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Diveica V, Muraki EJ, Binney RJ, Pexman PM. Mapping semantic space: Exploring the higher-order structure of word meaning. Cognition 2024; 248:105794. [PMID: 38653181 DOI: 10.1016/j.cognition.2024.105794] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Revised: 03/27/2024] [Accepted: 04/15/2024] [Indexed: 04/25/2024]
Abstract
Multiple representation theories posit that concepts are represented via a combination of properties derived from sensorimotor, affective, and linguistic experiences. Recently, it has been proposed that information derived from social experience, or socialness, represents another key aspect of conceptual representation. How these various dimensions interact to form a coherent conceptual space has yet to be fully explored. To address this, we capitalized on openly available word property norms for 6339 words and conducted a large-scale investigation into the relationships between 18 dimensions. An exploratory factor analysis reduced the dimensions to six higher-order factors: sub-lexical, distributional, visuotactile, body action, affective and social interaction. All these factors explained unique variance in performance on lexical and semantic tasks, demonstrating that they make important contributions to the representation of word meaning. An important and novel finding was that the socialness dimension clustered with the auditory modality and with mouth and head actions. We suggest this reflects experiential learning from verbal interpersonal interactions. Moreover, formally modelling the network structure of semantic space revealed pairwise partial correlations between most dimensions and highlighted the centrality of the interoception dimension. Altogether, these findings provide new insights into the architecture of conceptual space, including the importance of inner and social experience, and highlight promising avenues for future research.
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Affiliation(s)
- Veronica Diveica
- Cognitive Neuroscience Institute, Department of Psychology, Bangor University, Gwynedd LL57 2AS, UK; Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec H3A 2B4, Canada.
| | - Emiko J Muraki
- Department of Psychology and Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta T2N 1N4, Canada.
| | - Richard J Binney
- Cognitive Neuroscience Institute, Department of Psychology, Bangor University, Gwynedd LL57 2AS, UK.
| | - Penny M Pexman
- Department of Psychology and Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta T2N 1N4, Canada; Department of Psychology, Western University, London, Ontario N6A 5C2, Canada.
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2
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de Zubicaray GI, Arciuli J, Guenther FH, McMahon KL, Kearney E. Non-arbitrary mappings between size and sound of English words: Form typicality effects during lexical access and memory. Q J Exp Psychol (Hove) 2024; 77:943-963. [PMID: 37332149 PMCID: PMC11032636 DOI: 10.1177/17470218231184940] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Revised: 05/04/2023] [Accepted: 06/09/2023] [Indexed: 06/20/2023]
Abstract
A century of research has provided evidence of limited size sound symbolism in English, that is, certain vowels are non-arbitrarily associated with words denoting small versus large referents (e.g., /i/ as in teensy and /ɑ/ as in tall). In the present study, we investigated more extensive statistical regularities between surface form properties of English words and ratings of their semantic size, that is, form typicality, and its impact on language and memory processing. Our findings provide the first evidence of significant word form typicality for semantic size. In five empirical studies using behavioural megastudy data sets of performance on written and auditory lexical decision, reading aloud, semantic decision, and recognition memory tasks, we show that form typicality for size is a stronger and more consistent predictor of lexical access during word comprehension and production than semantic size, in addition to playing a significant role in verbal memory. The empirical results demonstrate that statistical information about non-arbitrary form-size mappings is accessed automatically during language and verbal memory processing, unlike semantic size that is largely dependent on task contexts that explicitly require participants to access size knowledge. We discuss how a priori knowledge about non-arbitrary form-meaning associations in the lexicon might be incorporated in models of language processing that implement Bayesian statistical inference.
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Affiliation(s)
- Greig I de Zubicaray
- School of Psychology and Counselling, Faculty of Health, Queensland University of Technology, Brisbane, QLD, Australia
| | - Joanne Arciuli
- College of Nursing and Health Sciences, Flinders University, Adelaide, SA, Australia
| | - Frank H Guenther
- Department of Speech, Language & Hearing Sciences, Boston University, Boston, MA, USA
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Katie L McMahon
- School of Clinical Sciences, Centre for Biomedical Technologies, Queensland University of Technology, Brisbane, QLD, Australia
- Herston Imaging Research Facility, Royal Brisbane and Women’s Hospital, Herston, QLD, Australia
| | - Elaine Kearney
- School of Psychology and Counselling, Faculty of Health, Queensland University of Technology, Brisbane, QLD, Australia
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3
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Muraki EJ, Pexman PM. Unseen but influential associates: Properties of words' associates influence lexical and semantic processing. Psychon Bull Rev 2024:10.3758/s13423-024-02485-5. [PMID: 38459396 DOI: 10.3758/s13423-024-02485-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/21/2024] [Indexed: 03/10/2024]
Abstract
In many models of lexical and semantic processing, it is assumed that single word processing is a function of the characteristics of the words presented and the distributional properties of the words' networks. Recent research suggests that semantic characteristics of a target word's associates may in fact influence target-word responses in lexical-semantic tasks. The present study extends that previous research to examine whether lexical and semantic properties of target-word associates are recruited during lexical and semantic decision tasks, and whether the type of associate information recruited varies as a function of task and concreteness of the target word. We found that lexical-semantic properties of words' first associates are related to accuracy of responses to words in lexical decision, and that semantic properties of words' first associates are related to both response time and accuracy in semantic decision. Further, these effects differ depending on the target word's concreteness. These findings provide new insight about the way words' associates contribute to semantic representation and processing, even though the associates are not actually presented, moving beyond previous assumptions about lexical-semantic processing of single words.
