The hypothesis that perceptual mechanisms could have more representational and logical power than usually assumed is interesting and provocative, especially with regard to brain evolution. However, the importance of embodiment and grounding is exaggerated, and the implication that there is no highly abstract representation at all, and that human-like knowledge cannot be learned or represented without human bodies, is very doubtful. A machine-learning model, Latent Semantic Analysis (LSA) that closely mimics human word and passage meaning relations is offered as a (...) counterexample. (shrink)

The foundation of health can be understood by the heart only : in pursuit of knowledge about health in the light of Blaise Pascal's thinking.

Whether computational algorithms such as latent semantic analysis (LSA) can both extract meaning from language and advance theories of human cognition has become a topic of debate in cognitive science, whereby accounts of symbolic cognition and embodied cognition are often contrasted. Albeit for different reasons, in both accounts the importance of statistical regularities in linguistic surface structure tends to be underestimated. The current article gives an overview of the symbolic and embodied cognition accounts and shows how meaning induction attributed to (...) a specific statistical process or to activation of embodied representations should be attributed to language itself. Specifically, the performance of LSA can be attributed to the linguistic surface structure, more than special characteristics of the algorithm, and embodiment findings attributed to perceptual simulations can be explained by distributional linguistic information. (shrink)

Over the past two decades, researchers have made great advances in the area of computational methods for extracting meaning from text. This research has to a large extent been spurred by the development of latent semantic analysis (LSA), a method for extracting and representing the meaning of words using statistical computations applied to large corpora of text. Since the advent of LSA, researchers have developed and tested alternative statistical methods designed to detect and analyze meaning in text corpora. This research (...) exemplifies how statistical models of semantics play an important role in our understanding of cognition and contribute to the field of cognitive science. Importantly, these models afford large-scale representations of human knowledge and allow researchers to explore various questions regarding knowledge, discourse processing, text comprehension, and language. This topic includes the latest progress by the leading researchers in the endeavor to go beyond LSA. (shrink)

In Latent Semantic Analysis (LSA) the meaning of a word is represented as a vector in a high‐dimensional semantic space. Different meanings of a word or different senses of a word are not distinguished. Instead, word senses are appropriately modified as the word is used in different contexts. In N‐VP sentences, the precise meaning of the verb phrase depends on the noun it is combined with. An algorithm is described to adjust the meaning of a predicate as it is applied (...) to different arguments. In forming a sentence meaning, not all features of a predicate are combined with the features of the argument, but only those that are appropriate to the argument. Hence, a different “sense” of a predicate emerges every time it is used in a different context. This predication algorithm is explored in the context of four different semantic problems: metaphor interpretation, causal inferences, similarity judgments, and homonym disambiguation. (shrink)

Empirical studies indicate that analogy consists of two main processes: retrieval and mapping. While current theories and models of analogy have revealed much about the mainly structural constraints that govern the mapping process, the similarities that underpin the correspondences between individual representational elements and drive retrieval are understood in less detail. In existing models symbol similarities are externally defined but neither empirically grounded nor theoretically justified. This paper introduces a new model (EMMA: the environmental model of analogy) which relies on (...) co‐occurrence information provided by LSA (Latent Semantic Analysis; Landauer & Dumais, 1997) to ground the relations between the symbolic elements aligned in analogy. LSA calculates a contextual distribution for each word encountered in a corpus by counting the frequency with which it co‐occurs with other words. This information is used to define a model that locates each word encountered in a high‐dimensional space, with relations between elements in this space representing contextual similarities between words. A series of simulation experiments demonstrate that the environmental approach to semantics embodied in LSA can produce appropriate patterns of analogical retrieval, but that this semantic measure is not sufficient to model analogical mapping. The implications of these findings, both for theories of representation in analogy research and more general theories of semantics in cognition, are explored. (shrink)

