Creating Detailed Metadata for an R Shiny Analysis of Rodent Behavior Sequence Data Detected Along One Light-Dark Cycle

Automated mouse phenotyping through the high-throughput analysis of home cage behavior has brought hope of a more effective and efficient method for testing rodent models of diseases. Advanced video analysis software is able to derive behavioral sequence data sets from multiple-day recordings. However, no dedicated mechanisms exist for sharing or analyzing these types of data. In this article, we present a free, open-source software actionable through a web browser (an R Shiny application), which performs an analysis of home cage behavioral sequence data, which is designed to spot differences in circadian activity while preventing p-hacking. The software aligns time-series data to the light/dark cycle, and then uses different time windows to produce up to 162 behavior variables per animal. A principal component analysis strategy detected differences between groups. The behavior activity is represented graphically for further explorative analysis. A machine-learning approach was implemented, but it proved ineffective at separating the experimental groups. The software requires spreadsheets that provide information about the experiment (i.e., metadata), thus promoting a data management strategy that leads to FAIR data production. This encourages the publication of some metadata even when the data are kept private. We tested our software by comparing the behavior of female mice in videos recorded twice at 3 and 7 months in a home cage monitoring system. This study demonstrated that combining data management with data analysis leads to a more efficient and effective research process.

Exploring Collaborative Problem Solving Behavioral Transition Patterns in Science of Taiwanese Students at Age 15 According to Mastering Levels

This study analyzed the collaborative problem solving (CPS) behavioral transition patterns of 53,859 Taiwanese students in science at age 15 by using an online Taiwanese CPS assessment that was designed according to the Programme for International Student Assessment 2015 CPS framework. Because of behavioral changes over the testing period, the CPS target skills that corresponded to the assessment items can be viewed as a CPS behavioral sequence. Hence, a lag sequential analysis was applied to explore the significance of the interactions among the CPS skills. The behavioral sequence is coded according to the level of mastery (0, 1, or 2) of items. The CPS transition patterns were analyzed in three gaps, namely the gender gap, the urban–rural gap, and the achievement gap. The findings showed that “Monitoring and repairing the shared understanding” was a crucial CPS skill in science. Moreover, the female students who would follow rules of engagement effectively exhibited higher scores than male students did in monitoring the results of their actions and evaluating their success in solving the problem. No obvious differences were observed in the urban–rural gap, whereas differences were observed in the achievement gap.

Creating detailed metadata for an R Shiny analysis of circadian behavior sequence data

Automated mouse phenotyping through the high-throughput analysis of home cage behavior has brought hope of a more effective and efficient method for testing rodent models of diseases. Advanced video analysis software is able to derive behavioral sequence data sets from multiple-day recordings. However, no dedicated mechanisms exist for sharing or analyzing these types of data. In this article, we present a free, open-source software actionable through a web browser (an R Shiny application), which can perform state-of-the-art multidimensional analysis of homecage behavioral sequence data. The software aligns time-series data to the light/dark cycle, and then uses different time windows to produce up to 162 behavior variables per animal. It prevents p-hacking by providing an analysis that uses a principal component analysis strategy, while also representing the behavior graphically for further explorative analysis. A machine-learning approach was implemented, but it proved ineffective at separating the experimental groups. The software requires spreadsheets that provide information about the experiment (i.e., metadata), thus promoting a data management strategy that leads to FAIR data production. This encourages the publication of some metadata even when the data are kept private. We tested our software by comparing the behavior of female mice in videos recorded twice at 3and 7 months in a home cage monitoring system. This study demonstrated that combining data management with data analysis leads to a more efficient and effective research process.

