August 2023
Volume 23, Issue 9
Open Access
Vision Sciences Society Annual Meeting Abstract  |   August 2023
Predicting performance of diverse color vision genotypes of wild primates when foraging for food.
Author Affiliations & Notes
  • Max Snodderly
    University of Texas at Austin
  • Delisa Ramos
    University of Texas at Austin
  • Andres Link
    Universidad de los Andes-Colombia
  • Anthony Di Fiore
    University of Texas at Austin
  • Footnotes
    Acknowledgements  NSF IOS-0843354, NSF BCS 1638822, and The University of Texas at Austin.
Journal of Vision August 2023, Vol.23, 4960. doi:
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      Max Snodderly, Delisa Ramos, Andres Link, Anthony Di Fiore; Predicting performance of diverse color vision genotypes of wild primates when foraging for food.. Journal of Vision 2023;23(9):4960.

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

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Most diurnal Neotropical primates have polymorphic color vision that can result in multiple types of dichromats and trichromats living in the same social group. We have modeled how well these genotypes would be predicted to distinguish dietary fruits from the visual background of leaf tops and leaf bottoms as a classification problem implemented by support vector machines (SVMs). We measured reflectance spectra of fruits and leaves, and irradiance spectra of incident light to compute quantum catches of genetically diverse cones of three sympatric primates in Amazonian Ecuador. We constructed MacLeod-Boynton chromaticity spaces for each genotype and trained SVMs to classify fruits and leaves based on color, then added a third dimension for luminance. For metrics of performance, we used classification accuracy and also the area under ROC curves (AUC), which allowed calculation of 95% confidence intervals (CIs, range 0-1). In the absence of luminance information, in direct sunlight trichromats are predicted to distinguish fruit from leaf tops with accuracies of 91-96% compared to 59-73% for dichromats; CIs of the AUCs do not overlap. When luminance was incorporated, predicted performance of trichromats did not improve, but predicted accuracy for dichromats increased to 71-79%; some CIs overlapped slightly with trichromats. For the more common (and more difficult) discrimination of fruit vs leaf bottoms, predicted accuracy based on color decreased to 76-92% for trichromats and 64-71% for dichromats; CIs had small amounts of overlap that varied among genotypes. Adding luminance information had little effect on predicted accuracy for trichromats (76%-88%), but increased predicted accuracy for dichromats to 66%-82%; now CIs of dichromats overlapped extensively with those of trichromats. Thus, dichromats may be able to use luminance information to minimize the potential advantage that trichromats have in a critical foraging discrimination. More detailed comparisons among types of trichromats and dichromats will also be presented.


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