Behavior analysis and neuroscience: Complementary disciplines

Disentangling negative reinforcement, working memory, and deductive reasoning deficits in elevated BMI

Neuropsychological data suggest that being overweight or obese is associated with a tendency to perseverate behavior despite negative feedback. This deficit might be observed due to other cognitive factors, such as working memory (WM) deficits or decreased ability to deduce model-based strategies when learning by trial-and-error. In the present study, a group of subjects with overweight or obesity (Ow/Ob, n = 30) was compared to normal-weight individuals (n = 42) in a modified Reinforcement Learning (RL) task. The task was designed to control WM effects on learning by manipulating cognitive load and to foster model-based learning via deductive reasoning. Computational modelling and analysis were conducted to isolate parameters related to RL mechanisms, WM use, and model-based learning (deduction parameter). Results showed that subjects with Ow/Ob had a higher number of perseverative errors and used a weaker deduction mechanism in their performance than control individuals, indicating impairments in negative reinforcement and model-based learning, whereas WM impairments were not responsible for deficits in RL. The present data suggests that obesity is associated with impairments in negative reinforcement and model-based learning.

Tactics of just, equitable, diverse, and inclusive scientific research

The principles of social justice, equity, diversity, inclusion (JEDI) have received increasing attention in behavior analysis circles, but the conversation has largely centered on implications for applied behavior analysis practice and research. It may be less clear to researchers who conduct basic and translational research how JEDI principles can inform and inspire their work. This article synthesizes publications from behavior analysis and other scientific fields about tactics of JEDI-informed research. We organized this scholarship across five stages of research from developing the research question to sharing findings and curated sources for an audience of behavioral science researchers. We discuss reflexive practice, representation, belongingness, participatory research, quantitative critical theory, and open science, among other topics. Some researchers may have already adopted some of the practices outlined, some may begin new practices, and some may choose to conduct experimental analyses of JEDI problems. Our hope is that those actions will be reinforced by the behavior analysis scientific community. We conclude by encouraging the leadership of this journal to continue to work toward the structural changes necessary to make the experimental analysis of behavior just, equitable, diverse, and inclusive.

How are overweight and obesity associated with reinforcement learning deficits? A systematic review

Reinforcement learning (RL) refers to the ability to learn stimulus-response or response-outcome associations relevant to the acquisition of behavioral repertoire and adaptation to the environment. Research data from correlational and case-control studies have shown that obesity is associated with impairments in RL. The aim of the present study was to systematically review how obesity and overweight are associated with RL performance. More specifically, the relationship between high body mass index (BMI) and task performance was explored through the analysis of specific RL processes associated with different physiological, computational, and behavioral manifestations. Our systematic analyses indicate that obesity might be associated with impairments in the use of aversive outcomes to change ongoing behavior, as revealed by results involving instrumental negative reinforcement and extinction/reversal learning, but further research needs to be conducted to confirm this association. Hypotheses regarding how obesity might be associated with altered RL were discussed.

A Neurobiological-Behavioral Approach to Predicting and Influencing Private Events

The primary goals of behavior analysis are the prediction and influence of behavior. These goals are largely achieved through the identification of functional relations between behaviors and the stimulating environment. Behavior-behavior relations are insufficient to meet these goals. Although this environment-behavior approach has been highly successful when applied to public behaviors, extensions to private events have been limited. This article discusses technical and conceptual challenges to the study of private events. We introduce a neurobiological-behavioral approach which seeks to understand private behavior as environmentally controlled in part by private neurobiological stimuli. These stimuli may enter into functional relations with both public and private behaviors. The analysis builds upon several current approaches to private events, delineates private behaviors and private stimulation, and emphasizes the reciprocal interaction between the two. By doing so, this approach can improve treatment and assessment of behavior and advance understanding of concepts such as motivating operations. We then describe the array of stimulus functions that neurobiological stimuli may acquire, including eliciting, discriminative, motivating, reinforcing, and punishing effects, and describe how the overall approach expands the concept of contextual influence. Finally, we describe how advances in behavioral neuroscience that enable the measurement and analysis of private behaviors and stimuli are allowing these once private events to affect the public world. Applications in the area of human-computer interfaces are discussed.

