Several psychiatric and neurological conditions affect the semantic organization and content of a patient's speech. Specifically, the discourse of patients with schizophrenia is frequently characterized as lacking coherence. The evaluation of disturbances in discourse is often used in diagnosis and in assessing treatment efficacy, and is an important factor in prognosis.Measuring these deviations, such as "loss of meaning" and incoherence, is difficult and requires substantial human effort. Computational procedures can be employed to characterize the nature of the anomalies in discourse. We present a set of new tools derived from network theory and information science that may assist in empirical and clinical studies of communication patterns in patients, and provide the foundation for future automatic procedures. First we review information science and complex network approaches to measuring semantic coherence, and then we introduce a representation of discourse that allows for the computation of measures of disorganization. Finally we apply these tools to speech transcriptions from patients and a healthy participant, illustrating the implications and potential of this novel framework.

Depression significantly affects interpersonal functioning. Social avoidance may play an important role in depression, limiting opportunities and social skills acquisition, contributing to the maintenance of social difficulties. In the last few years, the need for studying social interactions using interactive tasks has been highlighted. This study investigated social avoidance in unmedicated depressed (n ϭ 26) and matched healthy control (n ϭ 26) participants, using a novel computerized social decision-making task (the TEAM task). In this task, participants choose between a social option (playing in a team with a coplayer) and an individual option (playing alone). Although the social option is more profitable from a material point of view, it can also be challenging because of social comparison and guilt feelings for failing the team. It was found that the higher the rank of the coplayer, the stronger the negative emotions (shame, guilt) reported by participants and the more they opted for the individual option. Depressed participants reported significantly less positive (happiness) and more negative (shame, guilt, disappointment) feelings regarding the task. Importantly, depressed participants chose the individual option significantly more often than controls, which led to lower gains in this group. Furthermore, as the task progressed, controls selected the individual option less often, whereas depressed participants selected the individual option more often. Our findings illustrate the importance of social avoidance in depression and how this behavior can lead to negative consequences. They also highlight the role of social comparison and guilt-related processes in underlying social avoidance in depression. General Scientific SummaryIn this study, we used a novel computerized social decision-making task to investigate social avoidance in depression. Our findings suggest that social avoidance plays a key role in depression, limiting individuals from opportunities and contributing to poor life quality. Furthermore, this study supports the notion that social comparison and guilt-related processes may underlie social avoidance in depression.

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Relative meaning frequency is a critical factor to consider in studies of semantic ambiguity. In this work, we examined how this measure may change across the European and Rioplatense dialects of Spanish, as well as how the overall distributional properties differ between Spanish and English, using a computer-assisted norming approach based on dictionary definitions (Armstrong, Tokowicz, & Plaut, 2012). The results showed that the two dialects differ considerably in terms of the relative meaning frequencies of their constituent homonyms, and that the overall distributions of relative frequencies vary considerably across languages, as well. These results highlight the need for localized norms to design powerful studies of semantic ambiguity and suggest that dialectal differences may be responsible for some discrepant effects related to homonymy. In quantifying the reliability of the norms, we also established that as few as seven ratings are needed to converge on a highly stable set of ratings. This approach is therefore a very practical means of acquiring essential data in studies of semantic ambiguity, relative to past approaches, such as those based on the classification of free associates. The norms also present new possibilities for studying semantic ambiguity effects within and between populations who speak one or more languages. The norms and associated software are available for download at

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