While a compassionate face for Germans mirrors others\' suffering, for U.S. Americans, a compassionate face is one that expresses a slight smile, partly because U.S. Americans want to avoid feeling negative (\"avoided negative affect\"; ANA) more than do Germans. The present work examines what people in a non-WEIRD (i.e. Chinese) cultural context think a compassionate face looks like. Additionally, it investigates whether an individually-measured cultural variable (i.e. ANA) can explain differences in conceptualisations of compassion between Chinese and U.S. Americans. Participants in China and the U.S. selected the face that most resembles a compassionate face in a reverse correlation task and completed a measure of ANA. As predicted, Chinese mental representations of a compassionate face included more sadness and less happiness compared to U.S. American mental representations of a compassionate face, and Chinese participants wanted to avoid feeling negative less than did U.S. Americans. Finally, ANA mediated the cultural differences in conceptualisations of compassion. We discuss how ANA and conceptualisations of compassion might be related to how people view the experience versus the expression of different emotions. This work has important implications for therapeutic settings and the meaning of compassion in an increasingly globalised and connected world.

Goal: We validate a recent reverse correlation approach to tinnitus characterization by applying it to individuals with clinically-diagnosed tinnitus. Methods: Two tinnitus patients assessed the subjective similarity of their non-tonal tinnitus percepts and random auditory stimuli. Regression of the responses onto the stimuli yielded reconstructions which were evaluated qualitatively by playing back resynthesized waveforms to the subjects and quantitatively by response prediction analysis. Results: Subject 1 preferred their resynthesis to white noise; subject 2 did not. Response prediction balanced accuracies were significantly higher than chance across subjects: subject 1: 0.5963, subject 2: 0.6922. Conclusion: Reverse correlation can provide the foundation for reconstructing accurate representations of complex, non-tonal tinnitus in clinically diagnosed subjects. Further refinements may yield highly similar waveforms to individualized tinnitus percepts.

This work addresses the link between motivation and self-perception by systematically studying visual self-representations. We propose that the way individuals perceive themselves may be associated with dispositional and situationally induced approach and avoidance motivation. First, we investigate how dispositional differences in approach/avoidance motivation and self-esteem relate to self-perception. Second, we investigate how state differences in approach/avoidance motivation relate to self-perception. Based on motivation literature, we predicted that self-perception would reflect psychological equanimity at the dispositional level (seeing the self in a favorable light), and motivational flexibility at the situational level (situational avoidance motivation results in a more realistic view of one\'s qualities). We use up-to-date face modeling methods to measure individuals\' self-perceptions in a systematic, nuanced, and implicit way: Participants are repeatedly asked to indicate which of two portrait versions better represents themselves. Then we relate distortions in self-perceptions to agency and communion dimensions. We demonstrate that (1) participants low in self-esteem show more communion enhancement than participants high in self-esteem; (2) participants in an avoidance state show less agency enhancement (i.e., more realistic self-perceptions) than participants in an approach state. This research is first to demonstrate regularities in visual self-perception that are linked to approach and avoidance motivation.

U.S. immigration discourse has spurred interest in characterizing who illegalized immigrants are or perceived to be. What are the associated visual representations of migrant illegality? Across two studies with undergraduate and online samples (N = 686), we used face-based reverse correlation and similarity sorting to capture and compare mental representations of illegalized immigrants, native-born U.S. citizens, and documented immigrants. Documentation statuses evoked racialized imagery. Immigrant representations were dark-skinned and perceived as non-white, while citizen representations were light-skinned, evaluated positively, and perceived as white. Legality further differentiated immigrant representations: documentation conjured trustworthy representations, illegality conjured threatening representations. Participants spontaneously sorted unlabeled faces by documentation status in a spatial arrangement task. Faces\' spatial similarity correlated with their similarity in pixel luminance and \"American\" ratings, confirming racialized distinctions. Representations of illegalized immigrants were uniquely racialized as dark-skinned un-American threats, reflecting how U.S. imperialism and colorism set conditions of possibility for existing representations of migrant illegalization.

