Artificial intelligence for the measurement of vocal stereotypy

Both researchers and practitioners often rely on direct observation to measure and monitor behavior. When these behaviors are too complex or numerous to be measured in vivo, relying on direct observation using human observers increases the amount of resources required to conduct research and to monitor the effects of interventions in practice. To address this issue, we conducted a proof of concept examining whether artificial intelligence could measure vocal stereotypy in individuals with autism. More specifically, we used an artificial neural network with over 1,500 minutes of audio data from 8 different individuals to train and test models to measure vocal stereotypy. Our results showed that the artificial neural network performed adequately (i.e., session-by-session correlation near or above .80 with a human observer) in measuring engagement in vocal stereotypy for 6 of 8 participants. Additional research is needed to further improve the generalizability of the approach.     

Artificial consciousness and the consciousness-attention dissociation

Artificial Intelligence is at a turning point, with a substantial increase in projects aiming to implement sophisticated forms of human intelligence in machines. This research attempts to model specific forms of intelligence through brute-force search heuristics and also reproduce features of human perception and cognition, including emotions. Such goals have implications for artificial consciousness, with some arguing that it will be achievable once we overcome short-term engineering challenges. We believe, however, that phenomenal consciousness cannot be implemented in machines. This becomes clear when considering emotions and examining the dissociation between consciousness and attention in humans. While we may be able to program ethical behavior based on rules and machine learning, we will never be able to reproduce emotions or empathy by programming such control systems—these will be merely simulations. Arguments in favor of this claim include considerations about evolution, the neuropsychological aspects of emotions, and the dissociation between attention and consciousness found in humans. Ultimately, we are far from achieving artificial consciousness.     

Cross-sex versus same-sex experience and relationship satisfaction: addressing diverse social networks among Orthodox Jews

Romantic experiences in adolescence often predict relationship stability and marital status in adulthood. Religious practice and belief also have been linked to increased marital satisfaction and overall wellbeing. However, certain religions limit cross-sex interaction in areas of education, social and romantic relationships. Although gender segregation has been studied in educational and occupational contexts, no previous research has addressed religious gender segregation and its impact on relationship development, marital satisfaction, and overall wellbeing. The present study addressed the generalisability of data on cross-sex experience derived from normative populations to a religious subculture, outlining predictors for marital satisfaction and wellbeing in an Orthodox Jewish sample. Results showed some similarities between the general population and the Orthodox Jews represented by the study sample. However, some factors illustrated divergent paths and outcomes for this sample. This study demonstrates the influence of societal norms and the importance of addressing cultural context when evaluating marital satisfaction.     

Diversity and Community Can Coexist

We examine the (in)compatibility of diversity and sense of community by means of agent‐based models based on the well‐known Schelling model of residential segregation and Axelrod model of cultural dissemination. We find that diversity and highly clustered social networks, on the assumptions of social tie formation based on spatial proximity and homophily, are incompatible when agent features are immutable, and this holds even for multiple independent features. We include both mutable and immutable features into a model that integrates Schelling and Axelrod models, and we find that even for multiple independent features, diversity and highly clustered social networks can be incompatible on the assumptions of social tie formation based on spatial proximity and homophily. However, this incompatibility breaks down when cultural diversity can be sufficiently large, at which point diversity and clustering need not be negatively correlated. This implies that segregation based on immutable characteristics such as race can possibly be overcome by sufficient similarity on mutable characteristics based on culture, which are subject to a process of social influence, provided a sufficiently large “scope of cultural possibilities” exists.     

Feature Statistics Modulate the Activation of Meaning During Spoken Word Processing

Understanding spoken words involves a rapid mapping from speech to conceptual representations. One distributed feature‐based conceptual account assumes that the statistical characteristics of concepts’ features—the number of concepts they occur in (distinctiveness/sharedness) and likelihood of co‐occurrence (correlational strength)—determine conceptual activation. To test these claims, we investigated the role of distinctiveness/sharedness and correlational strength in speech‐to‐meaning mapping, using a lexical decision task and computational simulations. Responses were faster for concepts with higher sharedness, suggesting that shared features are facilitatory in tasks like lexical decision that require access to them. Correlational strength facilitated responses for slower participants, suggesting a time‐sensitive co‐occurrence‐driven settling mechanism. The computational simulation showed similar effects, with early effects of shared features and later effects of correlational strength. These results support a general‐to‐specific account of conceptual processing, whereby early activation of shared features is followed by the gradual emergence of a specific target representation.     

