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

Claidière, Nicolas; Gullstrand, Julie; Latouche, Aurélien; Fagot, Joël

doi:10.3758/s13428-015-0686-9

Cited by 19 publications

(39 citation statements)

References 21 publications

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“…Recent studies have demonstrated that monkeys can be trained to perform complex tasks using touchscreen devices that can be easily integrated into a naturalistic environment (Rumbaugh et al, 1989;Mandell and Sackett, 2008;Fagot and Paleressompoulle, 2009;Gazes et al, 2013;Calapai et al, 2017;Claidière et al, 2017;Tulip et al, 2017;Berger et al, 2018). Finally, it should be possible to obtain stable gaze signals from unrestrained animals.…”

Section: Recording Brain Activity During Natural and Complex Behaviormentioning

confidence: 99%

A naturalistic environment to study natural social behaviors and cognitive tasks in freely moving monkeys

Jacob

Katti

Cherian

et al. 2020

Preprint

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Macaque monkeys are widely used to study the neural basis of cognition. In the traditional approach, the monkey is brought into a lab to perform tasks while it is restrained to obtain stable gaze tracking and neural recordings. This unnatural setting prevents studying brain activity during natural, social and complex behaviors. Here, we designed a naturalistic environment with an integrated behavioral workstation that enables complex task training with viable gaze tracking in freely moving monkeys. We used this facility to train monkeys on a challenging same-different task. Remarkably, this facility enabled a naïve monkey to learn the task merely by observing trained monkeys. This social training was faster primarily because the naïve monkey first learned the task structure and then the same-different rule. We propose that such hybrid environments can be used to study brain activity during natural behaviors as well as during controlled cognitive tasks.

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Section: Recording Brain Activity During Natural and Complex Behaviormentioning

confidence: 99%

A naturalistic environment to study natural social behaviors and cognitive tasks in freely moving monkeys

Jacob

Katti

Cherian

et al. 2020

Preprint

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“…The links in bold are the ones present in both top ten rankings. aggregation window of one month is usually advocated [22,48]. In the case of data 360 collected through wearable sensors on the other hand, a large amount of contacts is 361 already recorded after a few hours.…”

Section: Rank Interaction Network (Observations) Contacts Network (Sementioning

confidence: 99%

“…Comparison between 70 sensors and direct observations or videos have yielded mixed results [46,47]. Among 71 animals, different types of networks built from the same data set of direct observations 72 have been shown to differ [41,42], while a social network deduced from co-presence in 73 cognitive testing booths has been shown to correlate with the one obtained from 74 directly observed interactions [48,49]. However, we are not aware of studies using data 75 collected in the same population with on the one hand wearable sensors and on the 76 other hand direct observations.…”

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confidence: 99%

“…objective and reliable definition of contact as a proximity event. Second, all individuals 47 equipped with a sensor are monitored together, continuously and potentially for a long 48 time without the need for constant human supervision. This enables in principle the 49 collection of large data sets covering long periods of times and, consequently, makes it 50 possible to investigate the evolution and stability of social relationships and social 51 groups on long timescales.…”

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confidence: 99%

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Measuring social networks in primates: wearable sensors vs. direct observations

Gelardi

Godard

Paleressompoulle

et al. 2020

Preprint

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Network analysis represents a valuable and flexible framework to understand the structure of individual interactions at the population level in animal societies. The versatility of network representations is moreover suited to different types of datasets describing these interactions. However, depending on the data collection method, different pictures of the social bonds between individuals could a priori emerge. Understanding how the data collection method influences the description of the social structure of a group is thus essential to assess the reliability of social studies based on different types of data. This is however rarely feasible, especially for animal groups, where data collection is often challenging. Here, we address this issue by comparing datasets of interactions between primates collected through two different methods: behavioral observations and wearable proximity sensors. We show that, although many directly observed interactions are not detected by the sensors, the global pictures obtained when aggregating the data to build interaction networks turn out to be remarkably similar. Sensors data yield moreover a reliable social network already over short timescales and can be used for long term campaigns, showing their important potential for detailed studies of the evolution of animal social groups. Introduction 1 Interactions between individuals are the foundation of complex social structures in 2 human and other animal societies. Network analysis represents a valuable framework to 3 understand the structure and evolution of these interactions, as it encodes a whole 4 hierarchy of patterns, from individual-level interactions to complex population-level 5 social structures. [1-3, 3-8].6 With the increasing deployment of digital devices, new ways of collecting data, 7 combined with new network analysis tools, have made possible the development of 8 quantitative measures of these relationships and patterns in modern human societies, 9 January 17, 2020 1/20leading to the emergence of computational social science in the last decades [9]. For 10 instance, social relationships have been inferred and studied using various data sources 11 ranging from phone calls [10], e-mails [11,12], online interactions [13], to face-to-face 12 interactions measured by wearable sensors [14][15][16][17][18][19][20]. 13The availability of large volumes of data with high temporal resolution has thus 14 contributed to the rapid expansion of data-driven computational studies of human 15 relationships and human social networks. On the contrary, the data collection remains 16 more challenging in the field of animal studies, because the data on animal interactions 17 are still largely obtained from direct observations [6,21]. Data resulting from such 18 observations are extremely valuable as they often include detailed information about the 19 nature, duration and location of the interactions between individuals. They thus allow 20 researchers to grasp and investigate complex social patterns in animal groups. 21 Unfortunat...

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“…For our study, we decided to focus solely on grooming as it is known to be a bonding activity in primates and therefore to represent affiliative relationships accurately (Seyfarth, 1977). More details on the behavioural observations can be found in (Claidière et al, 2017).…”

Section: Behavioural Datamentioning

confidence: 99%

Detecting social (in)stability in primates from their temporal co-presence network

et al. 2019

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The stability of social relationships is important to animals living in groups, and social network analysis provides a powerful tool to help characterize and understand their (in)stability and the consequences at the group level. However, the use of dynamic social networks is still limited in this context because it requires longterm social data and new analytical tools. Here, we study the dynamic evolution of a group of 29 Guinea baboons (Papio papio) using a dataset of automatically collected cognitive tests comprising more than 16M records collected over 3 years. We first built a monthly aggregated temporal network describing the baboon's copresence in the cognitive testing booths. We then used a null model, considering the heterogeneity in the baboons' activity, to define both positive (association) and negative (avoidance) monthly networks. We tested social balance theory by combining these positive and negative social networks. The results showed that the networks were structurally balanced and that newly created edges also tended to preserve social balance. We then investigated several network metrics to gain insights into the individual level and group level social networks long-term temporal evolution. Interestingly, a measure of similarity between successive monthly networks was able to pinpoint periods of stability and instability and to show how some baboons' egonetworks remained stable while others changed radically. Our study confirms the prediction of social balance theory but also shows that large fluctuations in the numbers of triads may limit its applicability to study the dynamic evolution of animal social networks. In contrast, the use of the similarity measure proved to be very versatile and sensitive in detecting relationships' (in)stabilities at different levels. The changes we identified can be linked, at least in some cases, to females changing primary male, as observed in the wild.

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

Cited by 19 publications

References 21 publications

A naturalistic environment to study natural social behaviors and cognitive tasks in freely moving monkeys

A naturalistic environment to study natural social behaviors and cognitive tasks in freely moving monkeys

Measuring social networks in primates: wearable sensors vs. direct observations

Detecting social (in)stability in primates from their temporal co-presence network

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