1,721,239 research outputs found
The use of number words in natural language obeys Weber's law
No full textIt has been suggested that the origins of number words can be traced back to an evolutionarily ancient approximate number system, which represents quantities on a compressed scale and complies with Weber's law. Here, we use a data-driven computational model, which learns to predict 1 event (a word in a text corpus) from associated events, to characterize verbal behavior relative to number words in natural language, without appeal to perception. We show that the way humans use number words in spontaneous language reliably depends on numerical ratio-a clear signature of Weber's law-thus, perfectly mirroring the human and nonhuman psychophysical performance in comparative judgments of numbers. Most notably, the adherence to Weber's law is robustly replicated in a wide range of different languages. Together, these findings suggest that the everyday use of number words in language rests upon a preverbal approximate number system, which would affect the handling of numerical information not only at the input level but also at the level of verbal production
Maps and Space Are Entangled with Language Experience
No full textThe view that conceptual knowledge is explored through mechanisms originally deputed to navigate the physical space is theoretically fascinating and supported by an increasing body of multidisciplinary data. In their article, Bottini and Doeller proposed that spatial organizational principles of the hippocampal–parietal network can be favorably exploited to handle mental representations. The priority assigned to spatial models, however, should be carefully interpreted in the context of the deep entanglement between linguistic and spatial information: in fact, while spatial principles can help navigate conceptual knowledge, linguistic data itself can produce spatial information without relying on the computations described by Bottini and Doeller
Toward a unified account of nonsymbolic and symbolic representations of number: Insights from a combined psychophysical-computational approach
No full textThere is an ongoing, vibrant debate about whether numerical information in both nonsymbolic and symbolic notations would be supported by different neurocognitive systems or rather by a common preverbal approximate number system, which is ratio dependent and follows Weber’s law. Here, we propose that the similarities between nonsymbolic and symbolic number processing can be explained based on the principle of efficient coding. To probe this hypothesis we employed a new empirical approach, by predicting the behavioural performance in number comparison tasks with symbolic (i.e., number words) and nonsymbolic (i.e., arrays of dots) information not only from numerical ratio, but for the first time also from natural language data. That is, we used data extracted from vector-space models that are informative about the distributional pattern of number-words usage in natural language. Results showed that linguistic estimates predicted the behavioural performance in both symbolic and nonsymbolic tasks. However, and critically, our results also showed a task-dependent dissociation: linguistic data better predicted the performance in the symbolic task, whereas real numerical ratio better predicted the performance in the nonsymbolic task. These findings indicate that efficient coding of environmental regularities is an explanatory principle of human behavior in tasks involving numerical information. They also suggest that the ability to discriminate a stimulus from similar ones varies as a function of the specific statistical structure of the considered learning environment
Vector-Space Models of Semantic Representation From a Cognitive Perspective: A Discussion of Common Misconceptions
No full textModels that represent meaning as high-dimensional numerical vectors—such as latent semantic analysis (LSA), hyperspace analogue to language (HAL), bound encoding of the aggregate language environment (BEAGLE), topic models, global vectors (GloVe), and word2vec—have been introduced as extremely powerful machine-learning proxies for human semantic representations and have seen an explosive rise in popularity over the past 2 decades. However, despite their considerable advancements and spread in the cognitive sciences, one can observe problems associated with the adequate presentation and understanding of some of their features. Indeed, when these models are examined from a cognitive perspective, a number of unfounded arguments tend to appear in the psychological literature. In this article, we review the most common of these arguments and discuss (a) what exactly these models represent at the implementational level and their plausibility as a cognitive theory, (b) how they deal with various aspects of meaning such as polysemy or compositionality, and (c) how they relate to the debate on embodied and grounded cognition. We identify common misconceptions that arise as a result of incomplete descriptions, outdated arguments, and unclear distinctions between theory and implementation of the models. We clarify and amend these points to provide a theoretical basis for future research and discussions on vector models of semantic representation
Predicting Hand Movements With Distributional Semantics: Evidence From Mouse-Tracking