The focus of this paper is two-fold. First, similarities generated from six semantic models were compared to human ratings of paragraph similarity on two datasets—23 World Entertainment News Network paragraphs and 50 ABC newswire paragraphs. Contrary to findings on smaller textual units such as word associations (Griffiths, Tenenbaum, & Steyvers, 2007), our results suggest that when single paragraphs are compared, simple nonreductive models (word overlap and vector space) can provide better similarity estimates than more complex models (LSA, Topic Model, SpNMF, (...) and CSM). Second, various methods of corpus creation were explored to facilitate the semantic models’ similarity estimates. Removing numeric and single characters, and also truncating document length improved performance. Automated construction of smaller Wikipedia-based corpora proved to be very effective, even improving upon the performance of corpora that had been chosen for the domain. Model performance was further improved by augmenting corpora with dataset paragraphs. (shrink)

This study linked background TV and socioeconomic status to minority children’s Persian vocabulary development. To this end, 80 Iranian preschool children from two minority groups of Arabs and Turks were selected using stratified random sampling. They were simultaneous bilinguals, i.e., their mother tongue was either Arabic or Azari and their first language was Persian. Language sample analysis was used to measure vocabulary development through a 15-min interview by language experts. The LSA measures included total number of utterances, total number of (...) words, total number of new words, and mean length of utterance. A series of independent-samples t test, paired-samples t test, and repeated measures MANOVA tests were ran to examine data. Results showed significant improvements in children’s vocabulary scores from pretest to posttest for all children. In addition, high-SES children scored higher on the vocabulary measures in pretest and posttest. Moreover, background TV was associated with higher means in the TNW and the NDW in groups. The researchers concluded that background TV may be related to higher vocabulary scores in low-SES families as it may compensate for some linguistics gaps in these families including lower amount of child-parent interaction, play, and parents’ level of literacy. (shrink)

We argue that word meanings are not stored in a mental lexicon but are generated in the context of working memory from long-term memory traces that record our experience with words. Current statistical models of semantics, such as latent semantic analysis and the Topic model, describe what is stored in long-term memory. The CI-2 model describes how this information is used to construct sentence meanings. This model is a dual-memory model, in that it distinguishes between a gist level and an (...) explicit level. It also incorporates syntactic information about how words are used, derived from dependency grammar. The construction of meaning is conceptualized as feature sampling from the explicit memory traces, with the constraint that the sampling must be contextually relevant both semantically and syntactically. Semantic relevance is achieved by sampling topically relevant features; local syntactic constraints as expressed by dependency relations ensure syntactic relevance. (shrink)

Distributional models of semantics learn word meanings from contextual co‐occurrence patterns across a large sample of natural language. Early models, such as LSA and HAL (Landauer & Dumais, 1997; Lund & Burgess, 1996), counted co‐occurrence events; later models, such as BEAGLE (Jones & Mewhort, 2007), replaced counting co‐occurrences with vector accumulation. All of these models learned from positive information only: Words that occur together within a context become related to each other. A recent class of distributional models, referred to as (...) neural embedding models, are based on a prediction process embedded in the functioning of a neural network: Such models predict words that should surround a target word in a given context (e.g., word2vec; Mikolov, Sutskever, Chen, Corrado, & Dean, 2013). An error signal derived from the prediction is used to update each word's representation via backpropagation. However, another key difference in predictive models is their use of negative information in addition to positive information to develop a semantic representation. The models use negative examples to predict words that should not surround a word in a given context. As before, an error signal derived from the prediction prompts an update of the word's representation, a procedure referred to as negative sampling. Standard uses of word2vec recommend a greater or equal ratio of negative to positive sampling. The use of negative information in developing a representation of semantic information is often thought to be intimately associated with word2vec's prediction process. We assess the role of negative information in developing a semantic representation and show that its power does not reflect the use of a prediction mechanism. Finally, we show how negative information can be efficiently integrated into classic count‐based semantic models using parameter‐free analytical transformations. (shrink)