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Affiliation(s)
- Emiko J Muraki
- Department of Psychology, University of Calgary, 2500 University Drive, Calgary, Alberta, T2N 1N4, Canada.
| | - Penny M Pexman
- Department of Psychology, University of Calgary, 2500 University Drive, Calgary, Alberta, T2N 1N4, Canada
- Department of Psychology, Western University, 1151 Richmond Street, London, Ontario, N6A 3K7, Canada
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4
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Miller J. How Many Participants? How Many Trials? Maximizing the Power of Reaction Time Studies. Behav Res Methods 2024; 56:2398-2421. [PMID: 37537492 PMCID: PMC10991062 DOI: 10.3758/s13428-023-02155-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/29/2023] [Indexed: 08/05/2023]
Abstract
Due to limitations in the resources available for carrying out reaction time (RT) experiments, researchers often have to choose between testing relatively few participants with relatively many trials each or testing relatively many participants with relatively few trials each. To compare the experimental power that would be obtained under each of these options, I simulated virtual experiments using subsets of participants and trials from eight large real RT datasets examining 19 experimental effects. The simulations compared designs using the first N T trials from N P randomly selected participants, holding constant the total number of trials across all participants,N P × N T . The [ N P , N T ] combination maximizing the power to detect each effect depended on how the mean and variability of that effect changed with practice. For most effects, power was greater in designs having many participants with few trials each rather than the reverse, suggesting that researchers should usually try to recruit large numbers of participants for short experimental sessions. In some cases, power for a fixed total number of trials across all participants was maximized by having as few as two trials per participant in each condition. Where researchers can make plausible predictions about how their effects will change over the course of a session, they can use those predictions to increase their experimental power.
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Affiliation(s)
- Jeff Miller
- Department of Psychology, University of Otago, PO Box 56, Dunedin, 9054, New Zealand.
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5
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Petilli MA, Günther F, Marelli M. The Flickr frequency norms: What 17 years of images tagged online tell us about lexical processing. Behav Res Methods 2024; 56:126-147. [PMID: 36509941 DOI: 10.3758/s13428-022-02031-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/19/2022] [Indexed: 12/15/2022]
Abstract
Word frequency is one of the best predictors of language processing. Typically, word frequency norms are entirely based on natural-language text data, thus representing what the literature typically refers to as purely linguistic experience. This study presents Flickr frequency norms as a novel word frequency measure from a domain-specific corpus inherently tied to extra-linguistic information: words used as image tags on social media. To obtain Flickr frequency measures, we exploited the photo-sharing platform Flickr Image (containing billions of photos) and extracted the number of uploaded images tagged with each of the words considered in the lexicon. Here, we systematically examine the peculiarities of Flickr frequency norms and show that Flickr frequency is a hybrid metrics, lying at the intersection between language and visual experience and with specific biases induced by being based on image-focused social media. Moreover, regression analyses indicate that Flickr frequency captures additional information beyond what is already encoded in existing norms of linguistic, sensorimotor, and affective experience. Therefore, these new norms capture aspects of language usage that are missing from traditional frequency measures: a portion of language usage capturing the interplay between language and vision, which - this study demonstrates - has its own impact on word processing. The Flickr frequency norms are openly available on the Open Science Framework (https://osf.io/2zfs3/).
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Affiliation(s)
| | | | - Marco Marelli
- University of Milano-Bicocca, Milan, Italy
- NeuroMI, Milan Center for Neuroscience, Milan, Italy
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6
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Bolognesi MM, Caselli T. Specificity ratings for Italian data. Behav Res Methods 2023; 55:3531-3548. [PMID: 36163541 PMCID: PMC10615975 DOI: 10.3758/s13428-022-01974-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/02/2022] [Indexed: 11/08/2022]
Abstract
Abstraction enables us to categorize experience, learn new information, and form judgments. Language arguably plays a crucial role in abstraction, providing us with words that vary in specificity (e.g., highly generic: tool vs. highly specific: muffler). Yet, human-generated ratings of word specificity are virtually absent. We hereby present a dataset of specificity ratings collected from Italian native speakers on a set of around 1K Italian words, using the Best-Worst Scaling method. Through a series of correlation studies, we show that human-generated specificity ratings have low correlation coefficients with specificity metrics extracted automatically from WordNet, suggesting that WordNet does not reflect the hierarchical relations of category inclusion present in the speakers' minds. Moreover, our ratings show low correlations with concreteness ratings, suggesting that the variables Specificity and Concreteness capture two separate aspects involved in abstraction and that specificity may need to be controlled for when investigating conceptual concreteness. Finally, through a series of regression studies we show that specificity explains a unique amount of variance in decision latencies (lexical decision task), suggesting that this variable has theoretical value. The results are discussed in relation to the concept and investigation of abstraction.
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Affiliation(s)
| | - Tommaso Caselli
- Faculty of Arts, CLCG, University of Groeningen, Groningen, The Netherlands
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7
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Davis CP, Yee E. Is time an embodied property of concepts? PLoS One 2023; 18:e0290997. [PMID: 37669298 PMCID: PMC10479924 DOI: 10.1371/journal.pone.0290997] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 08/18/2023] [Indexed: 09/07/2023] Open
Abstract
A haircut usually lasts under an hour. But how long does it take to recognize that something is an instance of a haircut? And is this "time-to-perceive" a part of the representation of concepts like haircut? Across three experiments testing lexical decision, word recognition, and semantic decision, we show that the amount of time people say it takes to perceive a concept in the world (e.g., haircut, dandelion, or merit) predicts how long it takes for them to respond to a word referring to that thing, over and above the effects of other lexical-semantic variables (e.g., word frequency, concreteness) and other variables related to conceptual complexity (e.g., how confusable a concept is with other, similar concepts, or the diversity of the contexts in which a concept appears). These results suggest that our experience of how long it takes to recognize an instance of a concept can become a part of its representation, and that we simulate this information when reading words. Consequently, we suggest that time may be an embodied property of concepts.