Decisions in an Innate Behavioral Sequence

Innate behaviors are comprised of ordered sequences of component actions that progress to satisfy drives. We have characterized the structure of egg-laying behavior in Drosophila in detail and observed that the sequence is not merely comprised of motor acts but also acts of sensory exploration that govern the transitions between component actions. We have identified a cluster of internal sensory neurons that provide information about the progression of the egg during ovipositor burrowing, a behavior necessary for the subterraneous deposition of the egg. These neurons impart sensory feedback that allows burrowing to continue to egg deposition or to abort in favor of further exploration. Diminished activity of these neurons upon completed egg expulsion may initiate the transition to the final phase of egg-laying, allowing the cycle to repeat. Sensory feedback therefore plays a critical role at decision points between transitions affording innate behaviors with an adaptive flexibility.

Behavioral responses to numerical differences when two invasive ants meet: the case of Lasius neglectus and Linepithema humile

Two of the world’s most invasive ants, Linepithema humile and Lasius neglectus, are destined to overlap in range as they continue to spread throughout Europe. Although L. humile arrived first, and is therefore more numerically abundant, L. neglectus is the more behaviorally dominant of the two. We performed lab trials to determine whether L. humile could use numerical abundance to overcome the behavioral dominance of L. neglectus and whether the ants’ behavioral patterns shifted when the species co-occurred. We found that L. neglectus was more aggressive when less abundant, whereas the opposite was true of L. humile. When L. neglectus was outnumbered, it employed aggressive behaviors, such as biting or chemical attacks, more frequently than L. humile; it also utilized a behavioral sequence that included mandible opening and biting. Our results for these species support the hypothesis that species modulate their behavior towards competitors, which facilitates the understanding of how multiple invasive ant species can co-occur in a given area. Moreover, our study shows that the co-occurrence of invasive species could result from the use of two strategies: (1) the Bourgeois strategy, in which aggressiveness changes based on numerical dominance and (2) the dear-enemy strategy, in which aggressiveness is reduced when competitors co-occur. Since these strategies may lead to territory partitioning, we suggest that the behavioral flexibility displayed by both species when they overlap may allow local co-occurrence and increase their likelihood of co-occurrence during their range expansion in Europe, which could have a negative cumulative impact on invaded areas.

Optimizing agent behavior over long time scales by transporting value

Humans prolifically engage in mental time travel. We dwell on past actions and experience satisfaction or regret. More than storytelling, these recollections change how we act in the future and endow us with a computationally important ability to link actions and consequences across spans of time, which helps address the problem of long-term credit assignment: the question of how to evaluate the utility of actions within a long-duration behavioral sequence. Existing approaches to credit assignment in AI cannot solve tasks with long delays between actions and consequences. Here, we introduce a paradigm where agents use recall of specific memories to credit past actions, allowing them to solve problems that are intractable for existing algorithms. This paradigm broadens the scope of problems that can be investigated in AI and offers a mechanistic account of behaviors that may inspire models in neuroscience, psychology, and behavioral economics.

The complex communication signals in the mating behavior of the tropical cricket Cranistus colliurides Stål, 1861 (Orthoptera: Grylloidea; Trigonidiidae: Phylloscyrtini)

Cricket mating behavior reflects different strategies developed by sexual selection throughout evolutionary time. To our knowledge, only one species of the Neotropical cricket Trigonidiinae had its mating behavior studied so far. Here we expand this knowledge by describing the mating behavior of Cranistus colliurides Stål, 1861, a cricket commonly found in bushes and grasses along open fields or the forest edge. Adult crickets were collected in the municipality of Capão do Leão, State of Rio Grande do Sul, Brazil. Trials were carried out in laboratory to characterize the mating sequence. We quantified elapsed time of each behavioral sequence and discussed its implications in the observed mating behavior. The males of C. colliurides attracted females by means of a continuous trill, and receptive female triggers the beginning of the courtship through antennation. During courtship, copulation and post-copulatory actions, males showed a complex communication system based on information send to female by substrate vibration and an elaborated repertoire composed by calling, courtship and post-copulatory song. The mating behavior here described reveals divergence between related species hitherto studied which give us clues to understand how the sexual selection shaped the complex behaviors exhibited by Trigonidiinae crickets presently.