Information Value: Measuring Utterance Predictability as Distance from Plausible Alternatives

Giulianelli and Fernández, 2021; Wallbridge When viewed as information transmission, successful et al., 2022). However, token-level autoregressive language production can be seen as an act approximations of utterance probability have a of reducing the uncertainty over future states that a few problematic properties. A well-known issue comprehender may be anticipating. Saying a word, is that different realisations of the same concept for example, may cut the space of possibilities in or communicative intent compete for probability half, while uttering a whole sentence may restrict mass (Holtzman et al., 2021), which implies that the comprehender's expectations to a far smaller the surprisal of semantically equivalent realisations space. Measuring the amount of information is overestimated. Moreover, token-level carried by a linguistic signal is fundamental to surprisal estimates conflate different dimensions of the computational modelling of human language predictability. As evidenced by recent studies (Arehalli processing. Such quantifications are used in et al., 2022; Kuhn et al., 2023), this makes psycholinguistic and neurobiological models of it difficult to appreciate whether the information language processing (Levy, 2008; Willems et al., carried by an utterance is a result, for example, of 2016; Futrell and Levy, 2017; Armeni et al., 2017), the unexpectedness of its lexical material, syntactic to study the processing mechanisms of neural arrangements, semantic content, or speech act type.