Cognition

A fundamental question in visual perception is how the human visual system transforms the rich kinematic information present in observed actions into coherent social meaning. We addressed this by validating a 5-dimensional action space model - defined by Formidableness, Friendliness, Locomotion, Abduction, and Environmental Interaction - as a perceptual representation of avatar-conveyed actions. Using Representational Similarity Analysis, we first demonstrated strong topological correspondence between the models geometry and the structure of independent perceptual judgements, with cross-validated regression confirming the model as a generative framework that reliably predicts how observers evaluate novel actions. A morphing paradigm further revealed that perceptual ratings scaled approximately linearly with geometric distances along model dimensions, with each dimension selectively predicting its corresponding perceptual quality, satisfying the criteria for a valid psychological metric space. Critically, the 5D model showed substantially stronger alignment with semantic representations of actions than with their raw skeletal kinematics - an association robust to statistical control for kinematic similarity. This dissociation suggests that higher-order social-evaluative dimensions of action perception are largely invariant to low-level motion statistics, consistent with a hierarchical visual processing architecture in which kinematic input is progressively abstracted into a compact, semantically organised representational space optimised for social inference.

Building upon foundational psychological theories of event segmentation, this study addresses the limitation of overreliance on temporal boundaries as the primary segmentation criterion. Drawing on two experiments of direct and indirect causation in Mandarin Chinese, this study demonstrates how cognitive segmentation granularity and semantic integration jointly shape syntactic encoding. Results reveal distinct event encoding patterns for direct and indirect causation: coarse-grained segmentation leads to compact syntactic structures (e.g., verb-resultatives), while fine-grained segmentation yields varied multi-clausal expressions. Chinese speakers update event models via prediction errors of intentionality and protagonists, and tend to establish event boundaries at goal-relevant action endpoints when construing causal chains. These conceptual dimensions exert a modulating influence on both event segmentation and semantic integration. We propose a triad model integrating event segmentation, semantic integration, and linguistic specificity, providing a unified framework for elucidating the mind-language interface in conceptual construction and event coding of causation.

Humans rely on multiple sensory modalities, such as vision, audition, and touch, to perceive materials in everyday life. Previous research shows that multisensory perception leads to facilitation, yet the mechanisms responsible for this facilitation remain poorly understood. One potential mechanism is crossmodal prediction, whereby input from one modality generates predictions about another. While substantial research on multisensory facilitation has focused on bottom-up processes, such as spatial, temporal, and semantic congruency, the role of crossmodal predictions, particularly in material perception, has received little attention. To address this gap, we conducted two experiments, a reaction time task and a material rating task, in which participants viewed computer-generated animations of familiar objects being dropped to the ground. The paradigm exploited the natural temporal structure of impact events: pre-impact visual appearance provides information about an objects material and therefore can generate expectations about the forthcoming impact sound. Critically, participants saw the event only until before the impact, after which the video was masked. Thus, vision and audition were temporally aligned but not presented concurrently, allowing us to isolate the influence of visually driven expectations on the incoming auditory information without a bottom-up conflict. In some trials, the sound matched the expected material, but in a subset, it was incongruent, violating expectations elicited by the preceding visual information. Across both experiments, participants took longer to respond on incongruent than congruent trials, suggesting increased processing demands. In the rating task, incongruent trials also shifted material judgments, such that ratings reflected a weighted combination of incoming auditory information and visually driven predictions, with large individual differences in relative cue weighting. These findings suggest that priors on material properties from one modality, specifically vision, not only establish high-level expectations within the modality about an objects future state, but also extend across modalities.

Perception is typically biased towards prior expectations. In some cases, however, it seems repelled away from expectations, such that percepts appear less like what is expected. Even more intriguingly, separate studies have recently reported that predictions derived from gravity may shape perception in opposing ways. Specifically, gravity causes unsupported objects to accelerate downwards, leading to two predictions; that objects will move downwards (location prior) and at an increasing speed (acceleration prior). There is evidence that perceptual judgements are attracted towards location priors yet repelled from acceleration ones. Here we examine these effects in the same paradigm to determine whether they result from different types of stimuli and judgement, or more interestingly, might result from opposite influences of common predictive mechanisms influencing perception. We first replicate previous reports of a systematic bias to report upward moving objects as more accelerating than downward moving objects: effectively a repulsion from acceleration priors. We then show that the effect applies both at the level of retinal space and due to contextual cues concerning gravitational direction. Finally, we find that participants errors in a location reproduction task are similarly consistent with a repulsion from acceleration priors and, simultaneously, with an attraction towards location priors. We conclude by considering the ways in which these concurrent attractive and repulsive biases may reflect mechanisms optimising fast, accurate, and informative experiences in our ever-changing sensory world, therefore optimising the interface between perception and learning. Public Significance StatementIn a series of behavioural experiments, we show that expectations about how objects move due to gravity concurrently attract perception towards the prediction that objects move downwards, and repel perception away from the prediction that they do so at an increasing rate. These opposing influences inform current theories of perceptual processing, which explain how expectations may generate percepts that are fast, veridical, and informative.

