Abstract knowledge versus direct experience in processing of binomial expressions

We ask whether word order preferences for binomial expressions of the form A and B (e.g. bread and butter) are driven by abstract linguistic knowledge of ordering constraints referencing the semantic, phonological, and lexical properties of the constituent words, or by prior direct experience with the specific items in questions. Using forced-choice and self-paced reading tasks, we demonstrate that online processing of never-before-seen binomials is influenced by abstract knowledge of ordering constraints, which we estimate with a probabilistic model. In contrast, online processing of highly frequent binomials is primarily driven by direct experience, which we estimate from corpus frequency counts. We propose a trade-off wherein processing of novel expressions relies upon abstract knowledge, while reliance upon direct experience increases with increased exposure to an expression. Our findings support theories of language processing in which both compositional generation and direct, holistic reuse of multi-word expressions play crucial roles.National Science Foundation (U.S.) (Award NSF0953870)Eunice Kennedy Shriver National Institute of Child Health and Human Development (U.S.) (Award NICHDR01HD065829

Frequency-(in)dependent Regularization in Language Production and Cultural Transmission

Binomial expressions are more regularized--i.e. their ordering preferences (e.g. “bread and butter” vs. “butter and bread”) are more extreme—-the higher their frequency. Although standard iterated-learning models of language evolution can encode overall regularization biases, the stationary distributions in these standard models do not exhibit a relationship between expression frequency and regularization. We show that introducing a frequency-INdependent regularization bias into the data-generation stage of a 2-Alternative Iterated Learning Model yields frequency-dependent regularization in the stationary distribution. We also show that this model accounts for the distribution of binomial ordering preferences in corpus data

Do RNNs learn human-like abstract word order preferences?

RNN language models have achieved state-of-the-art results on various tasks, but what exactly they are representing about syntax is as yet unclear. Here we investigate whether RNN language models learn humanlike word order preferences in syntactic alternations. We collect language model surprisal scores for controlled sentence stimuli exhibiting major syntactic alternations in English: heavy NP shift, particle shift, the dative alternation, and the genitive alternation. We show that RNN language models reproduce human preferences in these alternations based on NP length, animacy, and definiteness. We collect human acceptability ratings for our stimuli, in the first acceptability judgment experiment directly manipulating the predictors of syntactic alternations. We show that the RNNs\u27 performance is similar to the human acceptability ratings and is not matched by an n-gram baseline model. Our results show that RNNs learn the abstract features of weight, animacy, and definiteness which underlie soft constraints on syntactic alternations

A Rate–Distortion view of human pragmatic reasoning

What computational principles underlie human pragmatic reasoning? A prominent approach to pragmatics is the Rational Speech Act (RSA) framework, which formulates pragmatic reasoning as probabilistic speakers and listeners recursively reasoning about each other. While RSA enjoys broad empirical support, it is not yet clear whether the dynamics of such recursive reasoning may be governed by a general optimization principle. Here, we present a novel analysis of the RSA framework that addresses this question. First, we show that RSA recursion implements an alternating maximization for optimizing a tradeoff between expected utility and communicative effort. On that basis, we study the dynamics of RSA recursion and disconfirm the conjecture that expected utility is guaranteed to improve with recursion depth. Second, we show that RSA can be grounded in Rate-Distortion theory, while maintaining a similar ability to account for human behavior and avoiding a bias of RSA toward random utterance production. This work furthers the mathematical understanding of RSA models, and suggests that general information-theoretic principles may give rise to human pragmatic reasoning

A Closer Look at the Performance of Neural Language Models on Reflexive Anaphor Licensing

An emerging line of work uses psycholinguistic methods to evaluate the syntactic generalizations acquired by neural language models (NLMs). While this approach has shown NLMs to be capable of learning a wide range of linguistic knowledge, confounds in the design of previous experiments may have obscured the potential of NLMs to learn certain grammatical phenomena. Here we re-evaluate the performance of a range of NLMs on reflexive anaphor licensing. Under our paradigm, the models consistently show stronger evidence of learning than reported in previous work. Our approach demonstrates the value of well-controlled psycholinguistic methods in gaining a fine-grained understanding of NLM learning potential

The cascade of fear: policy implementation and financial management reform in the European Commission

The complexity of managing European Union (EU) spending programmes is the subject of much comment but relatively little academic analysis. Using a multi-disciplinary analytical framework drawn from the management, policy and social sciences, this fieldwork-based case study examines the reform of financial management within the European Commission. In constructing an agent focussed narrative of a specific reform episode, it contributes to a growing literature on public management reform analysed from this perspective and also to the lightly developed field of EU financial managemen

Testing the Predictions of Surprisal Theory in 11 Languages

A fundamental result in psycholinguistics is that less predictable words take a longer time to process. One theoretical explanation for this finding is Surprisal Theory (Hale, 2001; Levy, 2008), which quantifies a word's predictability as its surprisal, i.e. its negative log-probability given a context. While evidence supporting the predictions of Surprisal Theory have been replicated widely, most have focused on a very narrow slice of data: native English speakers reading English texts. Indeed, no comprehensive multilingual analysis exists. We address this gap in the current literature by investigating the relationship between surprisal and reading times in eleven different languages, distributed across five language families. Deriving estimates from language models trained on monolingual and multilingual corpora, we test three predictions associated with surprisal theory: (i) whether surprisal is predictive of reading times; (ii) whether expected surprisal, i.e. contextual entropy, is predictive of reading times; (iii) and whether the linking function between surprisal and reading times is linear. We find that all three predictions are borne out crosslinguistically. By focusing on a more diverse set of languages, we argue that these results offer the most robust link to-date between information theory and incremental language processing across languages.Comment: This is a pre-MIT Press publication version of the pape