Cover not available
Article published In:
The Mental Lexicon
Vol. 17:1 (2022) ► pp.1–33

Is it you you’re looking for?

Personal relevance as a principal component of semantics

| University of Alberta,
| Gonzaga University,
Previous evidence has implicated personal relevance as a predictive factor in lexical access. Westbury (2014) showed that personally relevant words were rated as having a higher subjective familiarity than words that were not personally relevant, suggesting that personally relevant words are processed more fluently than less personally relevant words. Here we extend this work by defining a measure of personal relevance that does not rely on human judgments but is rather derived from first-order co-occurrence of words with the first-person singular personal pronoun, I. We show that words estimated as most personally relevant are recognized more quickly, named faster, judged as more familiar, and used by infants earlier than words that are less personally relevant. Self-relevance is also a strong predictor of several measures that are usually measured only by human judgments or their computational estimates, such as subjective familiarity, age of acquisition, imageability, concreteness, and body-object interaction. We have made all self-relevance estimates (as well as the raw data and code from our experiments) available at https://​osf​.io​/gdb6h/.
Keywords: self-relevance, semantics, word2vec, co-occurrence, word processing, age of acquisition, subjective familiarity
Article outline
  • Study 1.Psychometric properties of a measure of personal relevance
  • Study 2.Lexical Decision and Word Naming
    • Background
    • Discussion
  • Method
    • Stimuli
    • Participants
    • Results: Lexical decision RT
    • Results: Lexical decision error rates
      • Discussion: LD
    • Results: Familiarity decision
      • Discussion: Familiarity
    • Conclusion
  • References
Available under the Creative Commons Attribution-NonCommercial (CC BY-NC) 4.0 license.

For any use beyond this license, please contact the publisher at [email protected].

