Simon De Deyne
Biographic Data
| ID | 3442341 |
|---|---|
| NAME | Simon De Deyne |
| GIVEN NAMES | Simon |
| FAMILY NAME | De Deyne |
| SIGNATURE | DE DEYNE S |
| AFFILIATIONS | The University of Melbourne |
| ORCID | 0000-0002-7899-6210 |
| VERIFIED | Yes |
| TOTAL WORKS | 9 |
| TOTAL CITATIONS | 10 |
| WORKS AS AUTHOR | 9 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2017 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 1 |
Core Vocabulary Reveals Differences Between Human Word Prediction and Large Language Models
The question of which words are the most central or important to a language has been explored in various ways. In this study, we propose definitions of core vocabulary that are based on how language is learned, represented, and processed from psychological perspectives, and test these on a word prediction task. We aim to (1) compare core vocabulary based on word frequency in natural language, word association network centrality, and age-of-acquis…
Cross-linguistic L1–L2 dis/similarity effect on mental imagery in incremental motion event processing
Despite abundant studies on motion events and mental simulation in first languages (L1s), research on how cross-linguistic dis/similarity – whether an L1 shares constructions with a second language (L2) – affects mental simulation during incremental L2 processing remains limited. This study used a novel self-paced reading task with video verification to investigate L1 influence on mental imagery of the dual (directional/locational) interpretation…
What we mean when we say semantic
Tulving characterized semantic memory as a vast repository of meaning that underlies language and many other cognitive processes. This perspective on lexical and conceptual knowledge galvanized a new era of research undertaken by numerous fields, each with their own idiosyncratic methods and terminology. For example, “concept” has different meanings in philosophy, linguistics, and psychology. As such, many fundamental constructs used to delineate…
Core Vocabulary in Language Representation and Processing
The question of which words are most important or fundamental to a language has been explored in many ways. However, many of these approaches place little emphasis on how humans learn, represent, and process language from a psychological perspective. In this study, we define and compare three distinct psycholinguistic measures of core vocabulary—word frequency, age‐of‐acquisition, and centrality in semantic networks—and test how well these core w…
Compositionality and Sentence Meaning
One of the major outstanding questions in computational semantics is how humans integrate the meaning of individual words into a sentence in a way that enables understanding of complex and novel combinations of words, a phenomenon known as compositionality. Many approaches to modeling the process of compositionality can be classified as either “vector-based” models, in which the meaning of a sentence is represented as a vector of numbers, or “syn…
The Pandemic in Words
Most words have a variety of senses that can be added, removed, or altered over time. Understanding how they change across different contexts and time periods is crucial for revealing the role of language in social and cultural evolution. In this study we aimed to explore the collective changes in the mental lexicon as a consequence of the COVID-19 pandemic. We performed a large-scale word association experiment in Rioplatense Spanish. The data w…
Concept Creep and Psychiatrization
Some aspects of psychiatrization can be understood as forms of concept creep, the progressive expansion of concepts of harm. This article compares the two concepts and explores how concept creep sheds light on psychiatrization. We argue that although psychiatrization is in some respects a broader concept than concept creep, addressing institutional and societal dimensions of the expanding reach of psychiatry in addition to conceptual change, conc…
The “Small World of Words” English word association norms for over 12,000 cue words
Word associations have been used widely in psychology, but the validity of their application strongly depends on the number of cues included in the study and the extent to which they probe all associations known by an individual. In this work, we address both issues by introducing a new English word association dataset. We describe the collection of word associations for over 12,000 cue words, currently the largest such English-language resource …
Quantifying the structure of free association networks across the life span
We investigate how the mental lexicon changes over the life span using free association data from over 8,000 individuals, ranging from 10 to 84 years of age, with more than 400 cue words per age group. Using network analysis, with words as nodes and edges defined by the strength of shared associations, we find that associative networks evolve in a nonlinear (U-shaped) fashion over the life span. During early life, the network converges and become…
Quantifying the structure of free association networks across the life span
We investigate how the mental lexicon changes over the life span using free association data from over 8,000 individuals, ranging from 10 to 84 years of age, with more than 400 cue words per age group. Using network analysis, with words as nodes and edges defined by the strength of shared associations, we find that associative networks evolve in a nonlinear (U-shaped) fashion over the life span. During early life, the network converges and become…
