Cross-cultural consistency of coding the strange situation

This study tested whether or not cross-cultural differences in attachment classification distributions result from systematic differences in coding practices. First, we investigated whether or not the interactive scales have been scored consistently in several different cross-cultural samples. Second, the Richters, Waters, and Vaughn (1988) functions were applied to address the question of whether or not attachment classifications were consistently based upon the same pattern of interactive behaviors. Third, cross-cultural coding differences were described from a multivariate perspective. Data sets from seven investigators in six countries were available for analysis. Analyses on this “multinational data set” revealed that except for distance interaction, the interactive scales in the two reunion episodes were scored in accordance with the original coding rules. Furthermore, a good to reasonable agreement appeared to exist between the original classifications and those computed by the functions, except for infants older than 20 months of age. The multivariate principal component analysis showed that classification groups across cultures were more alike than cultures across classification groups. Our data showed, therefore, that attachment classifications have been consistently coded across cultures.     

Rapid acquisition of bias in signal detection: dynamics of effective reinforcement allocation

We investigated changes in bias (preference for one response alternative) in signal detection when relative reinforcer frequency for correct responses varied across sessions. In Experiment 1, 4 rats responded in a two-stimulus, two-response identification procedure employing temporal stimuli (short vs. long houselight presentations). Relative reinforcer frequency varied according to a 31-step pseudorandom binary sequence and stimulus duration difference varied over two values across conditions. In Experiment 2, 3 rats responded in a five-stimulus, two-response classification procedure employing temporal stimuli. Relative reinforcer frequency was varied according to a 36-step pseudorandom ternary sequence. Results of both experiments were analyzed according to a behavioral model of detection. The model was extended to incorporate the effects of current and previous session reinforcer frequency ratios on current-session performance. Similar to findings with concurrent schedules, effects on bias of relative reinforcer frequency were highest for the current session. However, carryover from reinforcer ratios of previous sessions was evident. Generally, the results indicate that bias can come under control of frequent changes in relative reinforcer frequency in both identification and classification procedures.     

Repetition and Reconfiguration: Demand‐Based Confabulation in Initial Eyewitness Memory

Repeated recall, as a result of repeated questioning, is typical of situations involving eyewitness evidence. The present study addressed questions of eyewitness performance with reference to repeated questioning in an initial interview, in a format based on actual police procedures. This experiment focused on eyewitness accuracy, eyewitness confidence, and the addition of false details to eyewitness accounts. Ninety‐two adult respondents were asked to recall all the information they could from a single viewing of a scene depicting an assailant aiming a handgun at a victim. This initial question was followed by three additional questions, in which respondents were asked to report any additional details they could recall. On average, respondents provided several times as many correct as false details to the initial question. However, performance deteriorated significantly to the three subsequent questions; on average, across the three subsequent questions, witnesses recalled nearly as much false as accurate information. Witness confidence was positively related to amount of accurate information recalled. However, confidence was also positively related to the number of instances of erroneous recall. These results indicate that reconfigurative dynamics begin to operate, producing confabulated responses in response to questioning demands, as early as the initial interview. This work may also help to clarify the critical relationship between accuracy and confidence in eyewitness reportage, at least within the framework reflected by the present research. Copyright © 2012 John Wiley & Sons, Ltd.     

Early versus Late Disclosure of Evidence: Effects on Verbal Cues to Deception,Confessions, and Lie Catchers' Accuracy

The strategic use of evidence (SUE)—a method of using case information to elicit different verbal responses from guilty and innocent suspects—has been shown to increase cues to deception and lie detection accuracy. This study manipulated the timing of evidence presentation to determine its effect on cues to deception, lie detection accuracy, and confession rates. Liars were less consistent with the evidence, and SUE was associated with higher lie detection accuracy. Results showed no difference between early and late disclosure of evidence on suspects' confession rates, nor on the diagnosticity of the confessions. Copyright © 2011 John Wiley & Sons, Ltd.     

