Linguistic Distributional Knowledge and Sensorimotor Grounding both Contribute to Semantic Category Production

The human conceptual system comprises simulated information of sensorimotor experience and linguistic distributional information of how words are used in language. Moreover, the linguistic shortcut hypothesis predicts that people will use computationally cheaper linguistic distributional information where it is sufficient to inform a task response. In a pre-registered category production study, we asked participants to verbally name members of concrete and abstract categories and tested whether performance could be predicted by a novel measure of sensorimotor similarity (based on an 11-dimensional representation of sensorimotor strength) and linguistic proximity (based on word co-occurrence derived from a large corpus). As predicted, both measures predicted the order and frequency of category production but, critically, linguistic proximity had an effect above and beyond sensorimotor similarity. A follow-up study using typicality ratings as an additional predictor found that typicality was often the strongest predictor of category production variables, but it did not subsume sensorimotor and linguistic effects. Finally, we created a novel, fully grounded computational model of conceptual activation during category production, which best approximated typical human performance when conceptual activation was allowed to spread indirectly between concepts, and when candidate category members came from both sensorimotor and linguistic distributional representations. Critically, model performance was indistinguishable from typical human performance. Results support the linguistic shortcut hypothesis in semantic processing and provide strong evidence that both linguistic and grounded representations are inherent to the functioning of the conceptual system. All materials, data, and code are available at https://osf.io/vaq56/ .     

基于语义网络的创造性认知机制研究现状与展望

语义网络是主要的知识表征方式之一,对创造性研究具有重要意义。近年来不断发展的网络科学方法为相关研究提供了强大的方法论框架及量化工具,研究者得以分别从语义网络结构及语义相关认知操作的角度探讨语义网络与创造性的内在联系。已有证据表明语义网络中所储存的概念越丰富、概念间的连接通路越多元化或越灵活,则个体创造性水平越高;而个体创造性水平亦受制于个体对概念的检索、抑制、选择、整合及扩展等认知能力。在此方向上,未来研究工作应当更多地思考如何在一个相对整合的认知框架中深入探讨语义网络结构及相关认知过程对创造性的交互作用,并结合神经影像等技术手段,将语义网络之于创造性的认知机制嵌套于神经科学体系中进行解释。     

A multi-objective mathematical programming problem with fuzzy relation constraints

In this study we discuss the problem of multi-objective mathematical programming with constraints defined by ‘max-min’ composite fuzzy relation equations. Since the feasible region is normally non-convex, the properties of the efficient points of a non-convex feasible region under multi-objectives are investigated and illustrated by examples. The necessary and sufficient conditions are proposed and proved. To facilitate decisions, a procedure that transforms these efficient points of an interval-valued decision space into a constant-valued decision space is proposed when the level of confidence is given by a decision maker. Then the transformed problem becomes a multi-attribute decision problem that can be evaluated by Yager's method to find the optimal alternative.     

Word Order Typology Interacts With Linguistic Complexity: A Cross-Linguistic Corpus Study

Much previous work has suggested that word order preferences across languages can be explained by the dependency distance minimization constraint (Ferrer-i Cancho, 2008, 2015; Hawkins, 1994). Consistent with this claim, corpus studies have shown that the average distance between a head (e.g., verb) and its dependent (e.g., noun) tends to be short cross-linguistically (Ferrer-i Cancho, 2014; Futrell, Mahowald, & Gibson, 2015; Liu, Xu, & Liang, 2017). This implies that on average languages avoid inefficient or complex structures for simpler structures. But a number of studies in psycholinguistics (Konieczny, 2000; Levy & Keller, 2013; Vasishth, Suckow, Lewis, & Kern, 2010) show that the comprehension system can adapt to the typological properties of a language, for example, verb-final order, leading to more complex structures, for example, having longer linear distance between a head and its dependent. In this paper, we conduct a corpus study for a group of 38 languages, which were either Subject–Verb–Object (SVO) or Subject–Object–Verb (SOV), in order to investigate the role of word order typology in determining syntactic complexity. We present results aggregated across all dependency types, as well as for specific verbal (objects, indirect objects, and adjuncts) and nonverbal (nominal, adjectival, and adverbial) dependencies. The results suggest that dependency distance in a language is determined by the default word order of a language, and crucially, the direction of a dependency (whether the head precedes the dependent or follows it; e.g., whether the noun precedes the verb or follows it). Particularly we show that in SOV languages (e.g., Hindi, Korean) as well as SVO languages (e.g., English, Spanish), longer linear distance (measured as number of words) between head and dependent arises in structures when they mirror the default word order of the language. In addition to showing results on linear distance, we also investigate the influence of word order typology on hierarchical distance (HD; measured as number of heads between head and dependent). The results for HD are similar to that of linear distance. At the same time, in comparison to linear distance, the influence of adaptability on HD seems less strong. In particular, the results show that most languages tend to avoid greater structural depth. Together, these results show evidence for “limited adaptability” to the default word order preferences in a language. Our results support a large body of work in the processing literature that highlights the importance of linguistic exposure and its interaction with working memory constraints in determining sentence complexity. Our results also point to the possible role of other factors such as the morphological richness of a language and a multifactor account of sentence complexity remains a promising area for future investigation.     

