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"Behavioral Research methods."
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Exploring Behavioral Markers of Long-Term Physical Activity Maintenance: A Case Study of System Identification Modeling Within a Behavioral Intervention
Efficacious interventions to promote long-term maintenance of physical activity are not well understood. Engineers have developed methods to create dynamical system models for modeling idiographic (i.e., within-person) relationships within systems. In behavioral research, dynamical systems modeling may assist in decomposing intervention effects and identifying key behavioral patterns that may foster behavioral maintenance. The Active Adult Mentoring Program was a 16-week randomized controlled trial of a group-based, peer-delivered physical activity intervention targeting older adults. Timeintensive (i.e., daily) physical activity reports were collected throughout the intervention. We explored differential patterns of behavior among participants who received the active intervention (N = 34; 88% women, 64.1 ± 8.3 years of age) and either maintained 150 minutes/week of moderate to vigorous intensity physical activity (MVPA; = 10) or did not (n = 24) at 18 months following the intervention period. We used dynamical systems modeling to explore whether key intervention components (i.e., self-monitoring, access to an exercise facility, behavioral initiation training, behavioral maintenance training) and theoretically plausible behavioral covariates (i.e., indoor vs. outdoor activity) predicted differential patterns of behavior among maintainers and nonmaintainers. W e found that maintainers took longer to reach a steady-state of MVPA. At week 10 of the intervention, nonmaintainers began to drop whereas maintainers increased MVPA. Self-monitoring, behavioral initiation training, percentage of outdoor activity, and behavioral maintenance training, but not access to an exercise facility, were key variables that explained patterns of change among maintainers. Future studies should be conducted to systematically explore these concepts within a priori idiographic (i.e., N-of-1) experimental designs.
Journal Article
Infant observation at the heart of training
The study of infant observation is widely used as part of training to become a psychoanalytic psychotherapist; the skills learned through infant observation can be widely applied to practicing analysis with all ages. Through the delineation of the views of writers and teachers of infant observation and her own empirical research, the author addresses the reasons why infant observation is a vital part of training for all analysts.
eBook
A tutorial on open-source large language models for behavioral science
Large language models (LLMs) have the potential to revolutionize behavioral science by accelerating and improving the research cycle, from conceptualization to data analysis. Unlike closed-source solutions, open-source frameworks for LLMs can enable transparency, reproducibility, and adherence to data protection standards, which gives them a crucial advantage for use in behavioral science. To help researchers harness the promise of LLMs, this tutorial offers a primer on the open-source Hugging Face ecosystem and demonstrates several applications that advance conceptual and empirical work in behavioral science, including feature extraction, fine-tuning of models for prediction, and generation of behavioral responses. Executable code is made available at
github.com/Zak-Hussain/LLM4BeSci.git
. Finally, the tutorial discusses challenges faced by research with (open-source) LLMs related to interpretability and safety and offers a perspective on future research at the intersection of language modeling and behavioral science.
Journal Article
Reputation as a sufficient condition for data quality on Amazon Mechanical Turk
Data quality is one of the major concerns of using crowdsourcing websites such as Amazon Mechanical Turk (MTurk) to recruit participants for online behavioral studies. We compared two methods for ensuring data quality on MTurk: attention check questions (ACQs) and restricting participation to MTurk workers with high reputation (above 95% approval ratings). In Experiment
1
, we found that high-reputation workers rarely failed ACQs and provided higher-quality data than did low-reputation workers; ACQs improved data quality only for low-reputation workers, and only in some cases. Experiment
2
corroborated these findings and also showed that more productive high-reputation workers produce the highest-quality data. We concluded that sampling high-reputation workers can ensure high-quality data without having to resort to using ACQs, which may lead to selection bias if participants who fail ACQs are excluded post-hoc.
Journal Article
Tapped out or barely tapped? Recommendations for how to harness the vast and largely unused potential of the Mechanical Turk participant pool
Mechanical Turk (MTurk) is a common source of research participants within the academic community. Despite MTurk's utility and benefits over traditional subject pools some researchers have questioned whether it is sustainable. Specifically, some have asked whether MTurk workers are too familiar with manipulations and measures common in the social sciences, the result of many researchers relying on the same small participant pool. Here, we show that concerns about non-naivete on MTurk are due less to the MTurk platform itself and more to the way researchers use the platform. Specifically, we find that there are at least 250,000 MTurk workers worldwide and that a large majority of US workers are new to the platform each year and therefore relatively inexperienced as research participants. We describe how inexperienced workers are excluded from studies, in part, because of the worker reputation qualifications researchers commonly use. Then, we propose and evaluate an alternative approach to sampling on MTurk that allows researchers to access inexperienced participants without sacrificing data quality. We recommend that in some cases researchers should limit the number of highly experienced workers allowed in their study by excluding these workers or by stratifying sample recruitment based on worker experience levels. We discuss the trade-offs of different sampling practices on MTurk and describe how the above sampling strategies can help researchers harness the vast and largely untapped potential of the Mechanical Turk participant pool.
Journal Article
Automatic object detection for behavioural research using YOLOv8
Observational studies of human behaviour often require the annotation of objects in video recordings. Automatic object detection has been facilitated strongly by the development of YOLO (‘you only look once’) and particularly by YOLOv8 from Ultralytics, which is easy to use. The present study examines the conditions required for accurate object detection with YOLOv8. The results show almost perfect object detection even when the model was trained on a small dataset (100 to 350 images). The detector, however, does not extrapolate well to the same object in other backgrounds. By training the detector on images from a variety of backgrounds, excellent object detection can be restored. YOLOv8 could be a game changer for behavioural research that requires object annotation in video recordings.
Journal Article
Nonnaïveté among Amazon Mechanical Turk workers: Consequences and solutions for behavioral researchers
Crowdsourcing services—particularly Amazon Mechanical Turk—have made it easy for behavioral scientists to recruit research participants. However, researchers have overlooked crucial differences between crowdsourcing and traditional recruitment methods that provide unique opportunities and challenges. We show that crowdsourced workers are likely to participate across multiple related experiments and that researchers are overzealous in the exclusion of research participants. We describe how both of these problems can be avoided using advanced interface features that also allow prescreening and longitudinal data collection. Using these techniques can minimize the effects of previously ignored drawbacks and expand the scope of crowdsourcing as a tool for psychological research.
Journal Article
Real-Life Neuroscience
Owing to advances in neuroimaging technology, the past couple of decades have witnessed a surge of research on brain mechanisms that underlie human cognition. Despite the immense development in cognitive neuroscience, the vast majority of neuroimaging experiments examine isolated agents carrying out artificial tasks in sensory and socially deprived environments. Thus, the understanding of the mechanisms of various domains in cognitive neuroscience, including social cognition and episodic memory, is sorely lacking. Here we focus on social and memory research as representatives of cognitive functions and propose that mainstream, lab-based experimental designs in these fields suffer from two fundamental limitations, pertaining to person-dependent and situation-dependent factors. The persondependent factor addresses the issue of limiting the active role of the participants in lab-based paradigms that may interfere with their sense of agency and embodiment. The situation-dependent factor addresses the issue of the artificial decontextualized environment in most available paradigms. Building on recent findings showing that reallife as opposed to controlled experimental paradigms involve different mechanisms, we argue that adopting a reallife approach may radically change our understanding of brain and behavior. Therefore, we advocate in favor of a paradigm shift toward a nonreductionist approach, exploiting portable technology in semicontrolled environments, to explore behavior in real life.
Journal Article