BACKGROUND: Deficits in face emotion recognition are well documented in depression, but the underlying mechanisms are poorly understood. Psychophysical observer models provide a way to precisely characterize such mechanisms. Using model-based analyses, we tested 2 hypotheses about how depression may reduce sensitivity to detect face emotion: 1) via a change in selectivity for visual information diagnostic of emotion or 2) via a change in signal-to-noise ratio in the system performing emotion detection.
METHODS: Sixty adults, one half meeting criteria for major depressive disorder and the other half healthy control participants, identified sadness and happiness in noisy face stimuli, and their responses were used to estimate templates encoding the visual information used for emotion identification. We analyzed these templates using traditional and model-based analyses; in the latter, the match between templates and stimuli, representing sensory evidence for the information encoded in the template, was compared against behavioral data.
RESULTS: Estimated happiness templates produced sensory evidence that was less strongly correlated with response times in participants with depression than in control participants, suggesting that depression was associated with a reduced signal-to-noise ratio in the detection of happiness. The opposite results were found for the detection of sadness. We found little evidence that depression was accompanied by changes in selectivity (i.e., information used to detect emotion), but depression was associated with a stronger influence of face identity on selectivity.
CONCLUSIONS: Depression is more strongly associated with changes in signal-to-noise ratio during emotion recognition, suggesting that deficits in emotion detection are driven primarily by deprecated signal quality rather than suboptimal sampling of information used to detect emotion.

What makes faces seem trustworthy? We investigated how racial prejudice predicts the extent to which perceivers employ racially prototypical cues to infer trustworthiness from faces. We constructed participant-level computational models of trustworthiness and White-to-Black prototypicality from U.S. college students\' judgments of White (Study 1, N = 206) and Black-White morphed (Study 3, N = 386) synthetic faces. Although the average relationships between models differed across stimuli, both studies revealed that as participants\' anti-Black prejudice increased and/or intergroup contact decreased, so too did participants\' tendency to conflate White prototypical features with trustworthiness and Black prototypical features with untrustworthiness. Study 2 (N = 324) and Study 4 (N = 397) corroborated that untrustworthy faces constructed from participants with pro-White preferences appeared more Black prototypical to naive U.S. adults, relative to untrustworthy faces modeled from other participants. This work highlights the important role of racial biases in shaping impressions of facial trustworthiness.

Communicating emotional intensity plays a vital ecological role because it provides valuable information about the nature and likelihood of the sender\'s behavior.1,2,3 For example, attack often follows signals of intense aggression if receivers fail to retreat.4,5 Humans regularly use facial expressions to communicate such information.6,7,8,9,10,11 Yet how this complex signaling task is achieved remains unknown. We addressed this question using a perception-based, data-driven method to mathematically model the specific facial movements that receivers use to classify the six basic emotions-\"happy,\" \"surprise,\" \"fear,\" \"disgust,\" \"anger,\" and \"sad\"-and judge their intensity in two distinct cultures (East Asian, Western European; total n = 120). In both cultures, receivers expected facial expressions to dynamically represent emotion category and intensity information over time, using a multi-component compositional signaling structure. Specifically, emotion intensifiers peaked earlier or later than emotion classifiers and represented intensity using amplitude variations. Emotion intensifiers are also more similar across emotions than classifiers are, suggesting a latent broad-plus-specific signaling structure. Cross-cultural analysis further revealed similarities and differences in expectations that could impact cross-cultural communication. Specifically, East Asian and Western European receivers have similar expectations about which facial movements represent high intensity for threat-related emotions, such as \"anger,\" \"disgust,\" and \"fear,\" but differ on those that represent low threat emotions, such as happiness and sadness. Together, our results provide new insights into the intricate processes by which facial expressions can achieve complex dynamic signaling tasks by revealing the rich information embedded in facial expressions.