Published online: 18 March 2022
DOI logo
https://doi.org/10.1075/ml.20031.wes
References (45)
References
Akaike, H. (1973). Information theory and an extension of the maximum likelihood principle. In B. N. Petrov & F. Caski. (Eds.), Proceedings of the Second International Symposium on Information Theory (pp. 267–281). Budapest: Akademiai Kiado.Google Scholar
(1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19 1, 716–723. DOI logoGoogle Scholar
Alexopoulos, T., Muller, D., Ric, F., & Marendaz, C. (2012). I, me, mine: Automatic attentional capture by self-related stimuli. European Journal of Social Psychology, 421, 770–779. DOI logoGoogle Scholar
Baayen, R. H. (2010). Demythologizing the word frequency effect: A discriminative learning perspective. The Mental Lexicon, 5 (3), 436–461. DOI logoGoogle Scholar
Baayen, R. H., Feldman, L. B., & Schreuder, R. (2006). Morphological influences on the recognition of monosyllabic monomorphemic words. Journal of Memory and Language, 55 (2), 290–313. DOI logoGoogle Scholar
Balota, D. A., Pilotti, M., & Cortese, M. J. (2001). Subjective frequency estimates for 2,938 monosyllabic words. Memory & Cognition, 29(4), 639–647. DOI logoGoogle Scholar
Balota, D. A., Yap, M. J., Hutchison, K. A., Cortese, M. J., Kessler, B., Loftis, B., Neely, J. H., Nelson, D. L., Simpson, G. B. & Treiman, R. (2007). The English Lexicon Project. Behavior Research Methods, 39 (3), 445–459. DOI logoGoogle Scholar
Bird, H., Franklin, S., and Howard, D. (2001). Age of acquisition and imageability ratings for a large set of words, including verbs and function words. Behavior Research Methods, Instruments, & Computers, 331, 73–79. DOI logoGoogle Scholar
Bonin, P., Peereman, R., Malardier, N., Méot, A., & Chalard, M. (2003). A new set of 299 pictures for psycholinguistic studies: French norms for name agreement, image agreement, conceptual familiarity, visual complexity, image variability, age of acquisition, and naming latencies. Behavior Research Methods, Instruments, & Computers, 35(1), 158–167. DOI logoGoogle Scholar
Brown, G. D., & Watson, F. L. (1987). First in, first out: Word learning age and spoken word frequency as predictors of word familiarity and word naming latency. Memory & cognition, 15(3), 208–216. DOI logoGoogle Scholar
Cortese, M. J., & Khanna, M. M. (2008). Age of acquisition ratings for 3,000 monosyllabic words. Behavior Research Methods, 40(3), 791–794. DOI logoGoogle Scholar
Davies, M. (2010). The Corpus of Contemporary American English as the first reliable monitor corpus of English. Literary and Linguistic computing, 25 (4), 447–464. DOI logoGoogle Scholar
Ferrand, L., Bonin, P., Méot, A., Augustinova, M., New, B., Pallier, C., & Brysbaert, M. (2008). Age-of-acquisition and subjective frequency estimates for all generally known monosyllabic French words and their relation with other psycholinguistic variables. Behavior Research Methods, 40(4), 1049–1054. DOI logoGoogle Scholar
Ferrand, L., Grainger, J., & New, B. (2003). Normes d’âge d’acquisition pour 400 mots monosyllabiques [Age-of-acquisition norms for 400 monosyllabic French words]. L’Année Psychologique, 1031, 445–467. DOI logoGoogle Scholar
Flieller, A., & Tournois, J. (1994). Imagery value, subjective and objective frequency, date of entry into the language, and degree of polysemy in a sample of 998 French words. International Journal of Psychology, 29(4), 471–509. DOI logoGoogle Scholar
Frings, C., & Wentura, D. (2014). Self-priorization processes in action and perception. Journal of Experimental Psychology: Human Perception and Performance, 40 (5), 1737.Google Scholar
Gernsbacher, M.A. (1984). Resolving 20 years of inconsistent interactions between lexical familiarity and orthography, concreteness, and polysemy. Journal of Experimental Psychology: General, 113(2), 256. DOI logoGoogle Scholar
Ghyselinck, M., De Moor, W., & Brysbaert, M. (2000). Age-of-acquisition ratings for 2816 Dutch four-and five-letter nouns. Psychologica Belgica, 40(2), 77–98. DOI logoGoogle Scholar
Gilhooly, K.J., and Logie, R. H. (1980). Age-of-acquisition, imagery, concreteness, familiarity, and ambiguity measures for 1,944 words. Behavior Research, Methods Instruments, and Computers, 121, 395–427. DOI logoGoogle Scholar
Golubickis, M., Falben, J. K., Cunningham, W. A., & Macrae, C. N. (2018). Exploring the self-ownership effect: Separating stimulus and response biases. Journal of Experimental Psychology: Learning, Memory, and Cognition, 44 (2), 295.Google Scholar
Hart, B. (1991). Input frequency and children’s first words. First Language, 11 (32), 289–300. DOI logoGoogle Scholar
Hempel, C. G., & Oppenheim, P. (1948). Studies in the Logic of Explanation. Philosophy of Science, 15 (2), 135–175. DOI logoGoogle Scholar
Hollis, G. (2020). Delineating linguistic contexts, and the validity of context diversity as a measure of a word’s contextual variability. Journal of Memory and Language, 114 1, 104–146. DOI logoGoogle Scholar
Hollis, G., & Westbury, C. (2016). The principals of meaning: Extracting semantic dimensions from co-occurrence models of semantics. Psychonomic Bulletin & Review, 23 (6), 1744–1756. DOI logoGoogle Scholar
