Evidence for abstract spatial concept learning in young animals
Year: 2026
Type: article
Abstract: Encoding abstract spatial relations (above-below, left-right, inside-outside) is essential for navigation and object recognition. The ability to form abstract categories is particularly advantageous for young animals, as it supports efficient environmental encoding during development. However, research has largely focused on adult animals, with limited data on human infants and no evidence on young non-human animals. To investigate the emergence of abstract category learning and its underlying mechanisms in minimally experienced animals, we tested day-old domestic chicks (Gallus gallus). During training, chicks learned to discriminate between two stimuli composed of a cross and a disk vertically aligned: one with the cross positioned above the disk, and the other with the disk positioned above the cross. In Experiment 1, chicks transferred their learning to novel versions of the same stimuli with altered vertical spacing between elements, as well as to mismatched pairs, where the spatial distance differed across the two test stimuli. In Experiment 2, chicks generalized to new stimuli with the same cross-disk configurations at novel absolute heights, and with mismatched pairs where absolute height differed between alternatives. In Experiment 3, chicks demonstrated to transfer to novel configurations of two objects with different shapes and colours and not to only-disk stimuli. Our study demonstrated that chicks discriminate visual configurations based solely on spatial relations between two shapes, transferring learning to novel stimuli while excluding reliance on absolute distance or overall height, providing evidence of abstract spatial concept learning in young, inexperienced animals.
Source: Cognition
Institutions: University of Trento, University of Padua
Language: English
Subfield: Automotive Engineering
Field: Engineering
Domain: Physical Sciences
SDG: Quality education
Open Access status: hybrid
Estimated APC paid: $3,910
OpenAlex ID: W7166691497