Neuroscience of Consciousness

Flow, as defined by Mihalyi Csikszentmihalyi (1975), is a holistic sensation experienced when individuals are fully immersed in an activity, resulting in a mental state characterized by a diminished sense of self and altered perception of time. To investigate the global neural dynamics underlying flow, we employed EEG microstate analysis to capture the spatial and temporal properties of dominant transient global brain states (Lehmann et al., 1998). In a study involving 43 participants playing the video game Thumper for 25 minutes, we extracted three four-minute EEG segments from each session corresponding to reported experiences of flow, boredom, and frustration, as determined by self-reports and performance metrics. Across conditions, six distinct microstate topographies (A-F) accounted for most of the global variance. Given that reduced self-referential processing is a key feature of flow, we hypothesized that flow would modulate the properties of microstates C and E, which have been associated with brain regions resembling the default mode network (DMN). Compared to boredom and frustration, the flow condition showed significantly decreased global explained variance, mean duration, time coverage, and occurrence frequency of microstate E, as well as reduced mean duration and time coverage of microstate C. These findings suggest that microstates associated with self-referential processing are shorter and less frequent during flow than during boredom and frustration. This supports the notion that the flow experience modulates global brain dynamics, particularly within the DMN. Furthermore, our results align with previous research reporting reduced DMN activity during meditative and psychedelic states, reinforcing the idea of diminished self-awareness in such conditions.

The sensory content and temporal structure of stimuli have been shown to consistently bias duration perception. Temporal intervals filled with continuous sensory input ("filled intervals"), are often perceived as lasting longer than intervals marked only by their onset and offset ("empty intervals"). Despite this robust behavioral finding, it remains unclear whether filled and empty intervals rely on similar or distinct neural mechanisms and, more generally, how sensory format shapes the neural processing of millisecond time. To address this question, we asked twenty-one healthy participants to reproduce visual durations across different stimulus configurations while high-density scalp EEG was recorded. Behavioral results revealed differences in performance across stimulus configurations. Event-related potentials (ERPs) recorded at occipito-parietal and fronto-central electrodes between 0.1 and 0.4 s after duration offset were modulated in amplitude by both stimulus duration and format. These modulations scaled with the sensory load of the stimulus and its duration, suggesting a common underlying mechanism. A Representational Similarity Analysis (RSA) of the ERP data showed that perceived time was represented more strongly than physical time particularly at occipito-parietal electrodes, but only within the 0.2-0.3 s post-offset window, where stimulus format exerted a pronounced effect on the ERP signal. These findings highlight the role of sensory processing in shaping duration perception and its neural coding, and reveal an early neural signature of perceived time in occipito-parietal electrodes. 1 Significance statementOur perception of subsecond durations is distorted by the sensory content of stimuli. Here, we investigated how stimulus configuration shapes the neural correlates of visual duration perception. Specifically, we asked whether temporal intervals filled with continuous sensory input are processed differently from those lacking such content. We found that, between 0.2 and 0.3 s after interval offset, ERP amplitudes were modulated by stimulus content, and in this same temporal window the EEG signal reflected the perceptual bias. These findings support the view that duration processing and perception are deeply rooted in sensory processing.

As avatars become more commonplace, understanding how the brain processes emotional expressions in virtual faces is critical. We compared behavioral and neural responses to real and virtual faces expressing seven emotions (anger, disgust, fear, joy, sadness, surprise, neutral). In Experiment 1 (n=61), participants rated the similarity between paired faces. Expressions conveying the same emotion were rated as highly similar across face types, whereas mismatched emotions yielded substantially lower similarity ratings, indicating perceived emotional meaning was preserved despite differences in face realism. In Experiment 2 (n=91), functional near-infrared spectroscopy was used to measure brain activity while participants viewed the same stimuli. General-linear-model analyses revealed greater activation limited to visual areas for 1) virtual faces and 2) surprise and neutral expressions. Functional connectivity analyses, however, revealed network level differences between face type and emotion across the brain. Real faces elicited stronger connectivity patterns across frontal, central-temporal, and parietal regions, whereas high-arousal emotions (fear, anger, and joy) were associated with broader network engagement than other expressions. Our results suggest face-type processing occur in early visual areas, and despite perceptual similarity, different emotions on real and virtual faces are associated with distinct patterns of network level connectivity across the brain.

