Perception and cognition oscillate with fluctuating bodily states. For example, visual processing has been shown to change with alternating cardiac phases. Here, we study the heartbeat's role for active information sampling-testing whether humans implicitly act upon their environment so that relevant signals appear during preferred cardiac phases. During the encoding period of a visual memory experiment, participants clicked through a set of emotional pictures to memorize them for a later recognition test. By self-paced key press, they actively prompted the onset of short (100 ms) presented pictures. Simultaneously recorded electrocardiograms allowed us to analyze the self-initiated picture onsets relative to the heartbeat. We find that self-initiated picture onsets vary across the cardiac cycle, showing an increase during cardiac systole, while memory performance was not affected by the heartbeat. We conclude that active information sampling integrates heart-related signals, thereby extending previous findings on the association between body-brain interactions and behavior. K E Y W O R D Scardiovascular, emotion, interoception, memory, sensation/perception, young adults 2 of 16 | KUNZENDORF Et al. Cardiac activity occurs in a cycle of two phases: During diastole, the ventricles relax to be filled with blood; during systole, the ventricles contract and eject blood into the arteries, while visceral pathways send information about each heartbeat to the brain (Critchley & Harrison, 2013). Such natural phasic changes of the cardiovascular state have been mainly associated with variations in perception. For sensory processing, which is typically measured with detection tasks or reaction time tasks, response to passively presented stimuli has been shown to be attenuated during early cardiac phases (i.e., during systole) or relatively enhanced at later time points in the cardiac cycle (i.e., at diastole;

Virtual reality (VR) represents a key technology of the 21st century, attracting substantial interest from a wide range of scientific disciplines. With regard to clinical neuropsychology, a multitude of new VR applications are being developed to overcome the limitations of classical paradigms. Consequently, researchers increasingly face the challenge of systematically evaluating the characteristics and quality of VR applications to design the optimal paradigm for their specific research question and study population. However, the multifaceted character of contemporary VR is not adequately captured by the traditional quality criteria (ie, objectivity, reliability, validity), highlighting the need for an extended paradigm evaluation framework. To address this gap, we propose a multidimensional evaluation framework for VR applications in clinical neuropsychology, summarized as an easy-to-use checklist (VR-Check). This framework rests on 10 main evaluation dimensions encompassing cognitive domain specificity, ecological relevance, technical feasibility, user feasibility, user motivation, task adaptability, performance quantification, immersive capacities, training feasibility, and predictable pitfalls. We show how VR-Check enables systematic and comparative paradigm optimization by illustrating its application in an exemplary research project on the assessment of spatial cognition and executive functions with immersive VR. This application furthermore demonstrates how the framework allows researchers to identify across-domain trade-offs, makes deliberate design decisions explicit, and optimizes the allocation of study resources. Complementing recent approaches to standardize clinical VR studies, the VR-Check framework enables systematic and project-specific paradigm optimization for behavioral and cognitive research in neuropsychology.

1Perception and cognition oscillate with fluctuating bodily states. For example, visual pro-2 cessing has been shown to change with alternating cardiac phases. Here, we study the 3 heartbeat's role for active information sampling-testing whether humans implicitly act upon 4 their environment so that relevant signals appear during preferred cardiac phases. 5During the encoding period of a visual memory experiment, participants clicked 6 through a set of emotional pictures to memorize them for a later recognition test. By self-7 paced key press, they actively prompted the onset of shortly (100-ms) presented pictures. 8Simultaneously recorded electrocardiograms allowed us to analyse the self-initiated picture 9 onsets relative to the heartbeat. We find that self-initiated picture onsets vary across the car-10 diac cycle, showing an increase during cardiac systole, while memory performance was not 11 affected by the heartbeat. We conclude that active information sampling integrates heart-12 related signals, thereby extending previous findings on the association between body-brain 13 interactions and behaviour.

Virtual reality (VR) technology provides clinicians, therapists, and researchers with new opportunities to observe, assess, and train behavior in realistic yet well-controlled environments. However, VR also comes with a number of challenges. For example, compared to more abstract experiments and tests on 2D computer screens, VR-based tasks are more complex to create, which can make it more expensive and time-consuming. One way to overcome these challenges is to create, standardize, and validate VR content and to make it openly available for researchers and clinicians. Here we introduce the OpenVirtualObjects (OVO), a set of 124 realistic 3D household objects that people encounter and use in their everyday lives. The objects were rated by 34 younger and 25 older adults for recognizability, familiarity, details (i.e., visual complexity), contact, and usage (i.e., frequency of usage in daily life). All participants also named and categorized the objects. We provide the data and the experiment- and analysis code online. With OVO, we hope to facilitate VR-based research and clinical applications. Easy and free availability of standardized and validated 3D objects can support systematic VR-based studies and the development of VR-based diagnostics and therapeutic tools.