Facial expressions are critical social signals that support human communication. In digital contexts, emojis serve as a primary surrogate for nonverbal cues such as facial expressions; however, little is known about the extent to which emoji expressions are processed using neural mechanisms similar to those engaged by real human faces. To address this question, we used EEG-based multivariate pattern analysis (MVPA) to examine the neural dynamics of emotional expression processing in real faces and emoji faces. Across two experiments using identical paradigms, independent groups of participants viewed facial expressions (happy, angry, sad, neutral) in real faces (4 female and 4 male identities, n = 24) or emojis (6 platforms, n = 25) while performing a two-alternative forced-choice emotion recognition task. Time-resolved multivariate classification and spatio-temporal searchlight analyses revealed robust decoding of emotional expressions within and across experiments. Consistent effects emerged early and peaked between 145 and 160 ms over posterior-occipital and parietal regions. Notably, robust cross-classification between real and emoji faces demonstrated that face-like emoji stimuli evoke neural responses comparable to those elicited by real faces, with more sustained effects over right posterior sites. These findings suggest that the brain uses partially overlapping spatio-temporal codes for naturalistic and symbolic facial expressions, providing new insights into the neural coding of social signals and the representational overlap between natural and artificial emotional expressions.

Emotional facial expressions are known to bias face processing, yet it remains unclear whether such effects extend to neural signals associated with face familiarity. In this secondary analysis of openly available EEG data, we used cross‑dataset multivariate pattern analysis (MVPA) to test whether established neural signatures of face familiarity generalize across emotional expressions. Participants viewed faces in two independent experiments: one involving explicit emotion categorization (happy, angry, sad, neutral) and another involving personally familiar and unfamiliar identities. Classifiers trained to distinguish familiar from unfamiliar faces were cross‑applied to emotional expressions, and vice versa, using complementary relabeling strategies. Across analyses, neural patterns for angry expressions showed the strongest and most sustained generalization to familiarity‑related neural signals, emerging around 200 ms post‑stimulus and persisting throughout the trial (peak Cohen's d = 1.35 over posterior regions). Neural patterns for happy and sad expressions showed weaker and more transient generalization (200-400 ms), while neutral expressions consistently aligned with patterns for unfamiliarity. These findings demonstrate that threat‑related facial expressions exhibit neural dynamics that show convergence in pattern structure with established familiarity signals, extending prior evidence that emotional expressions, particularly anger, systematically modulate face representations beyond identity.

Background:The dorsolateral prefrontal cortex (DLPFC) is a key contributor to memory categorization. Brain-Derived Neurotrophic Factor (BDNF) Val66Met polymorphism affects the efficacy of neuronal plasticity induction. We investigated whether DLPFC-transcranial direct current stimulation (tDCS) influences categorization performance, and whether BNDF genotype modulates this effect. Methods:Sixty-two healthy individuals were randomized to receive 10 min of either anodal right DLPFC-tDCS at 1 mA, or sham tDCS during the training phase of a prototype distortion task. Categorization performance was assessed during, shortly after, and the morning following the stimulation. Val66Met polymorphism status was determined through BNDF genotyping. Results:Val66Met carriers showed poorer categorization ability than Val/Val homozygotes (p < 0.0001). tDCS did not enhance categorization performance overall, or reduce genotype-associated differences with these specific stimulation parameters. However, tDCS extinguished the prototype effect of the categorization task observed in our sample. Conclusions:Our findings underscore the importance of BDNF polymorphism in category learning. 1 mA anodal right DLPFC over the right DLPFC does not improve performance or offset genotype differences under tested conditions. Significance:BDNF Val66Met polymorphism influences category learning, and low-intensity tDCS does not counteract this effect, emphasizing the need to refine stimulation protocols for genotype-specific cognitive enhancement.

This study investigated the shared neural dynamics underlying encoding and recognition processes across diverse visual object stimulus types in short term experimental familiarization, using EEG-based representational similarity analysis and multivariate cross-classification. Building upon previous research, we extended our exploration to the encoding phase. We show early visual stimulus category effects around 150 ms post-stimulus onset and old/new effects around 400 to 600 ms. Notably, a divergence in neural responses for encoding, old, and new stimuli emerged around 300 ms, with items encountered during the study phase showing the highest differentiation from old items during the test phase. Cross-category classification demonstrated discernible memory-related effects as early as 150 ms. Anterior regions of interest, particularly in the right hemisphere, did not exhibit differentiation between experimental phases or between study and new items, hinting at similar processing for items first encountered, irrespective of experiment phase. While short-term experimental familiarity did not consistently adhere to the old >new pattern observed in long-term personal familiarity, statistically significant effects are observed specifically for experimentally familiarized faces, suggesting a potential unique phenomenon specific to facial stimuli. Further investigation is warranted to elucidate underlying mechanisms and determine the extent of face-specific effects. Lastly, our findings underscore the utility of multivariate cross-classification and cross-dataset classification as promising tools for probing abstraction and shared neural signatures of cognitive processing.

