The Mechanisms of Categorical Influences on Visual Search

 
Audio is AI-generated
149

Abstract

In order to compare the mechanisms of such influences, described in modern paradigms, we use two parameters: the accessibility of categorical information and the form of its representation. The parameter of accessibility concerns the temporal aspect: on which stage of a search task the participants have access to the categorical information. This information could beavailable during a search task or before it during the target presentation stage. The second parameter is a form of representation of categorical information. It could be represented directly as categorical knowledge, or indirectly by an object’s visual features. Our analysis showsthe following: If categorical information is available only at the target demonstration stageand represented indirectly by visual features, then it influences visual search by recalling a prototype of a target category, the visual features of which then form a search template. If a category of a scene is also available, then it helps to choose the most relevant zones in a scene for search. Additional information of distractors’ categories will draw participants’ attention to the objects which are categorically similar to a target. Finally, if categorical information isavailable directly during the search stage, it influences our attention more than visual features. There fore, we conclude that the mechanism of categorical influences on visual search depends on the amount of categorical information available to a participant. The more categorical information of objects and a scene is available to participants, the more it influences visual search performance

General Information

Keywords: Visual Search, Categories, Attention, Top-Down Processes, Bottom-Up Processes

Journal rubric: Theory and Methodology of Psychology

OpenAlex citations: 1

OpenAlex trends: Visual Attention and Saliency Detection, Olfactory and Sensory Function Studies, Visual perception and processing mechanisms

Information about the work in OpenAlex

Number of citations: 1

Topics

Visual Attention and Saliency Detection

This cluster of papers focuses on computational modeling and detection of visual saliency, including topics such as saliency detection, visual attention, deep learning for salient object detection, analysis of eye movements, image and video segmentation, and the interplay between bottom-up and top-down attention mechanisms.

Number of works: 27131  |  Total number of citations: 555024

Topic detailsв OpenAlex

Olfactory and Sensory Function Studies

This cluster of papers explores the various aspects of olfactory dysfunction, including its association with COVID-19, neurological implications, neural processing, and the impact on quality of life. It covers topics such as anosmia, olfactory receptors, pheromones, taste disorders, and the role of the olfactory system in health and disease.

Number of works: 93783  |  Total number of citations: 1877067

Topic detailsв OpenAlex

Visual perception and processing mechanisms

This cluster of papers explores the neural mechanisms underlying visual perception, including topics such as perceptual learning, cortical connectivity, sensory integration, retinotopic mapping, motion processing, binocular vision, neuronal adaptation, and psychophysical function.

Number of works: 125349  |  Total number of citations: 3481312

Topic detailsв OpenAlex

Work details in OpenAlex

Article type: scientific article

DOI: https://doi.org/10.21638/spbu16.2019.305

Published

For citation: Morozov, M.I., Spiridonov, V.F. (2019). The Mechanisms of Categorical Influences on Visual Search. Vestnik of Saint Petersburg University. Psychology, 9(3), 280–294. (In Russ.). https://doi.org/10.21638/spbu16.2019.305

References

Treisman A., Galade G. A feature-integration theory of attention. Cognitive psychology, 1980, vol. 12, pp. 97–136.

Bravo M., Farid H. The specificity of search template. Journal of vision, 2008, vol. 9 (1), no. 34, pp. 1–9.

Fodor J. The modularity of the mind. Cambridge, MIT Press, 1983. 200 p.

Lupyan G. Cognitive Penetrability of Perception in the Age of Prediction: Predictive Systems are Penetrable Systems. Review of philosophy and psychology, 2015, vol. 6 (4), pp. 547–569.

Duncan J., Humphreys G. W. Visual search and stimulus similarity. Psychological Review, 1989, vol. 96, pp. 433–458.

Wolfe J. M. Guided Search 4.0: Current Progress with a model of visual search. Integrated Models of Cognitive Systems. Ed. by W. Gray. New York, Oxford, Oxford University Press, 2007, pp. 99–119.