Human visual performance for basic visual dimensions (e.g., contrast sensitivity and acuity) peaks at the fovea and decreases with eccentricity. The eccentricity effect is related to the larger visual cortical surface area corresponding to the fovea, but it is unknown if differential feature tuning contributes to this eccentricity effect. Here, we investigated two system-level computations underlying the eccentricity effect: featural representation (tuning) and internal noise. Observers (both sexes) detected a Gabor embedded in filtered white noise which appeared at the fovea or one of four perifoveal locations. We used psychophysical reverse correlation to estimate the weights assigned by the visual system to a range of orientations and spatial frequencies (SFs) in noisy stimuli, which are conventionally interpreted as perceptual sensitivity to the corresponding features. We found higher sensitivity to task-relevant orientations and SFs at the fovea than that at the perifovea, and no difference in selectivity for either orientation or SF. Concurrently, we measured response consistency using a double-pass method, which allowed us to infer the level of internal noise by implementing a noisy observer model. We found lower internal noise at the fovea than that at the perifovea. Finally, individual variability in contrast sensitivity correlated with sensitivity to and selectivity for task-relevant features as well as with internal noise. Moreover, the behavioral eccentricity effect mainly reflects the foveal advantage in orientation sensitivity compared with other computations. These findings suggest that the eccentricity effect stems from a better representation of task-relevant features and lower internal noise at the fovea than that at the perifovea.

Uncovering cognitive representations is an elusive goal that is increasingly pursued using the reverse correlation method, wherein human subjects make judgments about ambiguous stimuli. Employing reverse correlation often entails collecting thousands of stimulus-response pairs, which severely limits the breadth of studies that are feasible using the method. Current techniques to improve efficiency bias the outcome. Here we show that this methodological barrier can be diminished using compressive sensing, an advanced signal processing technique designed to improve sampling efficiency. Simulations are performed to demonstrate that compressive sensing can improve the accuracy of reconstructed cognitive representations and dramatically reduce the required number of stimulus-response pairs. Additionally, compressive sensing is used on human subject data from a previous reverse correlation study, demonstrating a dramatic improvement in reconstruction quality. This work concludes by outlining the potential of compressive sensing to improve representation reconstruction throughout the fields of psychology, neuroscience, and beyond.

Even though human experience unfolds continuously in time, it is not strictly linear; instead, it entails cascading processes building hierarchical cognitive structures. For instance, during speech perception, humans transform a continuously varying acoustic signal into phonemes, words, and meaning, and these levels all have distinct but interdependent temporal structures. Time-lagged regression using temporal response functions (TRFs) has recently emerged as a promising tool for disentangling electrophysiological brain responses related to such complex models of perception. Here, we introduce the Eelbrain Python toolkit, which makes this kind of analysis easy and accessible. We demonstrate its use, using continuous speech as a sample paradigm, with a freely available EEG dataset of audiobook listening. A companion GitHub repository provides the complete source code for the analysis, from raw data to group-level statistics. More generally, we advocate a hypothesis-driven approach in which the experimenter specifies a hierarchy of time-continuous representations that are hypothesized to have contributed to brain responses, and uses those as predictor variables for the electrophysiological signal. This is analogous to a multiple regression problem, but with the addition of a time dimension. TRF analysis decomposes the brain signal into distinct responses associated with the different predictor variables by estimating a multivariate TRF (mTRF), quantifying the influence of each predictor on brain responses as a function of time(-lags). This allows asking two questions about the predictor variables: (1) Is there a significant neural representation corresponding to this predictor variable? And if so, (2) what are the temporal characteristics of the neural response associated with it? Thus, different predictor variables can be systematically combined and evaluated to jointly model neural processing at multiple hierarchical levels. We discuss applications of this approach, including the potential for linking algorithmic/representational theories at different cognitive levels to brain responses through computational models with appropriate linking hypotheses.