We present a longitudinal computational study on the connection between emotional and amodal word representations from a developmental perspective. In this study, children's and adult word representations were generated using the latent semantic analysis (LSA) vector space model and Word Maturity methodology. Some children's word representations were used to set a mapping function between amodal and emotional word representations with a neural network model using ratings from 9‐year‐old children. The neural network was trained and validated in the child semantic space. (...) Then, the resulting neural network was tested with adult word representations using ratings from an adult data set. Samples of 1210 and 5315 words were used in the child and the adult semantic spaces, respectively. Results suggested that the emotional valence of words can be predicted from amodal vector representations even at the child stage, and accurate emotional propagation was found in the adult word vector representations. In this way, different propagative processes were observed in the adult semantic space. These findings highlight a potential mechanism for early verbal emotional anchoring. Moreover, different multiple linear regression and mixed‐effect models revealed moderation effects for the performance of the longitudinal computational model. First, words with early maturation and subsequent semantic definition promoted emotional propagation. Second, an interaction effect between age of acquisition and abstractness was found to explain model performance. The theoretical and methodological implications are discussed. (shrink)

1. I have fond memories of the Linguistic Society of America meeting in New Orleans just after Christmas in 1988, the last time I was able to see all my humanist friends from graduate school who were attending the concurrent meeting of the MLA. Shortly after that, the LSA decided to forego the company of humanists and assemble by itself during the first week of January. It's hard to fault the decision. Over and above the obvious practical advantages, like not (...) having to wait in line for forty-five minutes outside popular restaurants, the groups had been growing apart intellectually for some time. Few linguists bothered to cross town for MLA sessions on topics like onomastics, English usage, and the history of lexicography. Nor for that matter were those sessions drawing many MLA attendees, either. Literary people didn't seem to be much interested in language study anymore; what filled their ballrooms now were the sessions on deconstruction and other poststructuralist enthusiasms. It was like going back for your annual drink with your high-school sweetheart and finding that you had less and less to talk about. They didn't know what subjacency was and we didn't know what alterity was, and neither seemed to care. If you were of a mind to look for milestones, you could take that as the time when linguistic semantics definitively cut itself loose from its philological roots. Like a lot of semanticists, I had mixed feelings about the break. It's undeniable that linguistic semantics couldn't have come of age as an autonomous empirical discipline if it hadn't.. (shrink)

Language sample analysis is an important practice for providing a culturally sensitive and accurate assessment of a child's language abilities. A child's usage of literate language devices in narrative samples has been shown to be a critical target for evaluation. While automated scoring systems have begun to appear in the field, no such system exists for conducting progress-monitoring on literate language usage within narratives. The current study aimed to develop a hard-coded scoring system called the Literate Language Use in Narrative (...) Assessment, to automatically evaluate six aspects of literate language in non-coded narrative transcripts. LLUNA was designed to individually score six literate language elements. The interrater reliability of LLUNA with an expert scorer, as well as its' reliability compared to certified undergraduate scorers was calculated using a quadratic weighted kappa. Results indicated that LLUNA met strong levels of interrater reliability with an expert scorer on all six elements. LLUNA also surpassed the reliability levels of certified, but non-expert scorers on four of the six elements and came close to matching reliability levels on the remaining two. LLUNA shows promise as means for automating the scoring of literate language in LSA and narrative samples for the purpose of assessment and progress-monitoring. (shrink)

Introduction Social anxiety disorder (SAD) symptoms are maintained by cognitive biases, which are overestimations of the severity and likelihood of negative social events (cost/probability biases), and by sensitivity to rewards and punishments that are determined according to behavioral inhibition/behavioral activation systems (BIS/BAS). Cost/probability biases might activate the behavioral immune system and exacerbate the avoidance of social events. Earlier studies have proposed that low BIS or high BAS decrease SAD symptoms; BIS/BAS may even change the effects of cognitive biases on SAD (...) symptoms. Hence, the current study investigates the interaction effects of BIS/BAS and cost/probability biases on SAD symptoms. Method Seventy-six Japanese undergraduate students completed the Japanese version of the Liebowitz Social Anxiety Scale (LSAS), which comprises Fear and Avoidance subscales, the BIS/BAS Scale, and the Social Cost Probability Scale. Results A multiple regression analysis was performed to examine whether cost/probability biases, BIS/BAS, and their interactions affected SAD symptoms; following this, the main effects of cost bias and BIS were determined for LSAS-Fear (β =.64, p <.001; β =.33, p <.01) and LSAS-Avoidance (β =.49, p <.001; β =.35, p <.01). The interaction effect between cost bias and BAS was significant for LSAS-Avoidance (β = −.32, p <.05). Simple slope analysis showed that the slope of cost bias was significant for low-BAS individuals (β =.77, p <.001) but not for high-BAS individuals (β = −.21, n.s.). The interaction effect between probability bias and BAS was significant for LSAS-Avoidance (β =.40, p <.01) as well. Further, simple slope analysis revealed that the slope of probability bias was significant for low-BAS individuals (β = −.53, p <.05) but not for high-BAS individuals (β =.17, n.s.). Discussion The study found interesting results with respect to the avoidance of social events. Low-BAS individuals with high cost or low probability biases regarding social events may have a tendency to avoid social events. In contrast, if high-BAS individuals overestimate the cost of social events or underestimate the probability of social events, their anticipation of rewards might prevent them from avoiding social events. (shrink)