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Affiliation(s)
- Charles P. Davis
- Department of Psychology & Neuroscience, Duke University, Durham, North Carolina, United States of America
- CT Institute for the Brain and Cognitive Sciences, University of Connecticut, Storrs, Connecticut, United States of America
| | - Eiling Yee
- CT Institute for the Brain and Cognitive Sciences, University of Connecticut, Storrs, Connecticut, United States of America
- Department of Psychological Sciences, University of Connecticut, Storrs, Connecticut, United States of America
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8
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Gao C, Shinkareva SV, Desai RH. SCOPE: The South Carolina psycholinguistic metabase. Behav Res Methods 2023; 55:2853-2884. [PMID: 35971041 PMCID: PMC10231664 DOI: 10.3758/s13428-022-01934-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/13/2022] [Indexed: 11/08/2022]
Abstract
The number of databases that provide various measurements of lexical properties for psycholinguistic research has increased rapidly in recent years. The proliferation of lexical variables, and the multitude of associated databases, makes the choice, comparison, and standardization of these variables in psycholinguistic research increasingly difficult. Here, we introduce The South Carolina Psycholinguistic Metabase (SCOPE), which is a metabase (or a meta-database) containing an extensive, curated collection of psycholinguistic variable values from major databases. The metabase currently contains 245 lexical variables, organized into seven major categories: General (e.g., frequency), Orthographic (e.g., bigram frequency), Phonological (e.g., phonological uniqueness point), Orth-Phon (e.g., consistency), Semantic (e.g., concreteness), Morphological (e.g., number of morphemes), and Response variables (e.g., lexical decision latency). We hope that SCOPE will become a valuable resource for researchers in psycholinguistics and affiliated disciplines such as cognitive neuroscience of language, computational linguistics, and communication disorders. The availability and ease of use of the metabase with comprehensive set of variables can facilitate the understanding of the unique contribution of each of the variables to word processing, and that of interactions between variables, as well as new insights and development of improved models and theories of word processing. It can also help standardize practice in psycholinguistics. We demonstrate use of the metabase by measuring relationships between variables in multiple ways and testing their individual contribution towards a number of dependent measures, in the most comprehensive analysis of this kind to date. The metabase is freely available at go.sc.edu/scope.
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Affiliation(s)
- Chuanji Gao
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands
| | - Svetlana V Shinkareva
- Department of Psychology, Institute for Mind and Brain, University of South Carolina, Columbia, SC, 29201, USA.
| | - Rutvik H Desai
- Department of Psychology, Institute for Mind and Brain, University of South Carolina, Columbia, SC, 29201, USA.
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9
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Taylor JE, Rousselet GA, Scheepers C, Sereno SC. Rating norms should be calculated from cumulative link mixed effects models. Behav Res Methods 2023; 55:2175-2196. [PMID: 36103049 PMCID: PMC10439063 DOI: 10.3758/s13428-022-01814-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/16/2022] [Indexed: 11/08/2022]
Abstract
Studies which provide norms of Likert ratings typically report per-item summary statistics. Traditionally, these summary statistics comprise the mean and the standard deviation (SD) of the ratings, and the number of observations. Such summary statistics can preserve the rank order of items, but provide distorted estimates of the relative distances between items because of the ordinal nature of Likert ratings. Inter-item relations in such ordinal scales can be more appropriately modelled by cumulative link mixed effects models (CLMMs). In a series of simulations, and with a reanalysis of an existing rating norms dataset, we show that CLMMs can be used to more accurately norm items, and can provide summary statistics analogous to the traditionally reported means and SDs, but which are disentangled from participants' response biases. CLMMs can be applied to solve important statistical issues that exist for more traditional analyses of rating norms.
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Affiliation(s)
- Jack E Taylor
- School of Psychology and Neuroscience, University of Glasgow, 62 Hillhead Street, Glasgow, G12 8QB, UK.
| | - Guillaume A Rousselet
- School of Psychology and Neuroscience, University of Glasgow, 62 Hillhead Street, Glasgow, G12 8QB, UK
| | - Christoph Scheepers
- School of Psychology and Neuroscience, University of Glasgow, 62 Hillhead Street, Glasgow, G12 8QB, UK
| | - Sara C Sereno
- School of Psychology and Neuroscience, University of Glasgow, 62 Hillhead Street, Glasgow, G12 8QB, UK.
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10
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Brown KS, Yee E, Joergensen G, Troyer M, Saltzman E, Rueckl J, Magnuson JS, McRae K. Investigating the Extent to which Distributional Semantic Models Capture a Broad Range of Semantic Relations. Cogn Sci 2023; 47:e13291. [PMID: 37183557 DOI: 10.1111/cogs.13291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Revised: 03/20/2023] [Accepted: 04/07/2023] [Indexed: 05/16/2023]
Abstract
Distributional semantic models (DSMs) are a primary method for distilling semantic information from corpora. However, a key question remains: What types of semantic relations among words do DSMs detect? Prior work typically has addressed this question using limited human data that are restricted to semantic similarity and/or general semantic relatedness. We tested eight DSMs that are popular in current cognitive and psycholinguistic research (positive pointwise mutual information; global vectors; and three variations each of Skip-gram and continuous bag of words (CBOW) using word, context, and mean embeddings) on a theoretically motivated, rich set of semantic relations involving words from multiple syntactic classes and spanning the abstract-concrete continuum (19 sets of ratings). We found that, overall, the DSMs are best at capturing overall semantic similarity and also can capture verb-noun thematic role relations and noun-noun event-based relations that play important roles in sentence comprehension. Interestingly, Skip-gram and CBOW performed the best in terms of capturing similarity, whereas GloVe dominated the thematic role and event-based relations. We discuss the theoretical and practical implications of our results, make recommendations for users of these models, and demonstrate significant differences in model performance on event-based relations.