Switching to online: Testing the validity of supervised remote testing for online reinforcement learning experiments

Online experiments are an alternative for researchers interested in conducting behavioral research outside the laboratory. However, an online assessment might become a challenge when long and complex experiments need to be conducted in a specific order or with supervision from a researcher. The aim of this study was to test the computational validity and the feasibility of a remote and synchronous reinforcement learning (RL) experiment conducted during the social-distancing measures imposed by the pandemic. An additional feature of this study was to describe how a behavioral experiment originally created to be conducted in-person was transformed into an online supervised remote experiment. Open-source software was used to collect data, conduct statistical analysis, and do computational modeling. Python codes were created to replicate computational models that simulate the effect of working memory (WM) load over RL performance. Our behavioral results indicated that we were able to replicate remotely and with a modified behavioral task the effects of working memory (WM) load over RL performance observed in previous studies with in-person assessments. Our computational analyses using Python code also captured the effects of WM load over RL as expected, which suggests that the algorithms and optimization methods were reliable in their ability to reproduce behavior. The behavioral and computational validation shown in this study and the detailed description of the supervised remote testing may be useful for researchers interested in conducting long and complex experiments online.

Analyzing motivating functions of consumer behavior: Evidence from attention and neural responses to choices and consumption

Academia and business have shown an increased interest in using neurophysiological methods, such as eye-tracking and electroencephalography (EEG), to assess consumer motivation. The current research contributes to this literature by verifying whether these methods can predict the effects of antecedent events as motivating functions of attention, neural responses, choice, and consumption. Antecedent motivational factors are discussed, with a specific focus on deprivation as such a situational factor. Thirty-two participants were randomly assigned to the experimental and control conditions. Water deprivation of 11-12 h was used as an establishing operation to increase the reinforcing effectiveness of water. We designed three experimental sessions to capture the complexity of the relationship between antecedents and consumer behavior. Experimental manipulations in session 1 established the effectiveness of water for the experimental group and abolished it for the control group. Results from session 2 show that participants in the experimental group had significantly higher average fixation duration for the image of water. Their frontal asymmetry did not provide significant evidence of greater left frontal activation toward the water image. Session 3 demonstrated that choice and consumption behavior of the relevant reinforcer was significantly higher for participants in the experimental group. These early findings highlight the potential application of a multi-method approach using neurophysiological tools in consumer research, which provides a comprehensive picture of the functional relationship between motivating events, behavior (attention, neural responses, choice, and consumption), and consequences.

Murray Sidman and Patient H. M.: The role of behavior analysis in the emergence of collaborative modern neuroscience

The intersection of the lives of 2 essential individuals in science, behavior analyst Murray Sidman (1923-2019), and Henry G. Molaison also known as Patient H.M. (1926-2008), warrants examination as it highlights the role of behavior analysis and other disciplines in the emergence of modern neuroscience and a collaborative approach to science. This paper describes the historical context and content of two publications by Sidman in which Molaison served as a research participant. The goal of the present paper is to emphasize this little-known facet of Sidman's rich career and to highlight the pioneering role of behavior analysis, and particularly the work of Murray Sidman, in the emergence of collaborative modern neuroscience.

Translations in Stimulus-Stimulus Pairing: Autoshaping of Learner Vocalizations

Stimulus-stimulus pairing (SSP) is a procedure used by behavior analysis practitioners that capitalizes on respondent conditioning processes to elicit vocalizations. These procedures usually are implemented only after other, more customary methods (e.g., standard echoic training via modeling) have been exhausted. Unfortunately, SSP itself has mixed research support, probably because certain as-yet-unidentified procedural variations are more effective than others. Even when SSP produces (or increases) vocalizations, its effects can be short-lived. Although specific features of SSP differ across published accounts, fundamental characteristics include presentation of a vocal stimulus proximal with presentation of a preferred item. In the present article, we draw parallels between SSP procedures and autoshaping, review factors shown to affect autoshaping, and interpret autoshaping research for suggested SSP tests and applications. We then call for extended use and reporting of SSP in behavior-analytic treatments. Finally, three bridges created by this article are identified: basic-applied, respondent-operant, and behavior analysis with other sciences.

A Neuro-Operant Analysis of Mnemonic Recognition

Historically, the fields of operant selection and recognition memory have not interacted substantially with one another. However, both deal with how behavioral repertoires change over time as a function of environmental stimulation. In this article, we propose neuro-operant interpretations of behavioral phenomena occurring in recognition memory procedures based on (a) the ability to discriminate changes in the strength of responses caused by environmental stimulation and (b) the occasioning of supplementary responses by current stimulation. A neuro-operant interpretation of mnemonic behavior may further the understanding of the phenomena in place and simplify the current taxonomy of learning and memory.