Meditation has been associated with improvements in attention, emotional regulation, and mental well-being, motivating increasing interest in objective methods for assessing meditative states. In this study, we investigate whether EEG-based machine learning can reliably distinguish between multiple meditation styles and mind-wandering states. EEG data were recorded from experienced meditators performing three meditation styles, Shamatha, Vipassana, and Metta, together with an eyes-closed mind-wandering condition. EEG signals were preprocessed to remove artifacts, and features were extracted from frequency, time-frequency, and time domains. Classification was evaluated using both intra-subject and inter-subject strategies with multiple machine learning classifiers. Results demonstrate high intra-subject classification accuracy across meditation-versus-mind-wandering and meditation-style comparisons, indicating strongly discriminative subject-specific neural signatures. In contrast, inter-subject performance decreased substantially, particularly for distinguishing meditation styles, suggesting considerable inter-individual variability in meditation-related EEG patterns. Furthermore, temporal analysis revealed that classification performance increase over time, indicating that the neural distinctions between meditation states become increasingly pronounced over time. Additionally, t-SNE visualization showed clear within-subject clustering but increased overlap across subjects, explaining the reduced inter-subject generalization. Overall, these findings highlight the potential of EEG-based machine learning for personalized assessment and monitoring of meditative states while emphasizing the challenges of developing subject-independent meditation classification systems.

Past work has indicated that expectation can modulate neural responses to visual stimuli, but it is unclear whether these effects remain consistent across different types of unexpected stimuli. Here, we measured and compared neural prediction effects associated with semantic category and presentation frequency-based expectations in real-world object stimuli. Participants (n = 35) viewed real-world object images in rapid serial visual presentation (RSVP) streams. Semantically unexpected stimuli occurred when a stimulus was presented in a semantically incongruent stream. Low-frequency violations occurred when a rarely presented stimulus was displayed in a semantically congruent stream. Multivariate pattern analysis of electroencephalography (EEG) was used to quantify and compare the degree of information represented in neural activity for stimuli in different prediction conditions. Semantically expected stimuli yielded lower decoding accuracy relative to random (unpredictable) stimuli (125-313 ms post-onset) while semantically unexpected stimuli exhibited increased decoding accuracy (199-238 ms & 523-559 ms). Low-frequency violations yielded decoding accuracy which was not significantly different from semantically expected stimuli. Exploratory analyses indicated that dissimilarity between expected and presented stimuli quantified in terms of higher-level stimulus features, but not low-level visual features, modulated the observed neural prediction effects. These results demonstrate that different types of prediction violations have distinct modulatory effects on neural responses, providing novel insight into the neural implementation of predictive processing.

Empathy, a key element of social interaction, involves both cognitive and affective processes and is commonly investigated through measures such as empathic accuracy and affective physiological synchrony. While physiological synchrony offers a continuous measure of affective processes, empathic accuracy typically relies on discrete self-reports, leaving their temporal relationship largely unexplored. Advancing this line of research requires datasets that integrate time-continuous self-reports with physiological signals, yet such datasets--particularly those focusing on the empathizee--remain limited. To fill this gap, we present EMPAC (Empathy Measurement: Physiological, Affective, and Cognitive), a multimodal dataset constructed. To create empathy-eliciting stimuli, professional actors performed emotionally intense, pseudo-autobiographical narratives while their physiological signals (e.g., ECG, EDA) and continuous self-reported emotional states were recorded. We then conducted two observer experiments using these video recordings. In Experiment 1, to validate the stimuli as empathy-eliciting materials, observers continuously rated emotional intensity without being informed of the specific emotion portrayed, following the protocol of previous studies on time-series empathic accuracy. Yet this approach sometimes revealed a gap between the emotion category portrayed by the target and that perceived by the observers. In Experiment 2, we introduced a revised procedure in which the target emotion category was disclosed prior to viewing, revealing that specifying the target emotion led to a different relationship between individual empathy traits and empathic accuracy than observed in Experiment 1. EMPAC thus provides a rich, temporally aligned resource for investigating empathy dynamics in naturalistic settings and for evaluating methodological variations in empathic accuracy paradigms.

