A key component of research on human sentence processing is to characterize the processing difficulty associated with the comprehension of words in context. Models that explain and predict this difficulty can be broadly divided into two kinds, expectation‐based and memory‐based. In this work, we present a new model of incremental sentence processing difficulty that unifies and extends key features of both kinds of models. Our model, lossy‐context surprisal, holds that the processing difficulty at a word in context is proportional to (...) the surprisal of the word given a lossy memory representation of the context—that is, a memory representation that does not contain complete information about previous words. We show that this model provides an intuitive explanation for an outstanding puzzle involving interactions of memory and expectations: language‐dependent structural forgetting, where the effects of memory on sentence processing appear to be moderated by language statistics. Furthermore, we demonstrate that dependency locality effects, a signature prediction of memory‐based theories, can be derived from lossy‐context surprisal as a special case of a novel, more general principle called information locality. (shrink)

ICAP is a theory of active learning that differentiates students’ engagement based on their behaviors. ICAP postulates that Interactive engagement, demonstrated by co‐generative collaborative behaviors, is superior for learning to Constructive engagement, indicated by generative behaviors. Both kinds of engagement exceed the benefits of Active or Passive engagement, marked by manipulative and attentive behaviors, respectively. This paper discusses a 5‐year project that attempted to translate ICAP into a theory of instruction using five successive measures: (a) teachers’ understanding of ICAP after (...) completing an online module, (b) their success at designing lesson plans using different ICAP modes, (c) fidelity of teachers’ classroom implementation, (d) modes of students’ enacted behaviors, and (e) students’ learning outcomes. Although teachers had minimal success in designing Constructive and Interactive activities, students nevertheless learned significantly more in the context of Constructive than Active activities. We discuss reasons for teachers’ overall difficulty in designing and eliciting Interactive engagement. (shrink)

Humor plays an essential role in human interactions. Precisely what makes something funny, however, remains elusive. While research on natural language understanding has made significant advancements in recent years, there has been little direct integration of humor research with computational models of language understanding. In this paper, we propose two information-theoretic measures—ambiguity and distinctiveness—derived from a simple model of sentence processing. We test these measures on a set of puns and regular sentences and show that they correlate significantly with human (...) judgments of funniness. Moreover, within a set of puns, the distinctiveness measure distinguishes exceptionally funny puns from mediocre ones. Our work is the first, to our knowledge, to integrate a computational model of general language understanding and humor theory to quantitatively predict humor at a fine-grained level. We present it as an example of a framework for applying models of language processing to understand higher level linguistic and cognitive phenomena. (shrink)

Deliberative democratic theory emphasises the importance of informed and reflective discussion and persuasion in political decision-making. The theory has important implications for constitutionalism - and vice versa - as constitutional laws increasingly shape and constrain political decisions. The full range of these implications has not been explored in the political and constitutional literatures to date. This unique Handbook establishes the parameters of the field of deliberative constitutionalism, which bridges deliberative democracy with constitutional theory and practice. Drawing on contributions from world-leading (...) authors, this volume will serve as the international reference point on deliberation as a foundational value in constitutional law, and will be an indispensable resource for scholars, students and practitioners interested in the vital and complex links between democratic deliberation and constitutionalism. (shrink)

Previous work has shown that English native speakers interpret sentences as predicted by a noisy‐channel model: They integrate both the real‐world plausibility of the meaning—the prior—and the likelihood that the intended sentence may be corrupted into the perceived sentence. In this study, we test the noisy‐channel model in Mandarin Chinese, a language taxonomically different from English. We present native Mandarin speakers sentences in a written modality (Experiment 1) and an auditory modality (Experiment 2) in three pairs of syntactic alternations. The (...) critical materials are literally implausible but require differing numbers and types of edits in order to form more plausible sentences. Each sentence is followed by a comprehension question that allows us to infer whether the speakers interpreted the item literally, or made an inference toward a more likely meaning. Similar to previous research on related English constructions, Mandarin participants made the most inferences for implausible materials that could be inferred as plausible by deleting a single morpheme or inserting a single morpheme. Participants were less likely to infer a plausible meaning for materials that could be inferred as plausible by making an exchange across a preposition. And participants were least likely to infer a plausible meaning for materials that could be inferred as plausible by making an exchange across a main verb. Moreover, we found more inferences in written materials than spoken materials, possibly a result of a lack of word boundaries in written Chinese. Overall, the fact that the results were so similar to those found in related constructions in English suggests that the noisy‐channel proposal is robust. (shrink)

