双眼视差的神经机制与知觉学习效应

双眼瞳距使得空间某物体在左右眼视网膜的成像存在微小位置差异, 这种差异被称为双眼视差(binocular disparity), 是立体视知觉的重要信息来源。对双眼视差的心理物理学研究始于18世纪初, 迄今已有接近两百年的历史。近年来, 双眼视差研究主要集中在两方面。其一是用电生理、脑成像技术考察双眼视差在视觉背、腹侧通路的模块化表征, 其脑区表征反映出视觉系统的层级式、平行式加工规律。其二是应用知觉学习范式研究双眼视差的可塑性。未来研究应综合脑成像和神经调控技术考察双眼视差的神经机制及其学习效应, 包括双眼视差与多种深度线索间的信息整合和交互作用。应用方向上, 可结合虚拟现实等技术优化训练范式, 实现立体视力的康复和增强。     

象似性和手语学习经验对中国手语-汉语跨模态语义启动效应的影响

采用启动条件下的词汇判断任务,考察熟练手语使用者和无手语经验成年听人的跨模态语义启动效应。结果发现：1)在象似词条件下,两组被试判断汉语语义相关词的反应时均快于语义无关词,说明手语象似词和汉语词之间存在跨模态语义启动效应。2)在非象似词条件下,仅手语熟练被试判断汉语语义相关词的反应时快于语义无关词,无手语经验被试判断汉语语义相关词和无关词的速度没有差异。这是由于前者心理词库中的手语词和口语词共享语义表征,而后者主要依赖手语象似词的视觉模拟性。整个研究表明,中国手语和汉语间存在跨模态语义启动效应,但该效应受到手语词象似性和手语学习经历的调节。     

正式反馈的发展与临床应用

摘要:正式反馈通过系统收集当事人的效果反馈来追踪其治疗进展,进而识别缺乏治疗进展的个案,并通过促进治疗策略的调整来阻止治疗失败。正式反馈是近20年来发展出的一种循证治疗手段,其目的为进一步提高当事人的咨询效果。正式反馈的发展大致经历了临床有效性检验和实用性发展两个阶段。正式反馈可用于促进个体、夫妻和团体咨询的效果,也可用于评估临床督导效果、确定督导个案和指导临床督导。未来研究可探讨咨询师对待正式反馈的态度,拓展正式反馈的使用价值,建立正式反馈系统的本土化常模。     

A systematic review of the effectiveness of machine learning for predicting psychosocial outcomes in acquired brain injury: Which algorithms are used and why?

Clinicians working in the field of acquired brain injury (ABI, an injury to the brain sustained after birth) are challenged to develop suitable care pathways for an individual client’s needs. Being able to predict psychosocial outcomes after ABI would enable clinicians and service providers to make advance decisions and better tailor care plans. Machine learning (ML, a predictive method from the field of artificial intelligence) is increasingly used for predicting ABI outcomes. This review aimed to examine the efficacy of using ML to make psychosocial predictions in ABI, evaluate the methodological quality of studies, and understand researchers’ rationale for their choice of ML algorithms. Nine studies were reviewed from five databases, predicting a range of psychosocial outcomes from stroke, traumatic brain injury, and concussion. Eleven types of ML were employed with a total of 75 ML models. Every model was evaluated as having high risk of bias, unable to provide adequate evidence for predictive performance due to poor methodological quality. Overall, there was limited rationale for the choice of ML algorithms and poor evaluation of the methodological limitations by study authors. Considerations for overcoming methodological shortcomings are discussed, along with suggestions for assessing the suitability of data and suitability of ML algorithms for different ABI research questions.     

