Категоріальні особливості представлення концепту СУПЕРНИЦТВО засобами сучасної англійської мови

Стаття присвячена дослідженню способів осмислення суперництва як явища, взаємодії, процесу й властивості, представлених одиницями сучасної англійської мови. Інваріантно-варіантна структура семантичного змісту концепту СУПЕРНИЦТВО впорядковує його категоріальні, класифікаційні та диференційні ознаки, а також лексичні одиниці, що його об’єктивують

Feature integration in natural language concepts

Two experiments measured the joint influence of three key sets of semantic features on the frequency with which artifacts (Experiment 1) or plants and creatures (Experiment 2) were categorized in familiar categories. For artifacts, current function outweighed both originally intended function and current appearance. For biological kinds, appearance and behavior, an inner biological function, and appearance and behavior of offspring all had similarly strong effects on categorization. The data were analyzed to determine whether an independent cue model or an interactive model best accounted for how the effects of the three feature sets combined. Feature integration was found to be additive for artifacts but interactive for biological kinds. In keeping with this, membership in contrasting artifact categories tended to be superadditive, indicating overlapping categories, whereas for biological kinds, it was subadditive, indicating conceptual gaps between categories. It is argued that the results underline a key domain difference between artifact and biological concepts

Beyond Covariation: Cues to Causal Structure

Causal induction has two components: learning about the structure of causal models and learning about causal strength and other quantitative parameters. This chapter argues for several interconnected theses. First, people represent causal knowledge qualitatively, in terms of causal structure; quantitative knowledge is derivative. Second, people use a variety of cues to infer causal structure aside from statistical data (e.g. temporal order, intervention, coherence with prior knowledge). Third, once a structural model is hypothesized, subsequent statistical data are used to confirm, refute, or elaborate the model. Fourth, people are limited in the number and complexity of causal models that they can hold in mind to test, but they can separately learn and then integrate simple models, and revise models by adding and removing single links. Finally, current computational models of learning need further development before they can be applied to human learning

A Piecemeal Processing Strategy Model for Causal-Based Categorization

Over the last 20 years, causal-model theory has produced muchknowledge about causal-based categorization. However, per-sistent violations to the normative causal-model theory areprevalent. In particular, violations to the Markov conditionhave been repeatedly found. These violations have receiveddifferent explanations. Here, we develop a model that startsfrom generally accepted cognitive phenomena (e.g., process-ing limitations, the relevance of inference in cognitive process-ing) and assumes that people are not fully causal nor fully asso-ciative when performing causal-based categorization, offeringa new explanation for Markov violations

Informational non-reductionist theory of consciousness that providing maximum accuracy of reality prediction

The paper considers a non-reductionist theory of consciousness, which is not reducible to theories of reality and to physiological or psychological theories. Following D.I.Dubrovsky's "informational approach" to the "Mind-Brain Problem", we consider the reality through the prism of information about observed phenomena, which, in turn, is perceived by subjective reality through sensations, perceptions, feelings, etc., which, in turn, are information about the corresponding brain processes. Within this framework the following principle of the Information Theory of Consciousness (ITS) development is put forward: the brain discovers all possible causal relations in the external world and makes all possible inferences by them. The paper shows that ITS built on this principle: (1) also base on the information laws of the structure of external world; (2) explains the structure and functioning of the brain functional systems and cellular ensembles; (3) ensures maximum accuracy of predictions and the anticipation of reality; (4) resolves emerging contradictions and (5) is an information theory of the brain's reflection of reality.Comment: 14 pages, 7 figure