Revealing Criterial Vagueness in Inconsistencies

Verheyen, Steven; White, Anne; Égré, Paul

doi:10.1162/opmi_a_00025

Cited by 5 publications

(7 citation statements)

References 50 publications

(64 reference statements)

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“…Verheyen et al. (2019) suggested that the inter‐individual differences relate to stable differences between groups that apply different categorization criteria. For example, the level of education (Verheyen & Storms, 2018) or individual traits (Verheyen, Dewil, & Égré, 2018) affect the choice of category criteria.…”

Section: Discussionmentioning

confidence: 99%

“…Following the literature on vagueness and categorization, we suggest that these two types of vagueness could be interpreted as criterial and degree vagueness (Verheyen, Droeshout, & Storms, 2019). Criterial vagueness is a disagreement between participants about the criteria or conditions to classify a given object into a given category (Verheyen et al., 2019). Degree vagueness, in turn, means that individuals agree about the classification criterion but disagree to what extent the given object satisfies this criterion (Verheyen et al., 2019).…”

Section: Discussionmentioning

confidence: 99%

“…Criterial vagueness is a disagreement between participants about the criteria or conditions to classify a given object into a given category (Verheyen et al., 2019). Degree vagueness, in turn, means that individuals agree about the classification criterion but disagree to what extent the given object satisfies this criterion (Verheyen et al., 2019). For example, one can believe that an abstract painting cannot be considered a piece of art because art should imitate reality (criterial vagueness).…”

Section: Discussionmentioning

confidence: 99%

“…Previous studies in the domain of categorization (e.g., Verheyen, White, & Égré, 2019) have shown that participants change categorization criteria over time. Experiment 2 intends to investigate if similar effects can be observed for functional words such as quantifiers.…”

Section: Introductionmentioning

confidence: 98%

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Uncovering the Structure of Semantic Representations Using a Computational Model of Decision‐Making

et al. 2023

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According to logical theories of meaning, a meaning of an expression can be formalized and encoded in truth conditions. Vagueness of the language and individual differences between people are a challenge to incorporate into the meaning representations. In this paper, we propose a new approach to study truth‐conditional representations of vague concepts. For a case study, we selected two natural language quantifiers most and more than half. We conducted two online experiments, each with 90 native English speakers. In the first experiment, we tested between‐subjects variability in meaning representations. In the second experiment, we tested the stability of meaning representations over time by testing the same group of participants in two experimental sessions. In both experiments, participants performed the verification task. They verified a sentence with a quantifier (e.g., “Most of the gleerbs are feezda.”) based on the numerical information provided in the second sentence, (e.g., “60% of the gleerbs are feezda”). To investigate between‐subject and within‐subject differences in meaning representations, we proposed an extended version of the Diffusion Decision Model with two parameters capturing truth conditions and vagueness. We fit the model to responses and reaction times data. In the first experiment, we found substantial between‐subject differences in representations of most as reflected by the variability in the truth conditions. Moreover, we found that the verification of most is proportion‐dependent as reflected in the reaction time effect and model parameter. In the second experiment, we showed that quantifier representations are stable over time as reflected in stable model parameters across two experimental sessions. These findings challenge semantic theories that assume the truth‐conditional equivalence of most and more than half and contribute to the representational theory of vague concepts. The current study presents a promising approach to study semantic representations, which can have a wide application in experimental linguistics.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

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Uncovering the Structure of Semantic Representations Using a Computational Model of Decision‐Making

et al. 2023

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“…They constituted approximately equidistant color patches between a blue and a green prototype. As a result, any interindividual differences in the stimuli's conceptual representation could only be a matter of degree (i.e., the extent to which different color patches are considered instances of blue) and not a matter of criteria, where different participants consider different dimensions as relevant or weigh several underlying dimensions differently to establish category membership (Verheyen, Droeshout, & Storms, 2019; Verheyen & Storms, 2018; Verheyen, White, & Égré, 2019). Moving to more abstract, multidimensional stimuli, as suggested earlier, will allow one to study whether degree and/or criteria differences in conceptual representations affect generalization.…”

Section: Discussionmentioning

confidence: 99%

The idiosyncratic nature of how individuals perceive, represent, and remember their surroundings and its impact on learning-based generalization.

Zaman¹,

Verheyen³

2023

Journal of Experimental Psychology: General

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The current study adopted a multimodal assessment approach to map the idiosyncratic nature of how individuals perceive, represent, and remember their surroundings and to investigate its impact on learning-based generalization. During an online differential conditioning paradigm, participants (n = 105) learned the pairing between a blue color patch (CS+) and an outcome (i.e., shock symbol) and the unpairing between a green color patch and the same outcome. After the learning task, the generalization of outcome expectancies was assessed to 14 stimuli spanning the entire blue-green color spectrum. Hereafter, a stimulus identification task assessed the ability to correctly identify the CS+ among this stimulus range. Continuous and binary color category membership judgments of the stimuli were assessed preconditioning. We found that a response model with color perception and identification performance as sole predictors was preferred to contemporary approaches that use stimulus as a predictor. Interestingly, incorporating interindividual differences in color perception, CS identification, and color categories significantly improved the models’ ability to account for different generalization patterns. Our findings suggest that insight into the idiosyncratic nature of how individuals perceive, represent, and remember their surroundings provides exciting opportunities to understand post-learning behaviors better.

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A Comparison of the Spatial Arrangement Method and the Total-Set Pairwise Rating Method for Obtaining Similarity Data in the Conceptual Domain

Verheyen

White

Storms

2020

Multivariate Behavioral Research

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Revealing Criterial Vagueness in Inconsistencies

Cited by 5 publications

References 50 publications

Uncovering the Structure of Semantic Representations Using a Computational Model of Decision‐Making

Uncovering the Structure of Semantic Representations Using a Computational Model of Decision‐Making

The idiosyncratic nature of how individuals perceive, represent, and remember their surroundings and its impact on learning-based generalization.

A Comparison of the Spatial Arrangement Method and the Total-Set Pairwise Rating Method for Obtaining Similarity Data in the Conceptual Domain

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