Category norm data and relationships with lexical frequency and typicality within verb semantic categories

Abstract

The aim of the present study was to expand the scope of category norm and typicality data to include verbs for use when investigating semantic memory in fields such as linguistics, psychology, and aphasiology. Two experiments were conducted. In the first, participants were asked to list verbs within 10 semantic categories (e.g. breaking, cleaning, cooking, etc.) and 10 noun categories (e.g. animals, fruit, tools, etc.). In the second experiment, participants were asked to rate the typicality of verbs within 8 of the previously investigated verb categories. Although participants listed fewer verbs in verb categories than nouns in noun categories, the overall patterns with regard to correlation analyses between production frequency, mean rank of responses, lexical frequency, and typicality were consistent with those observed in noun categories. These patterns are also consistent with those observed in previous research. Potential similarities and differences between nouns and verbs, as well as future applications of such data, are discussed.

Modelling verb selection within argument structure constructions

“…Yet, the order of verb listing must also be taken into account. For example, the verb position in a produced list has been shown to correlate with the frequency of production of this verb in a category-listing task (Plant, Webster, & Whitworth, 2011). Similarly, studies on sentence production show that, all things being equal, the more accessible (prototypical, frequent) word in a word pair tends to be placed earlier in a sentence than the less accessible one (e.g.…”

“…It is common in cognitive science to estimate the typicality of concepts within a semantic category using so-called category normsranked lists of items based on human production data (e.g. Kelly, Bock, & Keil, 1986;Plant et al, 2011). EOR, however, do not use this approach, as it would lead to circular reasoning: prototypicality is used to predict the production data, and thus can not be computed based on other production data.…”

Serial Recall Order and Semantic Features of Category Fluency Words to Study Semantic Memory in Normal Ageing

“…Alternative methodologies have been studied to overcome the multi-componential element that characterises the construct validity of standard CFT scoring, to obtain “purer” measures of SM. A large number of studies have investigated the semantic properties of words generated during performance on CFTs, such as “age of acquisition,” “typicality,” and “frequency,” i.e., “item-level features” ( Forbes-McKay et al, 2005 ; Biundo et al, 2011 ; Venneri et al, 2011 ; Vita et al, 2014 ; Quaranta et al, 2016 ; Wakefield et al, 2018 ; Vonk et al, 2019a , b ; Taler et al, 2020 ), under the assumption that the ability to generate less frequent, less typical and later acquired words would reflect efficient semantic processing ( Murray and Forster, 2004 ; Steyvers and Tenenbaum, 2005 ; Plant et al, 2011 ). Other studies have focussed on the semantic relationships between words (e.g., Goñi et al, 2011 ; Pakhomov et al, 2012 ; Bertola et al, 2014 ; Quaranta et al, 2019 ), on the assumption that the sequence of words could be indicative of the integrity of the underlying semantic-processing system.…”

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“…English words (N = 24) were selected from semantic categories as defined by the relevant word class norms (Francis &Kucera, 1982, andPlanter, Webster, &Whitworth, 2011) to represent the three different word types to be examined in this study (nouns, verbs, adjectival emotive). There were eight concrete nouns (e.g., duck), eight verbs in the infinitive form (e.g., to build), and eight adjectival emotive words (e.g., lonely); these word types were matched on word length and frequency in English (Francis & Kucera, 1982) and translated forms of these words (in German) were a maximum of one syllable shorter or longer than the equivalent word in English.…”

“…The concrete nouns were all members of the same semantic categoryanimalsand were selected using Francis and Kucera's (1982) frequency analysis of English usage (mean frequency = 9.13) 1 . The verbs were also members of the same semantic category 'making' (Plant, Webster, & Whitworth, 2011) and had a mean frequency of English usage (Francis & Kucera, 1982) of 8. The emotive words also had a similar mean frequency of English usage (8.38, Francis & Kucera, 1982) and, according to the Bradley and Lang (1999) norms, were negatively valenced (M = 2.39 on a 9-point scale, mean SD = 1.47) and moderate in arousal (M = 5.01 on a 9-point scale, mean SD = 2.43).…”