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Introduction: Individuals with depression who do not respond to two or more courses of serotonergic antidepressants tend to have greater deficits in reward processing and greater internalizing symptoms, yet there is no validated self-report method to determine the likelihood of treatment resistance based on these symptom dimensions. Methods: This online case-control study leverages machine learning techniques to identify differences in self-reported anhedonia and internalizing symptom profiles of antidepressant non-responders compared to responders and healthy controls, as an initial proof-of-concept for relating these indicators to medication responsiveness. Random forest classifiers were used to identify a subset from a set of 24 reward predictors that distinguished among serotonergic medication resistant, non-resistant, and non-depressed individuals recruited online (N = 393). Feature selection was implemented to refine model prediction and improve interpretability. Results: Accuracies for full predictor models ranged from .54 to .71, while feature selected models retained 3-5 predictors and generated accuracies of .42 to .70. Several models performed significantly above chance. Sensitivity for non- responders was greatest after feature selection when compared to only responders, reaching .82 with 3 predictors. The predictors retained from feature selection were then explored using factor analysis at the item level and cluster analysis of the full data to determine empirically driven data structures. Discussion: Non-responders displayed 3 distinct symptom profiles along internalizing dimensions of anxiety, anhedonia, motivation, and cognitive function. Results should be replicated in a prospective cohort sample for predictive validity; however, this study demonstrates validity for using a limited anhedonia and internalizing self-report instrument for distinguishing between antidepressant resistant and responsive depression profiles. [ABSTRACT FROM AUTHOR]

We present a feedback or recurrent, auto-associative model that captures several important aspects of causal learning and causal reasoning that cannot be handled by feed-forward models. First, our model learns asymmetric relations between cause and effect, and can reason in both directions between cause and effect. As a result it can represent an important distinction in causal reasoning, that between necessary and sufficient causes. Second, it predicts cue competition among effects and provides a mechanism for them, something which can only be done with feed-forward models by assuming that two separate networks are learned, a highly non parsimonious assumption. Finally, we show that contrary to previous claims, a feed-forward model cannot handle Discounting and Augmenting in causal reasoning, although a feedback model can. The success of our feedback model argues for a greater focus on such models of causal learning and reasoning.

A parallel distributed processing model was used to simulate a central bias in social reasoning, the correspondence bias (Jones & Harris, 1967). This bias is the tendency to overattribute social behavior to the actor's personality and to underestimate the impact of situations. Simulations indicate that the extent of the correspondence bias can be understood as due to differences in the accessibility of explanatory concepts and the strength of causal links between potential explanations and behavior.

We investigated whether training in how to encode the causal stnicture of problems would improve individual's use of analogies to previously encountered cases. Subjects were trained either in how to encode the causal structure of business cases or given a lecture of equal length on a variety of decision making procedures. They were then asked to study several business cases and their successful solution. One week later, when asked to solve new problems, subjects who were trained in causal analysis, compared to the control group, were more likely to use an appropriate analogy from the previously studied cases (positive transfer) and were less likely to use an inappropriate analogy (negative transfer). Further anaJyses showed that training in causal analysis increased subjects' ability to encode the causal structure of the problem and increased the likelihood of being reminded of the analogy. Thus, the ability to encode causal structure and use analogies appropriately is not responsive only to increasing domain knowledge, but can also be improved by general training in the identification and encoding of the central components of the causal structure of problems.

This paper examines the tremendous similarities between the Parallel Constraint Satisfaction Processes that are a central part of many connectionist models and the Gestalt principles that played a central role in the history of Psychology. Gestalt Psychology played a major role in a number of areas in psychology, such as perception, reasoning and problem solving, causal reasoning, and many key aspects of social psychology, such as social perception, group interaction, and belief consistency. Many of the key assumptions of Gestalt Psychology have resurfaced in recent connectionist models. W e propose that Parallel Constraint Satisfaction Processes provide a computational implementation of many of the central principles of Gestalt Psychology. In this paper we discuss the clear parallels between each of five key assumptions of Gestalt Psychology and aspects of Parallel Constraint Satisfaction Processes. The five assumptions we examine are: (1) psychological processing can be treated as interactions in fields of forces, (2) psychological processing is holistic, (3) the whole is greater than the sum its parts, (4) the importance of the structure of cognitive elements; how things are connected and related, and (5) the emphasis on cognitive dynamics, and such concepts as change, equilibrium, and tension.

