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

The mechanism underlying backward priming in a lexical decision task: Spreading activation versus semantic matching

Koriat (1981) demonstrated that an association from the target to a preceding prime, in the absence of an association from the prime to the target, facilitates lexical decision and referred to this effect as "backward priming". Backward priming is of relevance, because it can provide information about the mechanism underlying semantic priming effects. Following Neely (1991), we distinguish three mechanisms of priming: spreading activation, expectancy, and semantic matching/integration. The goal was to determine which of these mechanisms causes backward priming, by assessing effects of backward priming on a language-relevant ERP component, the N400, and reaction time (RT). Based on previous work, we propose that the N400 priming effect reflects expectancy and semantic matching/integration, but in contrast with RT does not reflect spreading activation. Experiment 1 shows a backward priming effect that is qualitatively similar for the N400 and RT in a lexical decision task. This effect was not modulated by an ISI manipulation. Experiment 2 clarifies that the N400 backward priming effect reflects genuine changes in N400 amplitude and cannot be ascribed to other factors. We will argue that these backward priming effects cannot be due to expectancy but are best accounted for in terms of semantic matching/integration

A distributional model of semantic context effects in lexical processinga

One of the most robust findings of experimental psycholinguistics is that the context in which a word is presented influences the effort involved in processing that word. We present a novel model of contextual facilitation based on word co-occurrence prob ability distributions, and empirically validate the model through simulation of three representative types of context manipulation: single word priming, multiple-priming and contextual constraint. In our simulations the effects of semantic context are mod eled using general-purpose techniques and representations from multivariate statistics, augmented with simple assumptions reflecting the inherently incremental nature of speech understanding. The contribution of our study is to show that special-purpose m echanisms are not necessary in order to capture the general pattern of the experimental results, and that a range of semantic context effects can be subsumed under the same principled account.›

Transient localized wave patterns and their application to migraine

Transient dynamics is pervasive in the human brain and poses challenging problems both in mathematical tractability and clinical observability. We investigate statistical properties of transient cortical wave patterns with characteristic forms (shape, size, duration) in a canonical reaction-diffusion model with mean field inhibition. The patterns are formed by a ghost near a saddle-node bifurcation in which a stable traveling wave (node) collides with its critical nucleation mass (saddle). Similar patterns have been observed with fMRI in migraine. Our results support the controversial idea that waves of cortical spreading depression (SD) have a causal relationship with the headache phase in migraine and therefore occur not only in migraine with aura (MA) but also in migraine without aura (MO), i.e., in the two major migraine subforms. We suggest a congruence between the prevalence of MO and MA with the statistical properties of the traveling waves' forms, according to which (i) activation of nociceptive mechanisms relevant for headache is dependent upon a sufficiently large instantaneous affected cortical area anti-correlated to both SD duration and total affected cortical area such that headache would be less severe in MA than in MO (ii) the incidence of MA is reflected in the distance to the saddle-node bifurcation, and (iii) the contested notion of MO attacks with silent aura is resolved. We briefly discuss model-based control and means by which neuromodulation techniques may affect pathways of pain formation.Comment: 14 pages, 11 figure

Affective state influences retrieval-induced forgetting for integrated knowledge

Selectively testing parts of learned materials can impair later memory for nontested materials. Research has shown that such retrieval-induced forgetting occurs for low-integrated materials but may be prevented for high-integrated materials. However, previous research has neglected one factor that is ubiquitous in real-life testing: affective stat