Automation has been shown to weaken the sense of agency (SoA), the experience of controlling ones actions and their outcomes, by disrupting the predictive link between intention and effect. Explainable AI (XAI) has been proposed as a solution, yet the neurocognitive mechanisms through which explanations restore agency remain unclear. Across three EEG experiments using an autonomous-driving paradigm, we examined how automation and different forms of AI explanations modulate explicit agency judgments and early neural markers of agency-related predictive processing. In Experiment 1, automation reduced explicit feelings of control and was associated with reduced sensory attenuation, as reflected by increased P1-N1 amplitudes, decreased N1-P2 amplitudes, and delayed N1 latencies. In Experiment 2, distal (goal-level) explanations partially restored agency and selectively modulated early auditory responses, decreasing P1-N1 and increasing N1-P2 amplitudes. In Experiment 3, combining distal and proximal (trajectory-level) explanations produced the strongest behavioural and neural restoration of agency, yielding a graded attenuation of P1-N1 and enhanced N1-P2 responses along with accelerated N1 latencies. Across all experiments, mismatch negativity (MMN) remained unaffected, indicating that pre-attentive deviance detection is preserved regardless of agency or explainability. Together, these results identify component-specific EEG markers that track fluctuations in the sense of agency and demonstrate that multi-level intention sharing by AI systems enhances both predictive engagement and explicit control experience. This work provides a neurocognitive foundation for designing explainable autonomous systems capable of maintaining user agency.

Human spatial representations are often assumed to represent Euclidean properties such as length, distance, and angle. Here we test an alternative (but not mutually exclusive) possibility - that spatial memory is structured primarily around topological relations. Across four experiments, adults and children memorized simple letter-like figures and reproduced them by drawing, allowing the contents of their spatial representations to be revealed directly. Drawings showed systematic distortions of metric features, including strong biases of angles toward 90{degrees} and compression of line length towards an average value. In contrast, topologically critical features -- such as T-junctions and holes -- were reliably preserved, even relative to closely matched but topologically irrelevant features like L-junctions. These effects were magnified in a serial reproduction paradigm, in which participants iteratively generated new drawings from previous participant drawings: At the end of each mnemonic chain, figures converged on simplified topological structures as metric detail degraded. Similar patterns were observed in children aged five to eight years. Together, these findings suggest that basic topological relations may function as primitive building blocks of human spatial representation, with metric detail encoded secondarily. Significance statementThe iconic map of the London Underground is one of the most famous maps in history, yet something special about it goes unnoticed: it is not a veridical representation of space. Distances are arbitrary, and angles are presented only in coarse terms. Yet the ubiquity and appeal of such maps suggests that topological representation is intuitive -- as if the mind is keen to receive information in exactly this way. Here, using drawing as a tool, we show directly that the most primitive form of spatial representation appears to be a topological skeleton. Remarkably, even children as young as five represent spatial structure in topological terms, with roughly the same fidelity as adults -- pointing to an underappreciated building block of spatial representation.