No full textAlthough mouse-tracking has been taken as a real-time window on different aspects of human decision-making processes, whether purely semantic information affects response conflict at the level of motor output as measured through mouse movements is still unknown. Here, across two experiments, we investigated the effects of semantic knowledge by predicting participants’ performance in a standard keyboard task and in a mouse-tracking task through distributional semantics, a usage-based modeling approach to meaning. In Experiment 1, participants were shown word pairs and were required to perform a two-alternative forced choice task selecting either the more abstract or the more concrete word, using standard keyboard presses. In Experiment 2, participants performed the same task, yet this time response selection was achieved by moving the computer mouse. Results showed that the involvement of semantic components in the task at hand is observable using both standard reaction times (Experiment 1) as well as using indexes extracted from mouse trajectories (Experiment 2). In particular, mouse trajectories reflected the response conflict and its temporal evolution, with a larger deviation for increasing word semantic relatedness. These findings support the validity of mouse-tracking as a method to detect deep and implicit decision-making features. Additionally, by demonstrating that a usage-based model of meaning can account for the different degrees of cognitive conflict associated with task achievement, these findings testify the impact of the human semantic memory on decision-making processes
Novel nucleic acid molecules
Full text linkThe inventors have engineered novel DNA constructs for the expression of the tRNApyl genes in eukaryotic cells, especially mammalian cells, under new and improved promoter systems. The tRNApyl gene sequence possesses two internal regions that resemble an eukaryotic A box and B box, with the B box-like region more closely resembling a functional B box. Although previous attempts at reconstituting a consensus A- Box and B-box were unsuccessful as reported in Hancock et al (2010) and Mukai et al. (US 8,168,407), the inventors have now surprisingly found that the tRNApyl sequence can be altered to enable a functioning intragenic promoter and obtain a tRNApyl able to mediate efficient amber suppression in combination with WT pylRS. Such new tRNApyl gene can be used to generate highly active and stable cell lines for the incorporation of nnAAs into cells. The inventors have also found that the new modified tRNApyl gene containing a functional intragenic promoter element can be further improved by placing them downstream of the 5’ regulatory elements of genes expressed under type 4 promoters, thereby reconstituting a functional type 4 promoter element containing both extragenic and intragenic elements. The inventors have also surprisingly found that the WT tRNApyl gene can be expressed under transcriptional control of a tRNAglu gene and/or a tRNAasp gene, when said tRNAglu gene and/or tRNAasp gene is placed upstream of the tRNApyl gene and altered to lack the transcription termination sequence in order to effectively form a bicistronic message. DNA constructs bearing tandem repeats of novel tRNApyl genes of the invention have shown to lead to increased amber suppression
Head position and the mental representation of Italian nominal compounds: a constituent priming study in Italian
No full textThere is a significant body of psycholinguistic evidence that supports the hypothesis of an access to constituent representation during the mental processing of compound words. However it is not clear whether the internal hierarchy of the constituents (i.e., headedness) plays a role in their mental lexical processing and it is not possible to disentangle the effect of headedness from that of constituent position in languages that admit only head-final compounds, like English or Dutch. The present study addresses this issue in two constituent priming experiments (SOA 250ms) with a lexical decision task. Italian endocentric (head-initial and head-final) and exocentric nominal compounds were employed as stimuli and the position of the primed constituent was manipulated. A first-level priming effect was found, confirming the automatic access to constituent representation. Moreover, in head-final compounds data reveal a larger priming effect for the head than for the modifying constituent. These results suggest that different kinds of compounds have a different representation at mental level: while head-final compounds are represented with an internal head-modifier hierarchy, head-initial and exocentric compounds have a lexicalised, internally flat representation
Out-of-Vocabulary but Not Meaningless: Evidence for Semantic-Priming Effects in Pseudoword Processing
No full textNonarbitrary phenomena in language, such as systematic association in the form–meaning interface, have been widely reported in the literature. Exploiting such systematic associations previous studies have demonstrated that pseudowords can be indicative of meaning. However, whether semantic activation from words and pseudowords is supported by the very same processes, activating a common semantic memory system, is currently not known. Here, we take advantage of recent progresses from computational linguistics models allowing to induce meaning representations for out-of-vocabulary strings of letters via domain-general associative-learning mechanisms applied to natural language. We combined these models with data from priming tasks, in which participants are showed two strings of letters presented sequentially one after the other and are then asked to indicate if the latter is a word or a pseudoword. In Experiment 1 we reanalyzed the data of the largest behavioral database on semantic priming, while in Experiment 2 we ran an independent replication on a new language, Italian, controlling for a series of possible confounds. Results were consistent across the two experiments and showed that the prime-word meaning interferes with the semantic pattern elicited by the target pseudoword (i.e., at increasing estimated semantic relatedness between prime word and target pseudoword, participants’ reaction times increased and accuracy decreased). These findings indicate that the same associative mechanisms governing word meaning also subserve the processing of pseudowords, suggesting in turn that human semantic memory can be conceived as a distributional system that builds upon a generalpurpose capacity of extracting knowledge from complex statistical pattern
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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