反思自我评价的性质

反思自我评价是指个体对于他人如何看待自己的知觉;它对自我概念、种族认同的形成起着重要作用。反思自我评价并不完全准确,它受熟悉性,自尊水平等多种因素的影响。关于反思自我评价的形成,目前主要有三种理论：直接观察模型、他人反馈模型和自我理论模型。由于中西方在文化、社会和历史背景上的不同,应加强建构适合我国文化背景的反思自我评价理论;另外,也应加强对反思自我评价研究的应用探讨。     

Context effects in multi-alternative decision making: empirical data and a Bayesian model

For decisions between many alternatives, the benchmark result is Hick's Law: that response time increases log-linearly with the number of choice alternatives. Even when Hick's Law is observed for response times, divergent results have been observed for error rates-sometimes error rates increase with the number of choice alternatives, and sometimes they are constant. We provide evidence from two experiments that error rates are mostly independent of the number of choice alternatives, unless context effects induce participants to trade speed for accuracy across conditions. Error rate data have previously been used to discriminate between competing theoretical accounts of Hick's Law, and our results question the validity of those conclusions. We show that a previously dismissed optimal observer model might provide a parsimonious account of both response time and error rate data. The model suggests that people approximate Bayesian inference in multi-alternative choice, except for some perceptual limitations.     

友善特质与信息准确性对3～5岁幼儿选择性信任的影响

幼儿选择性信任的发展对幼儿社会认知和社会行为的发展有重要的促进作用。本研究探讨当信息提供者的友善特质与所提供信息的准确性相冲突时,幼儿在认知信任问题与人际信任问题上的信任选择特点。以103名3～5岁幼儿为被试,研究结果发现3～4岁幼儿在与认知信任有关问题中做出的选择与随机水平相比无显著差异,只有5岁幼儿表现出了对准确命名但不友善的信息提供者的赞同和询问偏好; 而在人际信任判断中,3～5岁幼儿均选择与具有友善特质的人为友,并将物品交于其保管。研究结果说明不同年龄段的幼儿表现出不同的信任选择倾向,且幼儿能依据不同问题灵活做出信任选择。     

An intelligent facial expression recognition system with emotion intensity classification

Facial expressions play a crucial role in emotion recognition as compared to other modalities. In this work, an integrated network, which is capable of recognizing emotion intensity levels from facial images in real time using deep learning technique is proposed. The cognitive study of facial expressions based on expression intensity levels are useful in applications such as healthcare, coboting, Industry 4.0 etc. This work proposes to augment emotion recognition with 2 other important parameters, valence and emotion intensity. This helps in better automated responses by a machine to an emotion. The valence model helps in classifying emotion as positive and negative emotions and discrete model classifies emotions as happy, anger, disgust, surprise and neutral state using Convolution Neural Network (CNN). Feature extraction and classification are carried out using CMU Multi-PIE database. The proposed architecture achieves 99.1% and 99.11% accuracy for valence model and discrete model respectively for offline image data with 5-fold cross validation. The average accuracy achieved in real time for valance model and discrete model is 95% & 95.6% respectively. Also, this work contributes to build a new database using facial landmarks, with three intensity levels of facial expressions which helps to classify expressions into low, mild and high intensities. The performance is also tested for different classifiers. The proposed integrated system is configured for real time Human Robot Interaction (HRI) applications on a test bed consisting of Raspberry Pi and RPA platform to assess its performance.     

Toward a Cognitive-Behavioral Classification System for Mental Disorders

As is true for its predecessors, the recently published DSM-5 uses arbitrary criteria and cutoffs to define categories of mental disorders that are of questionable validity and that provide no guidance for treatment. Recently, the NIMH introduced an alternative classification system, the Research Domain Criteria (RDoC). Both the DSM-5 and the RDoC initiative make the strong assumption that psychological problems are expressions of specific latent disease entities. In contrast, the complex causal network approach conceptualizes psychological problems as mutually interacting, often reciprocally reinforcing, elements of a complex causal network. The cognitive behavioral model offers a classification framework that is compatible with the complex causal network approach and provides a treatment-relevant alternative to the latent disease model that is the basis for the DSM-5 and the RDoC initiative.