Facial expressions are critical social signals, essential for human communication. This study used EEG to investigate the neural dynamics of the processing of emotional expressions in real and emoji faces, using a data-driven approach. Across two experiments with identical paradigms, two separate sets of participants viewed facial expressions (happy, angry, sad, neutral) in real faces (4 female and 4 male identities, n = 24) or emojis (6 platforms, n = 25) while performing a two-alternative forced-choice emotion recognition task. Time-resolved multivariate classification and spatio-temporal searchlight analyses revealed robust decoding of emotional expressions within and across experiments. Consistent effects emerged early and peaked between 145-160 ms over posterior-occipital and parietal regions. Notably, robust cross-classification between real and emoji faces demonstrated that face-like emoji stimuli evoke neural responses comparable to those elicited by real faces, with more sustained effects over right posterior sites. These findings suggest that the brain uses overlapping spatio-temporal codes for naturalistic and symbolic facial expressions, providing new insights into the neural coding of social signals and the representational overlap between natural and artificial emotional expressions. ### Competing Interest Statement The authors have declared no competing interest.

The ability to recognize and interpret facial expressions is fundamental to human social cognition, enabling navigation of complex interpersonal interactions and understanding of others’ emotional states. The extent to which neural patterns associated with facial expression processing are shared between observers remains unexplored, and no study has yet examined the neural dynamics specific to different emotional expressions. Additionally, the neural processing dynamics of facial attributes such as sex and identity in relation to facial expressions have not been thoroughly investigated. In this study, we investigated the shared neural dynamics of emotional face processing using an explicit facial emotion recognition task, where participants made two-alternative forced choice (2AFC) decisions on the displayed emotion. Our data-driven approach employed cross-participant multivariate classification and representational dissimilarity analysis on EEG data. The results demonstrate that EEG signals can effectively decode the sex, emotional expression, and identity of face stimuli across different stimuli and participants, indicating shared neural codes for facial expression processing. Multivariate classification analyses revealed that sex is decoded first, followed by identity, and then emotion. Emotional expressions (angry, happy, sad) were decoded earlier when contrasted with neutral expressions. While identity and sex information were modulated by image-level stimulus features, the effects of emotion were independent of visual image properties. Importantly, our findings suggest enhanced processing of face identity and sex for emotional expressions, particularly for angry faces and, to a lesser extent, happy faces.

Recognition memory research has identified several electrophysiological indicators of successful memory retrieval, known as old-new effects. These effects have been observed in different sensory domains using various stimulus types, but little attention has been given to their similarity or distinctiveness and the underlying processes they may share. Here, a data-driven approach was taken to investigate the temporal evolution of shared information content between different memory conditions using openly available EEG data from healthy human participants of both sexes, taken from six experiments. A test dataset involving personally highly familiar and unfamiliar faces was used. The results show that neural signals of recognition memory for face stimuli were highly generalized starting from around 200 ms following stimulus onset. When training was performed on non-face datasets, an early (around 200-300 ms) to late (post-400 ms) differentiation was observed over most regions of interest. Successful cross-classification for non-face stimuli (music and object/scene associations) was most pronounced in late period. Additionally, a striking dissociation was observed between familiar and remembered objects, with shared signals present only in the late window for correctly remembered objects, while cross-classification for familiar objects was successful in the early period as well. These findings suggest that late neural signals of memory retrieval generalize across sensory modalities and stimulus types, and the dissociation between familiar and remembered objects may provide insight into the underlying processes.

Recent theories on the neural correlates of face identification stressed the importance of the available identity-specific semantic and affective information. However, whether such information is essential for the emergence of neural signal of familiarity has not yet been studied in detail. Here, we explored the shared representation of face familiarity between perceptually and personally familiarized identities. We applied a cross-experiment multivariate pattern classification analysis (MVPA), to test if EEG patterns for passive viewing of personally familiar and unfamiliar faces are useful in decoding familiarity in a matching task where familiarity was attained thorough a short perceptual task. Importantly, no additional semantic, contextual, or affective information was provided for the familiarized identities during perceptual familiarization. Although the two datasets originate from different sets of participants who were engaged in two different tasks, familiarity was still decodable in the sorted, same-identity matching trials. This finding indicates that the visual processing of the faces of personally familiar and purely perceptually familiarized identities involve similar mechanisms, leading to cross-classifiable neural patterns.

