CE02 - Terre vivante

Artificial Intelligence for Studying Communication in Wild Animals – WildCom-AI

Artificial Intelligence for the Study of Animal Communication in the Wild

Studying the brain processing of information is necessary to better understand how communication signals influence behaviours. However, the methods classically used in brain studies can be applied only to a limited number of model species. Artificial intelligence (AI) offers novel perspectives to model these brain processes and thus to investigate them outside laboratories

General objectives

In this project, we will use AI to explore the links between information processing in the brain of information and complex behaviours expressed by non-human primates in the natural environments. We will study the influence of face perception on socio-sexual behaviours in Mandrill (Mandrillus sphinx), a primate from Central Africa, relying on a long-term research program on a wild population of more than 300 mandrills launched in 2012 in Gabon..<br /><br />We will first study the links between facial resemblance and kin recognition. We will model the perception of facial resemblance using AI applied to our database of 19,000 pictures portraying individuals of the studied population. We will explore the mechanisms allowing mandrills to evaluate their resemblance to other individuals of their social group, the link between parent-offspring facial resemblance and parental investment, how resemblance among adults is used to limit inbreeding, and to which extent fortuitous facial resemblance generates behavioural biases towards unrelated individuals, as a by-product of kin selection.<br /><br />Then, we will study how the efficiency of the information processing in the brain influences socio-sexual behaviours. The cognitive sciences have recently revealed that a communication signal processed efficiently is evaluated positively and triggers attractive behaviours in receivers. These results come from studies carried on humans and in laboratories, but their implications in other species and in natural environments remain unknown. Here, for the first time in the cognitive sciences, we will use AI to quantify the efficiency of processing information. We will study the processing of facial information in mandrills, testing the hypothesis that a face processed efficiently is visually more attractive, and thus increases reproductive success, social integration and parental care.

Our scientific hypotheses will be tested with the wild population of mandrills using correlation analyses, but also using experiments with mandrills from a large captive population. For the first time in behavioural science, we will leverage the state-of-the-art generative algorithms of AI to synthetize complex communication signals (i.e. mandrill faces) while controlling their variation. We will generate artificial portraits modifying resemblance to another face and controlling the efficiency of their processing. Then, we will analyse how resemblance and processing efficiency influence behaviours by displaying the modified portraits to captive mandrills in large-scale bioassays.

In progress

This project aims to explore new applications of AI (modelling the processing of information in the brain, generating stimuli) for ecology and evolutionary biology. While the vast majority of studies at the interface between AI and ecology aim at characterizing the diversity of organisms and their interactions, this project will bring a new light onto the processes determining these interactions.

In progress

Studying the brain’s processing of information is necessary to better understand how communication signals influence behaviours. However, the methods classically used in brain studies can be applied only to a limited number of model species. Artificial intelligence (AI) offers novel perspectives to model these neural processes and thus to investigate them outside laboratories. In this project, we will use AI to explore the links between information processing in the brain and complex behaviours expressed by non-human primates in natural environments. We will study the influence of face perception on socio-sexual behaviours in mandrills (Mandrillus sphinx), a primate from Central Africa, relying on a long-term research program on a wild population of more than 300 mandrills launched in Gabon in 2012.
We will first study the links between facial resemblance and kin recognition. We will model the perception of facial resemblance using AI applied to our database of 19,000 pictures portraying individuals of the studied population. We will explore the mechanisms allowing mandrills to evaluate their resemblance to other individuals of their social group, the link between parent-offspring facial resemblance and parental investment, how resemblance among adults is used to limit inbreeding, and to what extent fortuitous facial resemblance generates behavioural biases towards unrelated individuals, as a by-product of kin selection.
Then, we will study how the efficiency of information processing in the brain influences socio-sexual behaviours. The cognitive sciences have recently revealed that a communication signal that is processed efficiently is evaluated positively and triggers attractive behaviours in receivers. These results come from studies carried out on humans in laboratories, but their implications for other species in natural environments remain unknown. Here, for the first time in the cognitive sciences, we will use AI to quantify the efficiency of information processing. We will study the processing of facial information in mandrills, testing the hypothesis that a face that is processed efficiently is visually more attractive, and thus increases reproductive success, social integration and parental care.
Our scientific hypotheses will be tested with the wild population of mandrills using not only correlation analyses, but also using experiments with mandrills from a large captive population. For the first time in behavioural science, we will leverage state-of-the-art generative algorithms of AI to synthesise complex communication signals (i.e. mandrill faces) while controlling their variation. We will generate artificial portraits modifying resemblance to another face and controlling the efficiency of their processing. Then, we will analyse how resemblance and processing efficiency influence behaviours by displaying the modified portraits to captive mandrills in large-scale bioassays.
This project aims to explore new applications of AI (i.e. modelling the processing of information in the brain and generating stimuli) for ecology and evolutionary biology. While the vast majority of studies at the interface between AI and ecology aim at characterising the diversity of organisms and their interactions, this project will shed new light on the processes determining these interactions.

Project coordination

Julien RENOULT (Centre d'Ecologie Fonctionnelle et Evolutive)

The author of this summary is the project coordinator, who is responsible for the content of this summary. The ANR declines any responsibility as for its contents.

Partner

CEFE Centre d'Ecologie Fonctionnelle et Evolutive

Help of the ANR 364,546 euros
Beginning and duration of the scientific project: November 2020 - 48 Months

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