Age-Specific Effects of Lexical–Semantic Networks on Word Production

The lexical–semantic organization of the mental lexicon is bound to change across the lifespan. Nevertheless, the effects of lexical–semantic factors on word processing are usually based on studies enrolling young adult cohorts. The current study aims to investigate to what extent age-specific semantic organization predicts performance in referential word production over the lifespan, from school-age children to older adults. In Study 1, we conducted a free semantic association task with participants from six age-groups (ranging from 10 to 80 years old) to compute measures that capture age-specific properties of the mental lexicon across the lifespan. These measures relate to lifespan changes in the Available Richness of the mental lexicon and in the lexical–semantic Network Prototypicality of concrete words. In Study 2, we used the collected data to predict performance in a picture-naming task on a new group of participants within the same age-groups as for Study 1. The results show that age-specific semantic Available Richness and Network Prototypicality affect word production speed while the semantic variables collected only in young adults do not. A richer and more prototypical semantic network across subjects from a given age-group is associated with faster word production speed. The current results indicate that age-specific semantic organization is crucial to predict lexical–semantic behaviors across the lifespan. Similarly, these results also provide cues to the understanding of the lexical–semantic properties of the mental lexicon and to lexical selection in referential tasks.     

高校教师教学水平评价概化理论预算限制下最佳样本量估计

使用“高校教师教学水平评价问卷”,要求566名学生对19名教师进行评价,对收集到的数据作不同的概化设计,包括t×i、（s：t）×i、（s：t）×（i：v）和（s：t）×（i：v）×o四种设计。基于概化理论,结合预算限制,统一LaGrange乘法公式,自行推导不同设计的最佳样本量公式,联合估计的方差分量,计算出不同设计的最佳样本量。结果表明：（1）LaGrange乘法统一公式表现出较强的通用性,能够适用于预算限制下各种概化设计;（2）评价场合是影响高校教师教学水平评价一个相当重要的因素;（3）（s：t）×（i：v）×o是高校教师教学水平评价概化理论预算限制下最优概化设计;（4）高校教师教学水平评价概化理论预算限制下,每位教师最佳评价学生人数为20人,每个维度最佳评价题目数为3题。     

Socially emotional brain-inspired cognitive architecture framework for artificial intelligence

Artificial Intelligence of the next generation needs to interact with users socially, convincing them in its ability to understand human minds, including emotions. For this to happen, an artificial emotional intelligence is needed, capable of adequate, believable behavior in social emotional interactions. Building on previous developments, the present work extends the general framework of emotional Biologically Inspired Cognitive Architecture (eBICA: Samsonovich, 2013, 2018), endowing it with fluents describing, in addition to appraisals, somatic markers, feelings, emotions, moods, emotional reactions and biases. Key building blocks that integrate them are moral schemas and semantic maps. The model describes interaction of three factors: plans and commitments, moral and ethical values, and somatic comfort. Learning in this framework includes self-organization of semantic maps that in turn may provide guidance for active humanlike learning. Implications for empirical studies and practical applications are discussed together with the expected impact.     

Semantic similarity and grammatical class in naming actions

Italian speakers were asked to name pictures of actions (e.g. "bere", to drink). Pictures were presented at the same time as distracter words that were semantically related or unrelated to the picture names, and were of the same or different grammatical class (verbs or nouns). Half of the participants named the actions as verbs in citation form, the other half as verbs inflected for third person singular or plural. We found a reliable semantic interference effect. Crucially, we also observed a significant interaction between naming context and grammatical class: naming latencies were slower for verb distracters in the inflected form condition, but not in the citation form condition. The results are taken to provide evidence for the separability of semantics and grammatical class.     

A secret agent? How infants learn about the identity of objects in a causal scene

Four experiments examined the role of correlations between dynamic and static parts on 12- to 16-month-olds' ability to learn the identity of agents and recipients in a simple causal event. Infants were habituated to events in which objects with a dynamic or static part acted as an agent or a recipient and then were tested with an event in which the part-causal role relations were switched. Experiment 1 revealed that 16-month-olds, but not 12-month-olds, associate a dynamic part with the role of agent and a static part with the role of recipient. Experiment 2 showed that 12- and 16-month-olds do not associate a static part with the role of agent or a dynamic part with the role of recipient. Experiment 3 demonstrated that 14-month-olds will learn the relations presented in Experiment 1 and Experiment 2. Experiment 4 revealed that 12-month-olds were able to discriminate the two geometric figures in the events. The results are discussed with respect to infants' developing ability to attend to correlations between dynamic and static cues and the mechanism underlying early object concept acquisition.     

The relation between children's reading comprehension level and their comprehension of idioms

We report an investigation of 9-year-olds' ability to interpret idioms in relation to their reading comprehension level. We manipulated whether the idioms were transparent or opaque, whether they were real or novel, whether they were presented in isolation or in a supportive narrative context. As predicted, children were better able to explain the meanings of idioms in context than in isolation. The good and poor comprehenders did not differ in their abilities to interpret transparent idioms in context, but the poor comprehenders were significantly worse at using context to work out the meanings of opaque idioms. The explanation task revealed the source of information used by the children to derive nontarget interpretations. We discuss these findings in relation to idiom processing strategies and Levorato and Cacciari's global elaboration model.