It is a hallmark of a good model to make accurate a priori predictions to new conditions (Busemeyer & Wang, 2000). This study compared 8 decision learning models with respect to their generalizability. Participants performed 2 tasks (the Iowa Gambling Task and the Soochow Gambling Task), and each model made a priori predictions by estimating the parameters for each participant from 1 task and using those same parameters to predict on the other task. Three methods were used to evaluate the models at the individual level of analysis. The first method used a post hoc fit criterion, the second method used a generalization criterion for short-term predictions, and the third method again used a generalization criterion for long-term predictions. The results suggest that the models with the prospect utility function can make generalizable predictions to new conditions, and different learning models are needed for making short-versus long-term predictions on simple gambling tasks.

Reinforcement learning and decision-making (RLDM) provide a quantitative framework and computational theories with which we can disentangle psychiatric conditions into the basic dimensions of neurocognitive functioning. RLDM offer a novel approach to assessing and potentially diagnosing psychiatric patients, and there is growing enthusiasm for both RLDM and computational psychiatry among clinical researchers. Such a framework can also provide insights into the brain substrates of particular RLDM processes, as exemplified by model-based analysis of data from functional magnetic resonance imaging (fMRI) or electroencephalography (EEG). However, researchers often find the approach too technical and have difficulty adopting it for their research. Thus, a critical need remains to develop a user-friendly tool for the wide dissemination of computational psychiatric methods. We introduce an R package called hBayesDM (hierarchical Bayesian modeling of Decision-Making tasks), which offers computational modeling of an array of RLDM tasks and social exchange games. The hBayesDM package offers state-of-the-art hierarchical Bayesian modeling, in which both individual and group parameters (i.e., posterior distributions) are estimated simultaneously in a mutually constraining fashion. At the same time, the package is extremely user-friendly: users can perform computational modeling, output visualization, and Bayesian model comparisons, each with a single line of coding. Users can also extract the trial-by-trial latent variables (e.g., prediction errors) required for model-based fMRI/EEG. With the hBayesDM package, we anticipate that anyone with minimal knowledge of programming can take advantage of cutting-edge computational-modeling approaches to investigate the underlying processes of and interactions between multiple decision-making (e.g., goal-directed, habitual, and Pavlovian) systems. In this way, we expect that the hBayesDM package will contribute to the dissemination of advanced modeling approaches and enable a wide range of researchers to easily perform computational psychiatric research within different populations.

Chronic cannabis users are known to be impaired on a test of decision-making, the Iowa Gambling Task (IGT). Computational models of the psychological processes underlying this impairment have the potential to provide a rich description of the psychological characteristics of poor performers within particular clinical groups. We used two computational models of IGT performance, the Expectancy-Valence Learning model (EVL) and the Prospect-Valence Learning model (PVL), to assess motivational, memory, and response processes in 17 chronic cannabis abusers and 15 control participants. Model comparison and simulation methods revealed that the PVL model explained the observed data better than the EVL model. Results indicated that cannabis abusers tended to be underinfluenced by loss magnitude, treating each loss as a constant and minor negative outcome regardless of the size of the loss. In addition, they were more influenced by gains, and made decisions that were less consistent with their expectancies relative to non-using controls.Keywords decision-making; cannabis; Iowa Gambling Task; cognitive modeling Substance abusers often are impaired on laboratory measures of decision-making (Bechara et al., 2001;Petry, 2003;Petry, Bickel, & Arnett, 1998;Rogers et al., 1999). For example, in a laboratory decision-making task known as the Iowa Gambling Task (IGT; Bechara, Damasio, Damasio, & Anderson, 1994), substance abusers often make choices that lead to small, immediate gains at the cost of larger losses over time (S. Grant, Contoreggi, & London, 2000). Cannabis (marijuana) users, like other substance-using populations, perform more poorly than non-using controls on the IGT (Lamers, Bechara, Rizzo, & Ramaekers, 2006;Whitlow et al., 2004), even after prolonged abstinence from the drug (Bolla, Eldreth, Matochik, & Cadet, 2005). This impairment may be due to underlying deficits or differences in psychological processes (e.g., memory impairments, loss insensitivity, etc.), but pinpointing such processes can be difficult with traditional behavioral measures from the IGT. Recent work Address correspondence to: Julie C. Stout, School of Psychology, Psychiatry, & Psychological Medicine, Room 534, Building 17 Clayton Campus, Monash University, Victoria 3800 AUSTRALIA, Tel: +61 3 99053987, Fax: +61 3 99053948, julie.stout@med.monash.edu.au. Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. NIH Public Access NIH-PA Author ManuscriptNIH-PA Author Manuscript NIH-PA Author Manuscript has attempted to disentangle component processes of the IGT by means of computational cognitive models (Buse...

Anxiety is characterized by altered responses under uncertain conditions, but the precise mechanism by which uncertainty changes the behaviour of anxious individuals is unclear. Here we probe the computational basis of learning under uncertainty in healthy individuals and individuals with a mix of mood and anxiety disorders. Participants chose between four competing slot machines with fluctuating, reward/punishment outcomes during safety and stress. We predicted that anxious individuals under stress would learn faster about punishments, and exhibit choices that were more affected by them, formalising our predictions as parameters in reinforcement-learning accounts of behaviour. Overall, data suggest that anxious individuals are quicker to update their behaviour in response to negative outcomes (i.e. increased punishment learning-rates). When treating anxiety, it may therefore be more fruitful to encourage anxious individuals to integrate information over longer horizons when bad things happen, rather than try to blunt responses to negative outcomes.

Persons with schizophrenia experience subjective sensory anomalies and objective deficits on assessment of sensory function. Such deficits could be produced by abnormal signaling in the sensory pathways and sensory cortex or later stage disturbances in cognitive processing of such inputs. Steady state responses (SSRs) provide a noninvasive method to test the integrity of sensory pathways and oscillatory responses in schizophrenia with minimal task demands. SSRs are electrophysiological responses entrained to the frequency and phase of a periodic stimulus. Patients with schizophrenia exhibit pronounced auditory SSR deficits within the gamma frequency range (35-50 Hz) in response to click trains and amplitude-modulated tones. Visual SSR deficits are also observed, most prominently in the alpha and beta frequency ranges (7-30 Hz) in response to high-contrast, high-luminance stimuli. Visual SSR studies that have used the psychophysical properties of a stimulus to target specific visual pathways predominantly report magnocellular-based deficits in those with schizophrenia. Disruption of both auditory and visual SSRs in schizophrenia are consistent with neuropathological and magnetic resonance imaging evidence of anatomic abnormalities affecting the auditory and visual cortices. Computational models suggest that auditory SSR abnormalities at gamma frequencies could be secondary to gamma-aminobutyric acid-mediated or N-methyl-D-aspartic acid dysregulation. The pathophysiological process in schizophrenia encompasses sensory processing that probably contributes to alterations in subsequent encoding and cognitive processing. The developmental evolution of these abnormalities remains to be characterized.