The relationships among counseling expectations, attitudes toward seeking psychological help, psychological distress, and intention to seek counseling have only been examined in one previous study (Vogel, Wester, Wei, & Boysen, 2005). The primary purpose of the current study was to replicate and address the limitations of the Vogel et al. (2005) study. First, a mediation analysis of attitudes on the relationship of expectations and intention to seek therapy was performed. Next, path analyses were used to test a model of the relationship among counseling expectations, attitudes toward seeking psychological help, psychological distress, and the intent to seek counseling, for men and women separately. In the hypothesized model, two separate paths were predicted to impact intentions to seek psychological help. First, three distinct expectations about counseling (personal commitment, facilitative conditions, and counselor expertise) were expected to influence attitudes toward seeking psychological help, which in turn, predicted intention to seek counseling. Second, psychological distress was expected to relate to the intent to seek therapy. Because the hypothesized model for both genders did not fit the data, exploratory path analyses were completed. In the final path model for men, additional paths from the expectancy factors personal commitment and counselor expertise to intent to seek therapy resulted in a well-fitting model. For women, an additional path between psychological distress and attitudes improved the model significantly. Impact of these findings for research and practice are discussed. / Department of Counseling Psychology and Guidance Services

A reinforcement-learning technique for enhancing human behavior models in a context-based learning architecture is presented. Prior to the introduction of this technique, human models built and developed in a Context-Based reasoning framework lacked learning capabilities. As such, their performance and quality of behavior was always limited by what the subject matter expert whose knowledge is modeled was able to articulate or demonstrate. Results from experiments performed show that subject matter experts are prone to making errors and at times they lack information on situations that are inherently necessary for the human models to behave appropriately and optimally in those situations. The benefits of the technique presented is two fold; 1) It shows how human models built in a context-based framework can be modified to correctly reflect the knowledge learnt in a simulator; and 2) It presents a way for subject matter experts to verify and validate the knowledge they share. The results obtained from this research show that behavior models built in a context-based framework can be enhanced by learning and reflecting the constraints in the environment. From the results obtained, it was shown that after the models are enhanced, the agents performed better based on the metrics evaluated. Furthermore, after learning, the agent was shown to recognize unknown situations and behave appropriately in previously unknown situations. The overall performance and quality of behavior of the agent improved significantly.