Reconstructing maps from text

Avery, Johnathan E.; Goldstone, Robert L.; Jones, Michael N.

doi:10.1016/j.cogsys.2021.07.007

Cited by 8 publications

(8 citation statements)

References 32 publications

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“…These findings lend further support to semantic memory search being a two stages process (see refs. 47,65,66 ).…”

Section: Discussionmentioning

confidence: 99%

“…Although alternative views exist (e.g., refs. 47,65,66 ), the framework of exploration and exploitation in semantic foraging offers a useful quantitative method to characterize the responses in diverse generation tasks relevant to creativity. For instance, a few studies have separated exploitation/exploration processes operationalized in a visuospatial creativity task 67 or clustering and switching behavior in a divergent thinking task [68][69][70] , suggesting that they reflect separable processes supporting creative ideation.…”

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confidence: 99%

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An investigation of the cognitive and neural correlates of semantic memory search related to creative ability

et al. 2022

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Creative ideas likely result from searching and combining semantic memory knowledge, yet the mechanisms acting on memory to yield creative ideas remain unclear. Here, we identified the neurocognitive correlates of semantic search components related to creative abilities. We designed an associative fluency task based on polysemous words and distinguished two search components related to clustering and switching between the different meanings of the polysemous words. Clustering correlated with divergent thinking, while switching correlated with the ability to combine remote associates. Furthermore, switching correlated with semantic memory structure and executive abilities, and was predicted by connectivity between the default, control, and salience neural networks. In contrast, clustering relied on interactions between control, salience, and attentional neural networks. Our results suggest that switching captures interactions between memory structure and control processes guiding the search whereas clustering may capture attentional controlled processes for persistent search, and that alternations between exploratory search and focused attention support creativity.

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“…These findings lend further support to semantic memory search being a two stages process (see refs. 47,65,66 ).…”

Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

An investigation of the cognitive and neural correlates of semantic memory search related to creative ability

et al. 2022

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“…This view is substantiated by a growing body of research emphasizing the crucial interplay between perceptual and linguistic experiences in the formation of cognitive maps. In particular, 4 studies exploiting distributional semantic models (DSMs) demonstrated that spatial information can be bootstrapped from the statistical structure of natural language (Avery et al, 2021;Gatti et al, 2022;Louwerse, 2018;Rinaldi & Marelli, 2020). For instance, by exploiting Latent Semantic Analysis (Landauer & Dumais, 1997), a traditional count-based DSMs whose first processing step builds on the computation of word co-occurrences, Louwerse and colleagues showed that it is possible to reconstruct the spatial layout (e.g., geographical distance between cities) of geographical maps from various real world regions using text corpora written in the corresponding languages (e.g., using American corpora to reproduce the maps of the USA; Louwerse & Zwaan, 2009;Louwerse et al, 2006;Louwerse et al, 2012), as well as fictional words such as the Middle Earth from J. R. R.…”

Section: Introductionmentioning

confidence: 99%

Decomposing geographical judgments into spatial, temporal and linguistic components

Gatti,

Anceresi,

Marelli

et al. 2023

Preprint

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When mentally exploring large-scale maps, humans are assumed to mainly rely on spatial information derived from direct perceptual experience (e.g., prior visual experience with the geographical map itself). In the present study, we rather tested whether also temporal and linguistic information could account for the way humans explore and ultimately represent large-scale geographical maps. We quantified temporal distance as the minimum time needed to travel by train across Italian cities, while linguistic distance was retrieved from natural language through cognitively plausible AI models based on non-spatial associative learning mechanisms (i.e., distributional semantic models). In a first experiment, we show that temporal and linguistic distances capture with high-confidence real geographical distances. Next, in a second behavioral experiment, we show that linguistic information can account for human performance over and above real spatial information in a task in which participants have to judge the distance between cities (while temporal information was found to be not relevant). These findings indicate that, when exploring large-scale geographical maps, humans do take advantage of both perceptual and linguistic information, suggesting in turn that the formation of cognitive maps possibly relies on a strict interplay between spatial and non-spatial learning principles.

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“…Mandera et al (2017) showed that word embeddings can be used to predict semantic priming as well as word associations, similarity/relatedness ratings and even perform well in a multiple-choice task. Further evidence in favour of the cognitive plausibility of word embeddings has been provided by Westbury (2014); Westbury and Hollis (2019) who predicted familiarity and humour ratings respectively, Abdou et al (2021) who showed that even color relations are accurately represented by purely textual embeddings, as well as Louwerse and Zwaan (2009); Avery et al (2021) who demonstrated that geographical locations of cities are reflected in purely textual embeddings. However, the cognitive plausibility of mechanisms generating word embeddings such as Word2Vec has not gone unchallenged (Mannering and Jones, 2021).…”

Section: Introductionmentioning

confidence: 99%

Language with Vision: a Study on Grounded Word and Sentence Embeddings

Shahmohammadi¹,

Heitmeier²,

Shafaei-Bajestan³

et al. 2022

Preprint

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Language grounding to vision is an active field of research aiming to enrich text-based representations of word meanings by leveraging perceptual knowledge from vision. Despite many attempts at language grounding, it is still unclear how to effectively inject visual knowledge into the word embeddings of a language in such a way that a proper balance of textual and visual knowledge is maintained. Some common concerns are the following. Is visual grounding beneficial for abstract words or is its contribution only limited to concrete words? What is the optimal way of bridging the gap between text and vision? How much do we gain by visually grounding textual embeddings? The present study addresses these questions by proposing a simple yet very effective grounding approach for pre-trained word embeddings. Our model aligns textual embeddings with vision while largely preserving the distributional statistics that characterize word use in text corpora. By applying a learned alignment, we are able to generate visually grounded embeddings for unseen words, including abstract words. A series of evaluations on word similarity benchmarks shows that visual grounding is beneficial not only for concrete words, but also for abstract words. We also show that our method for visual grounding offers advantages for contextualized embeddings, but only when these are trained on corpora of relatively modest size. Code and grounded embeddings for English are available at https://github. com/Hazel1994/Visually_Grounded_Word_Embeddings_2.

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Reconstructing maps from text

Cited by 8 publications

References 32 publications

An investigation of the cognitive and neural correlates of semantic memory search related to creative ability

An investigation of the cognitive and neural correlates of semantic memory search related to creative ability

Decomposing geographical judgments into spatial, temporal and linguistic components

Language with Vision: a Study on Grounded Word and Sentence Embeddings

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