Semantic memory encompasses one's knowledge about the world. Distributional semantic models, which construct vector spaces with embedded words, are a proposed framework for understanding the representational structure of human semantic knowledge. Unlike some classic semantic models, distributional semantic models lack a mechanism for specifying the properties of concepts, which raises questions regarding their utility for a general theory of semantic knowledge. Here, we develop a computational model of a binary semantic classification task, in which participants judged target words for the (...) referent's size or animacy. We created a family of models, evaluating multiple distributional semantic models, and mechanisms for performing the classification. The most successful model constructed two composite representations for each extreme of the decision axis (e.g., one averaging together representations of characteristically big things and another of characteristically small things). Next, the target item was compared to each composite representation, allowing the model to classify more than 1,500 words with human‐range performance and to predict response times. We propose that when making a decision on a binary semantic classification task, humans use task prompts to retrieve instances representative of the extremes on that semantic dimension and compare the probe to those instances. This proposal is consistent with the principles of the instance theory of semantic memory. (shrink)

Student academic dishonesty is a pervasive problem for universities all over the world. The development of innovative practices and interventions for decreasing dishonest behaviour requires understanding factors influencing academic dishonesty. Previous research showed that personal, environmental, and situational factors affect dishonest behaviour at a university. The set of factors and the strength of their influence can differ across countries. There is a lack of research on factors affecting student dishonesty in Russia. A sample of 15,159 undergraduate students from eight Russian (...) highly selective universities was surveyed to understand what factors influence their decision to engage in dishonest behaviour. Ajzen’s Theory of Planned Behaviour was employed to explain dishonest behaviour among students. The explained variance in the engagement in academic dishonesty equals 48% in the model for the full sample, and reaches 69% in the model for one of the considered institutions. The major findings of this study were: subjective norms appeared to dominate as the strongest predictor of academic dishonesty across the Russian universities; perceived behavioural control, appeared to be positively related to the dishonest behaviour. In the majority of universities, this factor was found to be insignificant. This finding indicates a specific feature of Russian students’ an ethical decision-making process discussed in the last part of the paper. (shrink)