Predisposed and learned preferences for multipoint visual statistics in visually naive newly hatched chicks

Journal article

Recent studies have revealed that human and non-human animals (rats) can detect luminance distribution and correlations between pixels in an image (ranging from 2-point to 4-point). This sensitivity is believed to stem from optimization processes in the visual system operating through efficient coding mechanisms that retain the most informative and significant features (here identified as the most variable correlations), thereby reducing costs to extract biologically relevant information from the environment. However, it is yet to be determined whether this optimization is evolutionarily given by inborn mechanisms or shaped by visual experience. Here we report that newly hatched visually naive domestic chicks (Galluls gallus) spontaneously prefer to approach luminance, 2-point and 4-point correlation patterns (respectively, horizontal lines and rectangular patterns), while showing no preference for 3-point correlation over noise controls. This parallels the ranking observed in adult humans and rats, thus suggesting that evolutionarily given biological predispositions largely drive efficient coding of natural images. We also found that learning by exposure to visual stimuli, as occurs naturally during visual imprinting, induced a preference for noise over point correlation patterns in chicks exposed to 3- and 4-point patterns. We hypothesize that this behaviour could reflect chicks’ preference for stimuli of lower statistical (correlation-based) complexity.

Authors: Zanon, M; Lemaire, BS; Piasini, E; Caramellino, R; Nallet, C

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: The Royal Society
• Journal: Proceedings of the Royal Society B: Biological Sciences
• Volume: 293
• Issue: 2066
• Article number: 20253157
• Publication date: 2026-03-04
• Acceptance date: 2026-01-13
• DOI: 10.1098/rspb.2025.3157
• EISSN: 1471-2954
• ISSN: 0962-8452
• Language: English
• Keywords: image statistics; innate predisposition; efficient coding; imprinting; multipoint correlation