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Experiments in research on memory, language, and in other areas of cognitive science are increasingly being analyzed using Bayesian methods. This has been facilitated by the development of probabilistic programming languages such as Stan, and easily accessible front-end packages such as brms. The utility of Bayesian methods, however, ultimately depends on the relevance of the Bayesian model, in particular whether or not it accurately captures the structure of the data and the data analyst's domain expertise. Even with powerful software, the analyst is responsible for verifying the utility of their model. To demonstrate this point, we introduce a principled Bayesian workflow (Betancourt, 2018) to cognitive science. Using a concrete working example, we describe basic questions one should ask about the model: prior predictive checks, computational faithfulness, model sensitivity, and posterior predictive checks. The running example for demonstrating the workflow is data on reading times with a linguistic manipulation of object versus subject relative clause sentences. This principled Bayesian workflow also demonstrates how to use domain knowledge to inform prior distributions. It provides guidelines and checks for valid data analysis, avoiding overfitting complex models to noise, and capturing relevant data structure in a probabilistic model. Given the increasing use of Bayesian methods, we aim to discuss how these methods can be properly employed to obtain robust answers to scientific questions.
While previous research underscores the role of leaders in stimulating employee voice behaviour, comparatively little is known about what affects leaders' support for such constructive but potentially threatening employee behaviours. We introduce leader member exchange quality (LMX) as a central predictor of leaders' support for employees' ideas for constructive change. Apart from a general benefit of high LMX for leaders' idea support, we propose that high LMX is particularly critical to leaders' idea support if the idea voiced by an employee constitutes a power threat to the leader. We investigate leaders' attribution of prosocial and egoistic employee intentions as mediators of these effects. Hypotheses were tested in a quasi-experimental vignette study (N = 160), in which leaders evaluated a simulated employee idea, and a field study (N = 133), in which leaders evaluated an idea that had been voiced to them at work. Results show an indirect effect of LMX on leaders' idea support via attributed prosocial intentions but not via attributed egoistic intentions, and a buffering effect of high LMX on the negative effect of power threat on leaders' idea support. Results differed across studies with regard to the main effect of LMX on idea support.
During reading, rapid eye movements (saccades) shift the reader's line of sight from one word to another for high-acuity visual information processing. While experimental data and theoretical models show that readers aim at word centers, the eye-movement (oculomotor) accuracy is low compared to other tasks. As a consequence, distributions of saccadic landing positions indicate large (i) random errors and (ii) systematic over- and undershoot of word centers, which additionally depend on saccade lengths (McConkie et al.Visual Research, 28(10), 1107-1118,1988). Here we show that both error components can be simultaneously reduced by reading texts from right to left in German language (N= 32). We used our experimental data to test a Bayesian model of saccade planning. First, experimental data are consistent with the model. Second, the model makes specific predictions of the effects of the precision of prior and (sensory) likelihood. Our results suggest that it is a more precise sensory likelihood that can explain the reduction of both random and systematic error components.