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  1. Iterating semantic automata.Shane Steinert-Threlkeld & Thomas F. Icard - 2013 - Linguistics and Philosophy 36 (2):151-173.
    The semantic automata framework, developed originally in the 1980s, provides computational interpretations of generalized quantifiers. While recent experimental results have associated structural features of these automata with neuroanatomical demands in processing sentences with quantifiers, the theoretical framework has remained largely unexplored. In this paper, after presenting some classic results on semantic automata in a modern style, we present the first application of semantic automata to polyadic quantification, exhibiting automata for iterated quantifiers. We also discuss the role of semantic automata in (...)
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  • Iterating semantic automata.Shane Steinert-Threlkeld & I. I. I. Thomas F. Icard - 2013 - Linguistics and Philosophy 36 (2):151-173.
    The semantic automata framework, developed originally in the 1980s, provides computational interpretations of generalized quantifiers. While recent experimental results have associated structural features of these automata with neuroanatomical demands in processing sentences with quantifiers, the theoretical framework has remained largely unexplored. In this paper, after presenting some classic results on semantic automata in a modern style, we present the first application of semantic automata to polyadic quantification, exhibiting automata for iterated quantifiers. We also discuss the role of semantic automata in (...)
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  • Easy Solutions for a Hard Problem? The Computational Complexity of Reciprocals with Quantificational Antecedents.Fabian Schlotterbeck & Oliver Bott - 2013 - Journal of Logic, Language and Information 22 (4):363-390.
    We report two experiments which tested whether cognitive capacities are limited to those functions that are computationally tractable (PTIME-Cognition Hypothesis). In particular, we investigated the semantic processing of reciprocal sentences with generalized quantifiers, i.e., sentences of the form Q dots are directly connected to each other, where Q stands for a generalized quantifier, e.g. all or most. Sentences of this type are notoriously ambiguous and it has been claimed in the semantic literature that the logically strongest reading is preferred (Strongest (...)
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  • Computational complexity explains neural differences in quantifier verification.Heming Strømholt Bremnes, Jakub Szymanik & Giosuè Baggio - 2022 - Cognition 223 (C):105013.
  • Jakub Szymanik, Quantifiers and Cognition. Logical and Computational Perspectives. Springer, 2016. Pp. xii+211. ISBN: 978-3-319-28749-2 (hardcover) EUR 106,99; eBook EUR 83,29. [REVIEW]Giosuè Baggio & Heming Strømholt Bremnes - 2017 - Studia Logica 105 (5):1015-1019.
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  • A Computational Learning Semantics for Inductive Empirical Knowledge.Kevin T. Kelly - 2014 - In Alexandru Baltag & Sonja Smets (eds.), Johan van Benthem on Logic and Information Dynamics. Springer International Publishing. pp. 289-337.
    This chapter presents a new semantics for inductive empirical knowledge. The epistemic agent is represented concretely as a learner who processes new inputs through time and who forms new beliefs from those inputs by means of a concrete, computable learning program. The agent’s belief state is represented hyper-intensionally as a set of time-indexed sentences. Knowledge is interpreted as avoidance of error in the limit and as having converged to true belief from the present time onward. Familiar topics are re-examined within (...)
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