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

Abstract

Behavioral systems, understanding it as an emergent system comprising the environment and organism subsystems, includes the spatial dynamics as a basic dimension in natural settings. Nevertheless, under the standard approaches in the experimental analysis of behavior that are based on the single response paradigm and the temporal distribution of these discrete responses, 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. Derived from them, 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. Machine Learning advancements, such as t-SNE, variable ranking, among others, provide invaluable tools to crystallize an integrated approach for the analysis and representation of multidimensional behavioral-data. Under this rational, the present work: 1) proposes a multidisciplinary approach for the integrative and multilevel analysis of behavioral systems; 2) shows behavioral aspects usually ignored under the standard approaches in the experimental analysis of behavior; and 3) provides sensitive behavioral measures, based on spatial dynamics, and useful data representations for the study of behavioral systems. In order to exemplify and evaluate our approach, the spatial-dynamics of behavior embedded in phenomena relevant to the behavioral science, namely water-seeking behavior and motivational operations, is examined, showing aspects of behavioral systems hidden until now. Keywords: Behavioral systems; spatial-behavioral dynamics; time-based schedules; water-seeking behavior; motivational operations; Machine Learning; t-SNE; entropy

An Ecological Approach to the Effects of Water-Source Locations and Time-Based Schedules on Entropy and Spatio-Temporal Behavioral Features

“…delimited zones, texture path and dispenser's location) and dynamic (e.g. water delivery and stimuli-schedule) arrangements of the environment; this would be referred to as: functional densification of space ; c) the proposed approach is a comprehensive and allows a broad characterization of the continuum of behavior that is difficult to obtain with approaches based only in discrete responses or other unique measure of the spatial dimension of behavior (Henton & Iversen, 2012); d) the behavior is an integrated functional system comprising an environmentsubsystem and an organism-subsystem, in which the ecological relevance of the events and segments of the space are co-determined by the qualities of the organism, defined in a phylogenetic and an ontogenetic way; e) recurrence plots and entropy are useful representations and non-first-order measures in order to characterize, in plausible way, the spatial dynamics of behavior, and its embedded features neglected under standard approaches (León et al, 2021).…”

“…The developed approach could be strengthened with automated multi-point tracking systems for automated pose records (Datta, 2019; Mathis & Mathis, 2020), sensible measures for Spatial-temporal organization, and integrated and multidimensional analysis based on computational algorithms to characterize in a more comprehensive way the continuum of Spatial-temporal organization of water-feeding behavior system (León et al 2021; León, 2022).…”

“…Figure 6, left panel, shows the entropy of displacement patterns for all rats in the last five sessions in each condition. This measure is a quantitative index of the variation of displacement, high values represents more variation in displacement patterns and low values represents less variation (for the mathematical description of this measure see Appendix C of León et al 2020 and for its behavioral interpretation see León et al 2021).…”

Habit formation viewed as structural change in the behavioral network

“…Animals are engaged in more than lever press, key peck, or nose poke, they approach and orient to the stimulus, and walk or sniff around and explore in the given environment. Although the importance of observation and measurement of the behavior during learning was attempted in classic behavioral studies (Guthrie and Horton, 1946; Skinner, 1948; Staddon and Simmelhag, 1971; Jenkins and Moore, 1973), current behavioral quantification methods are expected to reveal the relationship between behavior and its underlying mechanism in a way that integrates the different disciplines of psychology, neuroscience, and ethology (Datta, et al, 2019; Leon, et al 2021). However, conventional views on behavior in psychology and neuroscience are based on empirical results obtained from the approaches before the appearance of such a new quantification technique of the behavior.…”

“…These results were replicated in different experimental settings. Supplementary Figure2Bshows the replicated results in different numbers of nodes(25,50,75, and 100). In simulation 2, agents received rewards every time they engaged in other responses.…”