Online Training for Primary Care Staff: Skills development on the Mental Health Gap Action Programme (mhGAP) Intervention Guide / Entrenamiento en línea para personal de atención primaria: Desarrollo de habilidades sobre la Guía de Intervención para Superar las Brechas en Salud Mental (mhGAP)

Abstract Developing effective learning strategies to strengthen mental health professionals' capacities and deliver evidence-based interventions in their communities is urgent. We developed and evaluated an online training program for the Intervention Guide for Mental, Neurological and Substance Use Disorders in Non-specialized Health Settings. Nine hundred and seventy-five health professionals in Mexico were enrolled in the training program, during the period of social distancing brought about by the COVID-19 pandemic. Participants completed a pre-post online evaluation strategy including Knowledge screening, assessment of Learning Activities, and performance in Programmed-Simulated cases to evaluate knowledge and skills for the assessment, management, and follow-up of Mental, Neurological and Substance Use Disorders. We found that participants improved their knowledge and skills from training on the mhGAP online course. Notably we observed these positive results regardless of sex, profession, institution, or social vulnerability rating of participants, suggesting that this is a relevant training program for primary care staff. These results contribute to the Mental Health Gap Action Programme and advance the use of online teaching and evaluation technologies in this field.

Resumen El desarrollo de estrategias efectivas de aprendizaje para fortalecer las competencias de los profesionales de la salud mental y brindar intervenciones basadas en evidencia en sus comunidades es necesario. El objetivo del presente trabajo fue desarrollar y evaluar un programa de entrenamiento en línea para la Guía de Intervención en Trastornos Mentales, Neurológicos y por Uso de Sustancias en nivel de atención de salud no especializada. Participaron 975 profesionales de la salud mexicanos durante el período de distanciamiento social provocado por la pandemia de COVID-19. Los participantes completaron una evaluación previa y posterior que incluyó un cuestionario de conocimientos, actividades de aprendizaje y la ejecución en casos simulados programados para evaluar el conocimiento y las habilidades para la evaluación, el manejo y el seguimiento de los trastornos mentales, neurológicos y por uso de sustancias. Los resultados indicaron que los participantes mejoraron sus conocimientos y habilidades en función de su participación en el curso en línea, independientemente del sexo, la profesión, la institución o la vulnerabilidad social de los participantes, sugiriendo que se trata de un programa de formación relevante para el personal de atención primaria. Los resultados contribuyen al Programa de Acción para la Brecha de Salud Mental y promueven el uso de tecnologías de evaluación y enseñanza en línea en este campo.

Computational models of stimulus equivalence: An intersection for the study of symbolic behavior.

Stimulus equivalence is a central paradigm in the analysis of symbolic behavior, language, and cognition. It describes emergent relations between stimuli that were not explicitly trained and cannot be explained by primary stimulus generalization. In recent years, researchers have developed computational models to simulate the learning of equivalence relations. These models have been used to address primary theoretical and methodological issues in this field, such as exploring the underlying mechanisms that explain emergent equivalence relations and analyzing the effects of training and testing protocols on equivalence outcomes. Nonetheless, although these models build upon general learning principles, their operation is usually obscure for nonmodelers, and in the field of stimulus equivalence computational models have been developed with a variety of approaches, architectures, and algorithms that make it difficult to understand the scope and contributions of these tools. In this paper, we present the state of the art in computational modeling of stimulus equivalence. We seek to provide concise and accessible descriptions of the models' functioning and operation, highlight their main theoretical and methodological contributions, identify the existing software available for researchers to run experiments, and suggest future directions in the emergent field of computational modeling of stimulus equivalence.

No need to forget, just keep the balance: Hebbian neural networks for statistical learning.

Language processing in humans has long been proposed to rely on sophisticated learning abilities including statistical learning. Endress and Johnson (E&J, 2021) recently presented a neural network model for statistical learning based on Hebbian learning principles. This model accounts for word segmentation tasks, one primary paradigm in statistical learning. In this discussion paper we review this model and compare it with the Hebbian model previously presented by Tovar and Westermann (T&W, 2017a; 2017b; 2018) that has accounted for serial reaction time tasks, cross-situational learning, and categorization paradigms, all relevant in the study of statistical learning. We discuss the similarities and differences between both models, and their key findings. From our analysis, we question the concept of "forgetting" in the model of E&J and their suggestion of considering forgetting as the critical ingredient for successful statistical learning. We instead suggest that a set of simple but well-balanced mechanisms including spreading activation, activation persistence, and synaptic weight decay, all based on biologically grounded principles, allow modeling statistical learning in Hebbian neural networks, as demonstrated in the T&W model which successfully covers learning of nonadjacent dependencies and accounts for differences between typical and atypical populations, both aspects that have not been fully demonstrated in the E&J model. We outline the main computational and theoretical differences between the E&J and T&W approaches, present new simulation results, and discuss implications for the development of a computational cognitive theory of statistical learning.

Atypical shape bias and categorization in autism: Evidence from children and computational simulations.

The shape bias, a preference for mapping new word labels onto the shape rather than the color or texture of referents, has been postulated as a word-learning mechanism. Previous research has shown deficits in the shape bias in children with autism even though they acquire sizeable lexicons. While previous explanations have suggested the atypical use of color for label extension in individuals with autism, we hypothesize an atypical mapping of novel labels to novel objects, regardless of the physical properties of the objects. In Experiment 1, we demonstrate this phenomenon in some individuals with autism, but the novelty of objects only partially explains their lack of shape bias. In a second experiment, we present a computational model that provides a developmental account of the shape bias in typically developing children and in those with autism. This model is based on theories of neurological dysfunctions in autism, and it integrates theoretical and empirical findings in the literature of categorization, word learning, and the shape bias. The model replicates the pattern of results of our first experiment and shows how individuals with autism are more likely to categorize experimental objects together on the basis of their novelty. It also provides insights into possible mechanisms by which children with autism learn new words, and why their word referents may be idiosyncratic. Our model highlights a developmental approach to autism that emphasizes deficient representations of categories underlying an impaired shape bias.

From altered synaptic plasticity to atypical learning: A computational model of Down syndrome.

Learning and memory rely on the adaptation of synaptic connections. Research on the neurophysiology of Down syndrome has characterized an atypical pattern of synaptic plasticity with limited long-term potentiation (LTP) and increased long-term depression (LTD). Here we present a neurocomputational model that instantiates this LTP/LTD imbalance to explore its impact on tasks of associative learning. In Study 1, we ran a series of computational simulations to analyze the learning of simple and overlapping stimulus associations in a model of Down syndrome compared with a model of typical development. Learning in the Down syndrome model was slower and more susceptible to interference effects. We found that interference effects could be overcome with dedicated stimulation schedules. In Study 2, we ran a second set of simulations and an empirical study with participants with Down syndrome and typically developing children to test the predictions of our model. The model adequately predicted the performance of the human participants in a serial reaction time task, an implicit learning task that relies on associative learning mechanisms. Critically, typical and atypical behavior was explained by the interactions between neural plasticity constraints and the stimulation schedule. Our model provides a mechanistic account of learning impairments based on these interactions, and a causal link between atypical synaptic plasticity and associative learning.