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Affiliation(s)
- Kevin S Brown
- Department of Pharmaceutical Sciences, Oregon State University
- School of Chemical, Biological, and Environmental Engineering, Oregon State University
| | - Eiling Yee
- Department of Psychological Sciences, University of Connecticut
| | | | | | | | - Jay Rueckl
- Department of Psychological Sciences, University of Connecticut
| | - James S Magnuson
- Department of Psychological Sciences, University of Connecticut
- BCBL, Basque Center on Cognition, Brain, & Language
- Ikerbasque, Basque Foundation for Science
| | - Ken McRae
- Department of Psychology, University of Western Ontario
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11
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Diveica V, Pexman PM, Binney RJ. Quantifying social semantics: An inclusive definition of socialness and ratings for 8388 English words. Behav Res Methods 2023; 55:461-473. [PMID: 35286618 PMCID: PMC10027635 DOI: 10.3758/s13428-022-01810-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/04/2022] [Indexed: 01/03/2023]
Abstract
It has been proposed that social experience plays an important role in the grounding of concepts, and socialness has been proffered as a fundamental organisational principle underpinning semantic representation in the human brain. However, the empirical support for these hypotheses is limited by inconsistencies in the way socialness has been defined and measured. To further advance theory, the field must establish a clearer working definition, and research efforts could be facilitated by the availability of an extensive set of socialness ratings for individual concepts. Therefore, in the current work, we employed a novel and inclusive definition to test the extent to which socialness is reliably perceived as a broad construct, and we report socialness norms for over 8000 English words, including nouns, verbs, and adjectives. Our inclusive socialness measure shows good reliability and validity, and our analyses suggest that the socialness ratings capture aspects of word meaning which are distinct to those measured by other pertinent semantic constructs, including concreteness and emotional valence. Finally, in a series of regression analyses, we show for the first time that the socialness of a word's meaning explains unique variance in participant performance on lexical tasks. Our dataset of socialness norms has considerable item overlap with those used in both other lexical/semantic norms and in available behavioural mega-studies. They can help target testable predictions about brain and behaviour derived from multiple representation theories and neurobiological accounts of social semantics.
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Affiliation(s)
- Veronica Diveica
- School of Human and Behavioural Sciences, Bangor University, Gwynedd, Wales, LL57 2AS, UK.
| | - Penny M Pexman
- Department of Psychology and Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Richard J Binney
- School of Human and Behavioural Sciences, Bangor University, Gwynedd, Wales, LL57 2AS, UK.
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De Marco D, De Stefani E, Vecchiato G. Embodying Language through Gestures: Residuals of Motor Memories Modulate Motor Cortex Excitability during Abstract Words Comprehension. SENSORS (BASEL, SWITZERLAND) 2022; 22:7734. [PMID: 36298083 PMCID: PMC9610064 DOI: 10.3390/s22207734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Revised: 10/04/2022] [Accepted: 10/10/2022] [Indexed: 06/16/2023]
Abstract
There is a debate about whether abstract semantics could be represented in a motor domain as concrete language. A contextual association with a motor schema (action or gesture) seems crucial to highlighting the motor system involvement. The present study with transcranial magnetic stimulation aimed to assess motor cortex excitability changes during abstract word comprehension after conditioning word reading to a gesture execution with congruent or incongruent meaning. Twelve healthy volunteers were engaged in a lexical-decision task responding to abstract words or meaningless verbal stimuli. Motor cortex (M1) excitability was measured at different after-stimulus intervals (100, 250, or 500 ms) before and after an associative-learning training where the execution of the gesture followed word processing. Results showed a significant post-training decrease in hand motor evoked potentials at an early processing stage (100 ms) in correspondence to words congruent with the gestures presented during the training. We hypothesized that traces of individual semantic memory, combined with training effects, induced M1 inhibition due to the redundancy of evoked motor representation. No modulation of cortical excitability was found for meaningless or incongruent words. We discuss data considering the possible implications in research to understand the neural basis of language development and language rehabilitation protocols.
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Affiliation(s)
- Doriana De Marco
- Istituto di Neuroscienze, Consiglio Nazionale delle Ricerche, 43125 Parma, Italy
- Dipartimento di Medicina e Chirurgia, Università degli Studi di Parma, 43125 Parma, Italy
| | - Elisa De Stefani
- Child and Adolescent Neuropsychiatry-NPIA District of Scandiano, AUSL of Reggio Emilia, 42019 Reggio Emilia, Italy
| | - Giovanni Vecchiato
- Istituto di Neuroscienze, Consiglio Nazionale delle Ricerche, 43125 Parma, Italy
- Dipartimento di Medicina e Chirurgia, Università degli Studi di Parma, 43125 Parma, Italy
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Diachronic semantic change in language is constrained by how people use and learn language. Mem Cognit 2022; 50:1284-1298. [PMID: 35767153 PMCID: PMC9365724 DOI: 10.3758/s13421-022-01331-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/20/2022] [Indexed: 11/24/2022]
Abstract
While it has long been understood that the human mind evolved to learn language, recent studies have begun to ask the inverted question: How has language evolved under the cognitive constraints of its users and become more learnable over time? In this paper, we explored how the semantic change of English words is shaped by the way humans acquire and process language. In Study 1, we quantified the extent of semantic change over the past 200 years and found that meaning change is more likely for words that are acquired later in life and are more difficult to process. We argue that it is human cognition that constrains the semantic evolution of words, rather than the other way around, because historical meanings of words were not easily accessible to people living today, and therefore could not have directly influenced how they learn and process language. In Study 2, we went further to show that semantic change, while bringing the benefit of meeting communicative needs, is cognitively costly for those who were born early enough to experience the change: Semantic change between 1970 and 2000 hindered processing speeds among middle-aged adults (ages 45–55) but not in younger adults (ages <25) in a semantic decision task. This hampering effect may have, in turn, curbed the rate of semantic change so that language does not change too fast for the human mind to catch up. Taken together, our research demonstrates that semantic change is shaped by processing and acquisition patterns across generations of language users.
