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

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

Institutions

Publication Details

Journal
Proceedings of the Royal Society B Biological Sciences
Published
2026-03-04
DOI
https://doi.org/10.1098/rspb.2025.3157
Primary Topic
Visual perception and processing mechanisms
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

V. Balasubramanian, Davide Zoccolan, Eugenio Piasini, Giorgio Vallortígara et al.
Proceedings of the Royal Society B Biological Sciences
Visual perception and processing mechanisms
article

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

V. Balasubramanian, Davide Zoccolan, Eugenio Piasini, Giorgio Vallortígara, Caroline Nallet, Riccardo Caramellino, Bastien Lemaire, Judit Gervain, Mirko Zanon
article en

Abstract

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.

Proceedings of the Royal Society B Biological SciencesVol. 293(2066)
Centre National de la Recherche Scientifique (FR), Scuola Internazionale Superiore di Studi Avanzati (IT), University of Padua (IT), University of Fribourg (CH), University of Trento (IT), Université Paris Cité (FR), Université Rennes 2 (FR), California University of Pennsylvania (US), University of Oxford (GB), Sorbonne Paris Cité (FR), Normandie Université (FR), Université de Rennes (FR), University of Pennsylvania (US)
University of Oxford, European Commission, Balliol College, University of Oxford
Openalex Percentile: Top 37%
Visual perception and processing mechanisms
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.