The computational modeling of inferential and referential competence

Abstract

Abductive Reasoning in Cognitive Neuroscience:Weak and Strong Reverse Inference

Reverse inference is a crucial inferential strategy used in cognitive neuroscience to derive conclusions about the engagement of cognitive processes from patterns of brain activation. While widely employed in experimental studies, it is now viewed with increasing scepticism within the neuroscience community. One problem with reverse inference is that it is logically invalid, being an instance of abduction in Peirce’s sense. In this paper, we offer the first systematic analysis of reverse inference as a form of abductive reasoning and highlight some relevant implications for the current debate. We start by formalising an important distinction that has been entirely neglected in the literature, namely the distinction between weak (strategic) and strong (justificatory) reverse inference. Then, we rely on case studies from recent neuroscientific research to systematically discuss the role and limits of both strong and weak reverse inference; in particular, we offer the first exploration of weak reverse inference as a discovery strategy within cognitive neuroscience

La modellizzazione computazionale della competenza inferen-ziale e della competenza referenziale

In philosophy of language, a distinction has been proposed by Diego Marconi between two aspects of lexical competence, i.e. referential and inferential competence. The former accounts for the relation-ship of words to the world, the latter for the relationship of words among themselves. The aim of the pa-per is to offer a critical discussion of the kind of formalisms and computational techniques that can be used in Artificial Intelligence to model the two aspects of lexical competence, and of the main difficulties related to the use of these computational techniques. The first conclusion of our discussion is that the dis-tinction between inferential and referential semantics is instantiated in the literature of Artificial Intelli-gence by the distinction between symbolic and connectionist approaches. The second conclusion of our discussion is that the modelling of lexical competence needs the advent of hybrid models integrating symbolic and connectionist frameworks. Our hypothesis is that Conceptual Spaces, a framework devel-oped by Gärdenfors more than fifteen years ago, can offer a lingua franca that allows to unify and gener-alize many aspects of the representational approaches mentioned above and to integrate “inferential” (=symbolic) and “referential” (=connectionist) computational approaches on common ground

Eliminativismo semantico e competenza lessicale

According to Meaning Eliminativism [ME] (Recanati 2004), an explanation of our lexical competence does not need to associate the entries of the lexicon with a set of stable “meanings” or “semantic potentials”. According to such a view, the speaker calculates a word’s meaning on a specific occasion of use based on the similarity between that use and all the past uses of the same word, without the mediation of an abstract representation of its conventional meaning. My purpose in this paper is to show that ME is not viable as an empirical thesis about the acquisition and the exercise of lexical competence. If ME were correct, we would have to deny the existence of the so-called «semantic memory», i.e. the memory that is functionally dedicated to the storage of declarative semantic knowledge relative to a word. Conversely, we would have to accept that all the performances that are traditionally associated to semantic memory are underpinned by the so-called «episodic memory», i.e. the memory of past personal experience that occurred at a particular time and place. Nevertheless, a critical analysis of a series of available neuropsychological data (i.e. data coming from brain damaged patients) will show that such an empirical hypothesis is unsustainable