A process-oriented language for describing aspects of reading comprehension

Includes bibliographical references (p. 36-38)The research described herein was supported in part by the National Institute of Education under Contract No. MS-NIE-C-400-76-011

Attachment priming and avoidant personality features as predictors of social-evaluation biases

Personality research has shown that negativity in social situations (e.g., negative evaluations of others) can be reduced by the activation of participants' sense of attachment security. Individuals with avoidant personality disorder (APD), however, are theoretically less responsive to context or situational cues because of the inflexible nature of their personality disposition. This idea of individual differences in context-responsiveness was tested in a sample of 169 undergraduates who were assessed for APD features and assigned to positive, negative, or neutral attachment priming conditions. More pronounced APD features were associated with more negative responses to vignettes describing potentially distressing social situations. A significant interaction showed that participants with more avoidant features consistently appraised the vignettes relatively more negatively, regardless of priming condition. Those without APD features, by contrast, did not exhibit negative appraisals/evaluations unless negatively primed (curvilinear effect). This effect could not be explained by depression, current mood, or attachment insecurity, all of which related to negative evaluative biases, but none of which related to situation inflexibility. These findings provide empirical support for the notion that negative information-processing is unusually inflexible and context-unresponsive among individuals with more pronounced features of APD

Parsing with parallelism : a spreading-activation model of inference processing during text understanding

The past decade of reseatch in Natural Language Processing has universally recognized that, since natural language input is almost always ambiguous with respect to its pragmatic implications, its syntactic parse, and even its lexical analysis (i.e., choice of correct word-sense for an ambiguous word), processing natural language input requires decisions about word meanings, syntactic structure, and pragmatic inferences. The lexical, syntactic, and pragmatic levels of inferencing are not as disparate as they have often been treated in both psychological and artificial intelligence research. In fact, these three levels of analysis interact to form a joint interpretation of text.ATLAST (A Three-level Language Analysis SysTem) is an implemented integration of human language understanding at the lexical, the syntactic, and the pragmatic levels. For psychological validity, ATLAST is based on results of experiments with human subjects. The ATLAST model uses a new architecture which was developed to incorporate three features: spreading activation memory, two-stage syntax, and parallel processing of syntax and semantics. It is also a new framework within which to interpret and tackle unsolved problems through implementation and experimentation

Foregrounding, burying and plot construction

No abstract available

Backwards is the way forward: feedback in the cortical hierarchy predicts the expected future

Clark offers a powerful description of the brain as a prediction machine, which offers progress on two distinct levels. First, on an abstract conceptual level, it provides a unifying framework for perception, action, and cognition (including subdivisions such as attention, expectation, and imagination). Second, hierarchical prediction offers progress on a concrete descriptive level for testing and constraining conceptual elements and mechanisms of predictive coding models (estimation of predictions, prediction errors, and internal models)

The NOMAD system : expectation-based detection and correction of errors during understanding of syntactically and semantically ill-formed text

Most large text-understanding systems have been designed under the assumption that the input text will be in reasonably "neat" form (for example, newspaper stories and other edited texts). However, a great deal of natural language text (for example, memos, messages, rough drafts, conversation transcripts, etc.) have features that differ significantly from "neat" texts, posing special problems for readers, such as misspelled words, missing words, poor syntactic construction, unclear or ambiguous interpretation, missing crucial punctuation, etc. Our solution to these problems is to make use of expectations, based both on knowledge of surface English and on world knowledge of the situation being described. These syntactic and semantic expectations can be used to figure out unknown words from context, constrain the possible word senses of words with multiple meanings (ambiguity), fill in missing words (ellipsis), and resolve referents (anaphora). This method of using expectations to aid the understanding of "scruffy" texts has bee incorporated into a working computer program called NOMAD, which understands scruffy texts in the domain of Navy ship-to-shore messages

Action! suspense! culture! insight! : reading stories in the classroom

Running title: Reading stories in the classroomAt head of title: Center for the Study of Reading.Bibliography: leaves 32-39Supported in part by the National Institute of Education under contract no. US-HEW-C-400-81-003

The Content-Dependence of Imaginative Resistance

An observation of Hume’s has received a lot of attention over the last decade and a half: Although we can standardly imagine the most implausible scenarios, we encounter resistance when imagining propositions at odds with established moral (or perhaps more generally evaluative) convictions. The literature is ripe with ‘solutions’ to this so-called ‘Puzzle of Imaginative Resistance’. Few, however, question the plausibility of the empirical assumption at the heart of the puzzle. In this paper, we explore empirically whether the difficulty we witness in imagining certain propositions is indeed due to claim type (evaluative v. non-evaluative) or whether it is much rather driven by mundane features of content. Our findings suggest that claim type plays but a marginal role, and that there might hence not be much of a ‘puzzle’ to be solved