During sideways movement of an observer, optic flow parsing - in which an objects speed in the world is extracted from all the other visual movement present in the scene, self-generated and otherwise - has been shown to be incomplete, leading to biases in speed perception, particularly when object and observer are moving in opposite directions. Here, we assess how judgements about the speed of objects moving in depth (judged relative to the world) towards or away from an observer (6 m/s) are affected by simultaneous movement of the observer either in the same or opposite direction as the object. In a virtual reality display, participants (n = 25) viewed a sphere simulated as moving in a corridor either while they were stationary or during visually simulated self-motion in the same or opposite direction as the object. They judged the spheres movement relative to the world by comparing its motion to a probe sphere that travelled laterally across the corridor in front of them. In a second experiment (n = 28) participants performed the same task but during faster self-motion (10 m/s). The second cohort also judged the direction in which the object was perceived to move during the same combinations of self and object speeds. Object speed was overestimated when the object travelled in the direction opposite to the observer compared to how objects motion was judged when the observer was stationary. However, object speed was also overestimated during self-motion in the same direction as the object where participants were also much more likely to misjudge the direction of motion of the object. Precision of judgements was lower when self-motion was simulated than it was for stationary observers. A simple arithmetic model of flow parsing fails to capture these results satisfactorily, suggesting that different mechanisms may be at play when the observer travels in the same direction as a moving object and is vulnerable to misperceiving its direction of travel.

Sentence comprehension requires the incremental construction of syntactic structure and semantic interpretation. Prior neural work (Nelson et al., 2017) identified key neural events at major phrase boundaries during sentence comprehension. To investigate a behavioral correlation of these processes, we used self-paced reading to examine the impact of syntactic phase boundaries, semantic congruence, and sentence structure on sentence processing. Participants read object-relative, subject-relative, and canonical control sentences one word at a time and a subsequent comprehension task. Reading times were analyzed relative to phrase boundaries, node-closing operations, and semantic congruence. Object-relative sentences produced the greatest processing difficulty, demonstrated by increased reading times and decreased comprehension accuracy. Reading times peaked at the phrase boundaries, indicating that processing costs are tied to constituent completion rather than individual lexical categories. Reading times also increased with the number of syntactic constituents completed at a phrase boundary. Agent-patient semantic congruence produced its largest effects in object-relative sentences, suggesting that semantic information interacts with syntactic computations when processing demands are greatest. These findings demonstrate that self-paced reading is sensitive to the incremental processing associated with syntactic constituent completion. Processing costs are tied more closely to phrase completion than to individual lexical categories, scale with the amount of syntactic structure completed at a boundary and interact with agent-patient semantic interpretation during object-relative sentence comprehension. Together, these findings support a view of sentence comprehension in which syntactic structure building and semantic interpretation proceed incrementally and interact continuously throughout online language processing.

Bayesian decision theory proposes that people make statistically rational decisions by combining prior knowledge with sensory information (likelihoods). This framework successfully explains many aspects of human behaviour. However, debate persists over whether people perform precise Bayesian computations (i.e., explicit Bayesian strategy) or rely on less demanding strategies - such as approximations or heuristics - that produce Bayesian-like behaviour (i.e., implicit Bayesian strategy). To address this, we examined people's sensitivity to metamers: different prior-likelihood combinations yielding identical optimal policies. An explicit Bayesian observer would show a temporary performance drop immediately after a switch of prior-likelihood combination, followed by recovery, reflecting prior updating. In two studies, we trained participants to estimate hidden target locations drawn from a Gaussian prior. On each trial, scattered dots provided likelihood information. Over time, participants learned the prior and combined it with likelihood information to infer target locations. We then covertly introduced an untrained prior-likelihood metamer. Unlike explicit Bayesian observers, participants' performance declined after the switch and persisted throughout the untrained pair presentation. This finding challenges strict Bayesian interpretations of task performance and suggests that participants rely instead on likelihood-sensitive strategy that is neither explicit Bayesian nor does it not fully integrate prior information. Our study demonstrates how metamer manipulations can distinguish behaviour that merely appears Bayesian, from behaviour genuinely produced by Bayesian computations, and calls for the use of metamers for ruling out alternative explanations of Bayesian-like behaviours.

The Sense of Agency (SoA), the experience of being in control of ones own actions, is thought to emerge from the comparison of internal sensorimotor predictions with afferent feedback. Classical comparator models treat SoA as arising from a prediction-feedback comparison. Volitional action likely engages multiple forward models that predict distinct features of an outcome, such as its timing and spatial trajectory. Whether prediction errors arising from these distinct forward models are integrated into a domain-general representation of agency, and if so by what computational logic, remains unresolved. We addressed this question using a Virtual Reality reaching task. Participants observed a virtual hand replicating their movements while we independently manipulated two sensorimotor domains: temporal delay and spatial angle deviation, in isolation and in factorial combinations. After each trial, participants made an SoA judgment. We examined whether SoA responses show computational hallmarks of integration between different features of sensorimotor prediction. Specifically, we pre-registered three computational models (Multiplicative, Minimum, and Mean) and compared their fit to per-trial responses. Across an exploratory sample (N = 16) and a pre-registered replication (N = 38), SoA declined monotonically with conflict magnitude in both domains. Critically, a Multiplicative integration rule consistently outperformed the Minimum and Mean rules. These results provide direct evidence for domain-general integration between prediction errors in SoA, governed by a multiplicative computational logic.

