Computational techniques comparing co-occurrences of city names in texts allow the relative longitudes and latitudes of cities to be estimated algorithmically. However, these techniques have not been applied to estimate the provenance of artifacts with unknown origins. Here, we estimate the geographic origin of artifacts from the Indus Valley Civilization, applying methods commonly used in cognitive science to the Indus script. We show that these methods can accurately predict the relative locations of archeological sites on the basis of artifacts of (...) known provenance, and we further apply these techniques to determine the most probable excavation sites of four sealings of unknown provenance. These findings suggest that inscription statistics reflect historical interactions among locations in the Indus Valley region, and they illustrate how computational methods can help localize inscribed archeological artifacts of unknown origin. The success of this method offers opportunities for the cognitive sciences in general and for computational anthropology specifically. (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)

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)

Spatial mental representations can be derived from linguistic and non‐linguistic sources of information. This study tested whether these representations could be formed from statistical linguistic frequencies of city names, and to what extent participants differed in their performance when they estimated spatial locations from language or maps. In a computational linguistic study, we demonstrated that co‐occurrences of cities in Tolkien’s Lord of the Rings trilogy and The Hobbit predicted the authentic longitude and latitude of those cities in Middle Earth. In (...) a human study, we showed that human spatial estimates of the location of cities were very similar regardless of whether participants read Tolkien’s texts or memorized a map of Middle Earth. However, text‐based location estimates obtained from statistical linguistic frequencies better predicted the human text‐based estimates than the human map‐based estimates. These findings suggest that language encodes spatial structure of cities, and that human cognitive map representations can come from implicit statistical linguistic patterns, from explicit non‐linguistic perceptual information, or from both. (shrink)

In an evolutionary perspective Louwerse elaborates the Symbol Interdependency Hypothesis (Louwerse, 2011), arguing that language has evolved such that it maps onto the perceptual system, allowing to bootstrap meaning also when grounding is limited. The author concludes that in principle the processing of abstract and concrete words is the same and that in both cases language users tend to rely anyway on indexical relationships that words entertain with other words.

Previous studies have shown that object properties are processed faster when they follow properties from the same perceptual modality than properties from different modalities. These findings suggest that language activates sensorimotor processes, which, according to those studies, can only be explained by a modal account of cognition. The current paper shows how a statistical linguistic approach of word co-occurrences can also reliably predict the category of perceptual modality a word belongs to (auditory, olfactory–gustatory, visual–haptic), even though the statistical linguistic approach (...) is less precise than the modal approach (auditory, gustatory, haptic, olfactory, visual). Moreover, the statistical linguistic approach is compared with the modal embodied approach in an experiment in which participants verify properties that share or shift modalities. Response times suggest that fast responses can best be explained by the linguistic account, whereas slower responses can best be explained by the embodied account. These results provide further evidence for the theory that conceptual processing is both linguistic and embodied, whereby less precise linguistic processes precede precise simulation processes. (shrink)

Affective information can be retrieved simply by measuring words co‐occurrences in linguistic contexts. Lenci and colleagues demonstrate that the affective measures retrieved from linguistic occurrences predict words’ concreteness: abstract words are more heavily loaded with affective information than concrete ones. These results challenge the Affective grounding hypothesis, suggesting that abstract concepts may be ungrounded and coded only linguistically, and that their affective load may be a linguistic factor.

Traditional methods for deriving property-based representations of concepts from text have focused on either extracting only a subset of possible relation types, such as hyponymy/hypernymy (e.g., car is-a vehicle) or meronymy/metonymy (e.g., car has wheels), or unspecified relations (e.g., car—petrol). We propose a system for the challenging task of automatic, large-scale acquisition of unconstrained, human-like property norms from large text corpora, and discuss the theoretical implications of such a system. We employ syntactic, semantic, and encyclopedic information to guide our extraction, (...) yielding concept-relation-feature triples (e.g., car be fast, car require petrol, car cause pollution), which approximate property-based conceptual representations. Our novel method extracts candidate triples from parsed corpora (Wikipedia and the British National Corpus) using syntactically and grammatically motivated rules, then reweights triples with a linear combination of their frequency and four statistical metrics. We assess our system output in three ways: lexical comparison with norms derived from human-generated property norm data, direct evaluation by four human judges, and a semantic distance comparison with both WordNet similarity data and human-judged concept similarity ratings. Our system offers a viable and performant method of plausible triple extraction: Our lexical comparison shows comparable performance to the current state-of-the-art, while subsequent evaluations exhibit the human-like character of our generated properties. (shrink)