Recognizing a face as belonging to a given identity is essential in our everyday life. Clearly, the correct identification of a face is only possible for familiar people, but 'familiarity' covers a wide range-from people we see every day to those we barely know. Although several studies have shown that the processing of familiar and unfamiliar faces is substantially different, little is known about how the degree of familiarity affects the neural dynamics of face identity processing. Here, we report the results of a multivariate EEG analysis, examining the representational dynamics of face identity across several familiarity levels. Participants viewed highly variable face images of 20 identities, including the participants' own face, personally familiar (PF), celebrity and unfamiliar faces. Linear discriminant classifiers were trained and tested on EEG patterns to discriminate pairs of identities of the same familiarity level. Time-resolved classification revealed that the neural representations of identity discrimination emerge around 100 ms post-stimulus onset, relatively independently of familiarity level. In contrast, identity decoding between 200 and 400 ms is determined to a large extent by familiarity: it can be recovered with higher accuracy and for a longer duration in the case of more familiar faces. In addition, we found no increased discriminability for faces of PF persons compared to those of highly familiar celebrities. One's own face benefits from processing advantages only in a relatively late time-window. Our findings provide new insights into how the brain represents face identity with various degrees of familiarity and show that the degree of familiarity modulates the available identity-specific information at a relatively early time window.

Recognizing a stimulus as familiar is an important capacity in our everyday life. Recent investigation of visual processes has led to important insights into the nature of the neural representations of familiarity for human faces. Still, little is known about how familiarity affects the neural dynamics of non-face stimulus processing. Here we report the results of an EEG study, examining the representational dynamics of personally familiar scenes. Participants viewed highly variable images of their own apartments and unfamiliar ones, as well as personally familiar and unfamiliar faces. Multivariate pattern analyses were used to examine the time course of differential processing of familiar and unfamiliar stimuli. Time-resolved classification revealed that familiarity is decodable from the EEG data similarly for scenes and faces. The temporal dynamics showed delayed onsets and peaks for scenes as compared to faces. Familiarity information, starting at 200 ms, generalized across stimulus categories and led to a robust familiarity effect. In addition, familiarity enhanced category representations in early (250 – 300 ms) and later (>400 ms) processing stages. Our results extend previous face familiarity results to another stimulus category and suggest that familiarity as a construct can be understood as a general, stimulus-independent processing step during recognition. Highlights Whether a face or scene is familiar can be decoded from the EEG signal with very similar temporal dynamics, starting at 200 ms and peaking around 400 ms after stimulus onset. The neural dynamics of this familiarity information generalizes across stimulus categories. Familiarity modulates stimulus category representations from 200 ms after stimulus onset, indicating deeper processing of familiar as compared to unfamiliar stimuli already during early processing stages.

Recognizing a face as familiar is essential in our everyday life. However, 'familiarity' covers a wide range -from people we see every day to those we barely know. Although face recognition is studied extensively, little is known about how the degree of familiarity af-fects neural face processing, despite the critical social importance of this dimension. Here we report the results of a multivariate cross-classification EEG experiment, where we study the temporal representational dynamics of the degree of familiarity. Participants viewed highly variable face images of 20 identities. Importantly, we measured face familiarity using subjective familiarity ratings in addition to testing explicit knowledge and reaction times in a face matching task. A machine learning algorithm, trained to discriminate familiar and unfamiliar faces from a separate study, was used to predict the degree of face familiarity from the pattern of the EEG data. We found that the neural representations of the degree of familiarity emerge between 400 -600 msec post-stimulus onset for famous persons. The correlation between decoding performance and behavioral familiarity was more reliable, occurred earlier and lasted longer when personally familiar and viewers' own faces were included in the analysis. Our findings provide new insights into how the brain represents faces with various degrees of familiarity and show that the degree of familiarity can be decoded reliably from the EEG at a relatively late time window. These results support the idea that representations of familiar faces form part of a general neural signature of the familiarity component of recognition memory processes.(c) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