Recent research has suggested that the availability of non-instrumental information about the outcome of a risky choice increases risk appetite. In this study, we aimed to perform a conceptual replication of these findings and to examine the cognitive mechanisms underlying this effect. Across two experiments (N = 150, 102), we presented participants with mathematically fair gambles and allowed them to choose the size of their bet. Between trials, we varied the presence of non-instrumental information that would reveal the outcome ahead of time. In both experiments, we did not find consistent evidence for an effect of the availability of non-instrumental information on bet size. These findings suggest that the previously reported effects of non-instrumental information on risk appetite may have been an idiosyncratic feature of experimental design, rather than a more general phenomenon that characterises human decision making under risk.

Children often outperform adults in probabilistic statistical learning tasks, yet the mechanisms underlying this developmental advantage remain poorly understood. Here, we used eye-tracking measures of belief updating to examine how children and adults acquire and update predictions in a probabilistic sequence-learning task. Using the standard (oculomotor) reaction time measure, children showed stronger statistical learning than adults, replicating previous behavioral findings while revealing a more detailed profile of developmental differences in statistical learning. Critically, children updated their predictions more frequently: they were less likely to repeat previous predictions and more likely to shift their expectations in response to new input. Adults, in contrast, showed greater persistence, tending to maintain prior predictions even when those predictions were inconsistent with the underlying statistical structure. Despite these pronounced differences in updating behavior, the processing and use of prediction errors were remarkably similar across age groups. These findings indicate that developmental differences in statistical learning do not primarily arise from how prediction errors are computed, but rather from how prior beliefs and incoming information are weighted during belief updating. Children's enhanced learning may therefore reflect reduced reliance on stable priors and greater sensitivity to current sensory evidence, supporting a more exploratory learning strategy. Adults, by contrast, appear to favor an exploitative strategy that stabilizes existing predictions but reduces flexibility in probabilistic environments. More broadly, the results suggest that developmental changes in statistical learning may reflect age-related differences in how readily learners revise their predictions in response to incoming evidence. By integrating sensitive oculomotor measures with analyses that probe the mechanisms underlying belief updating, the present study provides a more fine-grained account of how predictive learning changes across development and offers a framework for reconciling previously inconsistent developmental findings in statistical learning.

Efficient face-to-face communication relies on the integration of auditory speech and visual articulatory signals. Over the past five decades, the McGurk illusion has been widely used as an index of audiovisual speech integration. However, substantial variabilities in susceptibility to the illusion across participants and speakers limit its reliability as a stable measure of audiovisual integration ability. Here, we introduce the McGurk illusion dataset (MID), which, to our knowledge, is the largest publicly available McGurk stimulus dataset to date. The MID comprises auditory (N = 400), visual (N = 400), and audiovisual (N = 640) speech stimuli generated from 80 Mandarin speakers and validated through behavioral judgments across 360,900 trials. Using this dataset, we characterized the acoustic and facial articulatory properties of McGurk stimuli, replicated substantial inter-participant and inter-speaker variabilities in illusion susceptibility, and revealed the associations between variations in McGurk illusion rate and the variations in unisensory perception, audiovisual correspondences, and speakers characteristics. Furthermore, the stimulus set enabled systematic comparisons of the reliability of different McGurk illusion-based indices of audiovisual speech integration. Overall, the MID not only provides a standardized resource for investigating audiovisual speech integration and its alterations across populations, but also supports research on speaker normalization, lip-reading, and speech perception.