Word co‐occurrence patterns in language corpora contain a surprising amount of conceptual knowledge. Large language models (LLMs), trained to predict words in context, leverage these patterns to achieve impressive performance on diverse semantic tasks requiring world knowledge. An important but understudied question about LLMs’ semantic abilities is whether they acquire generalized knowledge of common events. Here, we test whether five pretrained LLMs (from 2018's BERT to 2023's MPT) assign a higher likelihood to plausible descriptions of agent−patient interactions than to minimally (...) different implausible versions of the same event. Using three curated sets of minimal sentence pairs (total n = 1215), we found that pretrained LLMs possess substantial event knowledge, outperforming other distributional language models. In particular, they almost always assign a higher likelihood to possible versus impossible events (The teacher bought the laptop vs. The laptop bought the teacher). However, LLMs show less consistent preferences for likely versus unlikely events (The nanny tutored the boy vs. The boy tutored the nanny). In follow‐up analyses, we show that (i) LLM scores are driven by both plausibility and surface‐level sentence features, (ii) LLM scores generalize well across syntactic variants (active vs. passive constructions) but less well across semantic variants (synonymous sentences), (iii) some LLM errors mirror human judgment ambiguity, and (iv) sentence plausibility serves as an organizing dimension in internal LLM representations. Overall, our results show that important aspects of event knowledge naturally emerge from distributional linguistic patterns, but also highlight a gap between representations of possible/impossible and likely/unlikely events. (shrink)

The literature contains a disconnect between accounts of how humans learn lexical semantic representations for words. Theories generally propose that lexical semantics are learned either through perceptual experience or through exposure to regularities in language. We propose here a model to integrate these two information sources. Specifically, the model uses the global structure of memory to exploit the redundancy between language and perception in order to generate inferred perceptual representations for words with which the model has no perceptual experience. We (...) test the model on a variety of different datasets from grounded cognition experiments and demonstrate that this diverse set of results can be explained as perceptual simulation (cf. Barsalou, Simmons, Barbey, & Wilson, 2003) within a global memory model. (shrink)

We compared the ability of three different contextual models of lexical semantic memory and of a simple associative model to predict the properties of semantic networks derived from word association norms. None of the semantic models were able to accurately predict all of the network properties. All three contextual models over-predicted clustering in the norms, whereas the associative model under-predicted clustering. Only a hybrid model that assumed that some of the responses were based on a contextual model and others on (...) an associative network successfully predicted all of the network properties and predicted a word's top five associates as well as or better than the better of the two constituent models. The results suggest that participants switch between a contextual representation and an associative network when generating free associations. We discuss the role that each of these representations may play in lexical semantic memory. Concordant with recent multicomponent theories of semantic memory, the associative network may encode coordinate relations between concepts, and contextual representations may be used to process information about more abstract concepts. (shrink)

The study of emotional concepts stands at a very interesting intersection between the theoretical debate about the nature of emotions and the debate about the nature of processing concrete concepts and abstract concepts. On the one hand, it is debated whether it is possible to differentiate basic emotions from secondary emotions and, on the other hand, whether emotional concepts differ from abstract concepts. In this regard, the prototypical perceptual aspects are considered an important factor both for the differentiation between concrete (...) and abstract concepts and for the differentiation between basic and secondary emotions (facial expressions). Thus, the objective has been to determine if (a) the presence or absence of a prototypical perceptual referent, and (b) the type of concept (referring to emotion and not referring to emotion), produce differences between concepts of basic emotions, secondary emotions and concepts not related to emotions, concrete and abstract, in the tasks of qualification of concreteness, imageability and availability of context and the task of the list of properties, that have been used in previous studies. A total of 86 university students from the suburbs of La Serena - Coquimbo (Chile), all native Spanish speakers, participated in the study. The results show that in the perception of concreteness and in the total of enumerated properties, emotional concepts presented similar results to abstract concepts not related to emotion and there was no difference between basic and secondary emotion concepts. In imageability and context availability, emotional concepts were perceived as different from and more concrete than abstract concepts. In addition, the cause-effect type attributes allowed to clearly differentiate emotional concepts from those not related to emotion and to differentiate between basic and secondary emotion concepts. These types of attributes appear almost exclusively in emotional concepts and are more frequent in basic emotions. These results are partially consistent with the predictions of Neurocultural and Conceptual Act theories about emotions. (shrink)

