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A lack of automated, quantitative, and accurate assessment of social behaviors in mammalian animal models has limited progress toward understanding mechanisms underlying social interactions and their disorders such as autism. Here we present a new integrated hardware and software system that combines video tracking, depth sensing, and machine learning for automatic detection and quantification of social behaviors involving close and dynamic interactions between two mice of different coat colors in their home cage. We designed a hardware setup that integrates traditional video cameras with a depth camera, developed computer vision tools to extract the body “pose” of individual animals in a social context, and used a supervised learning algorithm to classify several well-described social behaviors. We validated the robustness of the automated classifiers in various experimental settings and used them to examine how genetic background, such as that of Black and Tan Brachyury (BTBR) mice (a previously reported autism model), influences social behavior. Our integrated approach allows for rapid, automated measurement of social behaviors across diverse experimental designs and also affords the ability to develop new, objective behavioral metrics.

From sleep and energy use to exercise and health, consumers have access to more information about their behavior than ever before. The appeal of personal quantification seems clear. By better understanding our behavior, we can make the necessary changes to live happier, healthier lives. But might the new tools people are using—quantifying life— rob them of some of the benefits of engaging in those activities? Six experiments demonstrate that while measurement increases how much of an activity people do (e.g., walk or read more), it can simultaneously reduce how much people enjoy those activities. This occurs because measurement can undermine intrinsic motivation. By drawing attention to output, measurement can make enjoyable activities feel more like work, which reduces their enjoyment. As a result, measurement can decrease continued engagement in the activity and subjective well-being. Even in the absence of explicit external incentives, measurement itself can thus have similar effects. The findings have implications for measurement’s use, as well as for the psychology of external incentives and intrinsic motivation.

Structural characteristics of games have been regarded as important aspects in the possible development of problematic gambling. The most important factors along with individual susceptibility and risk factors of the individual gambler are the structural characteristics such as the speed and frequency of the game (and more specifically event frequency, bet frequency, event duration, and payout interval). To date, the association between structural characteristics and behavior has not been studied in an online gambling environment. The present study investigated the association between structural characteristics and online gambling behavior in an ecologically valid setting using data from actual gamblers. The authors were given access to data from a large European online gambling operator with players from Germany, Austria, UK, Poland, and Slovenia. The sample comprised 763,490 sessions between November 27, 2020 and April 15, 2021 utilizing data from 43,731 players. A machine learning tree-based algorithm with structural characteristics and session metrics explained 26% of the variance of the number of games played in a session. The results also showed that only 7.7% of the variance in the number of bets placed in a session was explained by the game’s structural characteristics alone. The most important structural characteristic with respect to the number of games played in a session was the event frequency of the game followed by the maximum amount won on a single bet in a session.

Many research findings in the gambling studies field rely on self-report data. A very small body of empirical research also suggests that when using self-report, players report their gambling losses inaccurately. The aim of the present study was to evaluate the differences between objective and subjective gambling spent data by comparing gambler’s actual behavioral tracking data with their self-report data over a 1-month period. A total of 17,742 Norwegian online gamblers were asked to participate in an online survey. Of those surveyed, 1335 gamblers answered questions relating to gambling expenditure that could be compared with their actual gambling behavior. The study found that the estimated loss self-reported by gamblers was correlated with the actual objective loss and that players with higher losses tended to have more difficulty estimating their gambling expenditure (i.e., players who spent more money gambling also appeared to have more trouble estimating their expenses accurately). Overall, the findings demonstrate that caution is warranted when using self-report data relating to amount of money spent gambling in any studies that are totally reliant on self-report data.

