Age-Related Degree and Criteria Differences in Semantic Categorization

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

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.

Structural differences in the semantic networks of younger and older adults

Cognitive science invokes semantic networks to explain diverse phenomena, from memory retrieval to creativity. Research in these areas often assumes a single underlying semantic network that is shared across individuals. Yet, recent evidence suggests that content, size, and connectivity of semantic networks are experience-dependent, implying sizable individual and age-related differences. Here, we investigate individual and age differences in the semantic networks of younger and older adults by deriving semantic networks from both fluency and similarity rating tasks. Crucially, we use a megastudy approach to obtain thousands of similarity ratings per individual to allow us to capture the characteristics of individual semantic networks. We find that older adults possess lexical networks with smaller average degree and longer path lengths relative to those of younger adults, with older adults showing less interindividual agreement and thus more unique lexical representations relative to younger adults. Furthermore, this approach shows that individual and age differences are not evenly distributed but, rather, are related to weakly connected, peripheral parts of the networks. All in all, these results reveal the interindividual differences in both the content and the structure of semantic networks that may accumulate across the life span as a function of idiosyncratic experiences.

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

We compare two methods for obtaining similarity data in the conceptual domain. In the Spatial Arrangement Method (SpAM), participants organize stimuli on a computer screen so that the distance between stimuli represents their perceived dissimilarity. In Total-Set Pairwise Rating Method (PRaM), participants rate the (dis)similarity of all pairs of stimuli on a Likert scale. In each of three studies, we had participants indicate the similarity of four sets of conceptual stimuli with either PRaM or SpAM. Studies 1 and 2 confirm two caveats that have been raised for SpAM. (i) While SpAM takes significantly less time to complete than PRaM, it yields less reliable data than PRaM does. (ii) Because of the spatial manner in which similarity is measured in SpAM, the method is biased against feature representations. Despite these differences, averaging SpAM and PRaM dissimilarity data across participants yields comparable aggregate data. Study 3 shows that by having participants only judge half of the pairs in PRaM, its duration can be significantly reduced, without affecting the dissimilarity distribution, but at the cost of a smaller reliability. Having participants arrange multiple subsets of the stimuli does not do away with the spatial bias of SpAM.

New Spanish semantic feature production norms for older adults

Semantic feature production norms are a useful tool for researchers to have empirically collected data about the semantic representations of a particular population. As older adults have been shown to have certain differences in their semantic knowledge organization in comparison with younger adults, it is relevant for them to have their own normative data. Thus we present here the first Spanish semantic feature production norms for older adults. They contain information about the feature composition of 400 concrete concepts. We also provide information about some feature and concept variables as well as comparisons between young and old adults on these variables.

Category norms with a cross-sectional sample of adults in the United States: Consideration of cohort, age, and historical effects on semantic categories

This paper describes normative data for newly collected exemplar responses to 70 semantic categories described in previous norming studies (Battig & Montague, Journal of Experimental Psychology, 80(3, pt.2): 1-46, 1969; Van Overschelde, Rawson, & Dunlosky, Journal of Memory and Language, 50(3): 289-335, 2004; Yoon et al., Psychology and Aging, 19(3), 379-393, 2004). These categories were presented to 246 young (18-39 years), middle (40-59 years), and older (60 years and older) English-speaking adults living in the United States who were asked to generate as many category exemplars as possible for each of the 70 categories. In order to understand differences in normative responses, we analyzed these responses a) between age groups within the current sample and b) in comparison to three previously published sets of norms. Experimental studies using such norms typically assume invariance of normative likelihoods across age and historical time. We replicate previous findings such that exemplar frequency correlations suggest moderate stability in generated category members between age groups and cohorts for many, but not all, categories. Further, analyses of rank-order correlations highlight that the traditional measure of typicality may not capture all aspects of typicality, namely that for some categories there is high consistency in the frequency of exemplars across age groups and/or norms, but the ordering of those exemplars differs significantly. We include a cluster analysis to aid in grouping categories based on relative stability across time, cohort, and age groups. These results emphasize the importance of maintaining and updating age-differentiated category norms.