Vector-based Approach to Verbal Cognition

Human verbal thinking is an object of many multidisciplinary studies Verbal cognition is often an integration of complex mental activities such as neurocognitive and psychological processes In neuro-cognitive study of language neural architecture and neuropsychological mechanism of verbal cognition are basis of a vector based modeling Human mental states as constituents of mental continuum represent an infinite set of meanings Number of meanings is not limited but numbers of words and rules that are used for building complex verbal structures are limited Verbal perception and interpretation of the multiple meanings and propositions in mental continuum can be modeled by applying tensor methods A comparison of human mental space to a vector space is an effective way of analyzing of human semantic vocabulary mental representations and rules of clustering and mapping As such Euclidean and non-Euclidean spaces can be applied for a description of human semantic vocabulary and high order Additionally changes in semantics and structures can be analyzed in 3D and other dimensional spaces It is suggested that different forms of verbal representation should be analyzed in a light of vector tensor transformations Vector dot and cross product covariance and contra variance have been applied to analysis of semantic transformations and pragmatic change in high order syntax structures These ideas are supported by empirical data from typologically different languages such as Mongolian English and Russian Moreover the author argues that the vectorbased approach to cognitive linguistics offers new opportunities to develop an alternative version of quantitative semantics and thus to extend theory of Universal grammar in new dimension

Feature biases in early word learning : network distinctiveness predicts age of acquisition

Do properties of a word’s features influence the order of its acquisition in early word learning? Combining the principles of mutual exclusivity and shape bias, the present work takes a network analysis approach to understanding how feature distinctiveness predicts the order of early word learning. Distance networks were built from nouns with edge lengths computed using various distance measures. Feature distinctiveness was computed as a distance measure, showing how far an object in a network is from other objects based on shared and non-shared features. Feature distinctiveness predicted order of acquisition across all measures; words that were further away from other words in the network space were learned earlier. The best distance measures were based only on non-shared features (object dissimilarity) and did not include shared features (object similarity). This indicates that shared features may play less of a role in early word learning than non-shared features. In addition, the strongest effects were found for visual form and surface features. Cluster analysis further revealed that this effect is a localized effect in the object feature space, where objects’ distances from their cluster centroid were inversely correlated with their age of acquisition. Together, these results suggest a role for feature distinctiveness in early word learning

What have we learned from 15 years of research on cross-situational word learning? A focused review

In 2007 and 2008, Yu and Smith published their seminal studies on cross-situational word learning (CSWL) in adults and infants, showing that word-object-mappings can be acquired from distributed statistics despite in-the-moment uncertainty. Since then, the CSWL paradigm has been used extensively to better understand (statistical) word learning in different language learners and under different learning conditions. The goal of this review is to provide an entry-level overview of findings and themes that have emerged in 15 years of research on CSWL across three topic areas (mechanisms of CSWL, CSWL across different learner and task characteristics) and to highlight the questions that remain to be answered

Redundancy And Complementarity In Language And The Environment:How Intermodal Information Is Combined To Constrain Learning

To acquire language, learners have to map the language onto the environment, but languages vary in terms of how much information is present within the language to constrain how the sentence relates to the world. We investigated the conditions under which information within the language and the environment is combined for learning. In a cross-situational artificial language learning study, participants listened to transitive sentences and viewed two scenes, and selected which scene was described by the sentence. The language had free word order, and varied in terms of whether or not it contained morphosyntactic information in order to define the subject and object roles of nouns in the sentence. We found that participants were able to learn information about word order and vocabulary from each language, demonstrating that information within the language only was not necessary for learning. Instead, participants can combine constraints from language and environment to support acquisition

Better early than late : The temporal dynamics of pointing cues during cross-situational word learning

Learning the meaning of a word is a difficult task due to the variety of possible referents present in the environment. Visual cues such as gestures frequently accompany speech, and have the potential to reduce referential uncertainty and promote learning, but the dynamics of pointing cues and speech integration are not yet known. If word learning is influenced by when, as well as whether, a learner is directed correctly to a target, then this would suggest temporal integration of visual and speech information can affect the strength of association of word-referent mappings. Across two pre-registered studies, we tested the conditions under which pointing cues promote learning. In a cross-situational word learning paradigm, we showed that the benefit of a pointing cue was greatest when the cue preceded the speech label, rather than following the label (Study 1). In an eye-tracking study (Study 2) the early cue advantage was due to participants’ attention being directed to the referent during label utterance, and this advantage was apparent even at initial exposures of word-referent pairs. Pointing cues promote time-coupled integration of visual and auditory information that aids encoding of word-referent pairs, demonstrating the cognitive benefits of pointing cues occurring prior to speech

Caregivers use gesture contingently to support word learning

Children learn words in environments where there is considerable variability, both in terms of the number of possible referents for novel words, and the availability of cues to support word-referent mappings. How caregivers adapt their gestural cues to referential uncertainty has not yet been explored. We tested a computational model of cross-situational word learning that examined the value of a variable gesture cue during training across conditions of varying referential uncertainty. We found that gesture had a greater benefit for referential uncertainty, but unexpectedly also found that learning was best when there was variability in both the environment (number of referents) and gestural cue use. We demonstrated that these results are reflected behaviourally in an experimental word learning study involving children aged 18-24-month-olds and their caregivers. Under similar conditions to the computational model, caregivers not only used gesture more when there were more potential referents for novel words, but children also learned best when there was some referential ambiguity for words. Thus, caregivers are sensitive to referential uncertainty in the environment and adapt their gestures accordingly, and children are able to respond to environmental variability to learn more robustly. These results imply that training under variable circumstances may actually benefit learning, rather than hinder it

Quantifying the psychological properties of words

This thesis explores the psychological properties of words – the idea that words carry links to additional information beyond their dictionary meaning. It does so by presenting three distinct publications and an applied project, the Macroscope. The published research respectively covers: the modelling of language networks to explain lexical growth; the use of high dimensional vector representations of words to discuss language learning; and the collection of a normative dataset of single word humour ratings. The first publication outlines the use of network science in psycholinguistics. The methodology is discussed, providing clear guidelines on the application of networks when answering psychologically motivated questions. A selection of psychological studies is presented as a demonstration of use cases for networks in cognitive psychology. The second publication uses referent feature norms to represent words in a high dimensional vector space. A correlative link between referent distinctiveness and age of acquisition is proposed. The shape bias literature (the idea that children only pay attention to the shape of objects early on) is evaluated in relation to the findings. The third publication collects and shares a normative dataset of single word humour ratings. Descriptive properties of the dataset are outlined and the potential future use in the field of humour is discussed. Finally, the thesis presents the Macroscope, a collaborative project put together with Li Ying. The Macroscope is an online platform, allowing for easy analysis of the psychological properties of target words. The platform is showcased, and its full functionality is presented, including visualisation examples. Overall, the thesis aims to give researchers all that’s necessary to start working with psychological properties of words – the understanding of network science in psycholinguistics, high dimensional vector spaces, normative datasets and the applied use of all the above through the Macroscope