Environmental determinants of lexical processing effort
A central concern of psycholinguistic research is explaining the relative ease or difficulty involved in processing words. In this thesis, we explore the connection between lexical processing effort and measurable properties of the linguistic environment. Distributional information (information about a wordâs contexts of use) is easily extracted from large language corpora in the form of co-occurrence statistics. We claim that such simple distributional statistics can form the basis of a parsimonious model of lexical processing effort. Adopting the purposive style of explanation advocated by the recent rational analysis approach to understanding cognition, we propose that the primary function of the human language processor is to recover meaning from an utterance. We assume that for this task to be efficient, a useful processing strategy is to use prior knowledge in order to build expectations about the meaning of upcoming words. Processing effort can then be seen as reflecting the difference between âexpectedâ meaning and âactualâ meaning. Applying the tools of information theory to lexical representations constructed from simple distributional statistics, we show how this quantity can be estimated
McDonald, Scott
Environmental determinants of lexical processing effort. A central concern of psycholinguistic research is explaining the relative ease or difficulty involved in processing words. In this thesis, we explore the connection between lexical processing effort and measurable properties of the linguistic environment. Distributional information (information about a wordâs contexts of use) is easily extracted from large language corpora in the form of co-occurrence statistics. We claim that such simple distributional statistics can form the basis of a parsimonious model of lexical processing effort. Adopting the purposive style of explanation advocated by the recent rational analysis approach to understanding cognition, we propose that the primary function of the human language processor is to recover meaning from an utterance. We assume that for this task to be efficient, a useful processing strategy is to use prior knowledge in order to build expectations about the meaning of upcoming words. Processing effort can then be seen as reflecting the difference between âexpectedâ meaning and âactualâ meaning. Applying the tools of information theory to lexical representations constructed from simple distributional statistics, we show how this quantity can be estimated
environmental determinant lexical_processing effort central_concern psycholinguistic explain relative ease difficulty_involve processing word explore_connection lexical_processing effort measurable property linguistic_environment distributional information information word context use easily extract large language corpora form co_occurrence statistic claim simple distributional statistic form_basis parsimonious model_lexical processing effort adopting purposive style explanation advocate recent rational analysis cognition propose primary_function human_language processor recover meaning utterance assume task efficient useful processing strategy_use prior knowledge order build expectation mean word processing effort see reflect difference expect mean actual meaning applying tool information theory lexical representation construct simple distributional statistic show quantity estimate