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Kaestner E, Thesen T, Devinsky O, Doyle W, Carlson C, Halgren E. An Intracranial Electrophysiology Study of Visual Language Encoding: The Contribution of the Precentral Gyrus to Silent Reading. J Cogn Neurosci 2021; 33:2197-2214. [PMID: 34347873 PMCID: PMC8497063 DOI: 10.1162/jocn_a_01764] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Models of reading emphasize that visual (orthographic) processing provides input to phonological as well as lexical-semantic processing. Neurobiological models of reading have mapped these processes to distributed regions across occipital-temporal, temporal-parietal, and frontal cortices. However, the role of the precentral gyrus in these models is ambiguous. Articulatory phonemic representations in the precentral gyrus are obviously involved in reading aloud, but it is unclear if the precentral gyrus is recruited during reading silently in a time window consistent with participation in phonological processing contributions. Here, we recorded intracranial electrophysiology during a speeded semantic decision task from 24 patients to map the spatio-temporal flow of information across the cortex during silent reading. Patients selected animate nouns from a stream of nonanimate words, letter strings, and false-font stimuli. We characterized the distribution and timing of evoked high-gamma power (70-170 Hz) as well as phase-locking between electrodes. The precentral gyrus showed a proportion of electrodes responsive to linguistic stimuli (27%) that was at least as high as those of surrounding peri-sylvian regions. These precentral gyrus electrodes had significantly greater high-gamma power for words compared to both false-font and letter-string stimuli. In a patient with word-selective effects in the fusiform, superior temporal, and precentral gyri, there was significant phase-locking between the fusiform and precentral gyri starting at ∼180 msec and between the precentral and superior temporal gyri starting at ∼220 msec. Finally, our large patient cohort allowed exploratory analyses of the spatio-temporal reading network underlying silent reading. The distribution, timing, and connectivity results place the precentral gyrus as an important hub in the silent reading network.
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Affiliation(s)
| | | | | | - Werner Doyle
- New York University Comprehensive Epilepsy Center
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15
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Beekhuizen B, Armstrong BC, Stevenson S. Probing Lexical Ambiguity: Word Vectors Encode Number and Relatedness of Senses. Cogn Sci 2021; 45:e12943. [PMID: 34018227 DOI: 10.1111/cogs.12943] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2019] [Revised: 10/22/2020] [Accepted: 01/04/2021] [Indexed: 11/29/2022]
Abstract
Lexical ambiguity-the phenomenon of a single word having multiple, distinguishable senses-is pervasive in language. Both the degree of ambiguity of a word (roughly, its number of senses) and the relatedness of those senses have been found to have widespread effects on language acquisition and processing. Recently, distributional approaches to semantics, in which a word's meaning is determined by its contexts, have led to successful research quantifying the degree of ambiguity, but these measures have not distinguished between the ambiguity of words with multiple related senses versus multiple unrelated meanings. In this work, we present the first assessment of whether distributional meaning representations can capture the ambiguity structure of a word, including both the number and relatedness of senses. On a very large sample of English words, we find that some, but not all, distributional semantic representations that we test exhibit detectable differences between sets of monosemes (unambiguous words; N = 964), polysemes (with multiple related senses; N = 4,096), and homonyms (with multiple unrelated senses; N = 355). Our findings begin to answer open questions from earlier work regarding whether distributional semantic representations of words, which successfully capture various semantic relationships, also reflect fine-grained aspects of meaning structure that influence human behavior. Our findings emphasize the importance of measuring whether proposed lexical representations capture such distinctions: In addition to standard benchmarks that test the similarity structure of distributional semantic models, we need to also consider whether they have cognitively plausible ambiguity structure.
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Affiliation(s)
| | - Blair C Armstrong
- Department of Psychology and Department of Language Studies, Basque Center on Cognition, Brain, & Language, University of Toronto, Scarborough
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Recognition times for 62 thousand English words: Data from the English Crowdsourcing Project. Behav Res Methods 2020; 52:741-760. [PMID: 31368025 DOI: 10.3758/s13428-019-01272-8] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
We present a new dataset of English word recognition times for a total of 62 thousand words, called the English Crowdsourcing Project. The data were collected via an internet vocabulary test in which more than one million people participated. The present dataset is limited to native English speakers. Participants were asked to indicate which words they knew. Their response times were registered, although at no point were the participants asked to respond as quickly as possible. Still, the response times correlate around .75 with the response times of the English Lexicon Project for the shared words. Also, the results of virtual experiments indicate that the new response times are a valid addition to the English Lexicon Project. This not only means that we have useful response times for some 35 thousand extra words, but we now also have data on differences in response latencies as a function of education and age.
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Siew CSQ. Feature distinctiveness effects in language acquisition and lexical processing: Insights from megastudies. Cogn Process 2020; 21:669-685. [PMID: 31974763 DOI: 10.1007/s10339-019-00947-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2019] [Accepted: 12/18/2019] [Indexed: 01/08/2023]
Abstract
Semantic features are central to many influential theories of word meaning and semantic memory, but new methods of quantifying the information embedded in feature production norms are needed to advance our understanding of semantic processing and language acquisition. This paper capitalized on databases of semantic feature production norms and age-of-acquisition ratings, and megastudies including the English Lexicon Project and the Calgary Semantic Decision Project, to examine the influence of feature distinctiveness on language acquisition, visual lexical decision, and semantic decision. A feature network of English words was constructed such that edges in the network represented feature distance, or dissimilarity, between words (i.e., Jaccard and Manhattan distances of probability distributions of features elicited for each pair of words), enabling us to quantify the relative feature distinctiveness of individual words relative to other words in the network. Words with greater feature distinctiveness tended to be acquired earlier. Regression analyses of megastudy data revealed that Manhattan feature distinctiveness inhibited performance on the visual lexical decision task, facilitated semantic decision performance for concrete concepts, and inhibited semantic decision performance for abstract concepts. These results demonstrate the importance of considering the structural properties of words embedded in a semantic feature space in order to increase our understanding of semantic processing and language acquisition.