Plant protection problems are simulated by a system of ordinary differential equations with given initial conditions. The sensitivity and resistance of pathogen subpopulations to fungicide mixtures, fungicide weathering, plant growth, etc. are taken into consideration. The system of equations is solved numerically for each set of initial conditions and parameters of the disease and fungicide applications. Optimization algorithms were investigated and a computer program was developed for optimization of these solutions. 14 typical cases of the disease were simulated and optimized (...) in order to determine optimal fungicide treatments. The optimized strategy for fungicide application differs considerably from the commonly used method and seems to be an important new principle in plant protection. The approach developed in this study may be useful for a wide spectrum of purposes in the simulation of leaf diseases. It may also help the biologist to decrease or pinpoint experimental work and analyze its results and is perspective for plant disease control. (shrink)

I survey the difficulties of several probabilistic views of non-deductive argument and of inductive probability and propose to explicate non-deductive reasoning in terms of rational preference. Following a critical examination of Popper’s allegedly deductive theory of rational preference, I draw upon the work of Popper and Rescher to present my view which includes: (i) the conjecturing of a set of alternative answers to or theories or hypotheses about the questions prompting the inquiry and (ii) the “reduction” of this set via (...) plausibilistic principles of rational preference. (shrink)

I survey the difficulties of several probabilistic views of non-deductive argument and of inductive probability and propose to explicate non-deductive reasoning in terms of rational preference. Following a critical examination of Popper’s allegedly deductive theory of rational preference, I draw upon the work of Popper and Rescher to present my view which includes: (i) the conjecturing of a set of alternative answers to or theories or hypotheses about the questions prompting the inquiry and (ii) the “reduction” of this set via (...) plausibilistic principles of rational preference. (shrink)

This non-technical, easy-to-read introduction to symbolic logic discusses truth-functional and predicate logic in a simple and concise manner. Emphasizes indirect proof of an especially simple form, while avoiding entirely conditional proof. Proof construction is taught by using: finished proofs; partially completed proofs in which the students are to supply missing justifications for lines of proof; and partially completed proofs in which the students are to supply the missing lines of proof, given their justifications. The difficult topic of symbolization of sentences (...) is presented in a quasi-mechanical way which stresses the recognition of standard form expressions. (shrink)

In this paper I take Hempel’s raven paradox as the claim that statements of the form ‘∼Ru v Bu’, ‘u is not a raven or u is black,’ confirm the hypothesis h ‘(x)(Rx → Bx)’, ‘All ravens are black.’ Although Hempel discusses this using a criterion of confirmation expressed wholly in terms of deductive logic (see 1965, pp. 35-9), it has become more common to articulate criteria of confirmation using concepts of probability and, in particular, to employ the positive relevance (...) criterion of confirmation which says that, given background knowledge k, (i) e confirms h if and only if P(h/e.k)>P(h/k); (ii) e disconfirms h if and only if P(h/e.k)shrink)

Determining the semantic role of sentence constituents is a key task in determining sentence meanings lying behind a veneer of variant syntactic expression. We present a model of natural language generation from semantics using the FrameNet semantic role and frame ontology. We train the model using the FrameNet corpus and apply it to the task of automatic semantic role and frame identification, producing results competitive with previous work (about 70% role labeling accuracy). Unlike previous models used for this task, our (...) model does not assume that the frame of a sentence is known, and is able to identify null- instantiated roles, which commonly occur in our corpus and whose identification is crucial to natural language interpretation. (shrink)