Soon after hatching, the young of precocial species, such as domestic chicks or ducklings, learn to recognize their social partner by simply being exposed to it (imprinting process). Even artificial objects or stimuli displayed on monitor screens can effectively trigger filial imprinting, though learning is canalized by spontaneous preferences for animacy signals, such as certain kinds of motion or a face-like appearance. Imprinting is used as a behavioural paradigm for studies on memory formation, early learning and predispositions, as well as number and space cognition, and brain asymmetries. Here, we present an automatized setup to expose and/or test animals for a variety of imprinting experiments. The setup consists of a cage with two high-frequency screens at the opposite ends where stimuli are shown. Provided with a camera covering the whole space of the cage, the behaviour of the animal is recorded continuously. A graphic user interface implemented in Matlab allows a custom configuration of the experimental protocol, that together with Psychtoolbox drives the presentation of images on the screens, with accurate time scheduling and a highly precise framerate. The setup can be implemented into a complete workflow to analyse behaviour in a fully automatized way by combining Matlab (and Psychtoolbox) to control the monitor screens and stimuli, DeepLabCut to track animals’ behaviour, Python (and R) to extract data and perform statistical analyses. The automated setup allows neuro-behavioural scientists to perform standardized protocols during their experiments, with faster data collection and analyses, and reproducible results.

Steps towards a computational ethology: an automatized, interactive setup to investigate filial imprinting and biological predispositions / Zanon, Mirko; Lemaire, Bastien S.; Vallortigara, Giorgio. - In: BIOLOGICAL CYBERNETICS. - ISSN 0340-1200. - 2021, 115:6(2021), pp. 575-584. [10.1007/s00422-021-00886-6]

Steps towards a computational ethology: an automatized, interactive setup to investigate filial imprinting and biological predispositions

Zanon, Mirko;Vallortigara, Giorgio
2021-01-01

Abstract

Soon after hatching, the young of precocial species, such as domestic chicks or ducklings, learn to recognize their social partner by simply being exposed to it (imprinting process). Even artificial objects or stimuli displayed on monitor screens can effectively trigger filial imprinting, though learning is canalized by spontaneous preferences for animacy signals, such as certain kinds of motion or a face-like appearance. Imprinting is used as a behavioural paradigm for studies on memory formation, early learning and predispositions, as well as number and space cognition, and brain asymmetries. Here, we present an automatized setup to expose and/or test animals for a variety of imprinting experiments. The setup consists of a cage with two high-frequency screens at the opposite ends where stimuli are shown. Provided with a camera covering the whole space of the cage, the behaviour of the animal is recorded continuously. A graphic user interface implemented in Matlab allows a custom configuration of the experimental protocol, that together with Psychtoolbox drives the presentation of images on the screens, with accurate time scheduling and a highly precise framerate. The setup can be implemented into a complete workflow to analyse behaviour in a fully automatized way by combining Matlab (and Psychtoolbox) to control the monitor screens and stimuli, DeepLabCut to track animals’ behaviour, Python (and R) to extract data and perform statistical analyses. The automated setup allows neuro-behavioural scientists to perform standardized protocols during their experiments, with faster data collection and analyses, and reproducible results.
2021
6
Zanon, Mirko; Lemaire, Bastien S.; Vallortigara, Giorgio
Steps towards a computational ethology: an automatized, interactive setup to investigate filial imprinting and biological predispositions / Zanon, Mirko; Lemaire, Bastien S.; Vallortigara, Giorgio. - In: BIOLOGICAL CYBERNETICS. - ISSN 0340-1200. - 2021, 115:6(2021), pp. 575-584. [10.1007/s00422-021-00886-6]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/322063
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