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Affiliation(s)
- Cynthia S Q Siew
- Department of Psychology, National University of Singapore, 9 Arts Link, Block AS4 #02-23, Singapore, 117570, Singapore.
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Mapping semantic space: property norms and semantic richness. Cogn Process 2019; 21:637-649. [PMID: 31552508 DOI: 10.1007/s10339-019-00933-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2019] [Accepted: 09/10/2019] [Indexed: 10/26/2022]
Abstract
In semantic property listing tasks, participants list many features for some concepts and fewer for others. This variability in number of features (NoF) has been used in previous research as a measure of a concept's semantic richness, and such studies have shown that in lexical-semantic tasks responses tend to be facilitated for words with high NoF compared to those for words with low NoF, even when many other relevant factors are controlled (Pexman et al. in Psychon Bull Rev 9:542-549, 2002; Mem Cogn 31:842-855, 2003; Psychon Bull Rev 15:161-167, 2008; Goh et al. in Front Psychol, 2016. https://doi.org/10.3389/fpsyg.2016.00976 ). Furthermore, shared features (i.e., features that are shared by multiple words) appear to facilitate responses in lexical-semantic tasks to a greater degree than distinctive features (Devereux et al. in Cogn Sci 40:325-350, 2016; Grondin et al. in J Mem Lang 60:1-19, 2009). This previous work was limited, however, to relatively small sets of words, typically those extracted from the McRae norms (McRae et al. in Behav Res Methods 37(4):547-559, 2005). New property listing norms provide the opportunity to extract NoF values for many more items (Buchanan et al. in Behav Res Methods 51:1849-1863, 2019). The purpose of the present study was to test whether NoF effects generalize to this larger item set, and to explore how NoF is related to other measures of semantic richness, including subjective ratings of concreteness, imageability, body-object interaction, sensory experience, valence, arousal, and age of acquisition, as well as more objective measures like semantic diversity, number of associates, and lexical centrality. Using the new Buchanan norms, we found significant NoF effects in lexical decision (is it a word or a nonword?) and semantic decision (is it concrete or abstract?) tasks. We also found significant effects of words' number of shared (less distinctive) features in each task. Further, factor analyses of all semantic richness measures showed a distinct factor structure, suggesting that there are clusters of semantic richness dimensions that seem to correspond to more embodied semantic dimensions and more distributional semantic dimensions. Results are interpreted as evidence that semantic representation is multimodal and multidimensional, and provide new insights about the structure of semantic space.
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Reijnierse WG, Burgers C, Bolognesi M, Krennmayr T. How Polysemy Affects Concreteness Ratings: The Case of Metaphor. Cogn Sci 2019; 43:e12779. [PMID: 31446656 PMCID: PMC6771986 DOI: 10.1111/cogs.12779] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2018] [Revised: 06/24/2019] [Accepted: 07/01/2019] [Indexed: 11/26/2022]
Abstract
Concreteness ratings are frequently used in a variety of disciplines to operationalize differences between concrete and abstract words and concepts. However, most ratings studies present items in isolation, thereby overlooking the potential polysemy of words. Consequently, ratings for polysemous words may be conflated, causing a threat to the validity of concreteness-ratings studies. This is particularly relevant to metaphorical words, which typically describe something abstract in terms of something more concrete. To investigate whether perceived concreteness ratings differ for metaphorical versus non-metaphorical word meanings, we obtained concreteness ratings for 96 English nouns from 230 participants. Results show that nouns are perceived as less concrete when a metaphorical (versus non-metaphorical) meaning is triggered. We thus recommend taking metaphoricity into account in future concreteness-ratings studies to further improve the quality and reliability of such studies, as well as the consistency of the empirical studies that rely on these ratings.
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Affiliation(s)
| | | | | | - Tina Krennmayr
- Department of Language, Literature, and CommunicationVrije Universiteit Amsterdam
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Zdrazilova L, Sidhu DM, Pexman PM. Communicating abstract meaning: concepts revealed in words and gestures. Philos Trans R Soc Lond B Biol Sci 2019; 373:rstb.2017.0138. [PMID: 29915006 DOI: 10.1098/rstb.2017.0138] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/02/2017] [Indexed: 11/12/2022] Open
Abstract
words refer to concepts that cannot be directly experienced through our senses (e.g. truth, morality). How we ground the meanings of abstract words is one of the deepest problems in cognitive science today. We investigated this question in an experiment in which 62 participants were asked to communicate the meanings of words (20 abstract nouns, e.g. impulse; 10 concrete nouns, e.g. insect) to a partner without using the words themselves (the taboo task). We analysed the speech and associated gestures that participants used to communicate the meaning of each word in the taboo task. Analysis of verbal and gestural data yielded a number of insights. When communicating about the meanings of abstract words, participants' speech referenced more people and introspections. In contrast, the meanings of concrete words were communicated by referencing more objects and entities. Gesture results showed that when participants spoke about abstract word meanings their speech was accompanied by more metaphorical and beat gestures, and speech about concrete word meanings was accompanied by more iconic gestures. Taken together, the results suggest that abstract meanings are best captured by a model that allows dynamic access to multiple representation systems.This article is part of the theme issue 'Varieties of abstract concepts: development, use and representation in the brain'.