In this paper, we use simple techniques derived from on Latent Semantic Analysis (LSA) to provide a simple and robust way of generating extractive summaries for TAC 2008 Update Summarization task.

The developing academic field of machine ethics seeks to make artificial agents safer as they become more pervasive throughout society. In contrast to computer ethics, machine ethics is concerned with the behavior of machines toward human users and other machines. This study aims to use an action-based ethical theory founded on the combinational aspects of deontological and teleological theories of ethics in the construction of an artificial moral agent (AMA).,The decision results derived by the AMA are acquired via fuzzy logic (...) interpretation of the relative values of the steady-state simulations of the corresponding rule-based fuzzy cognitive map (RBFCM).,Through the use of RBFCMs, the following paper illustrates the possibility of incorporating ethical components into machines, where latent semantic analysis (LSA) and RBFCMs can be used to model dynamic and complex situations, and to provide abilities in acquiring causal knowledge.,This approach is especially appropriate for data-poor and uncertain situations common in ethics. Nonetheless, to ensure that a machine with an ethical component can function autonomously in the world, research in artificial intelligence will need to further investigate the representation and determination of ethical principles, the incorporation of these ethical principles into a system’s decision procedure, ethical decision-making with incomplete and uncertain knowledge, the explanation for decisions made using ethical principles and the evaluation of systems that act based upon ethical principles.,To date, the conducted research has contributed to a theoretical foundation for machine ethics through exploration of the rationale and the feasibility of adding an ethical dimension to machines. Further, the constructed AMA illustrates the possibility of utilizing an action-based ethical theory that provides guidance in ethical decision-making according to the precepts of its respective duties. The use of LSA illustrates their powerful capabilities in understanding text and their potential application as information retrieval systems in AMAs. The use of cognitive maps provides an approach and a decision procedure for resolving conflicts between different duties.,This paper suggests that cognitive maps could be used in AMAs as tools for meta-analysis, where comparisons regarding multiple ethical principles and duties can be examined and considered. With cognitive mapping, complex and abstract variables that cannot easily be measured but are important to decision-making can be modeled. This approach is especially appropriate for data-poor and uncertain situations common in ethics. (shrink)

This study considered representations of divine and human others in the self-understanding of monotheists from three religions. Self-understanding was conceptualized on the basis of semantic and episodic knowledge in narrative response data. Given the importance of social context in the formation of cognitive schemas, the project emphasized self-understanding in a comparative religious design. The sample included sixty nominated religious exemplars who responded to a structured interview. Schemas were subsequently mapped for Jews, Muslims, and Christians by comparison of self and other (...) representations in a computational model known as latent semantic analysis (LSA). Findings indicated that representation of the divine is far removed from parents in cognitive schemas for all participants. Unlike Jews and Christians, Muslims appear to represent human others on the basis of self-understanding which principally references the divine. When considered in a computational semantic space, exemplars generally represent the self in a manner corresponding with divine and peer figures. (shrink)

