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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.As anyone who has learnt a foreign language or travelled abroad will have noticed, languages differ in the sounds they employ, the names they give to things, and the rules of grammar. However, linguists have long observed that, beneath this surface diversity, all human languages share a number of fundamental structural similarities. Most obviously, all languages use sounds, all languages have words, and all languages have a grammar. More subtly and more surprisingly, similarities can also be observed in more fine-grained linguistic features: for instance, George Zipf famously observed that, across multiple languages, short words tend also to be more frequent, and in my own recent work I have shown that languages prefer to use words that sound alike (e.g., cat, mat, rat, bat, fat, ...). Why do all languages exhibit these shared features?
This project aims to tackle exactly this key question by studying how languages are shaped by the human mind. In particular, I will explore how the way we learn language and use it to communicate drives the emergence of important features of lexicons, the set of all words in a...
Terminology used is generally based on DDI controlled vocabularies: Time Method, Analysis Unit, Sampling Procedure and Mode of Collection, available at CESSDA Vocabulary Service.
Methodology
Data collection period
01/11/2016 - 27/11/2019
Country
United Kingdom
Time dimension
Not available
Analysis unit
Individual
Universe
Not available
Sampling procedure
Not available
Kind of data
Numeric
Text
Data collection mode
Participants were recruited through Amazon Mechanical Turk. All participants were included in the analysis and randomly assigned to one of the conditions.Participants were tested online. They were instructed that they were to learn a rule by being exposed to a series of trials containing a collection of objects. For each trial, they would have to decide whether the display is consistent with the rule by pressing a “yes” or a “no” button and would receive feedback on their response.
Funding information
Grant number
ES/N017404/1
Access
Publisher
UK Data Service
Publication year
2021
Terms of data access
The Data Collection is available to any user without the requirement for registration for download/access.