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  1. Jakub Szymanik & Marcin Zajenkowski (2011). Contribution of Working Memory in the Parity and Proportional Judgments. Belgian Journal of Linguistics 25:189-206.
    The paper presents an experimental evidence on differences in the sentence-picture verification under additional memory load between parity and proportional quantifiers. We asked subjects to memorize strings of 4 or 6 digits, then to decide whether a quantifier sentence is true at a given picture, and finally to recall the initially given string of numbers. The results show that: (a) proportional quantifiers are more difficult than parity quantifiers with respect to reaction time and accuracy; (b) maintaining either 4 or 6 (...)
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  2. Marcin Zajenkowski, Rafał Styła & Jakub Szymanik (2011). A Computational Approach to Quantifiers as an Explanation for Some Language Impairments in Schizophrenia. Journal of Communication Disorder 44:2011.
    We compared the processing of natural language quantifiers in a group of patients with schizophrenia and a healthy control group. In both groups, the difficulty of the quantifiers was consistent with computational predictions, and patients with schizophrenia took more time to solve the problems. However, they were significantly less accurate only with proportional quantifiers, like more than half. This can be explained by noting that, according to the complexity perspective, only proportional quantifiers require working memory engagement.
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  3. Jakub Szymanik & Marcin Zajenkowski (2010). Quantifiers and Working Memory. In Maria Aloni & Katrin Schulz (eds.), Amsterdam Colloquium 2009, LNAI 6042. Springer.
    The paper presents a study examining the role of working<br>memory in quantifier verification. We created situations similar to the<br>span task to compare numerical quantifiers of low and high rank, parity<br>quantifiers and proportional quantifiers. The results enrich and support<br>the data obtained previously in and predictions drawn from a computational<br>model.
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  4. Jakub Szymanik & Marcin Zajenkowski (2009). Comprehension of Simple Quantifiers. Empirical Evaluation of a Computational Model. Cognitive Science: A Multidisciplinary Journal 34 (3):521-532.
    We examine the verification of simple quantifiers in natural language from a computational model perspective. We refer to previous neuropsychological investigations of the same problem and suggest extending their experimental setting. Moreover, we give some direct empirical evidence linking computational complexity predictions with cognitive reality.
    In the empirical study we compare time needed for understanding different types of quantifiers. We show that the computational distinction between quantifiers recognized by finite-automata and push-down automata is psychologically relevant. Our research improves upon hypothesis and (...)
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  5. Jakub Szymanik & Marcin Zajenkowski (2009). Improving Methodology of Quantifier Comprehension Experiments. Neuropsychologia 47 (12):2682--2683.
    Szymanik (2007) suggested that the distinction between first-order and higher-order quantifiers does not coincide with the computational resources required to compute the meaning of quantifiers. Cognitive difficulty of quantifier processing might be better assessed on the basis of complexity of the minimal corresponding automata. For example, both logical and numerical quantifiers are first-order. However, computational devices recognizing logical quantifiers have a fixed number of states while the number of states in automata corresponding to numerical quantifiers grows with the rank of (...)
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  6. Jakub Szymanik & Marcin Zajenkowski (2009). Understanding Quantifiers in Language. In N. A. Taatgen & H. van Rijn (eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society.
    We compare time needed for understanding different types of quantifiers. We show that the computational distinction between quantifiers recognized by finite-automata and pushdown automata is psychologically relevant. Our research improves upon hypothesis and explanatory power of recent neuroimaging studies as well as provides evidence for the claim that human linguistic abilities are constrained by computational complexity.
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