Cognitive constraints and island effects

Competence-based theories of island effects play a central role in generative grammar, yet the graded nature of many syntactic islands has never been properly accounted for. Categorical syntactic accounts of island effects have persisted in spite of a wealth of data suggesting that island effects are not categorical in nature and that nonstructural manipulations that leave island structures intact can radically alter judgments of island violations. We argue here, building on work by Paul Deane, Robert Kluender, and others, that processing factors have the potential to account for this otherwise unexplained variation in acceptability judgments. We report the results of self-paced reading experiments and controlled acceptability studies that explore the relationship between processing costs and judgments of acceptability. In each of the three self-paced reading studies, the data indicate that the processing cost of different types of island violations can be significantly reduced to a degree comparable to that of nonisland filler-gap constructions by manipulating a single nonstructural factor. Moreover, this reduction in processing cost is accompanied by significant improvements in acceptability. This evidence favors the hypothesis that island-violating constructions involve numerous processing pressures that aggregate to drive processing difficulty above a threshold, resulting in unacceptability. We examine the implications of these findings for the grammar of filler-gap dependencies

The source ambiguity problem: Distinguishing the effects of grammar and processing on acceptability judgments

Judgments of linguistic unacceptability may theoretically arise from either grammatical deviance or significant processing difficulty. Acceptability data are thus naturally ambiguous in theories that explicitly distinguish formal and functional constraints. Here, we consider this source ambiguity problem in the context of Superiority effects: the dispreference for ordering a wh-phrase in front of a syntactically “superior” wh-phrase in multiple wh-questions, e.g., What did who buy? More specifically, we consider the acceptability contrast between such examples and so-called D-linked examples, e.g., Which toys did which parents buy? Evidence from acceptability and self-paced reading experiments demonstrates that (i) judgments and processing times for Superiority violations vary in parallel, as determined by the kind of wh-phrases they contain, (ii) judgments increase with exposure, while processing times decrease, (iii) reading times are highly predictive of acceptability judgments for the same items, and (iv) the effects of the complexity of the wh-phrases combine in both acceptability judgments and reading times. This evidence supports the conclusion that D-linking effects are likely reducible to independently motivated cognitive mechanisms whose effects emerge in a wide range of sentence contexts. This in turn suggests that Superiority effects, in general, may owe their character to differential processing difficulty

The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision

We propose the Neuro-Symbolic Concept Learner (NS-CL), a model that learns visual concepts, words, and semantic parsing of sentences without explicit supervision on any of them; instead, our model learns by simply looking at images and reading paired questions and answers. Our model builds an object-based scene representation and translates sentences into executable, symbolic programs. To bridge the learning of two modules, we use a neuro-symbolic reasoning module that executes these programs on the latent scene representation. Analogical to human concept learning, the perception module learns visual concepts based on the language description of the object being referred to. Meanwhile, the learned visual concepts facilitate learning new words and parsing new sentences. We use curriculum learning to guide the searching over the large compositional space of images and language. Extensive experiments demonstrate the accuracy and efficiency of our model on learning visual concepts, word representations, and semantic parsing of sentences. Further, our method allows easy generalization to new object attributes, compositions, language concepts, scenes and questions, and even new program domains. It also empowers applications including visual question answering and bidirectional image-text retrieval.Comment: ICLR 2019 (Oral). Project page: http://nscl.csail.mit.edu

The role of explicit memory in syntactic persistence : effects of lexical cueing and load on sentence memory and sentence production

