Computational psychiatry

Computational ideas pervade many areas of science and have an integrative explanatory role in neuroscience and cognitive science. However, computational depictions of cognitive function have had surprisingly little impact on the way we assess mental illness because diseases of the mind have not been systematically conceptualized in computational terms. Here, we outline goals and nascent efforts in the new field of computational psychiatry, which seeks to characterize mental dysfunction in terms of aberrant computations over multiple scales. We highlight early efforts in this area that employ reinforcement learning and game theoretic frameworks to elucidate decision-making in health and disease. Looking forwards, we emphasize a need for theory development and large-scale computational phenotyping in human subjects.     

Generation of referring expressions: assessing the Incremental Algorithm

A substantial amount of recent work in natural language generation has focused on the generation of 'one-shot' referring expressions whose only aim is to identify a target referent. Dale and Reiter's Incremental Algorithm (IA) is often thought to be the best algorithm for maximizing the similarity to referring expressions produced by people. We test this hypothesis by eliciting referring expressions from human subjects and computing the similarity between the expressions elicited and the ones generated by algorithms. It turns out that the success of the IA depends substantially on the 'preference order' (PO) employed by the IA, particularly in complex domains. While some POs cause the IA to produce referring expressions that are very similar to expressions produced by human subjects, others cause the IA to perform worse than its main competitors; moreover, it turns out to be difficult to predict the success of a PO on the basis of existing psycholinguistic findings or frequencies in corpora. We also examine the computational complexity of the algorithms in question and argue that there are no compelling reasons for preferring the IA over some of its main competitors on these grounds. We conclude that future research on the generation of referring expressions should explore alternatives to the IA, focusing on algorithms, inspired by the Greedy Algorithm, which do not work with a fixed PO.     

I feel good,therefore I am real: Testing the causal influence of mood on state authenticity

Although the literature has focused on individual differences in authenticity, recent findings suggest that authenticity is sensitive to context; that is, it is also a state. We extended this perspective by examining whether incidental affect influences authenticity. In three experiments, participants felt more authentic when in a relatively positive than negative mood. The causal role of affect in authenticity was consistent across a diverse set of mood inductions, including explicit (Experiments 1 and 3) and implicit (Experiment 2) methods. The link between incidental affect and state authenticity was not moderated by ability to down-regulate negative affect (Experiments 1 and 3) nor was it explained by negative mood increasing private self-consciousness or decreasing access to the self system (Experiment 3). The results indicate that mood is used as information to assess one's sense of authenticity.     

Developmental changes in effects of risk and valence on adolescent decision-making

Recent research on risky decision-making in adults has shown that both the risk in potential outcomes and their valence (i.e., whether those outcomes involve gains or losses) exert dissociable influences on decisions. We hypothesised that the influences of these two crucial decision variables (risk and valence) on decision-making would vary developmentally during adolescence. We adapted a risk-taking paradigm that provides precise metrics for the impacts of risk and valence. Decision-making in 11–16 year old female adolescents was influenced by both risk and valence. However, their influences assumed different developmental patterns: the impact of valence diminished with age, while there was no developmental change in the impact of risk. These different developmental patterns provide further evidence that risk and valence are fundamentally dissociable constructs and have different influences on decisions across adolescence.     

Four Systems Anxiety Questionnaire (FSAQ): A Self-Report Measure of Somatic,Cognitive, Behavioral,and Feeling Components

This article presents the development of a 60-item self-report known as the Four Systems Anxiety Questionnaire (ESAR). The FSAQ assesses anxiety on four components: somatic, cognitive, behavioral, and feeling. The FSAQ was given to 218 first-year university students, 54 anxiety patients attending clinical psychology departments, and 14 agoraphobic patients pre- and postpsychological treatment. Results reveal satisfactory reliability and validity levels. In addition, we proved that the FSAQ is sensitive to change following psychological treatment.     

Testing and modelling non‐normality within the one‐factor model

Maximum likelihood estimation in the one‐factor model is based on the assumption of multivariate normality for the observed data. This general distributional assumption implies three specific assumptions for the parameters in the one‐factor model: the common factor has a normal distribution; the residuals are homoscedastic; and the factor loadings do not vary across the common factor scale. When any of these assumptions is violated, non‐normality arises in the observed data. In this paper, a model is presented based on marginal maximum likelihood to enable explicit tests of these assumptions. In addition, the model is suitable to incorporate the detected violations, to enable statistical modelling of these effects. Two simulation studies are reported in which the viability of the model is investigated. Finally, the model is applied to IQ data to demonstrate its practical utility as a means to investigate ability differentiation.