We explored the neural signatures of face familiarity using cross-participant and cross-experiment decoding of event-related potentials, evoked by unknown and experimentally familiarized faces from a set of experiments with different participants, stimuli, and familiarization-types. Participants were either familiarized perceptually, via media exposure, or by personal interaction. We observed significant cross-experiment familiarity decoding involving all three experiments, predominantly over posterior and central regions of the right hemisphere in the 270 – 630 ms time window. This shared face familiarity effect was most prominent between the Media and Personal, as well as between the Perceptual and Personal experiments. Cross-experiment decodability makes this signal a strong candidate for a general neural indicator of face familiarity, independent of familiarization methods and stimuli. Furthermore, the sustained pattern of temporal generalization suggests that it reflects a single automatic processing cascade that is maintained over time. Highlights We investigated if a general neural signature of face familiarity exist A cross-experiment decoding analysis of EEG data was used The analysis involved perceptual, media and personal familiarization methods We found a preserved pattern of familiarity decoding across experiments between 270 and 630 ms post-stimulus This signature is consistent with previous reports on face familiarity effects

In a recent study using cross-experiment multivariate classification of EEG patterns, we found evidence for a shared familiarity signal for faces, patterns of neural activity that successfully separate trials for familiar and unfamiliar faces across participants and modes of familiarization. Here, our aim was to expand upon this research to further characterize the spatio-temporal properties of this signal. By utilizing the information content present for incidental exposure to personally familiar and unfamiliar faces, we tested how the information content in the neural signal unfolds over time under different task demands - giving truthful or deceptive responses to pho-tographs of genuinely familiar and unfamiliar individuals. For this goal, we re-analyzed data from two previously published experiments using within-experiment leave-one-subject-out and cross-experiment classification of face familiarity. We observed that the general face familiarity signal, consistent with its previously described spatio-temporal properties, is present for long-term personally familiar faces under passive viewing, as well as for acknowledged and concealed familiarity responses. Also, central-posterior regions contain information related to deception. We propose that signals in the 200-400 ms window are modulated by top-down task-related antic-ipation, while the patterns in the 400-600 ms window are influenced by conscious effort to deceive. To our knowledge, this is the first report describing the representational dynamics of concealed knowledge for faces, using time-resolved multivariate classification.

Identifying the faces of familiar persons requires the ability to assign several different images of a face to a common identity. Previous research showed that the occipito-temporal cortex, including the fusiform and the occipital face areas, is sensitive to personal identity. Still, the viewpoint, facial expression and image-independence of this information are currently under heavy debate. Here we adapted a rapid serial visual stimulation paradigm Johnston et al. (2016, https://doi.org/10.1016/j.cortex.2016.10.002) and presented highly variable ambient-face images of famous persons to measure functional magnetic resonance imaging (fMRI) adaptation. fMRI adaptation is considered as the neuroimaging manifestation of repetition suppression, a neural phenomenon currently explained as a correlate of reduced predictive error responses for expected stimuli. We revisited the question of image-invariant identity-specific encoding mechanisms of the occipito-temporal cortex, using fMRI adaptation with a particular interest in predictive mechanisms. Participants were presented with trials containing eight different images of a famous person, images of eight different famous persons or seven different images of a particular famous person followed by an identity change to violate potential expectation effects about person identity. We found an image-independent adaptation effect of identity for famous faces in the fusiform face area. However, in contrast to previous electrophysiological studies, using similar paradigms, no release of the adaptation effect was observed when identity-specific expectations were violated. Our results support recent multivariate pattern analysis studies, showing image-independent identity encoding in the core face-processing areas of the occipito-temporal cortex. These results are discussed in the frame of recent identity-processing models and predictive mechanisms.

The role of the dorsolateral prefrontal cortex (DLPFC) in implicit sequence/statistical learning has received considerable attention in recent cognitive neuroscience research. Studies have used non-invasive brain stimulation methods to test whether the DLPFC plays a role in the incidental acquisition and expression of implicit sequence learning. In a recent study, Prutean et al. has concluded that stimulating the left or the right DLPFC might not affect the expression of implicit sequence learning measured by the Serial Reaction Time (SRT) task. The authors speculated that the previous results revealing improved implicit sequence learning following DLPFC stimulation might have been found because explicit awareness accumulated with the use of Alternating Serial Reaction Time (ASRT) task. Our response presents solid evidence that the ASRT task measures implicit sequence learning that remains unconscious both at the judgment and structural level. Therefore, contrary to the conclusion of Prutean et al., we argue that the DLPFC could have a crucial effect on implicit sequence learning that may be task-dependent. We suggest that future research should focus on the specific cognitive processes that may be differentially involved in the SRT versus ASRT tasks, and test what the role of the DLPFC is in those specific cognitive processes.