Speakers' memory of sentence structure can persist and modulate the syntactic choices of subsequent utterances (i.e., structural priming). Much research on structural priming posited a multifactorial account by which an implicit learning process and a process related to explicit memory jointly contribute to the priming effect. Here, we tested two predictions from that account: (1) that lexical repetition facilitates the retrieval of sentence structures from memory; (2) that priming is partly driven by a short-term explicit memory mechanism with limited resources. In two pairs of structural priming and sentence structure memory experiments, we examined the effects of structural priming and its modulation by lexical repetition as a function of cognitive load in native Dutch speakers. Cognitive load was manipulated by interspersing the prime and target trials with easy or difficult mathematical problems. Lexical repetition boosted both structural priming (Experiments 1a-2a) and memory for sentence structure (Experiments 1b-2b) and did so with a comparable magnitude. In Experiment 1, there were no load effects, but in Experiment 2, with a stronger manipulation of load, both the priming and memory effects were reduced with a larger cognitive load. The findings support an explicit memory mechanism in structural priming that is cue-dependent and attention-demanding, consistent with a multifactorial account of structural priming

Just an Update on PMING Distance for Web-based Semantic Similarity in Artificial Intelligence and Data Mining

One of the main problems that emerges in the classic approach to semantics is the difficulty in acquisition and maintenance of ontologies and semantic annotations. On the other hand, the Internet explosion and the massive diffusion of mobile smart devices lead to the creation of a worldwide system, which information is daily checked and fueled by the contribution of millions of users who interacts in a collaborative way. Search engines, continually exploring the Web, are a natural source of information on which to base a modern approach to semantic annotation. A promising idea is that it is possible to generalize the semantic similarity, under the assumption that semantically similar terms behave similarly, and define collaborative proximity measures based on the indexing information returned by search engines. The PMING Distance is a proximity measure used in data mining and information retrieval, which collaborative information express the degree of relationship between two terms, using only the number of documents returned as result for a query on a search engine. In this work, the PMINIG Distance is updated, providing a novel formal algebraic definition, which corrects previous works. The novel point of view underlines the features of the PMING to be a locally normalized linear combination of the Pointwise Mutual Information and Normalized Google Distance. The analyzed measure dynamically reflects the collaborative change made on the web resources

Protecting your software updates

As described in many blog posts and the scientific literature, exploits for software vulnerabilities are often engineered on the basis of patches, which often involves the manual or automated identification of vulnerable code. The authors evaluate how this identification can be automated with the most frequently referenced diffing tools, demonstrating that for certain types of patches, these tools are indeed effective attacker tools. But they also demonstrate that by using binary code diversification, the effectiveness of the tools can be diminished severely, thus severely closing the attacker's window of opportunity

Pupillary Effects During Retrieval: Influenced by Cognitive Load and Strength of Memory

Memory retrieval is influenced by cognitive processes that occur during encoding, some of which can be measured with pupillary responses. For example, during retrieval, pupils dilate more to previously-seen old items compared to new items, a phenomenon called the pupil old/new effect. Encoding variables that influence the strength of the memory trace for encoded stimuli play a role in successful discrimination of new versus old items. Additionally, the cognitive load during encoding (i.e., the effort needed to encode information), also impacts memory success by taking up mental resources needed to successfully encode information. In this study, I conducted a meta-analysis to examine whether pupillary dilation effects are stronger after encoding manipulations that influence memory strength or cognitive load. This analysis showed that both memory strength and cognitive load affect pupil dilations. However, the impact was greater for cognitive load, suggesting that the amount of effort required to process information during encoding has a greater impact on pupil size than variables that affect the strength of the memory trace. Pupillometry can be a useful measure of memory effects, so future research could use pupil measures to study variables that affect other types of memory, such as explicit versus implicit memory

NATURAL LANGUAGE DOCUMENTS: INDEXING AND RETRIEVAL IN AN INFORMATION SYSTEM

A steadily increasing number of natural language (NL) documents are handled in information systems. Most of these documents typically contain some formatted data, which we call strong database data, and additionally some unformatted data, i.e., free text. The task of a modern information system is to characterize such unformatted (text) data automatically and, in doing so, to support the user in storing and retrieving natural language documents. The retrieval of natural language documents is a fuzzy process because the user will formulate fuzzy queries unless he uses some strong search keys. Retrieval of natural language documents can be facilitated with natural language queries; that is, with searches based on natural language text comparisons