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Affiliation(s)
- Lenka Zdrazilova
- Department of Psychology, Faculty of Arts, University of Calgary, Calgary, Alberta, Canada T2N 1N4
| | - David M Sidhu
- Department of Psychology, Faculty of Arts, University of Calgary, Calgary, Alberta, Canada T2N 1N4
| | - Penny M Pexman
- Department of Psychology, Faculty of Arts, University of Calgary, Calgary, Alberta, Canada T2N 1N4
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Quantifying sensorimotor experience: Body–object interaction ratings for more than 9,000 English words. Behav Res Methods 2018; 51:453-466. [DOI: 10.3758/s13428-018-1171-z] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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Rotaru AS, Vigliocco G, Frank SL. Modeling the Structure and Dynamics of Semantic Processing. Cogn Sci 2018; 42:2890-2917. [PMID: 30294932 PMCID: PMC6585957 DOI: 10.1111/cogs.12690] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2016] [Revised: 09/04/2018] [Accepted: 09/04/2018] [Indexed: 11/29/2022]
Abstract
The contents and structure of semantic memory have been the focus of much recent research, with major advances in the development of distributional models, which use word co-occurrence information as a window into the semantics of language. In parallel, connectionist modeling has extended our knowledge of the processes engaged in semantic activation. However, these two lines of investigation have rarely been brought together. Here, we describe a processing model based on distributional semantics in which activation spreads throughout a semantic network, as dictated by the patterns of semantic similarity between words. We show that the activation profile of the network, measured at various time points, can successfully account for response times in lexical and semantic decision tasks, as well as for subjective concreteness and imageability ratings. We also show that the dynamics of the network is predictive of performance in relational semantic tasks, such as similarity/relatedness rating. Our results indicate that bringing together distributional semantic networks and spreading of activation provides a good fit to both automatic lexical processing (as indexed by lexical and semantic decisions) as well as more deliberate processing (as indexed by ratings), above and beyond what has been reported for previous models that take into account only similarity resulting from network structure.
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Affiliation(s)
- Armand S. Rotaru
- Division of Psychology and Language SciencesUniversity College London
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The “Small World of Words” English word association norms for over 12,000 cue words. Behav Res Methods 2018; 51:987-1006. [DOI: 10.3758/s13428-018-1115-7] [Citation(s) in RCA: 99] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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A comparison of homonym meaning frequency estimates derived from movie and television subtitles, free association, and explicit ratings. Behav Res Methods 2018; 51:1399-1425. [PMID: 30203161 DOI: 10.3758/s13428-018-1107-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Most words are ambiguous, with interpretation dependent on context. Advancing theories of ambiguity resolution is important for any general theory of language processing, and for resolving inconsistencies in observed ambiguity effects across experimental tasks. Focusing on homonyms (words such as bank with unrelated meanings EDGE OF A RIVER vs. FINANCIAL INSTITUTION), the present work advances theories and methods for estimating the relative frequency of their meanings, a factor that shapes observed ambiguity effects. We develop a new method for estimating meaning frequency based on the meaning of a homonym evoked in lines of movie and television subtitles according to human raters. We also replicate and extend a measure of meaning frequency derived from the classification of free associates. We evaluate the internal consistency of these measures, compare them to published estimates based on explicit ratings of each meaning's frequency, and compare each set of norms in predicting performance in lexical and semantic decision mega-studies. All measures have high internal consistency and show agreement, but each is also associated with unique variance, which may be explained by integrating cognitive theories of memory with the demands of different experimental methodologies. To derive frequency estimates, we collected manual classifications of 533 homonyms over 50,000 lines of subtitles, and of 357 homonyms across over 5000 homonym-associate pairs. This database-publicly available at: www.blairarmstrong.net/homonymnorms/ -constitutes a novel resource for computational cognitive modeling and computational linguistics, and we offer suggestions around good practices for its use in training and testing models on labeled data.
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Lupyan G, Winter B. Language is more abstract than you think, or, why aren't languages more iconic? Philos Trans R Soc Lond B Biol Sci 2018; 373:20170137. [PMID: 29915005 PMCID: PMC6015821 DOI: 10.1098/rstb.2017.0137] [Citation(s) in RCA: 51] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/09/2018] [Indexed: 01/29/2023] Open
Abstract
How abstract is language? We show that abstractness pervades every corner of language, going far beyond the usual examples of freedom and justice In the light of the ubiquity of abstract words, the need to understand where abstract meanings come from becomes ever more acute. We argue that the best source of knowledge about abstract meanings may be language itself. We then consider a seemingly unrelated question: Why isn't language more iconic? Iconicity-a resemblance between the form of words and their meanings-can be immensely useful in language learning and communication. Languages could be much more iconic than they currently are. So why aren't they? We suggest that one reason is that iconicity is inimical to abstraction because iconic forms are too connected to specific contexts and sensory depictions. Form-meaning arbitrariness may allow language to better convey abstract meanings.This article is part of the theme issue 'Varieties of abstract concepts: development, use and representation in the brain'.
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Affiliation(s)
- Gary Lupyan
- Department of Psychology, University of Wisconsin, Madison, WI 53706, USA
| | - Bodo Winter
- Department of English Language and Applied Linguistics, University of Birmingham, Birmingham, UK
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Kaestner E, Morgan AM, Snider J, Zhan M, Jiang X, Levy R, Ferreira VS, Thesen T, Halgren E. Toward a Database of Intracranial Electrophysiology during Natural Language Presentation. LANGUAGE, COGNITION AND NEUROSCIENCE 2018; 35:729-738. [PMID: 35528322 PMCID: PMC9074941 DOI: 10.1080/23273798.2018.1500262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/02/2018] [Accepted: 07/05/2018] [Indexed: 06/14/2023]
Abstract
Intracranial electrophysiology (iEEG) studies using cognitive tasks contribute to the understanding of the neural basis of language. However, though iEEG is recorded continuously during clinical treatment, due to patient considerations task time is limited. To increase the usefulness of iEEG recordings for language study, we provided patients with a tablet pre-loaded with media filled with natural language, wirelessly synchronized to clinical iEEG. This iEEG data collected and time-locked to natural language presentation is particularly applicable for studying the neural basis of combining words into larger contexts. We validate this approach with pilot analyses involving words heard during a movie, tagging syntactic properties and verb contextual probabilities. Event-related averages of high-frequency power (70-170Hz) identified bilateral perisylvian electrodes with differential responses to syntactic class and a linear regression identified activity associated with contextual probabilities, demonstrating the usefulness of aligning media to iEEG. We imagine future multi-site collaborations building an 'intracranial neurolinguistic corpus'.