A unified model of social perception, integratingcausal reasoning and impression formation (Miller &Read, 1991), provides an account of h o w people ar-rive at coherent representations of others and explaintheir behavicM'. T h e model integrates work on aknowledge structure approach (Schank & Abelson,1977) with Kintsch's (1988) construction-integrationmodel and Thagard's (1989) model of explanatorycoherence. W e explore two issues in social percep-tion. First, w e show h o w the model can be used toexplain trait inferences, where traits are treated asframes, composed of goals, plans, resources andbeliefs. Second, w e examine h o w people might com-bine inconsistent traits to arrive at a coherent modelof another, an example of conceptual combination.

We present a neural network model that aims to bridge the historical gap between dynamic and structural approaches to personality. The model integrates work on the structure of the trait lexicon, the neurobiology of personality, temperament, goal-based models of personality, and an evolutionary analysis of motives. It is organized in terms of two overarching motivational systems, an approach and an avoidance system, as well as a general disinhibition and constraint system. Each overarching motivational system influences more specific motives. Traits are modeled in terms of differences in the sensitivities of the motivational systems, the baseline activation of specific motives, and inhibitory strength. The result is a motive-based neural network model of personality based on research about the structure and neurobiology of human personality. The model provides an account of personality dynamics and person-situation interactions and suggests how dynamic processing approaches and dispositional, structural approaches can be integrated in a common framework. (Contains 15 tables and 3 figures.)

A localist, parallel constraint satisfaction, artificial neural network model is presented that accounts for a broad collection of attitude and attitude-change phenomena. The network represents the attitude object and cognitions and beliefs related to the attitude, as well as how to integrate a persuasive message into this network. Short-term effects are modeled by activation patterns due to parallel constraint satisfaction processes, and long-term effects are modeled by weight changes due to the settling patterns of activation. Phenomena modeled include thought-induced attitude polarization, elaboration and attitude strength, motivated reasoning and social influence, an integrated view of heuristic versus systematic persuasion, and implicit versus explicit attitude change. Results of the simulations are consistent with empirical results. The same set of simple mechanisms is used to model all the phenomena, which allows the model to offer a parsimonious theoretical account of how structure can impact attitude change. This model is compared with previous computational approaches to attitudes, and implications for attitude research are discussed.

A socially optimized learning approach, which integrates diverse theoretical perspectives, places men who have sex with men (MSM) in an interactive virtual environment designed to simulate the emotional, interpersonal, and contextual narrative of an actual sexual encounter while challenging and changing MSM's more automatic patterns of risky responses. MSM assume a character's identity and are scaffolded in their decision-making and relearning process by supportive, and often humorous, peer coaches who encourage situated learning of self-regulatory and behavioral skills, and tailor and frame their responses to be responsive to the men's behavioral choices. In a longitudinal design, participants were randomly assigned to receive either an interactive video (IAV) intervention with peer counseling or to a peer-counseling-alone control. Compared to the counseling alone, men who also received the IAV reduced risky anal sex behaviors and increased protected anal sex behaviors.

We compare the structure and content of political experts' knowledge with that of novices. We were particularly interested in whether experts would show more causal and historical reasoning in explaining political events, as well as whether their knowledge was structured in the form of a narrative. Eight relative political experts (advanced political science graduate students), 19 intermediate knowledge subjects (7 psychology graduate students and 12 knowledgeable undergraduates), and 9 novices were extensively interviewed about 1 of 3 political conflicts in a 2-step knowledge extraction process advocated by Graesser and Clark (1985). Experts relied more on events; used a more historical analysis consisting of past states, events, goals, and actions; and, most important, relied heavily on causal reasoning to create a coherent, understandable causal scenario or narrative. In addition, experts' overall explanation networks were significantly more connected (but less centralized) than those of the other groups.