Hollis, G., Westbury, C., & Lefsrud, L. (2017). Extrapolating human judgments from skip-gram vector representations of word meaning. The Quarterly Journal of Experimental Psychology, 70 (8), 1603–1619. DOI logoGoogle Scholar
Keuleers, E., Lacey, P., Rastle, K., & Brysbaert, M. (2012). The British Lexicon Project: Lexical decision data for 28,730 monosyllabic and disyllabic English words. Behavior Research Methods, 44 ( 1 ), 287–304. DOI logoGoogle Scholar
Kuperman, V., Stadthagen-Gonzalez, H., & Brysbaert, M. (2012). Age-of-acquisition ratings for 30,000 English words. Behavior Research Methods, 44 (4), 978–990. DOI logoGoogle Scholar
Leech, G., Rayson, P. & Wilson, A. (2001). Companion website for: Word Frequencies in Written and Spoken English: based on the British National Corpus. [URL]
Marques, J. F., Fonseca, F. L., Morais, S., & Pinto, I. A. (2007). Estimated age of acquisition norms for 834 Portuguese nouns and their relation with other psycholinguistic variables. Behavior Research Methods, 39(3), 439–444. DOI logoGoogle Scholar
Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781.Google Scholar
Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., & Dean, J. (2013). Distributed representations of words and phrases and their compositionality. In Advances in Neural Information Processing Systems (Neural Information Processing Systems Conference, 2013); pp. 3111–3119).Google Scholar
Northoff, G., Heinzel, A., De Greck, M., Bermpohl, F., Dobrowolny, H., & Panksepp, J. (2006). Self-referential processing in our brain – a meta-analysis of imaging studies on the self. Neuroimage, 31 (1), 440–457. DOI logoGoogle Scholar
Schäfer, S., Wentura, D., & Frings, C. (2015). Self-prioritization beyond perception. Experimental Psychology. DOI logoGoogle Scholar
Schäfer, S., Frings, C., & Wentura, D. (2016). About the composition of self-relevance: Conjunctions not features are bound to the self. Psychonomic Bulletin & Review, 23 (3), 887–892. DOI logoGoogle Scholar
Shaoul, C. & Westbury, C. (2006). USENET Orthographic Frequencies for 111,627 English Words. (2005–2006) Edmonton, AB: University of Alberta (downloaded from [URL]
Schmitz, T. W., & Johnson, S. C. (2007). Relevance to self: A brief review and framework of neural systems underlying appraisal. Neuroscience & Biobehavioral Reviews, 31 (4), 585–596. DOI logoGoogle Scholar
Stadthagen-Gonzalez, H., & Davis, C. J. (2006). The Bristol norms for age of acquisition, imageability, and familiarity. Behavior Research Methods, 38 1, 598–605. DOI logoGoogle Scholar
Sui, J., He, X., & Humphreys, G. W. (2012). Perceptual effects of social salience: Evidence from self-prioritization effects on perceptual matching. Journal of Experimental Psychology: Human Perception and Performance, 381, 1105–1117. DOI logoGoogle Scholar
Symons, C. S., & Johnson, B. T. (1997). The self-reference effect in memory: a meta-analysis. Psychological Bulletin, 121 (3), 371. DOI logoGoogle Scholar
Tillotson, S. M., Siakaluk, P. D., & Pexman, P. M. (2008). Body-object interaction ratings for 1,618 monosyllabic nouns. Behavior Research Methods, 40 (4), 1075–1078. DOI logoGoogle Scholar
Westbury, C. (2014). You can’t drink a word: Lexical and individual emotionality affect subjective familiarity judgments. Journal of Psycholinguistic Research, 43 (5), 631–649. DOI logoGoogle Scholar
(2016). Pay no attention to that man behind the curtain: Explaining semantics without semantics. The Mental Lexicon, 11.3, 350–374. DOI logoGoogle Scholar
Westbury, C., & Hollis, G. (2018). Conceptualizing syntactic categories as semantic categories: Unifying part-of-speech identification and semantics using co-occurrence vector averaging. Behavior Research Methods, 1–28.Google Scholar
Westbury, C., Hollis, G., Sidhu, D. M., & Pexman, P. M. (2018). Weighing up the evidence for sound symbolism: Distributional properties predict cue strength. Journal of Memory and Language, 99 1, 122–150. DOI logoGoogle Scholar
Westbury, C., & Nicoladis, E. (1998). Meaning in children’s first words: Implications for a theory of lexical ontology. In Proceedings of the 22nd Annual Boston University Conference on Language Development (pp. 768–778). Cascadilla Press: Somerville, MA.Google Scholar
Cited by (5)

Cited by five other publications

Kuiken, Don
2024. The Epistemic Limits of Impactful Dreams: Metacognition, Metaphoricity, and Sublime Feeling. Brain Sciences 14:6 ► pp. 528 ff. DOI logo
Villani, Caterina, Adele Loia & Marianna M. Bolognesi
2024. The semantic content of concrete, abstract, specific, and generic concepts. Language and Cognition ► pp. 1 ff. DOI logo
Heitmeier, Maria, Yu-Ying Chuang & R. Harald Baayen
2023. How trial-to-trial learning shapes mappings in the mental lexicon: Modelling lexical decision with linear discriminative learning. Cognitive Psychology 146 ► pp. 101598 ff. DOI logo
Westbury, Chris
2023. Why are human animacy judgments continuous rather than categorical? A computational modeling approach. Frontiers in Psychology 14 DOI logo
Baayen, R. Harald, Dunstan Brown & Yu-Ying Chuang
2022. Explorations of morphological structure in distributional space. The Mental Lexicon 17:3 ► pp. 326 ff. DOI logo

This list is based on CrossRef data as of 5 july 2024. Please note that it may not be complete. Sources presented here have been supplied by the respective publishers. Any errors therein should be reported to them.