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Affiliation(s)
- Erik Kaestner
- Department of Neurosciences, University of California at San Diego, La Jolla, California
| | - Adam Milton Morgan
- Department of Psychology, University of California at San Diego, La Jolla, California
| | - Joseph Snider
- Institute for Neural Computation, University of California at San Diego, La Jolla, California
| | - Meilin Zhan
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts
| | - Xi Jiang
- Department of Neurosciences, University of California at San Diego, La Jolla, California
| | - Roger Levy
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts
| | - Victor S Ferreira
- Department of Psychology, University of California at San Diego, La Jolla, California
| | - Thomas Thesen
- Department of Neurology, New York University Comprehensive Epilepsy Center, New York, New York
| | - Eric Halgren
- Department of Neurosciences, University of California at San Diego, La Jolla, California
- Department of Radiology, University of California at San Diego, La Jolla, California
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When is best-worst best? A comparison of best-worst scaling, numeric estimation, and rating scales for collection of semantic norms. Behav Res Methods 2018; 50:115-133. [PMID: 29322399 DOI: 10.3758/s13428-017-1009-0] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Large-scale semantic norms have become both prevalent and influential in recent psycholinguistic research. However, little attention has been directed towards understanding the methodological best practices of such norm collection efforts. We compared the quality of semantic norms obtained through rating scales, numeric estimation, and a less commonly used judgment format called best-worst scaling. We found that best-worst scaling usually produces norms with higher predictive validities than other response formats, and does so requiring less data to be collected overall. We also found evidence that the various response formats may be producing qualitatively, rather than just quantitatively, different data. This raises the issue of potential response format bias, which has not been addressed by previous efforts to collect semantic norms, likely because of previous reliance on a single type of response format for a single type of semantic judgment. We have made available software for creating best-worst stimuli and scoring best-worst data. We also made available new norms for age of acquisition, valence, arousal, and concreteness collected using best-worst scaling. These norms include entries for 1,040 words, of which 1,034 are also contained in the ANEW norms (Bradley & Lang, Affective norms for English words (ANEW): Instruction manual and affective ratings (pp. 1-45). Technical report C-1, the center for research in psychophysiology, University of Florida, 1999).
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Scoring best-worst data in unbalanced many-item designs, with applications to crowdsourcing semantic judgments. Behav Res Methods 2017; 50:711-729. [DOI: 10.3758/s13428-017-0898-2] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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31
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Yap MJ, Pexman PM. Semantic Richness Effects in Syntactic Classification: The Role of Feedback. Front Psychol 2016; 7:1394. [PMID: 27695431 PMCID: PMC5023677 DOI: 10.3389/fpsyg.2016.01394] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2016] [Accepted: 08/31/2016] [Indexed: 11/29/2022] Open
Abstract
Words with richer semantic representations are recognized faster across a range of lexical processing tasks. The most influential account of this finding is based on the idea that semantic richness effects are mediated by feedback from semantic-level to lower-level representations. In an earlier lexical decision study, Yap et al. (2015) tested this claim by examining the joint effects of stimulus quality and four semantic richness dimensions (imageability, number of features, semantic neighborhood density, semantic diversity). The results of that study showed that joint effects of stimulus quality and richness were generally additive, consistent with the idea that semantic feedback does not typically reach the earliest levels of representation in lexical decision. The present study extends this earlier work by investigating the joint effects of stimulus quality and the same four semantic richness dimensions on syntactic classification performance (is this a noun or verb?), which places relatively more emphasis on semantic processing. Additive effects of stimulus quality and richness were found for two of the four targeted dimensions (concreteness, number of features) while semantic neighborhood density and semantic diversity did not seem to influence syntactic classification response times. These findings provide further support against the view that semantic information reaches early letter-level processes.
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Affiliation(s)
- Melvin J. Yap
- Department of Psychology, National University of SingaporeSingapore, Singapore
| | - Penny M. Pexman
- Department of Psychology, University of CalgaryCalgary, AB, Canada
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Goh WD, Yap MJ, Lau MC, Ng MMR, Tan LC. Semantic Richness Effects in Spoken Word Recognition: A Lexical Decision and Semantic Categorization Megastudy. Front Psychol 2016; 7:976. [PMID: 27445936 PMCID: PMC4923159 DOI: 10.3389/fpsyg.2016.00976] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2016] [Accepted: 06/13/2016] [Indexed: 11/30/2022] Open
Abstract
A large number of studies have demonstrated that semantic richness dimensions [e.g., number of features, semantic neighborhood density, semantic diversity , concreteness, emotional valence] influence word recognition processes. Some of these richness effects appear to be task-general, while others have been found to vary across tasks. Importantly, almost all of these findings have been found in the visual word recognition literature. To address this gap, we examined the extent to which these semantic richness effects are also found in spoken word recognition, using a megastudy approach that allows for an examination of the relative contribution of the various semantic properties to performance in two tasks: lexical decision, and semantic categorization. The results show that concreteness, valence, and number of features accounted for unique variance in latencies across both tasks in a similar direction—faster responses for spoken words that were concrete, emotionally valenced, and with a high number of features—while arousal, semantic neighborhood density, and semantic diversity did not influence latencies. Implications for spoken word recognition processes are discussed.
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Affiliation(s)
- Winston D Goh
- Department of Psychology, National University of Singapore Singapore, Singapore
| | - Melvin J Yap
- Department of Psychology, National University of Singapore Singapore, Singapore
| | - Mabel C Lau
- Department of Psychology, National University of Singapore Singapore, Singapore
| | - Melvin M R Ng
- Department of Psychology, National University of Singapore Singapore, Singapore
| | - Luuan-Chin Tan
- Department of Psychology, National University of Singapore Singapore, Singapore
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