Visual working memory (vWM) is often linked to conscious experience and visual imagery, but it is typically described as a system that stores separate, independent items. These assumptions are difficult to reconcile, given the unified nature of conscious experience. Here, we test the hypothesis that vWM relies on at least two distinct representations: an underlying, unconscious memory trace and a consciously accessible, integrated representation. A total of 216 participants performed a change-detection task, in which they rated their perceptual awareness of the memory display during the maintenance interval. Critically, we manipulated the statistical properties of the displays (average item size and size variability) to probe sensitivity to unified ensemble-level structure. Results revealed a dissociation between subjective and objective measures. Perceptual awareness increased for displays with larger, more variable items, whereas objective performance improved for displays with smaller, less variable items. Despite this difference, subjective awareness still predicted performance, and even incorrect responses showed consistent biases rather than random guesses. Importantly, individual differences in imagery vividness (VVIQ) were selectively associated with subjective awareness and estimation bias, but not with objective correctness. These precision biases were further shaped by display statistics, suggesting that multiple representations can guide behavior. Together, our findings support a reinterpretation of vWM performance in which task responses can draw on both unconscious and consciously accessible representations. One possible explanation for these behavioral patterns is that subjective experience reflects integrated, ensemble-like representations, while objective performance depends more strongly on item-specific information. Public significance statementsWorking memory allows us to temporarily hold and use information, and differences in this ability are closely linked to broader cognitive skills such as intelligence. This study shows that these differences may not depend only on how much information people can store, but also on how they experience it: some individuals appear to rely more on consciously accessible, image-like representations, especially when memory is uncertain or prone to error. By demonstrating that subjective experience and the vividness of imagery can shape behavior independently of objective accuracy, these findings suggest that how we use memory may be as important as how much we can store, with implications for understanding individual differences in cognition.

Much of a typical individuals mental life is characterized by spontaneous thoughts that occur independently of external stimuli. In prior studies, ongoing mental experiences and their neural correlates have been captured using thought probes presented at random intervals during functional Magnetic Resonance Imaging (fMRI). However, this approach results in temporally imprecise estimates of brain activity relative to the arising of mental experience. In this preregistered, proof-of-concept study, we aimed to improve temporal precision using a novel method termed real-time fMRI-triggered experience-sampling (rt-fMRI-ES). We analyzed blood-oxygenation-level-dependent signals in real time during a wakeful resting state (n=60) to trigger thought probes from spontaneous activations within two regions: the dorsal anterior insular cortex (daIC; a key region within salience network) and posteromedial cortex (PMC; a key region within default mode network). We tested two preregistered hypotheses: (H1) Ratings of arousal time-locked to daIC-activation trials are higher than ratings time-locked to non-daIC-activation trials; (H2) Ratings of external-attention time-locked to PMC-activation trials are lower than ratings time-locked to non-PMC-activation trials. After applying preregistered exclusion criteria, 42 participants (1243 trials) and 49 participants (1429 trials) were included in H1 and H2 analyses, respectively. We did not find evidence in support of H1, but we did find evidence in support of H2, as external-attention ratings were significantly lower for trials triggered by PMC activation compared to other trial types. Taken together, we successfully developed and validated the rt-fMRI-ES method, offering a novel technique to efficiently capture spontaneous thoughts based on ongoing neural activity. Preregistered Stage 1 Recommendationhttps://osf.io/sd4hu (Date of in-principle acceptance: 07/24/2024; under temporary private embargo)

BackgroundThis study examines a competition-based model (C-model) designed to capture the temporal dynamics of successive brain microstates derived from electroencephalography (EEG) recordings during eyes-open conditions. The analyzed data were obtained from a public repository comprising microstate sequences from 60 sessions of a single subject [1]. When applied to microstate dynamics, the C-model posits a stochastic competition among neural circuits underlying the expression of individual microstates. MethodsThe model is formulated at a conceptual level (computational level in Marrs framework) and employs a geometric distribution to account for the long right tail of microstate duration distributions, interpreted as the probability of "failure" of the currently active microstate to persist. To account for the short-lived left tail, the model incorporates a transient increase in the stability of the currently active network, or equivalently, a temporary decrease in the activation probability of competing microstates (refractory period). ResultsThe model provides a good fit to the microstate duration distributions across all 60 sessions. One third of sessions showed microstate identity sequential dependency with respect to the previous microstates. DiscussionThese results suggest that the C-model captures key aspects of microstate temporal structure. Moreover, because microstate probabilities can be modulated by psychophysiological conditions--including the influence of previously active networks--the model may serve as a building block for more comprehensive neurobiological frameworks of neural and behavioral dynamics. In such frameworks, microstate sequences could emerge from structured competition and flow among neural networks supporting microstate expression.

