Impact of acceptance and commitment therapy versus education on stigma toward people with psychological disorders

Acceptance and commitment therapy (ACT) has previously been shown to alter stigmatizing attitudes and to be relatively useful for psychologically inflexible participants. The present study is the first to bring those two findings together by comparing ACT to an education intervention for reducing stigma toward people with psychological disorders, and examining whether results differ for psychologically inflexible versus flexible individuals. A sample of college students (N =95) was randomly assigned to a 2(1)2h ACT or educational workshop. Measures were taken before and after the workshop and at a 1-month follow-up. ACT reduced mental health stigma significantly regardless of participants' pre-treatment levels of psychological flexibility, but education reduced stigma only among participants who were relatively flexible and non-avoidant to begin with. Acceptance could be an important avenue of exploration for stigma researchers.     

Running memory span: A comparison of behavioral capacity limits with those of an attractor neural network

We studied a computational model of short term memory capacity that performs a simulated running memory span task using Hebbian learning and rapid decay of connection strengths to keep recent items active for later recall. This model demonstrates recall performance similar to humans performing the same task, with a capacity limit of approximately three items and a prominent recency effect. The model also shows that this capacity depends on decay to release the model from accumulating interference. Model findings are compared with data from two behavioral experiments that used varying task demands to tax memory capacity limits. Following additional theoretical predictions from the computational model, behavioral data support that when task demands require attention to be spread too thin to keep items available for later recall, capacity limits suffer. These findings are important both for understanding the mechanisms underlying short term memory capacity, and also to memory researchers interested in the role of attention in capacity limitations.     

The Role of Negative Information in Distributional Semantic Learning

Distributional models of semantics learn word meanings from contextual co‐occurrence patterns across a large sample of natural language. Early models, such as LSA and HAL (Landauer & Dumais, 1997; Lund & Burgess, 1996), counted co‐occurrence events; later models, such as BEAGLE (Jones & Mewhort, 2007), replaced counting co‐occurrences with vector accumulation. All of these models learned from positive information only: Words that occur together within a context become related to each other. A recent class of distributional models, referred to as neural embedding models, are based on a prediction process embedded in the functioning of a neural network: Such models predict words that should surround a target word in a given context (e.g., word2vec; Mikolov, Sutskever, Chen, Corrado, & Dean, 2013). An error signal derived from the prediction is used to update each word's representation via backpropagation. However, another key difference in predictive models is their use of negative information in addition to positive information to develop a semantic representation. The models use negative examples to predict words that should not surround a word in a given context. As before, an error signal derived from the prediction prompts an update of the word's representation, a procedure referred to as negative sampling. Standard uses of word2vec recommend a greater or equal ratio of negative to positive sampling. The use of negative information in developing a representation of semantic information is often thought to be intimately associated with word2vec's prediction process. We assess the role of negative information in developing a semantic representation and show that its power does not reflect the use of a prediction mechanism. Finally, we show how negative information can be efficiently integrated into classic count‐based semantic models using parameter‐free analytical transformations.     

Attention Problems and Restlessness as Transdiagnostic Markers of Severity and Treatment Response in Youth with Internalizing Problems

Research on Child and Adolescent Psychopathology - Transdiagnostic models of psychopathology suggest that disorders may share common features that could influence their severity. Attention problems...