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Journal Cover   Clinical Psychological Science
  [7 followers]  Follow
    
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 2167-7026 - ISSN (Online) 2167-7034
   Published by Sage Publications Homepage  [814 journals]
  • Compound Extinction: Using the Rescorla-Wagner Model to Maximize Exposure
           Therapy Effects for Anxiety Disorders
    • Authors: Culver, N. C; Vervliet, B, Craske, M. G.
      Pages: 335 - 348
      Abstract: Although exposure therapy is an effective treatment for anxiety disorders, fear sometimes returns following successful therapy. The Rescorla–Wagner model predicts that presenting two fear-provoking stimuli simultaneously (compound extinction) will maximize learning during exposure and reduce the likelihood of relapse. Participants were presented with either single extinction trials only or single extinction trials followed by compound extinction trials. In addition, participants within each extinction group were randomized to caffeine or placebo ingestion prior to extinction to investigate the mechanism by which compound extinction may maximize learning (enhanced associative change or enhanced responding). Participants presented with compound trials demonstrated significantly less fear responding at spontaneous recovery compared with participants who received single extinction trials only. Ingestion of caffeine also provided some protection from spontaneous recovery (as measured by valence ratings). At the reinstatement test, only compound extinction trials predicted less fear responding; caffeine ingestion prior to extinction did not attenuate reinstatement effects.
      PubDate: 2015-05-05T00:00:27-07:00
      DOI: 10.1177/2167702614542103
      Issue No: Vol. 3, No. 3 (2015)
       
  • A Two-Hit Model of Autism: Adolescence as the Second Hit
    • Authors: Picci, G; Scherf, K. S.
      Pages: 349 - 371
      Abstract: Adolescence brings dramatic changes in behavior and neural organization. Unfortunately, for some 30% of individuals with autism, there is marked decline in adaptive functioning during adolescence. We propose a two-hit model of autism. First, early perturbations in neural development function as a "first hit" that sets up a neural system that is "built to fail" in the face of a second hit. Second, the confluence of pubertal hormones, neural reorganization, and increasing social demands during adolescence provides the "second hit" that interferes with the ability to transition into adult social roles and levels of adaptive functioning. In support of this model, we review evidence about adolescent-specific neural and behavioral development in autism. We conclude with predictions and recommendations for empirical investigation about several domains in which developmental trajectories for individuals with autism may be uniquely deterred in adolescence.
      PubDate: 2015-05-05T00:00:27-07:00
      DOI: 10.1177/2167702614540646
      Issue No: Vol. 3, No. 3 (2015)
       
  • Editor's Introduction to the Special Series: Computational Psychiatry
    • Authors: Kazdin; A. E.
      Pages: 372 - 373
      PubDate: 2015-05-05T00:00:27-07:00
      DOI: 10.1177/2167702614567351
      Issue No: Vol. 3, No. 3 (2015)
       
  • Introduction to the Series on Computational Psychiatry
    • Authors: Maia; T. V.
      Pages: 374 - 377
      PubDate: 2015-05-05T00:00:27-07:00
      DOI: 10.1177/2167702614567350
      Issue No: Vol. 3, No. 3 (2015)
       
  • Model-Based Cognitive Neuroscience Approaches to Computational Psychiatry:
           Clustering and Classification
    • Authors: Wiecki, T. V; Poland, J, Frank, M. J.
      Pages: 378 - 399
      Abstract: Psychiatric research is in crisis. We highlight efforts to overcome current challenges by focusing on the emerging field of computational psychiatry, which might enable the field to move from a symptom-based description of mental illness to descriptors based on objective computational multidimensional functional variables. We survey recent efforts toward this goal and describe a set of methods that together form a toolbox to aid this research program. We identify four levels in computational psychiatry: (a) behavioral tasks that index various psychological processes, (b) computational models that identify the generative psychological processes, (c) parameter-estimation methods concerned with quantitatively fitting these models to subject behavior by focusing on hierarchical Bayesian estimation as a rich framework with many desirable properties, and (d) machine-learning clustering methods that identify clinically significant conditions and subgroups of individuals. As a proof of principle, we apply these methods to two different data sets. Finally, we highlight challenges for future research.
      PubDate: 2015-05-05T00:00:27-07:00
      DOI: 10.1177/2167702614565359
      Issue No: Vol. 3, No. 3 (2015)
       