In this study, the differences in behavior between two groups of online gamblers were investigated. The first group comprised individuals who played casino games, and the second group comprised those who bet on sports events. The focal point of the study was on problem gambling, and the objective was to identify and quantify both common and distinct traits that are characteristic to casino and sports problem gamblers. To this end, a set of gamblers from the gaming operator  LeoVegas was studied. Each gambler was ascribed two binary variables: one separating casino players from sports bettors, and one indicating whether there was an exclusion related to problem gambling. For each of the four combinations of the two variables, 2500 gamblers were randomly selected for a thorough comparison, resulting in a total of 10,000 participants. The comparison was performed by constructing two predictive models, estimating risk scores using these models, and scrutinizing the risk scores by means of a technique originating from collaborative game theory. The number of cash wagers per active day contributed the most to problem-gambling-related exclusion in the case of sports betting, whereas the volume of money spent contributed the most to this exclusion in the case of casino players. The contribution of the volume of losses per active day was noticeable in the case of both casino players and sports bettors. For casino players, gambling via desktop computers contributed positively to problem-gambling-related exclusion. For sports bettors, it was more concerning when the individual used mobile devices. The number of approved deposits per active day contributed to problem-gambling-related exclusion to a larger extent for sports bettors than casino players. The main conclusion is that the studied explanatory variables contribute differently to problem-gambling-related exclusion among casino players and sports bettors.

Social responsibility and duty of care have become major cornerstones for gambling operators. This has led to the introduction of many different responsible gambling tools such as limit-setting, mandatory play breaks, and personalized messaging. In the present study, the authors were given access to two secondary datasets provided by a German online slots game operator. The first dataset was from an online survey carried out by the gambling operator among 1000 of its players concerning their attitude towards deposit limits as well as self-reported problem gambling. In addition to the survey responses, the authors were given access to a second dataset of account-based data concerning each customer’s wagers, wins, monetary deposits, and monetary withdrawals. These datasets were then combined. The majority of players had a positive attitude towards the maximum deposit monthly deposit limit which was introduced by the German State Treaty on Gambling in 2021. Players who disagreed with the maximum monthly deposit limit, deposited significantly more money in the 30 days prior to answering the survey questions compared to players who agreed with the monthly deposit limit. The tracking data found only 7.6% of players had deposited the maximum amount of money allowed in one month. However, 60.5% of players in the survey data said that they did so. Players who said that they continued to gamble after reaching the deposit limit wagered and deposited significantly more money in the 30 days prior to the survey compared to players who said they stopped gambling after reaching the deposit limit. Two-fifths of players said they continued to gamble after reaching the monthly deposit limit (42%). The majority of the players said they chose a personal deposit limit because it helped them to better control their gambling expenditure. A quarter of the players reported gambling problems using the Brief Biosocial Gambling Screen (27%). Self-reported problem gambling was not correlated with depositing, wagering or any other player tracking metric.

It is widely assumed that our actions shape our brains and that the resulting connections determine who we are. To test this idea in a reductionist setting, in which genes and environment are controlled, we investigated differences in neuroanatomy and structural covariance by ex vivo structural magnetic resonance imaging in mice whose behavioral activity was continuously tracked for 3 months in a large, enriched environment. We confirmed that environmental enrichment increases mouse hippocampal volumes. Stratifying the enriched group according to individual longitudinal behavioral trajectories, however, revealed striking differences in mouse brain structural covariance in continuously highly active mice compared to those whose trajectories showed signs of habituating activity. Network-based statistics identified distinct subnetworks of murine structural covariance underlying these differences in behavioral activity. Together, these results reveal that differentiated behavioral trajectories of mice in an enriched environment are associated with differences in brain connectivity. An individual's experiences and behavior shape their brain, thereby building and refining a network of connections between neurons. This unique network may affect an individual's brain resilience in the face of aging, injury or disease. Understanding how individual experiences shape brain connections could help scientists develop personalized treatments. It may also have important implications for preventing brain disease. Studying mice can provide a window into some of these brain processes. By using inbred mice, scientists can rule out the role of genetics in brain differences. Scientists can also control the animals’ environments and track the activity of individuals to study their behavior. Bogado Lopes et al. show that more active mice living in enriched environments have signs of more complex networks of brain connections. In the experiments, the researchers placed genetically identical mice in either standard laboratory mouse housing or in enriched environments. Mice in the enriched housing had access to multi-level enclosures connected with tubes and supplied with a rotating array of toys. A tiny tracking device was inserted under the skin of the mice to follow their movements. Finally, all mice underwent structural magnetic resonance imaging to assess their brain anatomy and connections. This revealed that the most active and adventurous mice in the enriched enclosures had the most robust signs of increased brain connectivity. However, mice with declining activity levels in the enriched enclosures had fewer brain connections. Brain connection patterns in these creatures of habit were nearly identical to the ones in mice housed in small unenriched enclosures. The results show that how individual mice respond to their environments affects their brain structure. More active behavior patterns lead to more robust networks of brain connections. Larger studies in mice could provide more about lifestyle-dependent brain resilience. It may also help scientists to develop individualized approaches to optimizing brain health.

