The numerical Stroop task involves presenting participants with two digits that differ in physical size and numerical value and asking them to report which digit had the larger size or value while ignoring the other dimension. Previous studies show that participants have difficulty ignoring the irrelevant dimension and thus have implications on the automaticity of numerical processing. The present study investigates the automatic influence of numerical value on numerosity processing in a novel Stroop-like task. In two experiments, participants were presented with digits made of colored stripes and asked to identify the number of different colors. In both experiments, interference and facilitation effects were found, supporting the automaticity of symbolic number processing and its influence on numerosity processing. These findings expand upon previous research on numerical as well as counting Stroop tasks, and have potential implications for studying interference and basic numerical processing in children and clinical populations.
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http://dx.doi.org/10.3758/s13421-024-01631-7 | DOI Listing |
J Vis
January 2025
Department of Cognitive Sciences and Neurobiology and Behavior, University of California, Irvine, California, USA.
A salience map is a topographic map that has inputs at each x,y location from many different feature maps and summarizes the combined salience of all those inputs as a real number, salience, which is represented in the map. Of the more than 1 million Google references to salience maps, nearly all use the map for computing the relative priority of visual image components for subsequent processing. We observe that salience processing is an instance of substance-invariant processing, analogous to household measuring cups, weight scales, and measuring tapes, all of which make single-number substance-invariant measurements.
View Article and Find Full Text PDFNat Commun
January 2025
Center for Information and Neural Networks (CiNet), Advanced ICT Research Institute, National Institute of Information and Communications Technology, Suita, Japan.
The ability to estimate numerical magnitude is essential for decision-making and is thought to underlie arithmetic skills. In humans, neural populations in the frontoparietal regions are tuned to represent numerosity. However, it remains unclear whether their response properties are fixed to a specific numerosity (i.
View Article and Find Full Text PDFPsychon Bull Rev
January 2025
Department of Business and Information Science, Japan International University, Tsukuba, Japan.
Previous research has suggested that numerosity estimation and counting are closely related to distributed and focused attention, respectively (Chong & Evans, WIREs Cognitive Science, 2(6), 634-638, 2011). Given the critical role of color in guiding attention, this study investigated its effects on numerosity processing by manipulating both color variety (single color, medium variety, high variety) and spatial arrangement (clustered, random). Results from the estimation task revealed that high color variety led to a perceptual bias towards larger quantities, regardless of whether colors were clustered or randomly arranged.
View Article and Find Full Text PDFPsychol Res
December 2024
School of Psychology, Central China Normal University (CCNU), Wuhan, 430079, China.
The serial dependence effect (SDE) is a perceptual bias where current stimuli are perceived as more similar to recently seen stimuli, possibly enhancing the stability and continuity of visual perception. Although SDE has been observed across many visual features, it remains unclear whether humans rely on a single mechanism of SDE to support numerosity processing across two distinct numerical ranges: subitizing (i.e.
View Article and Find Full Text PDFPsychol Rev
December 2024
School of Interactive Computing, Georgia Institute of Technology.
Although the importance of unsupervised learning has been recognized since William James's "blooming, buzzing confusion," it has received less attention in the literature than supervised learning. An important form of unsupervised learning is clustering, which involves determining the groups of distinct objects that belong together. Visual clustering is foundational for ensemble perception, numerosity judgments, spatial problem-solving, understanding information visualizations, and other forms of visual cognition, and yet surprisingly few researchers have directly investigated this human ability.
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