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

Time-pressure Does Not Alter the Bias Towards Canonical Interpretation of Quantifiers

The Interface Transparency Thesis (ITT) proposes that people tend to use a canonical interpretation of linguistic expressions, even when this interpretation is sub-optimal for the task at hand. The current paper sought to investigate this claim further by adding a time-pressure manipulation to a quantified sentence verification task and analyzing the results through a computational model of decision-making. The results indicate that time pressure -while effectively changing behavioral responses- does not alter cognitive processes associated with quantifier verification, thus supporting the ITT

Most, but not more than half, is proportion-dependent and sensitive to individual differences

In this study we test individual differences in the meaning representations of two natural language quantifiers – most and more than half – in a novel, purely linguistic task. We operationalized differences in meaning representations as differences in individual thresholds which were estimated using logistic regression. We show that the representation ofmost varies across subjects and its verification depends on proportion. Moreover, the choice of the representation of most affects the verification process. These effects are not present for more than half. The study demonstrates the cognitive differences between most and more than half and individual variation in meaning representations

Representational complexity and pragmatics cause the monotonicity effect

Psycholinguistic studies have repeatedly demonstrated that downward entailing (DE) quantifiers are more difficult to process than upward entailing (UE) ones. We contribute to the current debate on cognitive processes causing the monotonic-ity effect by testing predictions about the underlying processes derived from two competing theoretical proposals: two-step and pragmatic processing models. We model reaction times and accuracy from two verification experiments (a sentence-picture and a purely linguistic verification task), using the diffusion decision model (DDM). In both experiments, verification of UE quantifier more than half was compared to verification of DE quantifier fewer than half. Our analyses revealed the same pattern of results across tasks: Both non-decision times and drift rates, two of the free model parameters of the DDM, were affected by the monotonicity manipulation. Thus, our modeling results support both two-step (prediction: non-decision time is affected) and pragmatic processing models (prediction: drift rate is affected)

Most, but not more than half, is proportion-dependent and sensitive to individual differences

In this study we test individual differences in the meaning representations of two natural language quantifiers – most and more than half – in a novel, purely linguistic task. We operationalized differences in meaning representations as differences in individual thresholds which were estimated using logistic regression. We show that the representation ofmost varies across subjects and its verification depends on proportion. Moreover, the choice of the representation of most affects the verification process. These effects are not present for more than half. The study demonstrates the cognitive differences between most and more than half and individual variation in meaning representations