Feature-rich multiplex lexical networks reveal mental strategies of early language learning

Citraro, Salvatore; Vitevitch, Michael S.; Stella, Massimo; Rossetti, Giulio

doi:10.1038/s41598-022-27029-6

Cited by 10 publications

(4 citation statements)

References 68 publications

(144 reference statements)

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“…In addition to the suggestions above for future research, researchers may also consider using feature-rich networks [56], which merges the structural characteristics of networks with vector-based information about individual words (e.g., word frequency, word length, part of speech, etc.). The structural information can be examined independent of the vector-based information, or the two types of information can be combined to reveal patterns that neither approach could reveal by themselves.…”

Section: Plos Onementioning

confidence: 99%

Using network science to examine audio-visual speech perception with a multi-layer graph

Vitevitch,

Lachs

2024

PLoS ONE

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To examine visual speech perception (i.e., lip-reading), we created a multi-layer network (the AV-net) that contained: (1) an auditory layer with nodes representing phonological word-forms and edges connecting words that were phonologically related, and (2) a visual layer with nodes representing the viseme representations of words and edges connecting viseme representations that differed by a single viseme (and additional edges to connect related nodes in the two layers). The results of several computer simulations (in which activation diffused across the network to simulate word identification) are reported and compared to the performance of human participants who identified the same words in a condition in which audio and visual information were both presented (Simulation 1), in an audio-only presentation condition (Simulation 2), and a visual-only presentation condition (Simulation 3). Another simulation (Simulation 4) examined the influence of phonological information on visual speech perception by comparing performance in the multi-layer AV-net to a single-layer network that contained only a visual layer with nodes representing the viseme representations of words and edges connecting viseme representations that differed by a single viseme. We also report the results of several analyses of the errors made by human participants in the visual-only presentation condition. The results of our analyses have implications for future research and training of lip-reading, and for the development of automatic lip-reading devices and software for individuals with certain developmental or acquired disorders or for listeners with normal hearing in noisy conditions.

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Section: Plos Onementioning

confidence: 99%

Using network science to examine audio-visual speech perception with a multi-layer graph

Vitevitch,

Lachs

2024

PLoS ONE

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“…Using the valence labels for the key concepts and associated responses, we enriched the BFMNs, representing them as feature-rich cognitive networks [45] in which information about the sentiment of associative responses could be used to describe the properties of the cue word [27]. As in previous works, we leveraged the notion of a node's neighborhood, consisting of the set of adjacent nodes to a target node: in this case, the neighborhoods of a cue word were the sets of all the associative responses generated by the participants (the language models or humans) responding to the same set of instructions.…”

Section: Network Building and Semantic Frame Reconstructionmentioning

confidence: 99%

Cognitive Network Science Reveals Bias in GPT-3, GPT-3.5 Turbo, and GPT-4 Mirroring Math Anxiety in High-School Students

Abramski

Citraro

Lombardi

et al. 2023

BDCC

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Large Language Models (LLMs) are becoming increasingly integrated into our lives. Hence, it is important to understand the biases present in their outputs in order to avoid perpetuating harmful stereotypes, which originate in our own flawed ways of thinking. This challenge requires developing new benchmarks and methods for quantifying affective and semantic bias, keeping in mind that LLMs act as psycho-social mirrors that reflect the views and tendencies that are prevalent in society. One such tendency that has harmful negative effects is the global phenomenon of anxiety toward math and STEM subjects. In this study, we introduce a novel application of network science and cognitive psychology to understand biases towards math and STEM fields in LLMs from ChatGPT, such as GPT-3, GPT-3.5, and GPT-4. Specifically, we use behavioral forma mentis networks (BFMNs) to understand how these LLMs frame math and STEM disciplines in relation to other concepts. We use data obtained by probing the three LLMs in a language generation task that has previously been applied to humans. Our findings indicate that LLMs have negative perceptions of math and STEM fields, associating math with negative concepts in 6 cases out of 10. We observe significant differences across OpenAI’s models: newer versions (i.e., GPT-4) produce 5× semantically richer, more emotionally polarized perceptions with fewer negative associations compared to older versions and N=159 high-school students. These findings suggest that advances in the architecture of LLMs may lead to increasingly less biased models that could even perhaps someday aid in reducing harmful stereotypes in society rather than perpetuating them.

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“…Using the valence labels for the key concepts and associated responses, we enriched the BFMNs, representing them as feature-rich cognitive networks [15] in which information about the sentiment of associative responses can be used to describe the properties of the cue word [65]. As in previous works, we leveraged the notion of a node's neighborhood, consisting of the set of adjacent nodes to a target node: In this case, the neighborhoods of a cue word are the sets of all the associative responses generated by the participants (the language models or humans) responding to the same set of instructions.…”

Section: Network Building and Semantic Frame Reconstructionmentioning

confidence: 99%

Cognitive network science reveals bias in GPT-3, ChatGPT, and GPT-4 mirroring math anxiety in high-school students

Abramski,

Citraro,

Lombardi

et al. 2023

Preprint

View full text Add to dashboard Cite

Large language models are becoming increasingly integrated into our lives. Hence, it is important to understand the biases present in their outputs in order to avoid perpetuating harmful stereotypes, which originate in our own flawed ways of thinking. This challenge requires developing new benchmarks and methods for quantifying affective and semantic bias, keeping in mind that LLMs act as psycho-social mirrors that reflect the views and tendencies that are prevalent in society. One such tendency that has harmful negative effects is the global phenomenon of anxiety toward math and STEM subjects. Here, we investigate perceptions of math and STEM fields provided by cutting-edge language models, namely GPT-3, Chat-GPT, and GPT-4, by applying an approach from network science and cognitive psychology. Specifically, we use behavioral forma mentis networks (BFMNs) to understand how these LLMs frame math and STEM disciplines in relation to other concepts. We use data obtained by probing the three LLMs in a language generation task that has previously been applied to humans. Our findings indicate that LLMs have an overall negative perception of math and STEM fields, with math being perceived most negatively. We observe significant differences across the three LLMs. We observe that newer versions (i.e. GPT-4) produce richer, more complex perceptions as well as less negative perceptions compared to older versions and N=159 high-school students. These findings suggest that advances in the architecture of LLMs may lead to increasingly less biased models that could even perhaps someday aid in reducing harmful stereotypes in society rather than perpetuating them.

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Feature-rich multiplex lexical networks reveal mental strategies of early language learning

Cited by 10 publications

References 68 publications

Using network science to examine audio-visual speech perception with a multi-layer graph

Using network science to examine audio-visual speech perception with a multi-layer graph

Cognitive Network Science Reveals Bias in GPT-3, GPT-3.5 Turbo, and GPT-4 Mirroring Math Anxiety in High-School Students

Cognitive network science reveals bias in GPT-3, ChatGPT, and GPT-4 mirroring math anxiety in high-school students

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