Using Automated Learning Devices for Monkeys (ALDM) to study social networks

Behavior Research Methods, Dec 2015

Social network analysis has become a prominent tool to study animal social life, and there is an increasing need to develop new systems to collect social information automatically, systematically, and reliably. Here we explore the use of a freely accessible Automated Learning Device for Monkeys (ALDM) to collect such social information on a group of 22 captive baboons (Papio papio). We compared the social network obtained from the co-presence of the baboons in ten ALDM testing booths to the social network obtained through standard behavioral observation techniques. The results show that the co-presence network accurately reflects the social organization of the group, and also indicate under which conditions the co-presence network is most informative. In particular, the best correlation between the two networks was obtained with a minimum of 40 days of computer records and for individuals with at least 500 records per day. We also show through random permutation tests that the observed correlations go beyond what would be observed by simple synchronous activity, to reflect a preferential choice of closely located testing booths. The use of automatized cognitive testing therefore presents a new way of obtaining a large and regular amount of social information that is necessary to develop social network analysis. It also opens the possibility of studying dynamic changes in network structure with time and in relation to the cognitive performance of individuals.

A PDF file should load here. If you do not see its contents the file may be temporarily unavailable at the journal website or you do not have a PDF plug-in installed and enabled in your browser.

Alternatively, you can download the file locally and open with any standalone PDF reader:

https://link.springer.com/content/pdf/10.3758%2Fs13428-015-0686-9.pdf

Using Automated Learning Devices for Monkeys (ALDM) to study social networks

Behav Res Using Automated Learning Devices for Monkeys (ALDM) to study social networks Nicolas Claidière 0 1 2 Julie Gullstrand 0 1 2 Aurélien Latouche 0 1 2 Joël Fagot 0 1 2 0 EA 4629, Conservatoire National des Arts et Métiers , Paris , France 1 Laboratoire de Psychologie Cognitive UMR 7290, Aix Marseille Université and Centre Nationale de la Recherche Scientifique , Marseille 13331 , France 2 Nicolas Claidière Social network analysis has become a prominent tool to study animal social life, and there is an increasing need to develop new systems to collect social information automatically, systematically, and reliably. Here we explore the use of a freely accessible Automated Learning Device for Monkeys (ALDM) to collect such social information on a group of 22 captive baboons (Papio papio). We compared the social network obtained from the co-presence of the baboons in ten ALDM testing booths to the social network obtained through standard behavioral observation techniques. The results show that the co-presence network accurately reflects the social organization of the group, and also indicate under which conditions the co-presence network is most informative. In particular, the best correlation between the two networks was obtained with a minimum of 40 days of computer records and for individuals with at least 500 records per day. We also show through random permutation tests that the observed correlations go beyond what would be observed by simple synchronous activity, to reflect a preferential choice of closely located testing booths. The use of automatized cognitive testing therefore presents a new way of obtaining a large and regular amount of social information that is necessary to develop social network analysis. It also opens the possibility of studying dynamic changes in network structure with time and in relation to the cognitive performance of individuals. Animal behaviour; Baboon; Computerised testing; Social cognition - Social network analysis (SNA) has become a prominent tool to study the social life of animals in general (Croft, James, & Krause, 2008; Krause, Lusseau, & James, 2009; Kurvers, Krause, Croft, Wilson, & Wolf, 2014; Wey, Blumstein, Shen, & Jordán, 2008; Whitehead, 2008) . and of primates in particular (Brent, Lehmann, & Ramos-Fernandez, 2011) . However, when compared to humans, SNA with primates is often limited by the amount of data that can be gathered on the social relationships of individuals. Traditionally, primate social networks have been studied through standard observation techniques such as scan sampling or focal follows (Altmann, 1974) . but these methods are time consuming and provide irregular and sometime biased information (e.g., when one individual is more easily seen or recognized than another; Whitehead, 2008) . More recently, the development of GPS collars has provided new ways to gather relatively large amounts of data over substantial periods of time (e.g., Patzelt et al., 2014; Qi et al., 2014) . However, GPS techniques have a relatively poor spatial resolution and can only be used to track the movements of groups of individuals (between-group SNA), but not the proximity of individuals within groups. In Patzelt et al. (2014) . for instance, two individuals wearing GPS collar are considered associated when they are less than 100 m away. In this article, we describe a new method to study the sociality of nonhuman primate species on the basis of the automatic collection of proximity data during Automated Learning Device for Monkeys (ALDM; Fagot & Bonté, 2010; Fagot & Paleressompoulle, 2009) testing (a Bproximity network,^ for short). This new method complements the existing techniques of automatic collection of proximity data that can be used to collect large amounts of data over long periods of times for individuals within groups (within-group SNA). This method is based on an automatic reinforcement system that has been developed in our laboratory (ALDM test systems). With this system, a group of baboons have free access to computerized testing booths that are installed in trailers next to their enclosure. The baboons are automatically detected and recognized by the computer and are trained to perform cognitive experiments on touchscreens by using positive reinforcement (see the Method section for more details). Baboons can therefore select the testing booth of their choice and maintain visual contact with other individuals taking part in the experiment (through the transparent side walls of the testing booths; see Fig. 1). Earlier studies have shown that this system is an efficient tool for the assessment of cognitive functions in experimental tasks (e.g., memory: Fagot & De Lillo, 2011; reasoning: Flemming, Thompson, & Fagot, 2013; or perception: Parron & Fagot, 2007) and has a positive impact on animal welfare (Fagot, Gullstrand, Kemp, Defilles, & Mekaouche, 2014) . In the present study, we used standard behavioral observation te (...truncated)


This is a preview of a remote PDF: https://link.springer.com/content/pdf/10.3758%2Fs13428-015-0686-9.pdf

Nicolas Claidière, Julie Gullstrand, Aurélien Latouche, Joël Fagot. Using Automated Learning Devices for Monkeys (ALDM) to study social networks, Behavior Research Methods, 2017, pp. 24-34, Volume 49, Issue 1, DOI: 10.3758/s13428-015-0686-9