Abstract
Abstract As a formal theory, Bundesen’s theory of visual attention (TVA) enables the estimation of several theoretically meaningful parameters involved in attentional selection and visual encoding. As of yet, TVA has almost exclusively been used in restricted empirical scenarios such as whole and partial report and with strictly controlled stimulus material. We present a series of experiments in which we test whether the advantages of TVA can be exploited in more realistic scenarios with varying degree of stimulus control. This includes brief experimental sessions conducted on different mobile devices, computer games, and a driving simulator. Overall, six experiments demonstrate that the TVA parameters for processing capacity and attentional weight can be measured with sufficient precision in less controlled scenarios and that the results do not deviate strongly from typical laboratory results, although some systematic differences were found.
Highlights
In most areas, psychological research methods stress the value of strict control: According to common psychological thinking, drawing conclusions from data presupposes an appropriate choice and manipulation of independent variables, tight control of possible confounding factors, reduction of random noise, and randomization of participants
As a formal theory, Bundesen’s theory of visual attention (TVA) enables the estimation of several theoretically meaningful parameters involved in attentional selection and visual encoding
Six experiments demonstrate that the TVA parameters for processing capacity and attentional weight can be measured with sufficient precision in less controlled scenarios and that the results do not deviate strongly from typical laboratory results, some systematic differences were found
Summary
Psychological research methods stress the value of strict control: According to common psychological thinking, drawing conclusions from data presupposes an appropriate choice and manipulation of independent variables, tight control of possible confounding factors, reduction of random noise, and randomization of participants. In more everyday settings—“in the wild”—such control would not be possible which is one of the main reasons to study phenomena in the laboratory. Through the reproducibility crisis (e.g., Open Science Collaboration, 2015), we have learned that even data obtained in controlled laboratory studies are far less reliable than had been expected. Without theoretical frameworks and respective formal models, it is difficult to come up with precise and unambiguous predictions for yet unobserved situations that allow testing hypotheses. If these predictions are not precise and do not adhere to some formal framework, separating expected from unexpected results is difficult and undesired flexibility when interpreting results can hinder true progress.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