When we feel restless and easily distracted, continuously switching tasks (exploration), our pupils tend to be large. In contrast, when we are calmly focused on a single task (exploitation), our pupils tend to be small. According to the Adaptive Gain Theory (AGT), a switch from exploitation to exploration is associated with an increase in norepinephrine in the locus coeruleus, which in turn triggers pupil dilation. However, the AGT does not provide a functional explanation of why exploration triggers pupil dilation. One possibility is that visual sensitivity, which increases with pupil size, is especially important during exploration. We set out to provide evidence consistent with this functional explanation, as well as to replicate two key previous results. Participants performed a four-armed bandit task, which induces both exploration and exploitation behavior. During the task, participants also needed to detect an occasional and unpredictable near-threshold peripheral flash. We replicated two key results: pupils were larger during exploration than during exploitation; and increased pupil size (overall, independent of exploration status) was associated with increased visual sensitivity. However, most importantly, we did not find that visual sensitivity was higher during exploration than during exploitation; probably, the reliable-yet-tiny increase in pupil size during exploration was too small to affect visual sensitivity. We conclude that key previous results are replicable; however, common experimental paradigms, such as the four-armed bandit task, induce only small changes in exploration behavior. Therefore, more powerful paradigms are required in order to test functional explanations of pupil-size changes during exploration and exploitation.

Past sensory experience shapes our perceptual decision-making in the now. Popular models frame perceptual decisions as either attracted towards or repelled away from recent sensory information, but it is unclear when and why these distinct effects emerge. We here ask whether effects turn from attractive to repulsive depending on the level of surprise elicited by the precision-weighted discrepancy between past and present sensory states. This model is based upon the idea that attraction is adaptive for optimizing efficiency and accuracy when discrepancies are small, because they likely reflect sensory noise rather than real change in the environment. In contrast, repulsion may reflect the upweighting of counterfactual evidence when discrepancies are large because they more likely signal the need for model updating. We test this model on a large amount of recently-collated trial-by-trial serial dependence data and consistently find support for it across the dataset, participant, and trial-by-trial level. Specifically, serial dependence effects are attractive at low discrepancies between past and current sensory states but turn repulsive when discrepancies are larger. Higher sensory precision is found to accelerate this flip by reducing the modal discrepancy threshold required to trigger repulsion effects. We discuss how these findings necessitate extending existing theories of serial dependence, and how they may resolve conflicts in the broader predictive processing, learning and perception literatures.

Why does an angry face feel uncomfortable? The answer is that it signals a threat. However, a face is only part of an encounter, and distance, facial stimulus type, and gaze may shape discomfort regardless of perceived anger. To separate these cues, we conducted three within-subjects virtual reality experiments. In each experiment, 24 adults viewed avatars at intimate, personal, and social distances (30, 100, and 300 cm, respectively) and rated the faces perceived anger and their own discomfort; head movement was recorded in Experiments 2 and 3. In Experiment 1, the expression (angry, neutral) and facial color (natural, red) were crossed with distance; in Experiment 2, a featureless mannequin served as a nonface comparison; and in Experiment 3, the gaze direction (direct, averted) was manipulated. Expression primarily determined perceived anger, whereas distance predominantly determined discomfort: A nearby neutral face was uncomfortable despite low perceived anger (Experiment 1). A neutral human face was more uncomfortable than a mannequin, although both received similarly low perceived-anger ratings (Experiment 2). Direct gaze increased the discomfort without changing perceived anger (Experiment 3). Backward head movement exhibited a similar pattern, with participants leaning back more from human faces than from the mannequin. These results indicate that the discomfort associated with an angry face is not merely explained by perceived anger. Instead, social discomfort was differentially associated with interpersonal distance and gaze direction and differed between the human-face and mannequin conditions.

Bayesian accounts of autism propose that perception is less influenced by prior expectations and more strongly driven by incoming sensory information in autistic than non-autistic individuals, with this altered balance cascading through the cognitive hierarchy to also influence higher cognitive functions. However, empirical support for these accounts remains mixed. Previous work has mostly tested these ideas in the context of objective environmental statistics, but recent work suggests that it may be subjective experience of structure, rather than structure itself, that shapes perceptual processing. Characterising these subjective experiences in autistic individuals is therefore crucial for understanding predictive processing in autism. In the present study, we thus examined subjective experience of statistical structure in autistic and non-autistic adults and tested how this experience relates to perceptual decisions. Participants were exposed to statistical regularities between action cues and visual stimuli (shapes), and we measured their speed and accuracy in reporting which shape they had seen. At the end of the study, participants were asked to estimate the probability and rate their surprise for each action-shape combination. Autistic and non-autistic participants showed similar subjective probability and surprise ratings and a comparable relationship between these ratings and perceptual decisions. Across participants, subjective ratings explained perceptual decisions better than objective structure. Together, these findings show that autistic and non-autistic adults experience statistical structure similarly, with these experiences exerting a similar influence on perceptual decisions - therefore suggesting that subjective experience plays a comparable role in predictive processing in autistic and non-autistic adults.

