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
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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)