Alexander R. G., Zelinsky G. J. Visual similarity effects in categorical search. Journal of Vision, 2011, vol. 11 (8), no. 9, pp. 1–15.

Torralba A., Oliva A., Castelhano M., Henderson J. Contextual Guidance of Eye Movements and Attention in Real-World Scenes: The Role of Global Features in Object Search. Psychological Review, 2006, vol. 113, no. 4, pp. 766–786.

Hwang A., Wang H., Pomplun M. Semantic guidance of eye movements in real-world scenes. Vision Research, 2011, vol. 51, pp. 1192–1205.

Schmidt J., Zelinsky G. Search guidance is proportional to the categorical specificity of a target cue. The Quarterly Journal of Experimental Psychology, 2009, vol. 62 (10), pp. 1904–1914.

Maxfield J., Stalder W., Zelinsky G. Effects of target typicality on categorical search. Journal of Vision, 2014, vol. 14 (12), no. 1, pp. 1–11.

Potter M. C. Conceptual short-term memory in perception and thought.

Frontiers in Psychology, 2012, vol. 3, pp. 1–13.

Itti L., Koc C. Computational modelling of visual attention. Nature reviews neuroscience, 2001, vol. 2 (3), pp. 194–203.

Labelme Database. Available at: http://labelme.csail.mit.edu (accessed: 03.12.2018).

Chia-Chien W., Farahnaz A. W., Pomplun M. Guidance of visual attention by semantic information in real-world scenes. Frontiers in psychology, 2004, vol. 54, pp. 1–13.

Huettig F., Altmann G. Word meaning and the control of eye fixation: Semantic competitor effects and the visual world paradigm. Cognition, 2005, vol. 96 (1), pp. 23–32.

Mirman D., Magnuson J. Dynamics of activation of semantically similar concepts during spoken word recognition. Memory and Cognition, 2009, vol. 37, pp. 1026–1039.

Allopenna P. D., Magnuson J. S., Tanenhaus M. K. Tracking the Time course of Spoken Word Recognition Using Eye Movements: Evidence for Continuous Mapping Models. Journal of Memory and Language, 1998, vol. 38, pp. 419−439.

Huettig F., Altmann G. Visual-shape Competition During Language Mediated Attention is Based on Lexical Input and not Modulated by Contextual Appropriateness. Visual Cognition, 2007, vol. 15, pp. 985−1018.

Huettig F., McQueen J. The tug of war between phonological, semantic and shape information in language-mediated visual search. Journal of Memory and Language, 2007, vol. 57 (4), pp. 460–482.

Groot F., Huettig F., Olivers C. When Meaning Matters: The Temporal Dynamics of Semantic Influences on Visual Attention. Journal of Experimental Psychology: Human Perception and Performance, 2016, vol. 42 (2), pp. 180–196.

Huettig F., Olivers C., Hartsuiker R. J. Looking, language, and memory: Bridging research from the visual world and visual search paradigms. Acta Psychologica, 2011, vol. 137, pp. 138–150. 

Information About the Authors

Maxsim I. Morozov, researcher, The Moscow School of Social and Economic Sciences, Russian Presidential Academy of National Economy and Public Administration, Moscow, Russian Federation, e-mail: magnus_ingvarsson_frost@mail.ru

Vladimir F. Spiridonov, Doctor of Psychology, Professor Dean of Psychological Department, Head of Laboratory of Cognitive Research, Psychological Department, Russian Presidential Academy of National Economy and Public Administration, Moscow, Russian Federation, ORCID: https://orcid.org/0000-0001-5081-879X, e-mail: vfspiridonov@yandex.ru

Metrics

 Web Views

Whole time: 424
Previous month: 9
Current month: 12

 PDF Downloads

Whole time: 149
Previous month: 2
Current month: 6

 Total

Whole time: 573
Previous month: 11
Current month: 18