scholarly journals Connecting Content and Logical Words

2019 ◽  
Vol 36 (3) ◽  
pp. 531-547 ◽  
Author(s):  
Emmanuel Chemla ◽  
Brian Buccola ◽  
Isabelle Dautriche
Keyword(s):  
Rule Learning ◽  
Natural Languages ◽  
Learning Experiment ◽  
Connectedness Property

Abstract Content words (e.g. nouns and adjectives) are generally connected: there are no gaps in their denotations; no noun means ‘table or shoe’ or ‘animal or house’. We explore a formulation of connectedness which is applicable to content and logical words alike, and which compares well with the classic notion of monotonicity for quantifiers. On a first inspection, logical words satisfy this generalized version of the connectedness property at least as well as content words do — that is, both in terms of what may be observed in the lexicons of natural languages (although our investigations remain modest in that respect) and in terms of acquisition biases (with an artificial rule learning experiment). This reduces the putative differences between content and logical words, as well as the associated challenges that these differences would pose, e.g., for learners.

Effects of pretraining and stimulus composition on rule learning.

1969 ◽  
Vol 80 (3, Pt.1) ◽  
pp. 450-454 ◽  
Author(s):  
Peter J. Johnson ◽  
Roger H. White
Keyword(s):  
Rule Learning

Linguistic Data Model for Natural Languages and Artificial Intelligence. Part 5. Introduction to Logic

Discourse ◽  
2020 ◽  
Vol 6 (3) ◽  
pp. 109-117
Author(s):  
O. M. Polyakov
Keyword(s):  
Artificial Intelligence ◽  
Mathematical Logic ◽  
Natural Language ◽  
Semantic Interpretation ◽  
General View ◽  
World Model ◽  
Semantic Approach ◽  
Natural Languages ◽  
The World ◽  
Linguistic Aspect

Introduction. The article continues the series of publications on the linguistics of relations (hereinafter R–linguistics) and is devoted to an introduction to the logic of natural language in relation to the approach considered in the series. The problem of natural language logic still remains relevant, since this logic differs significantly from traditional mathematical logic. Moreover, with the appearance of artificial intelligence systems, the importance of this problem only increases. The article analyzes logical problems that prevent the application of classical logic methods to natural languages. This is possible because R-linguistics forms the semantics of a language in the form of world model structures in which language sentences are interpreted.Methodology and sources. The results obtained in the previous parts of the series are used as research tools. To develop the necessary mathematical representations in the field of logic and semantics, the formulated concept of the interpretation operator is used.Results and discussion. The problems that arise when studying the logic of natural language in the framework of R–linguistics are analyzed. These issues are discussed in three aspects: the logical aspect itself; the linguistic aspect; the aspect of correlation with reality. A very General approach to language semantics is considered and semantic axioms of the language are formulated. The problems of the language and its logic related to the most General view of semantics are shown.Conclusion. It is shown that the application of mathematical logic, regardless of its type, to the study of natural language logic faces significant problems. This is a consequence of the inconsistency of existing approaches with the world model. But it is the coherence with the world model that allows us to build a new logical approach. Matching with the model means a semantic approach to logic. Even the most General view of semantics allows to formulate important results about the properties of languages that lack meaning. The simplest examples of semantic interpretation of traditional logic demonstrate its semantic problems (primarily related to negation).

scholarly journals When and why do people act on flawed science? Effects of anecdotes and prior beliefs on evidence-based decision-making

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Audrey L. Michal ◽  
Yiwen Zhong ◽  
Priti Shah
Keyword(s):  
Educational Interventions ◽  
Evidence Based ◽  
Prior Beliefs ◽  
Learning Experiment ◽  
Apply Research ◽  
Evidence Quality ◽  
The Media ◽  
The Impact ◽  
Learning Interventions

AbstractToday’s citizens are expected to use evidence, frequently presented in the media, to inform decisions about health, behavior, and public policy. However, science misinformation is ubiquitous in the media, making it difficult to apply research appropriately. Across two experiments, we addressed how anecdotes and prior beliefs impact readers’ ability to both identify flawed science and make appropriate decisions based on flawed science in media articles. Each article described the results of flawed research on one of four educational interventions to improve learning (Experiment 1 included articles about having a tidy classroom and exercising while learning; Experiment 2 included articles about using virtual/augmented reality and napping at school). Experiment 1 tested the impact of a single anecdote and found no significant effect on either participants’ evidence evaluations or decisions to implement the learning interventions. However, participants were more likely to adopt the more plausible intervention (tidy classroom) despite identifying that it was unsupported by the evidence, suggesting effects of prior beliefs. In Experiment 2, we tested whether this intervention effect was driven by differences in beliefs about intervention plausibility and included two additional interventions (virtual reality = high plausible, napping = low plausible). We again found that participants were more likely to implement high plausible than low plausible interventions, and that evidence quality was underweighed as a factor in these decisions. Together, these studies suggest that evidence-based decisions are more strongly determined by prior beliefs than beliefs about the quality of evidence itself.

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