The recognition of facial identity is essential for social interactions. Despite extensive prior fMRI and EEG/MEG research on the neural representations of familiar faces, we know little about the spatio-temporal dynamics of face identity information. Therefore, we applied a novel multimodal approach by fusing the neuronal responses recorded in an fMRI and an EEG experiment. We analyzed the neural responses to naturally varying famous faces and traced how face identity emerges over time in different areas of the brain. We found that image invariant face identity information prevails over an extended time period (from 150 to 810 ms after stimulus onset) in the representational geometry of a broadly distributed network of parietal, temporal, and frontal areas with overlapping temporal profiles. These results challenge the current hierarchical models of face perception and suggest instead concerted and parallel activation of multiple nodes in the brain’s identity coding network while processing information of familiar faces.

The occipital face area (OFA) is hierarchically one of the first stages of the face processing network. It has originally been thought to be involved in early, structural processing steps, but currently more and more studies challenge this view and propose that it also takes part in higher level face processing, such as identification and recognition. Here we tested whether the OFA is involved in the initial steps of recognition memory and plays a causal role in the differential processing of familiar and unfamiliar faces. We used an offline, inhibitory continuous theta-burst stimulation (cTBS) protocol over the right OFA and the vertex as control site. Electroencephalographic (EEG) recording of event-related potentials (ERPs), elicited by visually presented familiar (famous) and unfamiliar faces was performed before and after stimulation. We observed a difference in ERPs for famous and unfamiliar faces in a time-window corresponding to the N250 component. Importantly, this difference was significantly increased by cTBS of the right OFA, suggesting its causal role in the differential processing of familiar and unfamiliar faces. The enhancement occurred focally, at electrodes close to the right hemispheric cTBS site, as well as over similar occipito-temporal sites of the contralateral hemisphere. To the best of our knowledge, this is the first study showing the causal role of the rOFA in the differential processing of familiar and unfamiliar faces, using combined cTBS and EEG recording methods. These results are discussed with respect to the nature of familiar face representations, supported by an extensive, bilateral network.

Changing pre-existing, automatized motor skills often requires interference control. Prepotent response inhibition - one subdimension of inhibition - has been theorized to be particularly associated with successful interference control in motor skills. Recent evidence suggests that different inhibition subdimensions elicit distinct ERP patterns (with larger P3 components for response inhibition). Therefore, we examined whether a similar ERP pattern would arise in a task demanding participants to overcome interference emerging from strong motor automatisms. This was realized within a typing paradigm involving a letter switch manipulation which is able to produce strong, immediate interference effects. Most importantly, stimulus-locked ERP analyses revealed an enhanced P3 component at frontal, central and most pronouncedly parietal sites for interference trials, in line with previous reported patterns for response inhibition. Together, different analyses provide first insights into the electrophysiological correlates of motor skill change, corroborating the pivotal role of response inhibition for successful interference control.

Modifying established motor skills is a challenging endeavor due to proactive interference from undesired old to desired new actions, calling for high levels of cognitive control. Motor restrictions may facilitate the modification of motor skills by rendering undesired responses physically impossible, thus reducing demands to response inhibition. Here we studied behavioral and EEG effects of rule changes to typing in skilled touch-typists. The respective rule change-typing without using the left index finger-was either implemented per instruction only or with an additional motor restriction. In both groups, the rule change elicited delays and more errors in typing, indicating the occurrence of proactive interference. While stimulus-locked ERPs did not exhibit prominent effects of rule change or group, response-locked ERPs revealed that the time courses of preparatory brain activity preceding typing responses depended on the presence of motor restriction. Although further research is necessary to corroborate our findings, they indicate a novel brain correlate that represents changes in inhibitory response preparation induced by short-term motor restrictions.

Androgens are critical regulators of prostate differentiation and function, as well as prostate cancer growth and survival. Therefore, androgen ablation is the preferred systemic treatment for disseminated prostate cancer. Androgen action is exerted in target tissues via binding the androgen receptor (AR), a nuclear receptor transcription factor. Historically, the gene expression program mediated by the AR has been poorly understood. However, recent gene expression profiling and more traditional single-gene characterization studies have revealed many androgen-regulated genes that are important mediators of androgen action in both normal and malignant prostate tissue. This review will focus on the androgen-regulated gene expression program, and examine how recently identified androgen-regulated genes are likely to contribute to the development and progression of prostate cancer. We will also summarize several recent studies that have attempted to unravel how these genes are deregulated in androgen depletion independent prostate cancer.