Avendano-Garrido, M. L. | bioRxiv Science

bioRxiv2021

Effect of the Ecological Location of a Water Source on Entropy and other Spatio-temporal Behavioral Features: An extended and systematic replication

In behavior analysis, the modulation of the effect of time-based schedules by the spatial characteristics of the environment has been scarcely studied. Furthermore, the spatial organization of behavior, despite its ubiquity and ecological relevance, has not been widely addressed. The purpose of the present work was to analyze the effect of water delivery location (peripheral vs. central) on the spatial organization of water-feeding behavior under time-based schedules. One group of rats was exposed to a Fixed Time 30 s-water-delivery schedule and a second group to a Variable Time 30 s schedule. For both groups, in the first phase, the water dispenser was located in the perimetral zone. In the second condition, the water dispenser was located in the central zone. Each location was presented for 20 sessions. Rats trajectories, distance to the dispenser, accumulated time in regions, and entropy measures were analyzed. A differential effect of the location of water delivery in interaction with the time-based schedule was observed on all the analyzed spatial qualities of behavior. The findings are discussed in relation to the ecological proposal of Timberlakes behavioral systems.

animal behavior and cognition↗

bioRxiv2021

Beyond single discrete responses: An integrative and multidimensional analysis of behavioral dynamics assisted by Machine Learning

Behavioral systems, understanding it as an emergent system comprising the environment and organism subsystems, include spatial dynamics as a primary dimension in natural settings. Nevertheless, under the standard approaches, the experimental analysis of behavior is based on the single response paradigm and the temporal distribution of discrete responses. Thus, the continuous analysis of spatial behavioral dynamics has been a scarcely studied field. The technological advancements in computer vision have opened new methodological perspectives for the continuous sensing of spatial behavior. With the application of such advancements, recent studies suggest that there are multiple features embedded in the spatial dynamics of behavior, such as entropy, and that they are affected by programmed stimuli (e.g., schedules of reinforcement), at least, as much as features related to discrete responses. Despite the progress, the characterization of behavioral systems is still segmented, and integrated data analysis and representations between discrete responses and continuous spatial behavior are exiguous in the Experimental Analysis of Behavior. Machine Learning advancements, such as t-SNE, variable ranking, provide invaluable tools to crystallize an integrated approach for analyzing and representing multidimensional behavioral data. Under this rationale, the present work: 1) proposes a multidisciplinary approach for the integrative and multilevel analysis of behavioral systems, 2) provides sensitive behavioral measures based on spatial dynamics and helpful data representations to study behavioral systems, and 3) reveals behavioral aspects usually ignored under the standard approaches in the experimental analysis of behavior. To exemplify and evaluate our approach, the spatial dynamics embedded in phenomena relevant to behavioral science, namely water-seeking behavior, and motivational operations, are examined, showing aspects of behavioral systems hidden until now.

animal behavior and cognition↗