Categorisation is often treated as a form of compression: a high-dimensional stimulus space is reduced to a smaller set of behaviourally or cognitively useful classes. However, compression alone does not determine whether a category map is useful. The present manuscript develops an information-theoretic framework for evaluating categorisation in terms of both category complexity and target-relevant information preservation. Across a set of synthetic demonstrations, alternative category maps over the same stimulus space are shown to preserve different target variables, including identity, action, nuisance, and hierarchical category structure. The framework is then extended to learned visual representations by analysing layer-derived category maps from a pretrained ResNet-50 network applied to CIFAR-10 images. Two scenarios are compared: a clean-only object run and a pooled nuisance run containing clean, blurred, pixelated, and noise-perturbed images. The results show that category maps can have substantial entropy while preserving information about a variable that is not aligned with the specified target, and that the value of a categorisation depends on the target variable to be preserved. The manuscript argues that categorisation should therefore be evaluated not only by compression or separability, but by the information retained about a specified cognitive, behavioural, or computational target.

In previous studies by the author on binocular vision with the asymmetric eye (AE), which models a healthy human eye with misaligned optical components, the results were primarily presented in the Rodrigues vector (RV) framework and supported by simulations and 3D visualizations in GeoGebras dynamic geometry environment. In this paper, the novel geometric kinematics of the human eye, that is, the eye with misaligned optics, and simplified assumptions about the eye rotations (the eyes translational movements are disregarded), are developed within the framework of rigid-body rotations. The originality of the analysis lies in a precise geometric decomposition of a full rotation of the eyes posture into a torsion-free rotation (the geodesic part) and a torsional rotation (the non-geodesic extension of the geodesic part). This decomposition is extended to the corresponding decomposition of the angular velocity. A novel derivation of the eyes angular velocity from the RV formulation of the eye kinematics is proposed.

Electroencephalographic (EEG) microstates provide a compact framework for characterizing the temporal organization of large-scale brain activity, yet their sensitivity to altered brain states remains insufficiently explored. In this study, we applied broadband and frequency-resolved EEG microstate analysis to resting-state EEG data from two publicly available datasets acquired under markedly different altered-state conditions: psilocybin microdosing and acute inhaled N,N-dimethyltryptamine (DMT). The aim was to determine whether narrowband microstate analysis reveals structured alterations in resting-state brain dynamics beyond those captured by broadband analysis alone. Psilocybin microdosing was associated with relatively subtle effects, including reduced global field power and frequency-specific alterations in delta- and theta-band microstate parameters, while no significant broadband spatiotemporal changes were observed. In contrast, acute inhaled DMT was associated with broader microstate alterations spanning broadband, delta, theta, and alpha activity, indicating more extensive reorganization of temporal microstate expression. Across both datasets, a descriptive overlap was observed in the delta band, where microstate C showed increased duration and microstate D showed decreased occurrence. Given the substantial differences between datasets in dose, route of administration, temporal dynamics, and study context, these overlapping effects should be interpreted cautiously. Overall, the findings support frequency-resolved EEG microstate analysis as a useful approach for characterizing altered resting-state brain dynamics and for detecting frequency-specific effects that may be obscured in broadband summaries.

Conversations can feel effortlessly engaging or, conversely, difficult and unrewarding. Multiple factors contribute to the experienced quality and outcomes of a conversation, among them how interlocutors align with each other. The present study investigated speech-to-speech, brain-to-speech, and brain-to-brain coordination as markers of interpersonal alignment, examining their relationship with jointly perceived interaction quality and mutual affinity between conversational partners. Pairs of previously unacquainted participants (dyads) engaged in multiple short, free-form conversations on topics of varying interest while their vocal and neural activity were simultaneously recorded in a dual-EEG ("hyperscanning") setup. We analyzed interlocutors prosodic adaptation, neural speech tracking, and neural coordination during each conversation. At the speech-to-speech level, our findings reveal that partners with more positive mutual impressions became more similar in their volume and voice quality over the course of the experiment session, reflecting greater prosodic convergence. At the brain-to-speech level, we found no reliable effect of interaction quality on neural tracking of unfolding speech within any individual region, although topographical differences suggested relative modulation across scalp sites. Finally, at the brain-to-brain level, our findings show that higher perceived interaction quality enhanced inter-brain relationships across frequency bands (alpha and theta) and temporal dependencies (concurrent/near-instantaneous and recurrent/listener-lagging), with the strongest effects observed for concurrent alpha-band coupling. These findings suggest that distinct coordination processes are involved in how interlocutors experience an interaction and how they establish relational affinity, casting new light into the mechanisms that make a conversation worthwhile.