言语想象的神经机制

言语想象不仅在大脑预处理机制方面起到重要的作用,还是目前脑机接口领域研究的热点。与正常言语产生过程相比,言语想象的理论模型、激活脑区、神经传导路径等均与其有较多相似之处。而言语障碍群体的言语想象、想象有意义的词语和句子时的脑神经机制与正常言语产生存在差异。鉴于人类言语系统的复杂性,言语想象的神经机制研究还面临一系列挑战,未来研究可在言语想象质量评价工具及神经解码范式、脑控制回路、激活通路、言语障碍群体的言语想象机制、词语和句子想象的脑神经信号等方面进一步探索,为有效提高脑机接口的识别率提供依据,为言语障碍群体的沟通提供便利。     

增塑剂DEHP的神经和行为发育毒性

增塑剂邻苯二甲酸二乙基己酯(Di-(2-ethylhexyl) phthalate,DEHP)是一种具有拟雌激素和抗雄激素活性的环境内分泌干扰物,人类和野生动物可通过不同途径终生暴露于DEHP.母体摄入的DEHP可通过胎盘和乳汁转入子代体内并进入脑组织.围生期DEHP暴露性别特异性地改变子代大鼠下丘脑视前区芳香酶活性而影响雌激素合成,并通过改变雌激素受体活性干扰雌激素对脑发育的调节作用.DEHP及其代谢产物可以影响神经细胞的增殖分化和突触形成,干扰性激素调控发育过程中的下丘脑氨基酸递质系统对促性腺激素释放激素分泌的刺激作用,影响中脑多巴胺递质系统发育而诱导自发性多动症.DEHP对脑发育的作用最终影响动物的早期行为、学习记忆和情感等行为发育.     

神经元活动的时相同步与脑功能整合

从大脑整合的角度分析心理的神经机制。神经元之间的交互和动态联结而构成神经集合被认为是每一个认知活动的基础。然而这一交互作用的具体性质,即脑整合的机制尚未明确。通过对有关实验结果的分析,认为神经元活动的时相同步可能是脑整合的机制。     

Plagues,Pagans, and Christians: Differential Survival,Social Networks,and the Rise of Christianity

In his book, The Rise of Christianity, Rodney Stark argues that the early Christian church indirectly benefitted from the plagues that struck the early Roman Empire in the 2nd and 3rd centuries of the Common Era. In particular, he argues that the early church's doctrines concerning love, charity, and social service would have led Christians to enjoy higher survival rates than pagans, which would have left the social networks of the former relatively intact, while those of the latter in disarray. Moreover, since recruitment to new religious movements occurs largely through social ties, the probability that an average pagan would have converted to Christianity would have been greater after the plagues than before. In this article, we use computer modeling to test Stark's hypothesis. Not only do we find support for his hypothesis, we also show that higher survival rates for either group not only would have benefitted their respective social networks but also would have benefitted their competitor's social networks, highlighting how network effects often flow in multiple and unexpected ways. We conclude with suggestions for future research, in particular how scholars can build upon our analyses and explore alternative assumptions.     

On the current status of the evolutionary theory of behavior dynamics

The evolutionary theory of behavior dynamics is a complexity theory that instantiates the Darwinian principles of selection, reproduction, and mutation in a genetic algorithm. The algorithm is used to animate artificial organisms that behave continuously in time and can be placed in any experimental environment. The present paper is an update on the status of the theory. It includes a summary of the evidence supporting the theory, a list of the theory's untested predictions, and a discussion of how the algorithmic operations of the theory may correspond to material reality. Based on the evidence reviewed here, the evolutionary theory appears to be a strong candidate for a comprehensive theory of adaptive behavior.