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Questionnaires are among the most common research tools in virtual reality (VR) user studies. Transitioning from virtuality to reality for giving self-reports on VR experiences can lead to systematic biases. VR allows to embed questionnaires into the virtual environment which may ease participation and avoid biases. To provide a cohesive picture of methods and design choices for questionnaires in VR (INVRQ), we discuss 15 INVRQ studies from the literature and present a survey with 67 VR experts from academia and industry. Based on the outcomes, we conducted two user studies in which we tested different presentation and interaction methods of INVRQS and evaluated the usability and practicality of our design. We observed comparable completion times between INVRQS and questionnaires outside VR (OUTVRQS) with higher enjoyment but lower usability for INVRQS. These findings advocate the application of INVRQS and provide an overview of methods and considerations that lay the groundwork for INVRQ design.

One class of theoretical accounts of associative learning suggests that reinforcers are processed according to learning rules that minimize the predictive error between the expected strength of future reinforcement and its actual strength. The omission of reinforcement in a situation where it is expected leads to inhibitory learning of stimuli indicative for such a violation of the prediction. There are, however, results indicating that inhibitory learning can also be induced by other mechanisms. Here, we present data from olfactory reward conditioning in honeybees that show that (1) one-and multiple-trial backward conditioning results in conditioned inhibition (CI); (2) the inhibition is maximal for a 15-sec interval between US and CS; (3) there is a nonmonotonic dependency on the degree of CI from the US-CS interval during backward pairing; and (4) the prior association of context stimuli with reinforcement is not necessary for the development of CI. These results cannot be explained by models that only minimize a prediction error. Rather, they are consistent with models of associative learning that, in addition, assume that learning depends on the 3Corresponding author. 4present address: European Media Lab,

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