Compositionality and Sentence Meaning
One of the major outstanding questions in computational semantics is how humans integrate the meaning of individual words into a sentence in a way that enables understanding of complex and novel combinations of words, a phenomenon known as compositionality. Many approaches to modeling the process of compositionality can be classified as either “vector-based” models, in which the meaning of a sentence is represented as a vector of numbers, or “syn…
Quantifying the structure of free association networks across the life span
We investigate how the mental lexicon changes over the life span using free association data from over 8,000 individuals, ranging from 10 to 84 years of age, with more than 400 cue words per age group. Using network analysis, with words as nodes and edges defined by the strength of shared associations, we find that associative networks evolve in a nonlinear (U-shaped) fashion over the life span. During early life, the network converges and become…
The “Small World of Words” English word association norms for over 12,000 cue words
Word associations have been used widely in psychology, but the validity of their application strongly depends on the number of cues included in the study and the extent to which they probe all associations known by an individual. In this work, we address both issues by introducing a new English word association dataset. We describe the collection of word associations for over 12,000 cue words, currently the largest such English-language resource …
Concept Creep and Psychiatrization
Some aspects of psychiatrization can be understood as forms of concept creep, the progressive expansion of concepts of harm. This article compares the two concepts and explores how concept creep sheds light on psychiatrization. We argue that although psychiatrization is in some respects a broader concept than concept creep, addressing institutional and societal dimensions of the expanding reach of psychiatry in addition to conceptual change, conc…
The Pandemic in Words
Most words have a variety of senses that can be added, removed, or altered over time. Understanding how they change across different contexts and time periods is crucial for revealing the role of language in social and cultural evolution. In this study we aimed to explore the collective changes in the mental lexicon as a consequence of the COVID-19 pandemic. We performed a large-scale word association experiment in Rioplatense Spanish. The data w…
Compositionality and Sentence Meaning
One of the major outstanding questions in computational semantics is how humans integrate the meaning of individual words into a sentence in a way that enables understanding of complex and novel combinations of words, a phenomenon known as compositionality. Many approaches to modeling the process of compositionality can be classified as either “vector-based” models, in which the meaning of a sentence is represented as a vector of numbers, or “syn…
Cross-linguistic L1–L2 dis/similarity effect on mental imagery in incremental motion event processing
Despite abundant studies on motion events and mental simulation in first languages (L1s), research on how cross-linguistic dis/similarity – whether an L1 shares constructions with a second language (L2) – affects mental simulation during incremental L2 processing remains limited. This study used a novel self-paced reading task with video verification to investigate L1 influence on mental imagery of the dual (directional/locational) interpretation…
What we mean when we say semantic
Tulving characterized semantic memory as a vast repository of meaning that underlies language and many other cognitive processes. This perspective on lexical and conceptual knowledge galvanized a new era of research undertaken by numerous fields, each with their own idiosyncratic methods and terminology. For example, “concept” has different meanings in philosophy, linguistics, and psychology. As such, many fundamental constructs used to delineate…
Core Vocabulary in Language Representation and Processing
The question of which words are most important or fundamental to a language has been explored in many ways. However, many of these approaches place little emphasis on how humans learn, represent, and process language from a psychological perspective. In this study, we define and compare three distinct psycholinguistic measures of core vocabulary—word frequency, age‐of‐acquisition, and centrality in semantic networks—and test how well these core w…
Core Vocabulary Reveals Differences Between Human Word Prediction and Large Language Models
The question of which words are the most central or important to a language has been explored in various ways. In this study, we propose definitions of core vocabulary that are based on how language is learned, represented, and processed from psychological perspectives, and test these on a word prediction task. We aim to (1) compare core vocabulary based on word frequency in natural language, word association network centrality, and age-of-acquis…
Computer Science (6 works) · Psychology (6 works) · Linguistics (4 works) · Natural language processing (4 works) · Semantic similarity (4 works) · Artificial Intelligence (3 works) · Cognition (3 works) · Word Association (3 works) · Action Observation and Synchronization (2 works) · Artificial Intelligence (2 works)