Theories of embodied cognition assume that concepts are grounded in non-linguistic, sensorimotor experience. In support of this assumption, previous studies have shown that upwards response movements are faster than downwards movements after participants have been presented with words whose referents are typically located in the upper vertical space. This is taken as evidence that processing these words reactivates sensorimotor experiential traces. This congruency effect was also found for novel words, after participants learned these words as labels for novel objects that (...) they encountered either in their upper or lower visual field. While this indicates that direct experience with a word's referent is sufficient to evoke said congruency effects, the present study investigates whether this direct experience is also a necessary condition. To this end, we conducted five experiments in which participants learned novel words from purely linguistic input: Novel words were presented in pairs with real up- or down-words ; they were presented in natural sentences where they replaced these real words ; they were presented as new labels for these real words ; and they were presented as labels for novel combined concepts based on these real words. In all five experiments, we did not find any congruency effects elicited by the novel words; however, participants were always able to make correct explicit judgements about the vertical dimension associated to the novel words. These results suggest that direct experience is necessary for reactivating experiential traces, but this reactivation is not a necessary condition for understanding the corresponding aspects of word meaning. (shrink)

Three experiments test the theory that verb meanings are more malleable than noun meanings in different semantic contexts, making a previously seen verb difficult to remember when it appears in a new semantic context. Experiment 1 revealed that changing the direct object noun in a transitive sentence reduced recognition of a previously seen verb, whereas changing the verb had little impact on noun recognition. Experiment 2 revealed that verbs exhibited context effects more similar to those shown by superordinate nouns rather (...) than basic‐level nouns. Experiment 3 demonstrated that the degree of meaning change in a target word resulting from changes in semantic context influenced the magnitude of context effects, but context effects remained larger for verbs than for nouns even when the degree of meaning change was similar for nouns and verbs. These results are discussed with respect to the imageability and grammatical roles played by nouns and verbs in a sentence. (shrink)

Recently, there has been a great deal of interest in the idea that natural language enhances and extends our cognitive capabilities. Supporters of embodied cognition have been particularly interested in the way in which language may provide a solution to the problem of abstract concepts. Toward this end, some have emphasized the way in which language may act as form of cognitive scaffolding and others have emphasized the potential importance of language-based distributional information. This essay defends a version of the (...) cognitive enhancement thesis that integrates and builds on both of these proposals. I argue that the embodied representations associated with language processing serve as a supplementary medium for conceptual processing. The acquisition of a natural language provides a means of extending our cognitive reach by giving us access to an internalized combinatorial symbol system that augments and supports the context-sensitive embodied representational systems that exist independently of language. (shrink)

The Phenomenal Concept Strategy offers the physicalist perhaps the most promising means of explaining why the connection between mental facts and physical facts appears to be contingent even though it is not. In this article, we show that the large body of evidence suggesting that our concepts are often embodied and grounded in sensorimotor systems speaks against standard forms of the PCS. We argue, nevertheless, that it is possible to formulate a novel version of the PCS that is thoroughly in (...) keeping with embodied cognition, focuses on features of physical concepts, and succeeds in explaining the appearance of contingency. (shrink)

One of the main limitations of natural language‐based approaches to meaning is that they do not incorporate multimodal representations the way humans do. In this study, we evaluate how well different kinds of models account for people's representations of both concrete and abstract concepts. The models we compare include unimodal distributional linguistic models as well as multimodal models which combine linguistic with perceptual or affective information. There are two types of linguistic models: those based on text corpora and those derived (...) from word association data. We present two new studies and a reanalysis of a series of previous studies. The studies demonstrate that both visual and affective multimodal models better capture behavior that reflects human representations than unimodal linguistic models. The size of the multimodal advantage depends on the nature of semantic representations involved, and it is especially pronounced for basic‐level concepts that belong to the same superordinate category. Additional visual and affective features improve the accuracy of linguistic models based on text corpora more than those based on word associations; this suggests systematic qualitative differences between what information is encoded in natural language versus what information is reflected in word associations. Altogether, our work presents new evidence that multimodal information is important for capturing both abstract and concrete words and that fully representing word meaning requires more than purely linguistic information. Implications for both embodied and distributional views of semantic representation are discussed. (shrink)

Cognitive Science, Volume 46, Issue 7, July 2022.

