The paper introduces a general model of recovery from errors in parsing. The mechanism proposed returns selectively on the choice points, in order to identify the one that was badly resolved, and could have caused the error. Then, it selects an alternative previously discarded and finally selectively repairs the appropriate fragments between the ambiguous region and the breakdown region. Both the psycholinguistic and the computational features of the model are put in evidence.

In incremental parsing, infinite local ambiguity occurs when the input word can be combined with the syntactic structure built so far in a infinite number of ways. A common example is left recursion (e.g. "'railway station clock" or "his sister's boyfriend's shirt"), where local information cannot tell us the depth of embedding of the left descendent chain of nodes. From the processing point of view, infinite local ambiguity causes a technical problem, which a model must solve in order to implement incrementality fully. This paper provides a general solution to the problem of infinite local ambiguity, by introducing the concept of Minimal Recursive Structure. We give two examples of parsers in which the solution is used.

The increasing lexicalization of syntactic theories poses new difficulties for incremental models of language processing. In this paper, we describe an incremental interpreter that makes use of knowledge on categories to keep the syntactic structure always connected. This, in turn, guarantees a fine-grained syntax-semantics interaction. The paper introduces the general problem of formalizing the notion of incremental interpretation, and analyzes the current approaches in the cognitive literature.

A cognitive account of propositional reasoning must consider both the representation of the propositions (premises and states of affairs) and the context in which the propositions are used. This paper is concerned with reasoning processes involving three different connectives (conjunctive, conditional and disjunctive connectives) in three different tasks (accomplishing a request for action expressed by a premise, judging a state of affairs as true or false with respect to a premise, drawing an inference from two premises). Our claim is that the ability to reason with connectives is explained in terms of construction and manipulation of mental models. We present a computer model that takes as input the modelistic representations of the premises and the speciHc state of affairs, compares such models and gives rise to a series of model manipulations in order to produce a result, i.e. an action, a judgement or an inference. A computer program reproduces the performances of subjects of different age groups, predicting both correct and erroneous inferences.