Probability Pyramiding Revisited: Univariate, Multivariate, and Neural Network Analyses of Complex Data

Network analysis of loneliness, mental, and physical health in Czech adolescents

BACKGROUND

The increasing urgency to address rising loneliness among adolescents has become a critical issue, underscoring the need for further studies on its association with mental and physical health. The objective was to examine the changes in loneliness and its relation to mental and physical health issues in three adolescent age groups.

METHODS

A total sample of 14,588 Czech pupils (50.7% boys, mean age 13.6 ± 1.7 years) in grades 5, 7 and 9 was used from a representative dataset of the Health Behaviour in School-aged Children (HBSC) study. The network analysis based on undirected graphical models was used as an exploratory technique to assess and test the structure of the data.

RESULTS

The association between loneliness and health decreased with age. There was a significant positive association between loneliness, feeling low, and irritability. No significant direct association between loneliness and physical health complaints was found.

CONCLUSION

Further studies, preferably of longitudinal character, are needed to confirm the changes in associations between loneliness and mental and physical health outcomes.

Out of the Lab: Shaping an Ecological and Constructional Cultural Systems Science

Contemporary societies face critical, interlocking, "wicked" challenges, including economic inequities and marginalization, personal and collective violence, ethnic and religious conflicts, degradation of "the commons," climate change, and more, and all of these issues clearly are grounded in behavior. An adequate culturo-behavior science could be positioned to advance and leverage research and interventions supporting community well-being, and contribute to overcoming urgent societal and global challenges. The current state of cultural systems science, however, is limited by theory and methodology, and by competition for attention with well-established research and practice opportunities related to individual-level challenges. In this article, the author explores those limitations, and suggests a more expansive perspective drawing on historical and contemporary ecological science and contemporary theories of complex systems. Research guided by established science within those disciplines offers opportunities to move cultural systems science out of the lab, and into a more adequate, environmentally rich stance drawing on ecological strategies, recursively integrating contextual observations, conceptual advances, and in vivo experimentation. Examples of each of those strategies and exploration of developmental programs of research grounded in such integration are explored.

The Emergence of Stimulus Relations: Human and Computer Learning

Traditionally, investigations in the area of stimulus equivalence have employed humans as experimental participants. Recently, however, artificial neural network models (often referred to as connectionist models [CMs]) have been developed to simulate performances seen among human participants when training various types of stimulus relations. Two types of neural network models have shown particular promise in recent years. RELNET has demonstrated its capacity to approximate human acquisition of stimulus relations using simulated matching-to-sample (MTS) procedures (e.g., Lyddy & Barnes-Holmes Journal of Speech and Language Pathology and Applied Behavior Analysis, 2, 14-24, 2007). Other newly developed connectionist algorithms train stimulus relations by way of compound stimuli (e.g., Tovar & Chavez The Psychological Record, 62, 747-762, 2012; Vernucio & Debert The Psychological Record, 66, 439-449, 2016). What makes all of these CMs interesting to many behavioral researchers is their apparent ability to simulate the acquisition of diversified stimulus relations as an analogue to human learning; that is, neural networks learn over a series of training epochs such that these models become capable of deriving novel or untrained stimulus relations. With the goal of explaining these quickly evolving approaches to practical and experimental endeavors in behavior analysis, we offer an overview of existing CMs as they apply to behavior-analytic theory and practice. We provide a brief overview of derived stimulus relations as applied to human academic remediation, and we argue that human and simulated human investigations have symbiotic experimental potential. Additionally, we provide a working example of a neural network referred to as emergent virtual analytics (EVA). This model demonstrates a process by which artificial neural networks can be employed by behavior-analytic researchers to understand, simulate, and predict derived stimulus relations made by human participants.