Functional connectivity (FC) is a statistical measure that reflects the degree of phase consistency between two signals and provides insights about potential interactions between two brain regions. Previous studies have reported conflicting results on the effect of meditation on FC, with some showing enhancement while others reporting suppression of FC. However, even though meditation increases power over a broad frequency range between 15-200 Hz and beyond, most FC studies have reported changes over fixed and narrow frequency bands below 50 Hz. Further, meditation-induced changes in power spectral density (PSD) and FC have never been compared with changes with other factors such as age, gender and stimulus. We recorded electroencephalogram (EEG) from open-eyed meditators (N=35) and their gender-and age-matched controls (N=36) and found that meditation was associated with a state decrease in FC across a broad frequency range (15-200 Hz), while PSD showed both trait and state enhancement. Furthermore, visual gratings, which are known to enhance narrow-band gamma power, led to reduced gamma FC in both meditators and controls. We also compared the effect of aging and gender on a different dataset of healthy middle-aged (N=78) and elderly (N=89) participants and found differences in distinct frequency bands that were limited to a narrow range. We also found that often-used average referencing heavily distorted the FC and gave uninterpretable results. Overall, our results suggest distinct neural mechanisms underlying healthy aging, vision, and meditation and further recommend caution while using average referencing to study phase-based metrics. Significance statementMeditation research has reported inconsistent effects on functional connectivity (FC), partly because most studies examined only narrow low-frequency bands despite meditation altering brain activity across a much broader frequency band. This study demonstrates that meditation produces a broadband state reduction in FC across 15-200 Hz, while simultaneously enhancing power. In contrast, healthy aging, gender, and visual stimulation showed frequency-specific effects confined to alpha (8-12 Hz) and high-beta (20-36 Hz) bands, highlighting meditations unique large-scale neural signature. The study also shows that average referencing can severely distort phase-based FC estimates, leading to misleading interpretations. These findings clarify conflicting literature, distinguish meditation from other neural modulators, and provide important methodological guidance for EEG connectivity research.

Focusing on an organisms task at hand is instrumental for intelligent and goal-driven behavior. However, humans and other animals often fail to pay sustained attention across long time intervals. Failing to stay on-task may cause one to miss crucial task-relevant signals, leading to impaired performance, which can have serious consequences. Therefore, it is important to understand the neural basis of attentional lapses. One promising neural marker of attentional lapses is the frontal P3 (fP3) EEG component, which has been suggested to reflect the susceptibility to incoming sensory input. Following this, we hypothesized that the fP3 1) predicts imminent lapses of attention, and 2) that it should predict upcoming lapses of attention across modalities. In two experiments, we found that the fP3 reliably tracked lapses of attention of sustained attention already seconds preceding the crucial visual signal. We further extended this to the auditory domain: Already 1.5s ahead of the incoming auditory target, the fP3 revealed whether that target was detected or not. Detailed topographic analyses did, however, reveal a slight dissociation between modalities in underlying intracranial source configurations. In sum, this work revealed a supramodal neural signature of susceptibility, which tracks lapses of sustained attention seconds ahead of the critical incoming sensory input.

Does attention operate within afterimages? Here we show that it does, using a novel pupillometry-based paradigm. Participants fixated centrally while bright and dark peripheral stimuli were presented, and a central cue directed attention to one of them. Over time, the stimuli perceptually faded due to adaptation and were then removed, leaving strong, negative afterimages. We found that pupil size tracked the brightness of the attended stimulus both during perceptual fading, when stimuli were present but perceptually weakened, and during perception of afterimages, when no physical stimuli were present. In the latter case, pupil size reflected the brightness of the negative afterimage rather than the preceding physical stimulus. This finding shows that covert attention can be directed within afterimages. More broadly, the results suggest that attention to afterimages bridges the gap between external and internal attention, challenging the notion of a strict dichotomy and supporting the view that this distinction is better understood as a continuum.

