TEACHING JOB INTERVIEW SKILLS TO RETARDED CLIENTS

Six retarded adults were taught job application and interview skills including introducing oneself, filling out a standard job application form, answering questions, and asking questions. A combination of instructions, modeling, role playing, and positive and corrective feedback was used across a multiple baseline experimental design. After training, the clients' performance in each area improved substantially over baseline levels. In addition, the newly taught skills appeared to generalize to a different office, application form, and interviewer.     

"Flash for life": Community-based prompting for safety belt promotion

A community-based strategy for promoting safety belt use was field-tested in two adjacent rural communities, one populated by a preponderance of students, faculty, and staff of a major university. The intervention involved the front-seat passenger of a stopped vehicle displaying to the driver of an adjacent, stopped vehicle an 11 x 14 inch flash card that read, "PLEASE BUCKLE UP-I CARE." If the driver buckled up, the "flasher" flipped over the card and displayed the message, "THANK YOU." This flash card was shown to 1,087 unbuckled drivers; 82% of these drivers looked at the flash card and 22% of these complied with the buckle-up request. Compliance was not influenced by the age or gender of the "flasher" (young child vs. college student), nor by the gender of the driver; but significantly more drivers in the university town buckled up following the flash card presentation (25% mean compliance in the college town vs. 14% in the other community). To date, over 2,000 individuals have received a buckle-up flash card for their own use.     

Learning How to Generalize

Generalization is a fundamental problem solved by every cognitive system in essentially every domain. Although it is known that how people generalize varies in complex ways depending on the context or domain, it is an open question how people learn the appropriate way to generalize for a new context. To understand this capability, we cast the problem of learning how to generalize as a problem of learning the appropriate hypothesis space for generalization. We propose a normative mathematical framework for learning how to generalize by learning inductive biases for which properties are relevant for generalization in a domain from the statistical structure of features and concepts observed in that domain. More formally, the framework predicts that an ideal learner should learn to generalize by either taking the weighted average of the results of generalizing according to each hypothesis space, with weights given by how well each hypothesis space fits the previously observed concepts, or by using the most likely hypothesis space. We compare the predictions of this framework to human generalization behavior with three experiments in one perceptual (rectangles) and two conceptual (animals and numbers) domains. Across all three studies we find support for the framework's predictions, including individual‐level support for averaging in the third study.     

Object‐Label‐Order Effect When Learning From an Inconsistent Source

Learning in natural environments is often characterized by a degree of inconsistency from an input. These inconsistencies occur, for example, when learning from more than one source, or when the presence of environmental noise distorts incoming information; as a result, the task faced by the learner becomes ambiguous. In this study, we investigate how learners handle such situations. We focus on the setting where a learner receives and processes a sequence of utterances to master associations between objects and their labels, where the source is inconsistent by design: It uses both “correct” and “incorrect” object‐label pairings. We hypothesize that depending on the order of presentation, the result of the learning may be different. To this end, we consider two types of symbolic learning procedures: the Object‐Label (OL) and the Label‐Object (LO) process. In the OL process, the learner is first exposed to the object, and then the label. In the LO process, this order is reversed. We perform experiments with human subjects, and also construct a computational model that is based on a nonlinear stochastic reinforcement learning algorithm. It is observed experimentally that OL learners are generally better at processing inconsistent input compared to LO learners. We show that the patterns observed in the learning experiments can be reproduced in the simulations if the model includes (a) an ability to regularize the input (and also to do the opposite, i.e., undermatch) and (b) an ability to take account of implicit negative evidence (i.e., interactions among different objects/labels). The model suggests that while both types of learners utilize implicit negative evidence in a similar way, there is a difference in regularization patterns: OL learners regularize the input, whereas LO learners undermatch. As a result, OL learners are able to form a more consistent system of image‐utterance associations, despite the ambiguous learning task.     

The mood-enhancement function of autobiographical memories: Comparisons with other functions in terms of emotional valence

In two studies, we examined the emotional valence of memories used for mood-enhancement in relation to memories serving self, social and directive functions. Our sample included a total of 263 participants aged between 45 and 82 years. In Study 1, participants recalled memories in response to 51 cue words. In Study 2, participants recalled 32 memories that served the four functions (eight memories per function). We used multilevel modeling to take into consideration the hierarchical nature of our datasets (memories nested within individuals). Study 1 showed that emotional valence was positively associated with mood-enhancement and social functions, whereas negatively related to self and directive functions. This relation was strongest for the mood-enhancement function. In Study 2, mood-enhancing memories were rated as more positive than self, social and directive memories. We discussed results in terms of the tripartite model of memory functions and proposed that mood-enhancement should represent a distinct function.     

