October 2020
Volume 20, Issue 11
Open Access
Vision Sciences Society Annual Meeting Abstract  |   October 2020
Correlation structure does not affect number judgment
Author Affiliations & Notes
  • Ford Atwater
    University of British Columbia
  • Madison Elliott
    University of British Columbia
  • Caitlin Coyiuto
    Carnegie Mellon University
  • Ronald Rensink
    University of British Columbia
  • Footnotes
    Acknowledgements  UBC 4 year PhD fellowship
Journal of Vision October 2020, Vol.20, 1511. doi:https://doi.org/10.1167/jov.20.11.1511
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      Ford Atwater, Madison Elliott, Caitlin Coyiuto, Ronald Rensink; Correlation structure does not affect number judgment. Journal of Vision 2020;20(11):1511. doi: https://doi.org/10.1167/jov.20.11.1511.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Past work showed a performance cost for discriminating two-class scatterplots formed of a “target” population and an irrelevant “distractor” population (Elliott, 2016). As long as a second population was present, discrimination was the same regardless of color similarity between the two, resulting in “all-or-nothing performance” (Elliott & Rensink, VSS 2019). When the same dots were randomly distributed in a numerosity estimation task, severe performance costs were found for similar target and distractor colors, but no cost for opposite-colored distractors, showing “graded performance” similar to that encountered in visual search (Duncan & Humphreys, 1989). A possible explanation for this difference is that the structured spatial distributions of stimuli in the correlation task differed from the random distributions in the number estimation task. This prompted us to investigate whether or not numerosity estimates would be affected by correlation structure, and vice versa. Observers performed two within-subjects tasks: a correlation task and a numerosity discrimination task. Crucially, the stimuli were always the same: two-class scatterplots with target and distractor populations distinguished by color. Trial blocks were fully counterbalanced according to target dot number (50, 100, 150) and target dot correlation (.3, .6, .9). Distractor populations were always drawn with 100 dots at Pearson’s r = .3. Results showed that correlation JND slopes were unaffected by number of dots in the target population, and JND intercepts increased as the number of dots decreased, consistent with one-class scatterplot performance (Rensink, 2017). Number JNDs increased with the number of dots, consistent with past findings (Feigenson et al., 2004). Critically, number JNDs did not vary with target correlation, showing that the structure and geometric density of the target population does not affect our ability to select and estimate number information.

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