The Natural Selection of Prediction Heuristics: Anchoring and Adjustment versus Representativeness

There are several heuristics which people use in making numerical predictions and these heuristics compete for the determination of prediction output. Some of them (e.g. representativeness) lead to excessively extreme predictions while others (e.g. anchoring and adjustment) lead to regressive (and even over-regressive) predictions. In this paper we study the competition between these two heuristics by varying the representation of predictor and outcome. The results indicate that factors which facilitate reliance on representativeness (e.g. compatibility between predictor and outcome) indeed lead to an increase in extremity, while factors that facilitate reliance on anchoring and adjustment (e.g. increased salience of a potential anchor) lead to a decrease in extremity.     

ON THE BENEFITS OF PHILOSOPHY AS A WAY OF LIFE IN A GENERAL INTRODUCTORY COURSE

Philosophy as a way of life (PWOL) places investigations of value, meaning, and the good life at the center of philosophical investigation, especially of one’s own life. This essay argues that PWOL is compatible with general introductory philosophy courses, further arguing that PWOL-based general introductions have several philosophical and pedagogical benefits. These include the ease with which high-impact practices, situated skill development, and students’ ability to “think like a disciplinarian” may be incorporated into such courses, relative to more traditional introductory courses, as well as the demonstration of philosophy’s value to students by explicitly tying philosophical investigation to students’ own lives.     

Emotional facial expressions affect visual rule learning in 7- to 8-month-old infants

Rule learning (RL) is an implicit learning mechanism that allows infants to detect and generalize rule-like repetition-based patterns (such as ABB and ABA) from a sequence of elements. Increasing evidence shows that RL operates both in the auditory and the visual domain and is modulated by the perceptual expertise with the to-be-learned stimuli. Yet, whether infants’ ability to detect a high-order rule from a sequence of stimuli is affected by affective information remains a largely unexplored issue. Using a visual habituation paradigm, we investigated whether the presence of emotional expressions with a positive and a negative value (i.e., happiness and anger) modulates 7- to 8-month-old infants’ ability to learn a rule-like pattern from a sequence of faces of different identities. Results demonstrate that emotional facial expressions (either positive and negative) modulate infants’ visual RL mechanism, even though positive and negative facial expressions affect infants’ RL in a different manner: while anger disrupts infants’ ability to learn the rule-like pattern from a face sequence, in the presence of a happy face infants show a familiarity preference, thus maintaining their learning ability. These findings show that emotional expressions exert an influence on infants’ RL abilities, contributing to the investigation on how emotion and cognition interact in face processing during infancy.     

Modeling the Influence of Language Input Statistics on Children's Speech Production

We trained a computational model (the Chunk-Based Learner; CBL) on a longitudinal corpus of child–caregiver interactions in English to test whether one proposed statistical learning mechanism—backward transitional probability—is able to predict children's speech productions with stable accuracy throughout the first few years of development. We predicted that the model less accurately reconstructs children's speech productions as they grow older because children gradually begin to generate speech using abstracted forms rather than specific “chunks” from their speech environment. To test this idea, we trained the model on both recently encountered and cumulative speech input from a longitudinal child language corpus. We then assessed whether the model could accurately reconstruct children's speech. Controlling for utterance length and the presence of duplicate chunks, we found no evidence that the CBL becomes less accurate in its ability to reconstruct children's speech with age.     

Psychometric and Validity Issues in Machine Learning Approaches to Personality Assessment: A Focus on Social Media Text Mining

In the age of big data, substantial research is now moving toward using digital footprints like social media text data to assess personality. Nevertheless, there are concerns and questions regarding the psychometric and validity evidence of such approaches. We seek to address this issue by focusing on social media text data and (i) conducting a review of psychometric validation efforts in social media text mining (SMTM) for personality assessment and discussing additional work that needs to be done; (ii) considering additional validity issues from the standpoint of reference (i.e. ‘ground truth’) and causality (i.e. how personality determines variations in scores derived from SMTM); and (iii) discussing the unique issues of generalizability when validating SMTM for personality assessment across different social media platforms and populations. In doing so, we explicate the key validity and validation issues that need to be considered as a field to advance SMTM for personality assessment, and, more generally, machine learning personality assessment methods. © 2020 European Association of Personality Psychology