How Perceptual Field Variables Effect on Local -Global Visual Processing? A Systematic Review and Meta-analysis
محل انتشار: هشتمین همایش بین المللی علوم شناختی
سال انتشار: 1399
نوع سند: مقاله کنفرانسی
زبان: انگلیسی
مشاهده: 43
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شناسه ملی سند علمی:
ICCS08_092
تاریخ نمایه سازی: 8 تیر 1405
چکیده مقاله:
Background and Aim: In local-global visual processing, Global Precedence Effect refers to the finding that states global aspects of a scene are processed more rapidly than local details. Global precedence has been examined using various tasks and conditions. Here, we classify the variables affecting global/local processing, into two primary categories: i) individual characteristics, like, age, disorder, culture, and, ii) perceptual field variables, like, stimulus size, eccentricity, sparsity, and spatial frequency. Methods: We conduct a systematic review based on PRISMA framework to identify the most important perceptual field variables in local-global processing. Subsequently, we perform a meta-analysis to estimate pooled effect size among all the studies investigating the effect of those variables. Results: The main goals of this study are identifying the most important perceptual field variables and investigating the effect of them on global precedence. Conclusion According to Baron-Cohen classification for effect size values, 'relevance", 'sparsity' and 'type (filled or outlined)' are classified in the group of small effect variables. "Visual field', 'level repetition', 'spatial frequency' and 'type (object vs letter)' has a medium effect and 'congruency', 'eccentricity' and 'size' has a near large effect. The perceptual field variables 'congruency', 'eccentricity' and 'size' should be controlled in designing stimulus for any visual task.
کلیدواژه ها:
نویسندگان
Zahra Rezvani
Department of Cognitive Modeling, Institute for Cognitive and Brain Sciences, Shahid Beheshti University G.C., Tehran, Iran.
Ali Katanforoush
Department of Computer and Data Sciences, Shahid Beheshti University G.C., Tehran, Iran.
Hamidreza Pouretemad
Department of Cognitive Modeling, Institute for Cognitive and Brain Sciences, Shahid Beheshti University G.C., Tehran, Iran.