October 2020
Volume 20, Issue 11
Open Access
Vision Sciences Society Annual Meeting Abstract  |   October 2020
Further Evidence that Probability Density Shape is a Proxy for Correlation
Author Affiliations & Notes
  • Madison Elliott
    University of British Columbia
  • Ronald Rensink
    University of British Columbia
  • Footnotes
    Acknowledgements  UBC 4 Year PhD Fellowship
Journal of Vision October 2020, Vol.20, 1481. doi:https://doi.org/10.1167/jov.20.11.1481
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      Madison Elliott, Ronald Rensink; Further Evidence that Probability Density Shape is a Proxy for Correlation. Journal of Vision 2020;20(11):1481. doi: https://doi.org/10.1167/jov.20.11.1481.

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

  • Supplements

Previous work demonstrated a discrimination performance cost for selecting “target” correlation populations among irrelevant “distractor” populations in two-class scatterplots (Elliott, 2016). This cost cannot be eliminated by increasing featural differences, e.g., color, between the dots in the two populations (Elliott & Rensink, VSS 2018). These findings do not agree with predictions from feature-based attention models (Wolfe, 1994), motivating us to investigate whether feature information can in fact be used to select target correlation populations. Observers performed a correlation discrimination task for scatterplots containing a target and a distractor population. Both populations had the same mean, standard deviation, color, and number of dots; the resulting two-class plots were distinguished by the correlation of the target population only. In the first of two counterbalanced conditions, targets were more correlated than the distractors; in the second, they were less. Results showed that observers can successfully discriminate two-class plots based on the correlation of their target populations. Increased JNDs were found when targets had higher correlations than distractors, replicating the results of Elliott (2016); however, there was no cost for targets with lower correlations. This asymmetry supports the proposal (Rensink, 2017) that estimation of correlation in scatterplots is based on the width of the probability density function corresponding to the dot cloud; for a two-class plot this appears to be a single density function dominated by the width of the lower-correlation (and thus wider) population. In addition, there is a resistance to feature selection: performance is the same regardless of the difference in features between target and distractor populations. This suggests that a two-class scatterplot is coded as a single ensemble, with observers unable to select items based on the value of their features because ensemble structure is prioritized over item-level feature information (Brady & Alvarez, 2011).


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