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Publications

Journal Article Ahead of print

PubMed: 42481833 DOI: 10.3758/s13420-026-00723-5

Repeatable colour preferences predict information use in nest-building birds.

Learn Behav

Authors: Benjamin A Whittaker, Henry R Swartwood García, Simran K Gill, Julia L Self, Lauren M Guillette

Affiliations

  • Department of Psychology, University of Alberta, Edmonton, AB, T6G 2R3, Canada.
  • Department of Psychology, University of Alberta, Edmonton, AB, T6G 2R3, Canada. Department of Chemical and Biological Sciences, Universidad de Las Américas Puebla, Ex-Hacienda Sta Catarina Mártir S/N, San Andrés Cholula, 72810, Puebla, México.
  • Department of Psychology, University of Alberta, Edmonton, AB, T6G 2R3, Canada.

Abstract

More than a century ago, naturalist Henry Smith Williams observed that birds nesting in his garden differed in their colour preferences for nest-building materials. His observations challenge the assumption that individuals within the same species respond similarly to the same colour cues, but the stability and consequences of these apparent preferences remain largely unexplored. Here, we tested the colour preferences of laboratory-raised zebra finches (Taeniopygia guttata) with access to pink and orange nest-building string. Birds were tested under conditions lacking relevant environmental cues, social information, or previous experience with the experimental colours. Individuals differed in both their preferred colour and the strength of preferences, which were more strongly repeatable across short-term than long-term intervals. We next asked whether these stable individual preferences influenced how birds respond to information, reanalyzing published social-learning datasets spanning different informational contexts. Stronger initial preferences generally predicted reduced use of socially demonstrated nonpreferred colours during nest building. These findings suggest that stable individual preferences represent an underappreciated source of variation in how animals acquire and use information during decision making.

MeSH

Learning