The critical brain hypothesis contends that brains operate near a phase transition where excitation and inhibition are balanced, enabling neural dynamics to rapidly adapt and reorganize for cognitive demands. Allocating control resources to maintain stable task representations likely shifts brains away from criticality. Here, we test whether proximity to criticality indexes the balance between flexible adaptation and effortful task engagement. To do so, we adapt a time-resolved measure of scale invariance in EEG amplitude fluctuations (d2), capable of quantifying distance-to-criticality under non-stationary conditions - as during cognitive tasks. We benchmark our measure using ground-truth simulations of a neural mass model and show that d2 is lowest when excitation and inhibition are balanced. Next, we apply d2 to data collected during a task-switching paradigm and find that more demanding trials increased deviation from criticality, whereas greater flexibility, faster responses, and higher accuracy occurred closer to criticality. These effects were region-specific: deviation at posterior electrodes predicted worse performance, while deviations at frontal midline electrodes predicted better performance. Together, these results suggest that deviations from criticality reflect both cognitive load and effort exertion, highlighting EEG amplitude scale-invariance as a sensitive marker of adaptive neural dynamics under cognitive demand.

Creativity fluctuates markedly from moment to moment, even within the same individual, yet the neural dynamics that determines whether a given attempt produces a highly creative idea remains poorly understood. Prior studies have identified static EEG correlates of creative thinking, but these do not explain how brain activity is dynamically organized before and during successful idea generation. Here, we model creative cognition as trajectories through a neural state space using energy landscape analysis (ELA) of EEG recorded during two complementary problem-solving paradigms: the divergent Alternative Uses Test (AUT) and the convergent, goal-directed Fusion Innovation Test (FIT). Across both tasks, creative success was associated with dissociable dynamical signatures in the resting and ideation stages. Before ideation, greater diversity of resting-state patterns of activity, corresponding to possible attractors, indexed a preparatory substrate of creative potential, showing weak trial-level effects but robust subject-level coupling with performance. During ideation, higher creativity was predicted by how the brain traversed its accessible state space: successful trials were characterized by traversal biased toward sustained exploration within stable attractor basins rather than frequent switching between basins of attraction ({beta} = 0.104, p = 0.004, trial-level). These findings identify a task-invariant, biologically grounded dynamical mechanism of creative cognition and show that creative performance depends not only on which neural states are available, but also on how neural activity traverses that state space.

Mental rotation in 3D is a key cognitive skill involving dynamic spatial transformations, for which pronounced individual differences have been documented. Here we ask whether individual differences in 3D abilities can be explained by analogous differences in 2D abilities. 3D mental-rotation was assessed by the Vandenberg & Kruse Mental Rotation Test (3D-MRT) and examined for association with performance and underlying electrocortical mechanisms during a 2D letter rotation task. Participants (N=40) first completed the MRT and then performed a computerized 2-D letter rotation task in which they had to identify whether letters were oriented in a standard or a mirrored direction (parity judgment) when rotated at 0{degrees}, 60{degrees}, 120{degrees}, and 180{degrees} while EEG was recorded. Reaction times (RTs) and error rates increased with angular disparity. The angular disparity effect on RT was smaller for mirrored letters. Low, relative to high, 3D-MRT scoring participants showed more pronounced accuracy declines at higher rotation angles. An EEG Event Related Potential (ERP) known as the Rotation-Related Negativity (RRN) became more pronounced with increasing angular disparity. High 3D-MRT scores were associated with a stronger RRN response at central-parietal sites. In addition, the ERP-P3b wave was more pronounced at central-parietal sites for low 3D-MRT scorers, independent of angular disparity. It is concluded that 3D rotational ability is positively associated with 2D mental rotation performance, and more strongly with enhanced recruitment of neural visual-spatial cortical representations than with enhanced recruitment of more general cognitive resources.

Evidence suggests that information represented more reliably in neural activity patterns across repeated exposures is more likely to be remembered. However, this relationship varies across category-selective regions of the ventral visual cortex. Specifically, for house stimuli neural reliability has been robustly linked to memory outcomes in the parahippocampal place area (PPA), but less consistently for faces in the fusiform face area (FFA). The reason for this mismatch is unknown. To address this discrepancy, we implemented a novel within-category manipulation by presenting highly face-like and non-face-like house stimuli during fMRI, followed by a memory test. Non-face-like houses were more likely to be remembered than face-like houses. Although face-likeness did not elicit face-selective responses in the FFA, representational reliability in ventral visual cortices, particularly in the FFA, showed an association with individual differences in memory performance. Finally, symmetry emerged as a potential perceptual factor underlying differences in mnemonic outcomes.