The field of cognitive aging has seen considerable advances in describing the linguistic and semantic changes that happen during the adult life span to uncover the structure of the mental lexicon (i.e., the mental repository of lexical and conceptual representations). Nevertheless, there is still debate concerning the sources of these changes, including the role of environmental exposure and several cognitive mechanisms associated with learning, representation, and retrieval of information. We review the current status of research in this field and outline a framework that promises to assess the contribution of both ecological and psychological aspects to the aging lexicon.
Two visual world eyetracking experiments investigated how acoustic cue value and statistical variance affect perceptual uncertainty during Cantonese consonant (Experiment 1) and tone perception (Experiment 2). Participants heard low- or high-variance acoustic stimuli. Euclidean distance of fixations from target and competitor pictures over time was analysed using Generalised Additive Mixed Modelling. Distance of fixations from target and competitor pictures varied as a function of acoustic cue, providing evidence for gradient, nonlinear sensitivity to cue values. Moreover, cue value effects significantly interacted with statistical variance, indicating that the cue distribution directly affects perceptual uncertainty. Interestingly, the time course of effects differed between target distance and competitor distance models. The pattern of effects over time suggests a global strategy in response to the level of uncertainty: as uncertainty increases, verification looks increase accordingly. Low variance generally creates less uncertainty, but can lead to greater uncertainty in the face of unexpected speech tokens. (C) 2016 Elsevier Inc. All rights reserved.
Linear mixed-effects models have increasingly replaced mixed-model analyses of variance for statistical inference in factorial psycholinguistic experiments. Although LMMs have many advantages over ANOVA, like ANOVAs, setting them up for data analysis also requires some care. One simple option, when numerically possible, is to fit the full variance covariance structure of random effects (the maximal model; Barr, Levy, Scheepers & Tily, 2013), presumably to keep Type I error down to the nominal a in the presence of random effects. Although it is true that fitting a model with only random intercepts may lead to higher Type I error, fitting a maximal model also has a cost: it can lead to a significant loss of power. We demonstrate this with simulations and suggest that for typical psychological and psycholinguistic data, higher power is achieved without inflating Type I error rate if a model selection criterion is used to select a random effect structure that is supported by the data. (C) 2017 The Authors. Published by Elsevier Inc.