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Word recognition in sentence reading is influenced by information from both preview and context. Recently, semantic preview effect (SPE) was observed being modulated by the constraint of context, indicating that context might accelerate the processing of semantically related preview words. Besides, SPE was found to depend on preview time, which suggests that SPE may change with different processing stages of preview words. Therefore, it raises the question of whether preview time-dependent SPE would be modulated by contextual constraint. In this study, we not only investigated the impact of contextual constraint on SPE in Chinese reading but also examined its dependency on preview time. The preview word and the target word were identical, semantically related or unrelated to the target word. The results showed a significant three-way interaction: The SPE depended on contextual constraint and preview time. In separate analyses for low and high contextual constraint of target words, the SPE significantly decreased with an increase in preview duration when the target word was of low constraint in the sentence. The effect was numerically in the same direction but weaker and statistically nonsignificant when the target word was highly constrained in the sentence. The results indicate that word processing in sentences is a dynamic process of integrating information from both preview (bottom-up) and context (top-down).
Eye movements during fixation of a stationary target prevent the adaptation of the visual system to continuous illumination and inhibit fading of the image. These random, involuntary, small movements are restricted at long time scales so as to keep the target at the center of the field of view. Here we use detrended fluctuation analysis in order to study the properties of fixational eye movements at different time scales. Results show different scaling behavior between horizontal and vertical movements. When the small ballistic movements, i.e., microsaccades, are removed, the scaling exponents in both planes become similar. Our findings suggest that microsaccades enhance the persistence at short time scales mostly in the horizontal component and much less in the vertical component. This difference may be due to the need for continuously moving the eyes in the horizontal plane, in order to match the stereoscopic image for different viewing distances
Benefits of graphic design expertise in old age : compensatory effects of a graphical lexicon?
(2008)
Additive and interactive effects of word frequency, stimulus quality, and semantic priming have been used to test theoretical claims about the cognitive architecture of word-reading processes. Additive effects among these factors have been taken as evidence for discrete-stage models of word reading. We present evidence from linear mixed-model analyses applied to 2 lexical decision experiments indicating that apparent additive effects can be the product of aggregating over- and underadditive interaction effects that are modulated by recent trial history, particularly the lexical status and stimulus quality of the previous trial's target. Even a simple practice effect expressed as improved response speed across trials was powerfully modulated by the nature of the previous target item. These results suggest that additivity and interaction between factors may reflect trial-to-trial variation in stimulus representations and decision processes rather than fundamental differences in processing architecture.
On the ambiguity of interaction and nonlinear main effects in a regime of dependent covariates
(2017)
The analysis of large experimental datasets frequently reveals significant interactions that are difficult to interpret within the theoretical framework guiding the research. Some of these interactions actually arise from the presence of unspecified nonlinear main effects and statistically dependent covariates in the statistical model. Importantly, such nonlinear main effects may be compatible (or, at least, not incompatible) with the current theoretical framework. In the present literature, this issue has only been studied in terms of correlated (linearly dependent) covariates. Here we generalize to nonlinear main effects (i.e., main effects of arbitrary shape) and dependent covariates. We propose a novel nonparametric method to test for ambiguous interactions where present parametric methods fail. We illustrate the method with a set of simulations and with reanalyses (a) of effects of parental education on their children’s educational expectations and (b) of effects of word properties on fixation locations during reading of natural sentences, specifically of effects of length and morphological complexity of the word to be fixated next. The resolution of such ambiguities facilitates theoretical progress.
The Smoothing Spline ANOVA (SS-ANOVA) requires a specialized construction of basis and penalty terms in order to incorporate prior knowledge about the data to be fitted. Typically, one resorts to the most general approach using tensor product splines. This implies severe constraints on the correlation structure, i.e. the assumption of isotropy of smoothness can not be incorporated in general. This may increase the variance of the spline fit, especially if only a relatively small set of observations are given. In this article, we propose an alternative method that allows to incorporate prior knowledge without the need to construct specialized bases and penalties, allowing the researcher to choose the spline basis and penalty according to the prior knowledge of the observations rather than choosing them according to the analysis to be done. The two approaches are compared with an artificial example and with analyses of fixation durations during reading.
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.