Current debates regarding the relative contribution of saliency versus semantics to gaze control often rely on comparing the predictive power of saliency and meaning maps. We argue that such indirect, global approaches are fundamentally limited because fixations arise from heterogeneous, local causes that are conflated in whole-scene comparisons. To substantiate this claim, we used a direct method where participants explicitly identified the reasons for fixation at specific clusters of high fixation density, distinguishing between low-level saliency and various semantic categories, as well as the most important one. The obtained judgments revealed that multiple factors contribute simultaneously to gaze control. Although their influence varied across fixation clusters, semantics generally dominated saliency. Notably, abstract semantic categories, particularly "unknown/unusual," proved important, highlighting the role of prior knowledge and novelty besides personal relevance in guiding attention. To interpret these findings in the context of existing models, we propose a framework distinguishing between processes highlighting interesting locations in the image from a sampling strategy translating this information into scanpaths. Within this framework, classic saliency and meaning maps are viewed as restricted inputs to the strategy, whereas deep learning-based models (e.g., DeepGaze IIE) are more general and may also implicitly encode aspects of the strategy itself. Consistent with this, we found that the predictive performance of DeepGaze IIE varied less significantly with the specific reasons for fixation than that of classic saliency and meaning map approaches.

The absence of voluntary visual imagery, known as aphantasia, offers a unique lens into the role of visual imagery in visual memory processes such as face recognition. While aphantasics often report difficulties, behavioral differences in standard tasks have generally been small. One possibility is that the contribution of visual imagery becomes apparent only when face recognition is especially demanding. We compared age- and gender-matched aphantasics and typical imagers on the more challenging long-form version of the Cambridge Face Memory Test (CFMT+) and on a measure of inverted face recognition. We also included tasks assessing object recognition and face perception. No group differences emerged for face perception or object recognition. By contrast, typical imagers outperformed aphantasics under high visual and mnemonic demands in face recognition in the laboratory cohort, particularly at the highest difficulty level of the CFMT+ and in inverted face recognition. This effect was attenuated or even absent in the online cohort. Drift-diffusion modelling indicated that this discrepancy was primarily driven by reduced response caution in online typical imagers. A meta-analysis of published short-form CFMT studies (total N = 432) revealed a moderate and reliable advantage for typical imagers (hedges g [≤] 0.41). Finally, across cohorts and tasks, aphantasics reported consistently lower subjective confidence, independent of accuracy. Overall, these findings suggest that visual imagery benefits face recognition, and highlight the need for caution in online testing, the predominant approach in aphantasia research.

Empathy has been linked to facial emotion recognition, but whether empathy is associated with the structural representation of facial affect (how observers position different affects relative to one another in face-shape space) remains largely unexplored. 53 adults completed a genetic-algorithm face task that generated prototypes for 13 affects using the Basel Face Model, and completed the 60-item Empathy Quotient (EQ). The genetic-algorithm task produced structurally distinct prototypes for all 13 affects (all paired tests p < .001 Bonferroni-corrected; Cohens dz 1.74-3.13), confirming that participants generated reliable, affect-specific face representations. A 13 x 13 between-affect distance matrix was then compared between higher-EQ (n = 30) and lower-EQ (n = 23) groups. 1 pair survived full correction across all 156 off-diagonal cells: Amusement x Contempt (Cohens d = -1.31), with higher-empathy participants representing these two affects as structurally closer to each other. Both amusement and contempt are social-evaluative affects that share overlapping facial action components, and this convergence may reflect heightened sensitivity to shared expressive structure among higher-empathy observers. In exploratory analyses, permutation testing and continuous-EQ correlations pointed to a broader pattern centered on social-evaluative affects (Amusement, Awe, Contempt, Fear, Happiness, Pride, Sadness, Interest). Individual differences in empathy appear most prominently associated with how social-evaluative affects are structurally positioned in face-shape space, suggesting that empathy modulates not just emotion recognition accuracy but the representational geometry of facial affect itself.