BackgroundType-Token Ratio, given its relatively simple hand computation, is one of the few LSA measures calculated by clinicians in everyday practice. However, it has significant well-documented shortcomings; these include instability as a function of sample size, and absence of clear developmental profiles over early childhood. A variety of alternative measures of lexical diversity have been proposed; some, such as Number of Different Words/100 can also be computed by hand. However, others, such as Vocabulary Diversity and the Moving Average Type Token (...) Ratio rely on complex resampling algorithms that cannot be conducted by hand. To date, no large-scale study of all four measures has evaluated how well any capture typical developmental trends over early childhood, or whether any reliably distinguish typical from atypical profiles of expressive child language ability.Materials and MethodsWe conducted linear and non-linear regression analyses for TTR, NDW, VocD, and MATTR scores for samples taken from 946 corpora from typically developing preschool children, engaged in adult-child toy play, from the Child Language Data Exchange System. These were contrasted with 504 samples from children known to have delayed expressive language skills. We also conducted a separate sub-analysis which examined possible contextual effects of sampling environment on lexical diversity.ResultsOnly VocD showed significantly different mean scores between the typically -developing children and delayed developing children group. Using TTR would actually misdiagnose typical children and miss children with known language impairment. However, computation of VocD as a function of toy interactions was significant and emerges as a further caution in use of lexical diversity as a valid proxy index of children’s expressive vocabulary skill.DiscussionThis large scale statistical comparison of computer-implemented algorithms for expressive lexical profiles in young children with traditional, hand-calculated measures showed that only VocD met criteria for evidence-based use in LSA. However, VocD was impacted by sample elicitation context, suggesting that non-linguistic factors, such as engagement with elicitation props, contaminate estimates of spoken lexical skill in young children. Implications and suggested directions are discussed. (shrink)

ABSTRACTSocial attentional biases are a core component of social anxiety disorder, but research has not yet determined their direction due to methodological limitations. Here we present preliminary findings from a novel, dynamic eye-tracking paradigm allowing spatial–temporal measurement of attention and gaze-following, a mechanism previously unexplored in social anxiety. 105 participants took part, with those high and low in social anxiety traits entered into the analyses. Participants watched a video of an emotionally-neutral social scene, where two actors periodically shifted their gaze (...) towards the periphery. HSA participants looked more at the actors’ faces during the initial 2s than the LSA group but there were no group differences in proportion of first fixations to the face or latency to first fixate the face, although HSA individuals’ first fixations to the face were shorter. No further differences in eye movements were found, nor in gaze-following beha... (shrink)

Cognitive models propose that both, negative interpretations of ambiguous social situations and ruminative thoughts about social events contribute to the maintenance of social anxiety disorder. It has further been postulated that ruminative thoughts fuel biased negative interpretations, however, evidence is rare. The present study used a multi-method approach to assess ruminative processing following a social interaction and negative interpretation bias in a student sample screened for high and low social anxiety. Results support the hypothesis that group differences in negative interpretations (...) of ambiguous social situations in HSAs vs. LSAs are mediated by higher levels of post-event processing assessed in the questionnaire. Exploratory analyses highlight the potential role of comorbid depressive symptoms. The current findings help to advance the understanding of the association between two cognitive processes involved in social anxiety and stress the importance of ruminative post-event processing. (shrink)

Current work in the evolution of language and communication is emphasising a close relationship between the evolution of speech, language and gestural action. This paper briefly explores some evolutionary implications of a range of closely related impairments of speech, language and gesture arising from left frontal brain lesions. I discuss aphasic lexical speech automatisms and their resolution with some recovery into agrammatism with apraxia of speech, an impairment of speech planning and programming. I focus attention on the most common forms (...) of LSAs, expletives and the pronoun+modal/aux subtype, and propose that further research into these phenomena can contribute to the debate. I briefly discuss recent studies of progressively degenerating neurological conditions resulting in progressive patterns of cognitive impairments that promises to provide insight into the evolutionary relationships between speech, language and gesture. (shrink)

Can an Artificial Intelligence make distinctions among major works of politics, philosophy, and fiction without human assistance? In this paper, latent semantic analysis is used to find patterns in a relatively small sample of notable works archived by Project Gutenberg. It is shown that an LSA-equipped AI can distinguish quite sharply between fiction and non-fiction works, and can detect some differences between political philosophy and history, and between conventional fiction and fantasy/science fiction. It is conjectured that this capability is a (...) step in the direction of “M-comprehension” by AIs. (shrink)