Mechanisms of developmental change in infant categorization

Computational models are tools for testing mechanistic theories of learning and development. Formal models allow us to instantiate theories of cognitive development in computer simulations. Model behavior can then be compared to real performance. Connectionist models, loosely based on neural information processing, have been successful in capturing a range of developmental phenomena, in particular on-line within-task category learning by young infants. Here we describe two new models. One demonstrates how age dependent changes in neural receptive field sizes can explain observed changes in on-line category learning between 3 and 10 months of age. The other aims to reconcile two conflicting views of infant categorization by focusing on the different task requirements of preferential looking and manual exploration studies. A dual-memory hypothesis posits that within-task category learning that drives looking time behaviors is based on a fast-learning memory system, whereas categorization based on background experience and assessed by paradigms requiring complex motor behavior relies on a second, slow-learning system. The models demonstrate how emphasizing the mechanistic causes of behaviors leads to discovery of deeper, more explanatory accounts of learning and development.     

A Bayesian Approach to More Stable Estimates of Group-Level Effects in Contextual Studies

Multilevel analyses are often used to estimate the effects of group-level constructs. However, when using aggregated individual data (e.g., student ratings) to assess a group-level construct (e.g., classroom climate), the observed group mean might not provide a reliable measure of the unobserved latent group mean. In the present article, we propose a Bayesian approach that can be used to estimate a multilevel latent covariate model, which corrects for the unreliable assessment of the latent group mean when estimating the group-level effect. A simulation study was conducted to evaluate the choice of different priors for the group-level variance of the predictor variable and to compare the Bayesian approach with the maximum likelihood approach implemented in the software Mplus. Results showed that, under problematic conditions (i.e., small number of groups, predictor variable with a small ICC), the Bayesian approach produced more accurate estimates of the group-level effect than the maximum likelihood approach did.     

The genetic architecture of effortful control and its interplay with psychological adjustment in adolescence

The role of genes and environment in the relation between self-regulation and adjustment is unclear. We investigated, with the twin design, genetic and environmental components of the association between effortful control (EC) and indicators of psychological adjustment using adolescents’ and parents’ reports for 774 twins. Genetic factors explained a substantial proportion of variance in EC (58%) and the outcome variables of optimism (55%), general self-esteem (45%), happiness (48%), and self-derogation (29%). Perceived competence had no significant genetic component. Aside from perceived competence, uncorrelated with EC, phenotypic correlations of EC with measures of well-being/adjustment were moderate and predominantly explained by shared genetic effects. Results suggest a significant genetic contribution in adolescents’ EC and in its relation to various aspects of adjustment.     

虚拟团队共享心理模型与团队效能的关系

通过对62个虚拟团队的调研,检验了虚拟团队认同式共享心理模型与分布式共享心理模型构思,并运用多层线性建模技术,对团队特征变量、共享心理模型与虚拟团队效能间的关系进行了分析。结果表明：两类共享心理模型与团队效能间存在显著正相关;团队时间会影响共享心理模型与虚拟团队任务效能间的关系,团队时间会削弱认同式共享心理模型对任务效能间的正向关系,但能加强分布式共享心理模型对任务效能的正面影响;团队规模则主要影响共享心理模型对合作效能的效应,团队规模增加会加强认同式共享心理模型与合作效能间的相关,却会削弱分布式共享心理模型与合作效能间的关系     

The use of mixed modeling to evaluate the impact of treatment integrity on learning

Treatment integrity, or the degree to which an intervention is implemented as intended, is a critical feature of skill acquisition tasks. Single‐case design consistently demonstrates that low treatment integrity slows or inhibits learning, but the relative impact of different types of instructional errors, or the presence of multiple errors, is less clear. The present study utilized a multilevel modeling approach to evaluate the impact of type of error (omission versus omission and commission) and error component (reinforcer delivery versus feedback) on learning. Findings revealed that learning outcomes worsened based on the type of error and as the complexity of errors increased; more specifically, participants performed better when only a single type of error occurred and when only a single error component was manipulated. Additionally, individual characteristics contributed to learning outcomes, highlighting the use of multilevel modeling as a helpful tool to supplement single‐case design. The differential impact of integrity errors on learning may be due to timing of errors (i.e., commission errors more likely to occur early in learning) or how errors affect reinforcement schedule versus discriminative control.