Probing Lexical Ambiguity: Word Vectors Encode Number and Relatedness of Senses

Lexical ambiguity—the phenomenon of a single word having multiple, distinguishable senses—is pervasive in language. Both the degree of ambiguity of a word (roughly, its number of senses) and the relatedness of those senses have been found to have widespread effects on language acquisition and processing. Recently, distributional approaches to semantics, in which a word's meaning is determined by its contexts, have led to successful research quantifying the degree of ambiguity, but these measures have not distinguished between the ambiguity of words with multiple related senses versus multiple unrelated meanings. In this work, we present the first assessment of whether distributional meaning representations can capture the ambiguity structure of a word, including both the number and relatedness of senses. On a very large sample of English words, we find that some, but not all, distributional semantic representations that we test exhibit detectable differences between sets of monosemes (unambiguous words; N = 964), polysemes (with multiple related senses; N = 4,096), and homonyms (with multiple unrelated senses; N = 355). Our findings begin to answer open questions from earlier work regarding whether distributional semantic representations of words, which successfully capture various semantic relationships, also reflect fine-grained aspects of meaning structure that influence human behavior. Our findings emphasize the importance of measuring whether proposed lexical representations capture such distinctions: In addition to standard benchmarks that test the similarity structure of distributional semantic models, we need to also consider whether they have cognitively plausible ambiguity structure.     

Face Recognition Depends on Specialized Mechanisms Tuned to View-Invariant Facial Features: Insights from Deep Neural Networks Optimized for Face or Object Recognition

Face recognition is a computationally challenging classification task. Deep convolutional neural networks (DCNNs) are brain-inspired algorithms that have recently reached human-level performance in face and object recognition. However, it is not clear to what extent DCNNs generate a human-like representation of face identity. We have recently revealed a subset of facial features that are used by humans for face recognition. This enables us now to ask whether DCNNs rely on the same facial information and whether this human-like representation depends on a system that is optimized for face identification. In the current study, we examined the representation of DCNNs of faces that differ in features that are critical or non-critical for human face recognition. Our findings show that DCNNs optimized for face identification are tuned to the same facial features used by humans for face recognition. Sensitivity to these features was highly correlated with performance of the DCNN on a benchmark face recognition task. Moreover, sensitivity to these features and a view-invariant face representation emerged at higher layers of a DCNN optimized for face recognition but not for object recognition. This finding parallels the division to a face and an object system in high-level visual cortex. Taken together, these findings validate human perceptual models of face recognition, enable us to use DCNNs to test predictions about human face and object recognition as well as contribute to the interpretability of DCNNs.     

肢体运动信息如何在工作记忆中存储?

肢体运动(空间位置运动与身体模式运动)是个体与环境交互作用的重要途径。以往行为学和脑成像研究分别探讨了空间位置运动信息和身体模式运动信息的工作记忆存储问题, 发现两种肢体运动信息的存储均独立于语音环、视空间画板的视觉子系统, 需要视空间画板的空间子系统的参与; 两种肢体运动信息激活的脑区(运动相关皮层)独立于语音环、视空间画板的视觉子系统和空间子系统, 并存在差异。这表明, 现有的工作记忆多成分模型不能完全解释肢体运动信息的存储。据此可推论, 工作记忆系统中可能存在一个负责处理肢体运动信息的“肢体运动系统”, 其隶属于视空间画板, 与视觉子系统和空间子系统并存; 其激活脑区因肢体运动的不同而存在差异。     

威廉·斯特恩的儿童人格发展观及其历史效应

威廉·斯特恩是被历史遮蔽的现代儿童发展心理学的重要创建者,对儿童人格发展理论作出了原创性卓越贡献。他以批判人格主义为哲学基础提出了人文科学取向的儿童人格发展观,主要表现在整体论、过程论和情境论三个维面。他认为儿童人格发展是多元统一的整合性发展,是主动、动力、辩证、独特和开放的过程性发展,是内在于生活世界的情境性发展。他的观点为奥尔波特和勒温、维果茨基、皮亚杰、彪勒及里格尔等人的儿童发展理论提供了直接或间接思想来源。     

Projective Geometry in Logical Space: Rethinking Tractarian Thoughts

Customary interpretations state that Tractarian thoughts are pictures, and, a fortiori, facts. I argue that important difficulties are unavoidable if we assume this standard view, and I propose a reading of the concept taking advantage of an analogy that Wittgenstein introduces, namely, the analogy between thoughts and projective geometry. I claim that thoughts should be understood neither as pictures nor as facts, but as acts of geometric projection in logical space. The interpretation I propose thus removes the root of the identified difficulties. Moreover, it allows important clarification concerning some central aspects of the Tractarian theory of representation, and it yields a unifying elucidation regarding Wittgenstein’s remarks on the solipsistic thesis.     

Kant on the original synthesis of understanding and sensibility

In this paper, I propose a novel interpretation of the role of the understanding in generating the unity of space and time. On the account I propose, we must distinguish between the unity that belongs to determinate spaces and times – which is a result of category-guided synthesis and which is Kant’s primary focus in §26 of the B-Deduction, including the famous B160–1n – and the unity that belongs to space and time themselves as all-encompassing structures. Non-conceptualist readers of Kant have argued that this latter unity cannot be the product of categorial synthesis. While they are correct that this unity is not the product of any particular act of category-guided synthesis, I argue that conceptualists are right to nevertheless attribute this unity to the understanding. I argue that it is a result of what we can think of as the ‘original’ synthesis of understanding and sensibility themselves – it is a synthesis, moreover, in which the whole is logically prior to the parts.     

群际接触中的群际隔离微生态

群际隔离微生态认为当公共空间中群际接触自然发生时,相比较内群体成员个体在空间距离上远离外群体成员;介绍了群际隔离微生态的维度、心理机制、实施过程、测度指标;分析了群际隔离微生态的相关研究现状和不足,在未来的研究中需要加强立论点、解释机制、研究方法的探索的研究,为群际接触研究提供了新的视角。     

Grounding by Attention Simulation in Peripersonal Space: Pupils Dilate to Pinch Grip But Not Big Size Nominal Classifier

Grammatical categories represent implicit knowledge, and it is not known if such abstract linguistic knowledge can be continuously grounded in real‐life experiences, nor is it known what types of mental states can be simulated. A former study showed that attention bias in peripersonal space (PPS) affects reaction times in grammatical congruency judgments of nominal classifiers, suggesting that simulated semantics may include reenactment of attention. In this study, we contrasted a Chinese nominal classifier used with nouns denoting pinch grip objects with a classifier for nouns with big object referents in a pupil dilation experiment. Twenty Chinese native speakers read grammatical and ungrammatical classifier‐noun combinations and made grammaticality judgment while their pupillary responses were measured. It was found that their pupils dilated significantly more to the pinch grip classifier than to the big object classifier, indicating attention simulation in PPS. Pupil dilations were also significantly larger with congruent trials on the whole than in incongruent trials, but crucially, congruency and classifier semantics were independent of each other. No such effects were found in controls.