Using performance feedback to improve treatment integrity of classwide behavior plans: an investigation of observer reactivity

The current study replicated the positive effects of performance feedback on treatment integrity and extended previous work by examining reactivity using a multiple baseline design with alternating treatments for observer-present and observer-absent conditions on teachers' implementation of a classwide behavior plan. No differences were found between conditions, and treatment integrity improved across all teachers, suggesting that performance feedback, rather than observer reactivity, was responsible for reported behavior changes.     

以貌取人可行吗？——基于面孔的特质推理及准确性

面孔—特质推理（face - traits inference）是知觉者凭借面孔推断他人特质的心理过程。面孔特征、构型以及表情是特质推理的主要线索。其心理机制可从生理、进化和认知取向等方面进行解释。以往研究发现,面孔-特质推理具有适度准确性。其准确性主要受到知觉对象的面孔吸引力,知觉者的身心特征、知识经验和文化背景的影响;情境也是影响准确性的重要因素。未来研究需在面部线索、准确性指标、准确性的影响因素以及理论框架等方面进行探讨。     

The Self As a Complex Adaptive System Part II: Levels of Analysis and the Position of the Observer

This article continues the project begun in the previous article titled: “The Self as a Complex Adaptive System. Part I: Complexity, Metapsychology, and Developmental Theories” (Palombo, 2013). Using systems theory and a complexity perspective, that article provided a critique of psychoanalytic developmental theories. This contribution addresses some of the methodological issues related to data collection on development and the effects of the observer on the observed. It introduces the Level of Analysis perspective as a heuristic that permits the use of a complexity view applicable to the construct of the self as a complex adaptive system. It proposes three levels, each of which is associated with a platform from which phenomena are observed, a neuropsychological level (L-1), an introspective level (L-2), and an interpersonal level (L-3). The article concludes with a plea for a unifying psychoanalytic paradigm that brings together the data from these three levels and that would lay the groundwork for a clinical theory that would bring together the major existing psychoanalytic theories.     

Causal attributions for heart disease: A systematic review

Abstract

The aims of this systematic literature review are to describe the pattern of attributions made for the causes of heart disease, and to determine how this pattern varies with the method by which attributions are elicited, and the respondent group.

A search yielded 47 papers and reports, containing 54 datasets. Lifestyle factors and chronic stress were the most common causes cited across all datasets. Attributions to stressors and fate or luck were more likely to be reported in studies that used interval rating scales than in studies that used dichotomous ratings. Cardiac patients were more likely to mention stressors and fate or luck as causes of heart disease; non-patients rated being overweight and hypertensive as more important

The differences observed between the responses of patients and non-patients may be due to actor - observer differences, or to a methodological difference: patients are often asked to report their own experiences whereas non-patients are asked about the general case.     

Classification objects, ideal observers & generative models

A successful vision system must solve the problem of deriving geometrical information about three-dimensional objects from two-dimensional photometric input. The human visual system solves this problem with remarkable efficiency, and one challenge in vision research is to understand how neural representations of objects are formed and what visual information is used to form these representations. Ideal observer analysis has demonstrated the advantages of studying vision from the perspective of explicit generative models and a specified visual task, which divides the causes of image variations into the separate categories of signal and noise. Classification image techniques estimate the visual information used in a task from the properties of “noise” images that interact most strongly with the task. Both ideal observer analysis and classification image techniques rely on the assumption of a generative model. We show here how the ability of the classification image approach to understand how an observer uses visual information can be improved by matching the type and dimensionality of the model to that of the neural representation or internal template being studied. Because image variation in real world object tasks can arise from both geometrical shape and photometric (illumination or material) changes, a realistic image generation process should model geometry as well as intensity. A simple example is used to demonstrate what we refer to as a “classification object” approach to studying three-dimensional object representations.     

Characterizing perceptual learning with external noise

Performance in perceptual tasks often improves with practice. This effect is known as ‘perceptual learning,’ and it has been the source of a great deal of interest and debate over the course of the last century. Here, we consider the effects of perceptual learning within the context of signal detection theory. According to signal detection theory, the improvements that take place with perceptual learning can be due to increases in internal signal strength or decreases in internal noise. We used a combination of psychophysical techniques (external noise masking and double-pass response consistency) that involve corrupting stimuli with externally added noise to discriminate between the effects of changes in signal and noise as observers learned to identify sets of unfamiliar visual patterns. Although practice reduced thresholds by as much as a factor of 14, internal noise remained virtually fixed throughout training, indicating learning served to predominantly increase the strength of the internal signal. We further examined the specific nature of the changes that took place in signal strength by correlating the externally added noise with observer’s decisions across trials (response classification). This technique allowed us to visualize some of the changes that took place in the linear templates used by the observers as learning occurred, as well as test the predictions of a linear template-matching model. Taken together, the results of our experiments offer important new theoretical constraints on models of perceptual learning.