Computerized speech recognition in psychological assessment: A Macintosh prototype for screening depressive symptoms

Computerized speech-recognition technology holds strong promise for psychological assessment. This paper focuses on a computerized speech recognition application for screening depressive symptomatology. A Macintosh-based prototype has been developed that uses the Center for Epidemiological Studies-Depression scale (CES-D). The prototype is a HyperCard stack interfaced with the Voice Navigator II speech recognition application. The “talking” program represents a viable depression screening tool that is fully voice operated by the respondent. A pilot study will assess the feasibility and acceptability of the computerized and written versions of the CES-D with a nonclinical adult population. A counterbalanced design will test for order effects and analyze the psychometric equivalence of the two methods. The limitations and future directions for speech-recognition applications in psychological assessment are discussed.     

The impact of variability and prechoice experience on taking safety measures: The case of security updates

The goal of this research is to clarify the conditions that trigger reluctance to take cost-effective safety measures. We present three experiments. In two of the experiments, the participants were asked to operate a simulated system for 20 periods, each with 10 trials. They could “update the system” to eliminate the risk of a “security failure” that led to a loss of 100 points. The updating cost was either fixed (at 10 points) or variable (initially 10 points, and some probability of free—0 points—updates). The optimal strategy prescribed updating at the first opportunity. Another experiment focused on one-shot decision under risk. The results highlight two factors that reduce the tendency to update and impair performance: cost variability and prechoice experience. Importantly, we show that the negative impact of cost variability is the product of two tendencies. First, experiencing periods with free updates slowed learning to select the optimal policy. Second, in many cases, the participants behaved as if they plan to update when the cost of updating is low but forget to do so. The results suggest that security can be enhanced by asking users to select a default updating policy before gaining experience and by replacing “free updates” with automatic updates. Information concerning the existence of automatic updates reduced manual updating, but this effect was eliminated by experience.     

Crossing the Threshold: The Spillover of Community Racial Diversity and Diversity Climate to the Workplace

We examined the spillover of community diversity to the workplace using a sample of 2,045 professionals living in communities across the U.S. Spillover effects were examined using 2 measures of community diversity: the degree to which employees were racially or ethnically similar to others in their community and perceptions of their community's diversity climate. Aligned with theories of group threat and racial segregation, Whites who were racially dissimilar to their communities expressed stronger intentions to leave their communities, and ultimately their workplaces, than those living in primarily White communities. However, community diversity climate offset these relationships; Whites who lived in communities that were racially dissimilar to them, but experienced the climate as inclusive, had lower moving intentions than those in communities that were experienced as racially intolerant. In contrast, for people of color, community diversity climate, rather than racial similarity to the community, predicted moving intentions. For both groups, the diversity climate in the community predicted moving intentions, which in turn predicted work turnover intentions, job search behaviors, and physical symptoms of stress at work. These findings suggest that the intention to leave one's community, and ultimately one's workplace, is influenced by community experiences and the community's perceived diversity climate.     

Neighborhood context and adjustment in African American youths from single mother homes: the intervening role of hopelessness

African American youths from single mother homes are more likely to live in neighborhoods characterized by greater risk and fewer resources than youth from two parent homes or European American youths; in turn, such adverse conditions are associated with increased adjustment problems. Despite this well-established vulnerability, relatively little is known about variables linking neighborhood context to youth adjustment. With the aim of identifying a potential youth-focused intervening variable amenable to intervention, this study examined the intervening role of hopelessness in the association between neighborhood context and adjustment problems in a sample of 171 African American youths (11-16 year olds) from single mother homes. Findings revealed direct associations between neighborhood context and youth adjustment, as well as indirect associations through youth hopelessness, although findings varied by the marker of neighborhood context (sense of community or perceived crime) and adjustment (internalizing or externalizing problems). Building on prior work noting that hopelessness is amenable to psychosocial intervention, the present findings suggest that hopelessness may afford a valuable target for prevention and intervention programming among African American youths from single mother homes in the context of adverse neighborhood conditions.     

Instance‐based Learning: A General Model of Repeated Binary Choice

A common practice in cognitive modeling is to develop new models specific to each particular task. We question this approach and draw on an existing theory, instance‐based learning theory (IBLT), to explain learning behavior in three different choice tasks. The same instance‐based learning model generalizes accurately to choices in a repeated binary choice task, in a probability learning task, and in a repeated binary choice task within a changing environment. We assert that, although the three tasks are different, the source of learning is equivalent and therefore, the cognitive process elicited should be captured by one single model. This evidence supports previous findings that instance‐based learning is a robust learning process that is triggered in a wide range of tasks from the simple repeated choice tasks to the most dynamic decision making tasks. Copyright © 2010 John Wiley & Sons, Ltd.