A drift-diffusion decomposition of conditions that influence shallow ("good enough") processing of heard sentences
Ryan M O'Leary1, Natalie Omori-Hoffe1, Griffin Dugan1
1Department of Psychology and Volen National Center for Complex Systems, Brandeis University, Waltham, MA, USA.
Abstract:
Although listeners may have the competence to engage in a word-by-word analysis to build a syntactic representation of a heard sentence, in everyday conversation, listeners may, almost by necessity, process the sentence only to a shallow or "good enough" level to derive its meaning. The possibility has been raised that processing schemata may be flexible, such that under some circumstances, comprehension decisions are more likely to be based on an incomplete analysis. We report two experiments in which adult participants were tested on their ability to determine the meaning of auditorily presented plausible or implausible sentences. In both experiments, participants were given two different orienting instructions while completing the listening task (instructions that emphasized either the speed or accuracy of the comprehension decision). In Experiment 1, we also manipulated spectral clarity such that speech was either heard clearly or degraded in spectral richness. A unique use of hierarchical drift-diffusion modeling was employed to probe latent decision-making processes that influenced the participants' comprehension decision. Results indicate that orienting instructions that emphasize speed and perceptual challenge both increase the likelihood that the assumed meaning of implausible sentences will be based on plausibility. Drift-diffusion modeling revealed a dissociation where orienting instructions selectively influenced the amount of evidence required for the participant to make a comprehension decision while sentence plausibility selectively influenced the rate of evidence accumulation. These drift-diffusion model findings were replicated in Experiment 2. Results suggest that comprehension processes are highly flexible and can be characterized in terms of underlying decision-making mechanisms.
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