2016-03-21T19:08:36

It has been reported in non-contingent schedules that the variety of patterns of behavior is affected by the temporal variation of water deliveries. While temporal variation is accomplished by delivering water at fixed or variable times, spatial variation is usually accomplished by varying the number of dispensers and distance among them. Such criteria do not consider the possible ecological relevance of the location of water dispensers. Nevertheless, it is plausible to suppose that the intersection of the programed contingencies (e.g., time-based schedules), the ecological differentiated space (e.g., open vs. closed zones), and the relative location of relevant objects and events (e.g., location of the water source—peripherical vs. center zone) could set up an integrated system with the behavioral patterns of the organism. In the present study, we evaluated the eco-functional relevance of two locations of the dispensers upon behavioral dynamics in Wistar rats using fixed and variable time schedules in a modified open-field system. In Experiment 1, three subjects were exposed to a fixed time 30-s water delivery schedule. In the first condition, the water dispenser was located at the center of the experimental chamber. In the second condition, the water dispenser was located at the center of a wall of the experimental chamber. Each location was present for 20 sessions. In Experiment 2, conditions were the same, but a variable time schedule was used. Routes, distance to the dispenser, recurrence patterns, time spent in zones, entropy, and divergence were analyzed. Our findings suggest a robust differential relevance of the location of the dispensers that should be considered in studies evaluating behavioral dynamics. Results are discussed from an integrative, ecological-parametric framework.

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

Understanding behavioral systems as emergent systems 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 is 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-distributed stochastic neighbor embedding and 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.

We observed changes in the rates of response topographies during the demand condition of functional analyses for participants who demonstrated problem behavior maintained by escape. Over the course of the functional analysis for each participant, the number of topographies decreased from the first to the last session. Additionally, after the first session of the demand condition the rate of responding for one topography increased or remained at high levels while the rates of all other topographies decreased. The implications of these results when conducting functional analysis are discussed.