How do semantic theories fit into the psychology of language more generally? A number of recent theoretical and experimental findings suggest that specifications of truth-conditions generate biases for different verification procedures. In this paper, we show how considerations of different representations of a visual scene in the semantic automata framework can generate predictions for differential working memory activation in proportional quantifier sentence verification. We present experimental results showing that different representations do impact working memory in sentence verification and that ‘more than half ’ and ‘most ’ behave differently in this regard.
Do negative quantifiers like “few” reduce people’s ability to rapidly evaluate incoming language wit...
In this paper we study if semantic complexity can influence the distribution of generalized quantifi...
Subjects listened to a story containing sentences with five different quantifiers (all, many, some, ...
The paper explores the cognitive mechanisms involved in the verification of sentences with proportio...
We study possible algorithmic models for the picture verification task with double-quantified senten...
This thesis presents experimental and computational modeling studies on the mental representations o...
This paper presents experimental evidence on the differences in a sentence-picture verification task...
We examine the verification of simple quantifiers in natural language from a computational model per...
The present dissertation is concerned with the processing difficulty of quantified sentences and how...
Abstract. We provide experimental evidence suggesting that the logical structure of linguistic expre...
Strategies used by people to verify quantified sentences, like `Most cars are white', have been a po...
The paper presents a study examining the role of working memory in quantifier verification. We creat...
I provide experimental evidence that quantifier semantics is transparently associated with a canonic...
The semantic complexity of a quantifier can be defined as the computational complexity of the finite...
According to the perceptual symbols theory (Barsalou, 1999), sensorimotor simulations underlie the r...
Do negative quantifiers like “few” reduce people’s ability to rapidly evaluate incoming language wit...
In this paper we study if semantic complexity can influence the distribution of generalized quantifi...
Subjects listened to a story containing sentences with five different quantifiers (all, many, some, ...
The paper explores the cognitive mechanisms involved in the verification of sentences with proportio...
We study possible algorithmic models for the picture verification task with double-quantified senten...
This thesis presents experimental and computational modeling studies on the mental representations o...
This paper presents experimental evidence on the differences in a sentence-picture verification task...
We examine the verification of simple quantifiers in natural language from a computational model per...
The present dissertation is concerned with the processing difficulty of quantified sentences and how...
Abstract. We provide experimental evidence suggesting that the logical structure of linguistic expre...
Strategies used by people to verify quantified sentences, like `Most cars are white', have been a po...
The paper presents a study examining the role of working memory in quantifier verification. We creat...
I provide experimental evidence that quantifier semantics is transparently associated with a canonic...
The semantic complexity of a quantifier can be defined as the computational complexity of the finite...
According to the perceptual symbols theory (Barsalou, 1999), sensorimotor simulations underlie the r...
Do negative quantifiers like “few” reduce people’s ability to rapidly evaluate incoming language wit...
In this paper we study if semantic complexity can influence the distribution of generalized quantifi...
Subjects listened to a story containing sentences with five different quantifiers (all, many, some, ...