Electrophysiological correlates of face and object perception: A comparative analysis of 2D laboratory and virtual reality conditions.

Human face perception is a specialized visual process with inherent social significance. The neural mechanisms reflecting this intricate cognitive process have evolved in spatially complex and emotionally rich environments. Previous research using VR to transfer an established face perception paradigm to realistic conditions has shown that the functional properties of face-sensitive neural correlates typically observed in the laboratory are attenuated outside the original modality. The present study builds on these results by comparing the perception of persons and objects under conventional laboratory (PC) and realistic conditions in VR. Adhering to established paradigms, the PC- and VR modalities both featured images of persons and cars alongside standard control images. To investigate the individual stages of realistic face processing, response times, the typical face-sensitive N170 component, and relevant subsequent components (L1, L2; pre-, post-response) were analyzed within and between modalities. The between-modality comparison of response times and component latencies revealed generally faster processing under realistic conditions. However, the obtained N170 latency and amplitude differences showed reduced discriminative capacity under realistic conditions during this early stage. These findings suggest that the effects commonly observed in the lab are specific to monitor-based presentations. Analyses of later and response-locked components showed specific neural mechanisms for identification and evaluation are employed when perceiving the stimuli under realistic conditions, reflected in discernible amplitude differences in response to faces and objects beyond the basic perceptual features. Conversely, the results do not provide evidence for comparable stimulus-specific perceptual processing pathways when viewing pictures of the stimuli under conventional laboratory conditions.

Induced oscillatory brain responses under virtual reality conditions in the context of repetition priming.

In the human electroencephalogram (EEG), induced oscillatory responses in various frequency bands are regarded as valuable indices to examine the neural mechanisms underlying human memory. While the advent of virtual reality (VR) drives the investigation of mnemonic processing under more lifelike settings, the joint application of VR and EEG methods is still in its infancy (e.g., due to technical limitations impeding the signal acquisition). The objective of the present EEG study was twofold. First, we examined whether the investigation of induced oscillations under VR conditions yields equivalent results compared to standard paradigms. Second, we aimed at obtaining further insights into basic memory-related brain mechanisms in VR. To these ends, we relied on a standard implicit memory design, namely repetition priming, for which the to-be-expected effects are well-documented for conventional studies. Congruently, we replicated a suppression of the evoked potential after stimulus onset. Regarding the induced responses, we observed a modulation of induced alphaband in response to a repeated stimulus. Importantly, our results revealed a repetition-related suppression of the high-frequency induced gammaband response (>30 Hz), indicating the sharpening of a cortical object representation fostering behavioral priming effects. Noteworthy, the analysis of the induced gammaband responses required a number of measures to minimize the influence of external and internal sources of artefacts (i.e., the electrical shielding of the technical equipment and the control for miniature eye movements). In conclusion, joint VR-EEG studies with a particular focus on induced oscillatory responses offer a promising advanced understanding of mnemonic processing under lifelike conditions.

SHAP value-based ERP analysis (SHERPA): Increasing the sensitivity of EEG signals with explainable AI methods.

Conventionally, event-related potential (ERP) analysis relies on the researcher to identify the sensors and time points where an effect is expected. However, this approach is prone to bias and may limit the ability to detect unexpected effects or to investigate the full range of the electroencephalography (EEG) signal. Data-driven approaches circumvent this limitation, however, the multiple comparison problem and the statistical correction thereof affect both the sensitivity and specificity of the analysis. In this study, we present SHERPA - a novel approach based on explainable artificial intelligence (XAI) designed to provide the researcher with a straightforward and objective method to find relevant latency ranges and electrodes. SHERPA is comprised of a convolutional neural network (CNN) for classifying the conditions of the experiment and SHapley Additive exPlanations (SHAP) as a post hoc explainer to identify the important temporal and spatial features. A classical EEG face perception experiment is employed to validate the approach by comparing it to the established researcher- and data-driven approaches. Likewise, SHERPA identified an occipital cluster close to the temporal coordinates for the N170 effect expected. Most importantly, SHERPA allows quantifying the relevance of an ERP for a psychological mechanism by calculating an "importance score". Hence, SHERPA suggests the presence of a negative selection process at the early and later stages of processing. In conclusion, our new method not only offers an analysis approach suitable in situations with limited prior knowledge of the effect in question but also an increased sensitivity capable of distinguishing neural processes with high precision.

Real-life relevant face perception is not captured by the N170 but reflected in later potentials: A comparison of 2D and virtual reality stimuli.

The perception of faces is one of the most specialized visual processes in the human brain and has been investigated by means of the early event-related potential component N170. However, face perception has mostly been studied in the conventional laboratory, i.e., monitor setups, offering rather distal presentation of faces as planar 2D-images. Increasing spatial proximity through Virtual Reality (VR) allows to present 3D, real-life-sized persons at personal distance to participants, thus creating a feeling of social involvement and adding a self-relevant value to the presented faces. The present study compared the perception of persons under conventional laboratory conditions (PC) with realistic conditions in VR. Paralleling standard designs, pictures of unknown persons and standard control images were presented in a PC- and a VR-modality. To investigate how the mechanisms of face perception differ under realistic conditions from those under conventional laboratory conditions, the typical face-specific N170 and subsequent components were analyzed in both modalities. Consistent with previous laboratory research, the N170 lost discriminatory power when translated to realistic conditions, as it only discriminated faces and controls under laboratory conditions. Most interestingly, analysis of the later component [230-420 ms] revealed more differentiated face-specific processing in VR, as indicated by distinctive, stimulus-specific topographies. Complemented by source analysis, the results on later latencies show that face-specific neural mechanisms are applied only under realistic conditions (A video abstract is available in the Supplementary material and via YouTube: https://youtu.be/TF8wiPUrpSY).