The topic is characterized by a highly interdisciplinary approach to the issue of action and language integration. Such an approach, combining computational models and cognitive robotics experiments with neuroscience, psychology, philosophy, and linguistic approaches, can be a powerful means that can help researchers disentangle ambiguous issues, provide better and clearer definitions, and formulate clearer predictions on the links between action and language. In the introduction we briefly describe the papers and discuss the challenges they pose to future research. We identify (...) four important phenomena the papers address and discuss in light of empirical and computational evidence: (a) the role played not only by sensorimotor and emotional information but also of natural language in conceptual representation; (b) the contextual dependency and high flexibility of the interaction between action, concepts, and language; (c) the involvement of the mirror neuron system in action and language processing; (d) the way in which the integration between action and language can be addressed by developmental robotics and Human-Robot Interaction. (shrink)

Our ability to deal with abstract concepts is one of the most intriguing faculties of human cognition. Still, we know little about how such concepts are formed, processed, and represented in mind. Current views are presented in their most recent and advanced form in this special issue, and directly compared and discussed in a lively debate, reported at the end of each chapter. The main results are reported in the editors’ introduction.

The human conceptual system comprises simulated information of sensorimotor experience and linguistic distributional information of how words are used in language. Moreover, the linguistic shortcut hypothesis predicts that people will use computationally cheaper linguistic distributional information where it is sufficient to inform a task response. In a pre‐registered category production study, we asked participants to verbally name members of concrete and abstract categories and tested whether performance could be predicted by a novel measure of sensorimotor similarity (based on an 11‐dimensional (...) representation of sensorimotor strength) and linguistic proximity (based on word co‐occurrence derived from a large corpus). As predicted, both measures predicted the order and frequency of category production but, critically, linguistic proximity had an effect above and beyond sensorimotor similarity. A follow‐up study using typicality ratings as an additional predictor found that typicality was often the strongest predictor of category production variables, but it did not subsume sensorimotor and linguistic effects. Finally, we created a novel, fully grounded computational model of conceptual activation during category production, which best approximated typical human performance when conceptual activation was allowed to spread indirectly between concepts, and when candidate category members came from both sensorimotor and linguistic distributional representations. Critically, model performance was indistinguishable from typical human performance. Results support the linguistic shortcut hypothesis in semantic processing and provide strong evidence that both linguistic and grounded representations are inherent to the functioning of the conceptual system. All materials, data, and code are available at https://osf.io/vaq56/. (shrink)

Over the past 15 years, there have been two increasingly popular approaches to the study of meaning in cognitive science. One, based on theories of embodied cognition, treats meaning as a simulation of perceptual and motor states. An alternative approach treats meaning as a consequence of the statistical distribution of words across spoken and written language. On the surface, these appear to be opposing scientific paradigms. In this review, we aim to show how recent cross-disciplinary developments have done much to (...) reconcile these two approaches. The foundation to these developments has been the recognition that intralinguistic distributional and sensory-motor data are interdependent. We describe recent work in philosophy, psychology, cognitive neuroscience, and computational modeling that are all based on or consistent with this conclusion. We conclude by considering some possible directions for future research that arise as a consequence of these developments. (shrink)

Translated from: W.V.O.Quine, W. H. O. (1986): Philosophy of Logic. Second Edition. Harvard University Press. Cambridge, Massachusetts and London, England, 47-61.

A great deal of research has focused on the question of whether or not concepts are embodied as a rule. Supporters of embodiment have pointed to studies that implicate affective and sensorimotor systems in cognitive tasks, while critics of embodiment have offered nonembodied explanations of these results and pointed to studies that implicate amodal systems. Abstract concepts have tended to be viewed as an important test case in this polemical debate. This essay argues that we need to move beyond a (...) pretheoretical notion of abstraction. Against the background of current research and theory, abstract concepts do not pose a single, unified problem for embodied cognition but, instead, three distinct problems: the problem of generalization, the problem of flexibility, and the problem of disembodiment. Identifying these problems provides a conceptual framework for critically evaluating, and perhaps improving upon, recent theoretical proposals. (shrink)