英国心理学会介绍

英国心理学会成立于1901年,有较长的历史;现有会员4万4千多人,在英国心理学研究和应用方面发挥了重要的领导和协调作用。该文介绍了英国心理学会的组织结构及它在2006年的活动情况。     

泰卜里厄哲玛提研究述评

泰卜里厄哲玛提是目前最大的跨国伊斯兰宣教运动,其以松散的组织化形式,以成员的流动性宣教而流布于很多地区。学术界主要从历史学、宗教学、政治学、社会学和人类学等学科的视角,关注了其起源、背景、领导人、思想、组织、成员、工作方式、使用文本、本土化、与政治的关系、社会影响、在不同国家和地区的活动等。研究者以西方学者为主,个别观点值得商榷。研究困难在于多学科知识、研究资料、语言三个方面。研究视野和范围仍有限,特别是缺乏深入的田野调查个案,对这一运动在不同地域中的本土化问题关照不够,同时应该重视研究的伦理和价值问题。     

Validating Regulatory Sensory Processing Disorders Using the Sensory Profile and Child Behavior Checklist (CBCL 1?–5)

The objective was to validate Regulatory Sensory Processing Disorders’ criteria (DC:0-3R, 2005) using empirical data on the presence and severity of sensory modulation deficits and specific psychiatric symptoms in clinical samples. Sixty toddlers who attended a child mental health unit were diagnosed by a clinical team. The following two groups were created: toddlers with RSPD (N?=?14) and those with “other diagnoses in Axis I/II of the DC:0-3R″(OD3R) (N?=?46). Independently of the clinical process, parents completed the Infant Toddler Sensory Profile (as a checklist for sensory symptoms) and the Achenbach Behavior Checklist for ages 1?–5 (CBCL 1?–5). The scores from the two groups were compared. The results showed the following for the RSPD group: a higher number of affected sensory areas and patterns than in the OD3R group; a higher percentage of sensory deficits in specific sensory categories; and a higher severity of behavioral symptoms such as withdrawal, inattention, other externalizing problems and pervasive developmental problems in CBCL 1?–5. The results confirmed our hypotheses by indicating a higher severity of sensory symptoms and identifying specific behavioral problems in children with RSPD. The results revealed convergent validity between the instruments and the diagnostic criteria for RSPD and supported the validity of RSPD as a unique diagnosis. The findings also suggested the importance of identifying sensory modulation deficits in order to develop an early intervention to enhance the sensory capacities of children who do not fully satisfy the criteria for some DSM-IV-TR disorders.     

The Development and Validation of a Family Wellbeing Index for Hong Kong Chinese Families

Journal of Child and Family Studies - Family wellbeing is studied worldwide. However, there is a dearth of studies on the wellbeing of families in Chinese societies such as Hong Kong, nor is there...     

How Do Artificial Neural Networks Classify Musical Triads? A Case Study in Eluding Bonini's Paradox

How might artificial neural networks (ANNs) inform cognitive science? Often cognitive scientists use ANNs but do not examine their internal structures. In this paper, we use ANNs to explore how cognition might represent musical properties. We train ANNs to classify musical chords, and we interpret network structure to determine what representations ANNs discover and use. We find connection weights between input units and hidden units can be described using Fourier phase spaces, a representation studied in musical set theory. We find the total signal coming through these weighted connection weights is a measure of the similarity between two Fourier structures: the structure of the hidden unit's weights and the structure of the stimulus. This is surprising because neither of these Fourier structures is computed by the hidden unit. We then show how output units use such similarity measures to classify chords. However, we also find different types of units—units that use different activation functions—use this similarity measure very differently. This result, combined with other findings, indicates that while our networks are related to the Fourier analysis of musical sets, they do not perform Fourier analyses of the kind usually described in musical set theory. Our results show Fourier representations of music are not limited to musical set theory. Our results also suggest how cognitive psychologists might explore Fourier representations in musical cognition. Critically, such theoretical and empirical implications require researchers to understand how network structure converts stimuli into responses.