Gambling, like many other leisure activities, has been greatly affected by the novel coronavirus disease 2019 (COVID-19) pandemic. The present study investigated the behavior of a sample of online casino gamblers before and after the COVID-19 pandemic was announced in March 2020. The authors were given access to behavioral tracking data of a representative sample of 133,286 online casino gamblers by a large European online gambling operator with several online casino Swedish licenses. Online casino gambling activity utilizing daily cross-sectional data was examined over a 5-month period from January 1 to May 31 (2020). Results indicated that the (i) number of active online casino gamblers significantly increased over time, (ii) mean average amount of money bet by online casino gamblers daily significantly decreased over time, (iii) mean average daily bet by online casino gamblers at both the 90th and 99th percentiles significantly decreased over time, and (iv) mean average daily bet by online casino gamblers at the 10th and 25th percentiles significantly increased over time. The analysis also indicated that the number of high-risk players significantly decreased during the 5-month study period. While many different groups have claimed that gambling and problem gambling would increase during the pandemic due to more time being spent at home, evidence from the present study suggests that this is not the case because gambling intensity decreased, at least among Swedish gamblers.

Recent technological advances greatly improved the possibility to study freely behaving animals in natural conditions. However, many systems still rely on animal-mounted devices, which can already bias behavioral observations. Alternatively, animal behaviors can be detected and tracked in recordings of stationary sensors, e.g. video cameras. While these approaches circumvent the influence of animal-mounted devices, identification of individuals is much more challenging. We take advantage of the individual-specific electric fields electric fish generate by discharging their electric organ (EOD) to record and track their movement and communication behaviors without interfering with the animals themselves. EODs of complete groups of fish can be recorded with electrode arrays submerged in the water and then be tracked for individual fish. Here, we present an improved algorithm for tracking electric signals of wave-type electric fish with unprecedented accuracy. Our algorithm benefits from combining and refining previous approaches of tracking individual specific EOD frequencies (EODf) and spatial electric field properties. In this process, the similarity of signal pairs in extended data windows determines their tracking order, making the algorithm more robust against detection losses and intersections. We quantify the performance of the algorithm and show its application for a data set recorded with a 64-electrode array in a stream in the Llanos, Colombia, where we managed, for the first time, to track Apteronotus leptorhynchus over many days. These technological advances make electric fish a unique modelsystem for a detailed analysis of social and communication behaviors, with strong implications for our research on sensory coding.

Oxytocin/vasopressin-like peptides are important regulators of physiology and social behavior in vertebrates. However, the function of inotocin, the homologous peptide in arthropods, remains largely unknown. Here, we show that the level of expression of inotocin and inotocin receptor are correlated with task allocation in the ant Camponotus fellah. Both genes are up-regulated when workers age and switch tasks from nursing to foraging. in situ hybridization revealed that inotocin receptor is specifically expressed in oenocytes, which are specialized cells synthesizing cuticular hydrocarbons which function as desiccation barriers in insects and for social recognition in ants. dsRNA injection targeting inotocin receptor, together with pharmacological treatments using three identified antagonists blocking inotocin signaling, revealed that inotocin signaling regulates the expression of cytochrome P450 4G1 (CYP4G1) and the synthesis of cuticular hydrocarbons, which play an important role in desiccation resistance once workers initiate foraging.