Cognitive load, fatigue and aversive simulator symptoms but not manipulated zeitgebers affect duration perception in virtual reality.

The perceived duration of an interval depends on numerous aspects of the passed event both endogenous, including physiological arousal, level of wakefulness, attention, and surprise, as well as exogenous such as valence, salience, or context in the environment. There is some evidence that "time-giving" cues from the environment (zeitgebers) are coupled with time perception. The movement of the sun on the horizon was demonstrated to affect interval perception in a study conducted by Schatzschneider et al. (2016) claiming that the sun's motion is a zeitgeber that influences time perception. In the present study, we undertake the first to our knowledge replication of this effect, extending the analysis to confounding aspects of the used paradigm. We aimed to test the effect of immersion, cognitive load, and changes in the speed of the sun on the horizon of the virtual environment on the perceived interval duration. We did not replicate the original effect, as reported by Schatzschneider et al., however, we did find that the perceived duration of an interval was affected by cognitive load, fatigue, and unpleasant symptoms caused by VR. In our analysis, we used Bayesian statistics to support our conclusion and offer its results as having some important consequences for the field.

Causal Inferences in Repetitive Transcranial Magnetic Stimulation Research: Challenges and Perspectives.

Transcranial magnetic stimulation (TMS) is used to make inferences about relationships between brain areas and their functions because, in contrast to neuroimaging tools, it modulates neuronal activity. The central aim of this article is to critically evaluate to what extent it is possible to draw causal inferences from repetitive TMS (rTMS) data. To that end, we describe the logical limitations of inferences based on rTMS experiments. The presented analysis suggests that rTMS alone does not provide the sort of premises that are sufficient to warrant strong inferences about the direct causal properties of targeted brain structures. Overcoming these limitations demands a close look at the designs of rTMS studies, especially the methodological and theoretical conditions which are necessary for the functional decomposition of the relations between brain areas and cognitive functions. The main points of this article are that TMS-based inferences are limited in that stimulation-related causal effects are not equivalent to structure-related causal effects due to TMS side effects, the electric field distribution, and the sensitivity of neuroimaging and behavioral methods in detecting structure-related effects and disentangling them from confounds. Moreover, the postulated causal effects can be based on indirect (network) effects. A few suggestions on how to manage some of these limitations are presented. We discuss the benefits of combining rTMS with neuroimaging in experimental reasoning and we address the restrictions and requirements of rTMS control conditions. The use of neuroimaging and control conditions allows stronger inferences to be gained, but the strength of the inferences that can be drawn depends on the individual experiment's designs. Moreover, in some cases, TMS might not be an appropriate method of answering causality-related questions or the hypotheses have to account for the limitations of this technique. We hope this summary and formalization of the reasoning behind rTMS research can be of use not only for scientists and clinicians who intend to interpret rTMS results causally but also for philosophers interested in causal inferences based on brain stimulation research.