  • Decision-Theoretic Psychiatry
    • Authors: Huys, Q. J. M; Guitart-Masip, M, Dolan, R. J, Dayan, P.
      Pages: 400 - 421
      Abstract: Psychiatric disorders profoundly impair many aspects of decision making. Poor choices have negative consequences in the moment and make it very hard to navigate complex social environments. Computational neuroscience provides normative, neurobiologically informed descriptions of the components of decision making that serve as a platform for a principled exploration of dysfunctions. Here, we identify and discuss three classes of failure modes arising in these formalisms. They stem from abnormalities in the framing of problems or tasks, from the mechanisms of cognition used to solve the tasks, or from the historical data available from the environment.
      PubDate: 2015-05-05T00:00:27-07:00
      DOI: 10.1177/2167702614562040
      Issue No: Vol. 3, No. 3 (2015)
       
  • Single-Stimulus Functional MRI Produces a Neural Individual Difference
           Measure for Autism Spectrum Disorder
    • Authors: Lu, J. T; Kishida, K. T, De Asis-Cruz, J, Lohrenz, T, Treadwell-Deering, D, Beauchamp, M, Montague, P. R.
      Pages: 422 - 432
      Abstract: Functional MRI typically makes inferences about neural substrates of cognitive phenomena at the group level. We report the use of a single-stimulus blood-oxygen-level-dependent (BOLD) response in the cingulate cortex that differentiates individual children with autism spectrum disorder from matched typically developing control children with sensitivity and specificity of 63.6% and 73.7%, respectively. The approach consists of passive viewing of self and other faces from which an individual difference measure is derived from the BOLD response to the first self-face image only. The method, penalized logistic regression, requires no averaging over stimulus presentations or individuals. These findings show that single-stimulus functional MRI responses can be extracted from individual subjects and used profitably as a neural individual difference measure. The results suggest that single-stimulus functional MRI can be developed to produce quantitative neural biomarkers for other developmental disorders and may even be useful in the rapid typing of cognition in healthy individuals.
      PubDate: 2015-05-05T00:00:27-07:00
      DOI: 10.1177/2167702614562042
      Issue No: Vol. 3, No. 3 (2015)
       
  • Bridging Levels of Understanding in Schizophrenia Through Computational
           Modeling
    • Authors: Anticevic, A; Murray, J. D, Barch, D. M.
      Pages: 433 - 459
      Abstract: Schizophrenia is an illness with a remarkably complex symptom presentation that has thus far been out of reach of neuroscientific explanation. This presents a fundamental problem for developing better treatments that target specific symptoms or root causes. One promising path forward is the incorporation of computational neuroscience, which provides a way to formalize experimental observations and, in turn, make theoretical predictions for subsequent studies. We review three complementary approaches: (a) biophysically based models developed to test cellular-level and synaptic hypotheses, (b) connectionist models that give insight into large-scale neural-system-level disturbances in schizophrenia, and (c) models that provide a formalism for observations of complex behavioral deficits, such as negative symptoms. We argue that harnessing all of these modeling approaches represents a productive approach for better understanding schizophrenia. We discuss how blending these approaches can allow the field to progress toward a more comprehensive understanding of schizophrenia and its treatment.
      PubDate: 2015-05-05T00:00:27-07:00
      DOI: 10.1177/2167702614562041
      Issue No: Vol. 3, No. 3 (2015)
       
  • The Role of Serotonin in Orbitofrontal Function and Obsessive-Compulsive
           Disorder
    • Authors: Maia, T. V; Cano-Colino, M.
      Pages: 460 - 482
      Abstract: Serotonin is crucial for orbitofrontal cortex function and for the treatment of obsessive-compulsive disorder. Using a neurocomputational model of the role of serotonin in orbitofrontal function, we show that (a) low serotonin leads to perseverative neuronal activity, with the network getting "stuck" in specific states; (b) low serotonin leads to an increased tendency both to develop obsessions—strong attractors to which the network activity tends and which are difficult to escape—and to fall into existing obsessions; (c) excessive glutamatergic activity, which may occur in obsessive-compulsive disorder, also leads to an increased tendency to develop obsessions and fall into existing obsessions; (d) increasing serotonin decreases these pathological tendencies, even if they are caused by excessive glutamatergic activity; and (e) the different effects of 5-HT1A and 5-HT2A serotonin receptors on neuronal activity explain the differential effects of drugs that target these receptors.
      PubDate: 2015-05-05T00:00:27-07:00
      DOI: 10.1177/2167702614566809
      Issue No: Vol. 3, No. 3 (2015)
       
 
 
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