attentional systems operate with more realistic objects and scenes? The aim of this study was to investigate whether and how global and local levels of attention influence the discrimination of objects.A number of models have been proposed to account for the effects of global and local processing. These can be divided into two broad categories. One set of models considers the spatial extent of attention, in which attention is focused on a region of space that can vary in size (Lamb & Robertson, 1988;Lamb & Yund, 2000;Robertson, 1996;Robertson, Egly, Lamb, & Kerth, 1993). The size of the attended region would be relatively larger for global processing than for local processing. Other models are based on principles of perceptual organization where visual information is divided into global and local processing streams by grouping local elements for global identifications or by parsing local elements for local identifications (Han & Humphreys, 2002;Han, Humphreys, & Chen, 1999a, 1999bKimchi, 1998Kimchi, , 2000. In both models, a distinction can be made between attending to the coarse structure of an object's visual properties at the global level and attending to fine details at the local level.This notion of coarse and fine-detailed visual information is also relevant to object categorization (Archambault, Gosselin, & Schyns, 2000;Morrison & Schyns, 2001;Oliva & Schyns, 1997;Schyns & Oliva, 1999). Objects can be categorized at various levels of specificity. At the most general level (superordinate), the object dog could be called an animal; at an intermediate level (basic), it could be called a dog; and at a specific level (subordinate), it could be called a beagle. It is at the basic level that objects are most likely to be identified and named most quickly (Jolicoeur, Gluck, & Kosslyn, 1984;Murphy & Brownell, 1985;Murphy & Lassaline, 1997; Rosch, Mervis, Gray, Johnson, & Boyes-Braem, 1976). One of the reasons that subordinate categorizations may take longer is that the per...

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Subordinate-level categorization relies on high spatial frequencies to a greater degree than basic-level categorization

Cited by 77 publications

References 47 publications

Estimating the usefulness of distorted natural images using an image contour degradation measure

Estimating the usefulness of distorted natural images using an image contour degradation measure

How does information from low and high spatial frequencies interact during scene categorization?

Hierarchical attention in discriminating objects at different levels of specificity

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