Biology subjects

Stecher, R.

Publications and source records attributed to Stecher, R..

7 recordsLinked to original sources

bioRxiv2026

A neuro-computational approximation of the qualities of mental images

Mental images are challenging to study, given that our conscious experience is notoriously hard to access. The currently prevalent introspective methods are inherently subjective and can thus only provide limited access to their qualities. Here, we developed a neuro-computational approach that approximates and assesses the properties of mental images without the need for introspection. To enable this approach, we collected a large-scale EEG dataset (10 participants, 10 sessions each, 43,200 trials total) of participants imagining 16 scenes based on text prompts. We employed AI image generation to create candidate image sets that approximate the content of mental images (based on the imagined text prompts), computationally simulated visual cortex responses to these images and then assessed their representational alignment with rhythmic EEG responses during imagery. In line with previous reports, mid- to high-level features of the AI-generated candidate images yielded reliable alignment with human alpha activity. By manipulating the qualities of the candidate images, we then tested which qualities predisposed higher representational alignment with cortical imagery representations. We found an increased representational alignment for spatially blurred and low contrast images, providing evidence for the prevalent notion of a reduced sensory quality of mental images. We further found that mental imagery may be characterized by a psychedelic image style, which envelops the images in visual flows that distort the image proportions. These results show that our approach can objectively capture qualities of mental images without the need of introspection, providing a hypothesis-based alternative to emerging reconstruction approaches.

neuroscience↗

bioRxiv2026

Distinct spatiotemporal representations of image beauty and image quality

Humans rapidly assess the beauty of natural scene images. Previous EEG work suggests neural representations of beauty emerge early and are temporally sustained. Complementary fMRI work pinpoints the neural correlates of beauty to visual, frontal, and default-mode network areas. An integrated view of the spatiotemporal dynamics that give rise to the perception of beauty, however, is lacking. Beyond the beauty of the depicted scene, the quality of the image itself influences its perceived beauty, and it is unknown how the brain separates these two factors. To address these questions, we recorded EEG (N = 52) and fMRI (N = 29) data while participants rated the beauty of 100 natural scene photographs. Another group of participants (N = 46) rated the image quality of the same photographs. Separate representational similarity analyses on the EEG and fMRI data revealed early and sustained beauty-related representations across widespread cortical areas. In contrast, representations of image quality emerged earlier, had markedly different representational dynamics, and were predominantly localized to visual cortex. In a model-based EEG-fMRI fusion analysis, we investigated how the correspondence between temporally resolved EEG signals and spatially resolved fMRI signals is explained by beauty ratings. Our results suggest that beauty-related representations emerge early (from around 165ms and peaking at 275ms post-onset), are long-lasting, and primarily originate from high-level visual cortex. This spatiotemporal signature persisted when controlling for image-quality ratings. Our findings emphasize the importance of perceptual processing for perceived beauty and suggest that the brain represents aesthetic appeal independently of image quality.

neuroscience↗

bioRxiv2026

Primate visual cortex spontaneously computes the beauty of objects

During everyday vision, beauty is a spontaneous experience. Perceived beauty varies substantially across object categories: We find puppies more beautiful than moths and roses more beautiful than potatoes. Here, we provide evidence that early processing in human and non-human primate visual cortex already reflects perceived beauty, even without deliberate judgments of beauty. First, in a combined EEG-fMRI analysis, we show that beauty ratings for hundreds of object categories from the THINGS database are predicted from neural responses measured in separate groups of human participants viewing the same images without explicit beauty judgments. Beauty-related representations emerged in occipital visual cortex within 100ms and peaked around 130ms. Second, multi-unit activity in macaque visual cortex also predicted human beauty judgments, despite the animals highly limited knowledge of the objects. These results indicate a perceptual basis for the beauty of visual categories, which may underlie spontaneous preference formation in everyday situations. TeaserEarly visual areas in both human and non-human primates rapidly compute the beauty of real-world objects

neuroscience↗

bioRxiv2023

Imaginary scenes are represented in cortical alpha activity

Imagining natural scenes enables us to engage with a myriad of simulated environments. How do our brains generate such complex mental images? Recent research suggests that cortical alpha activity carries information about individual objects during visual imagery. However, it remains unclear if more complex imagined contents such as natural scenes are similarly represented in alpha activity. Here, we answer this question by decoding the contents of imagined scenes from rhythmic cortical activity patterns. In an EEG experiment, participants imagined natural scenes based on detailed written descriptions, which conveyed four complementary scene properties: openness, naturalness, clutter level and brightness. By conducting classification analyses on EEG power patterns across neural frequencies, we were able to decode both individual imagined scenes as well as their properties from the alpha band, showing that also the contents of complex visual images are represented in alpha rhythms. An additional cross-classification analysis between alpha power patterns during the imagery task and during a perception task, in which participants were presented images of the described scenes, showed that scene representations in the alpha band are shared between imagery and late stages of perception. This suggests that alpha activity mediates the top-down re-activation of scene-related visual contents during imagery.

neuroscience↗

bioRxiv2023

Your place or mine? The neural dynamics of personally familiar scene recognition suggests category independent familiarity encoding

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. HighlightsO_LIWhether 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. C_LIO_LIThe neural dynamics of this familiarity information generalizes across stimulus categories. C_LIO_LIFamiliarity 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. C_LI

neuroscience↗

bioRxiv2023

EEG decoding reveals neural predictions for naturalistic material behaviors

Material properties like softness or stickiness determine how an object can be used. Based on our real-life experience, we form strong expectations about how objects should behave under force, given their typical material properties. Such expectations have been shown to modulate perceptual processes, but we currently do not know how expectation influences the temporal dynamics of the cortical visual analysis for objects and their materials. Here, we tracked the neural representations of expected and unexpected material behaviors using time-resolved EEG decoding in a violation-of-expectation paradigm, where objects fell to the ground and deformed in expected or unexpected ways. Participants were 25 men and women. Our study yielded three key results: First, both objects and materials were represented rapidly and in a temporally sustained fashion. Second, objects exhibiting unexpected material behaviors were more successfully decoded than objects exhibiting expected behaviors within 190ms after the impact, which might indicate additional processing demands when expectations are unmet. Third, general signals of expectation fulfillment that generalize across specific objects and materials were found within the first 150ms after the impact. Together, our results provide new insights into the temporal neural processing cascade that underlies the analysis of real-world material behaviors. They reveal a sequence of predictions, with cortical signals progressing from a general signature of expectation fulfillment towards increased processing of unexpected material behaviors. Significance StatementIn the real world, we can make accurate predictions about how an objects material shapes its behavior: For instance, we know that cups are typically made of porcelain and shatter when we accidentally drop them. Here, we use EEG to experimentally test how expectations about material behaviors impact neural processing. We showed our participants videos of objects that exhibited expected material behaviors (such as a glass shattering when falling to the ground) or unexpected material behaviors (such as a glass melting upon impact). Our results reveal a hierarchy of predictions in cortex: The visual system rapidly generates signals that index whether expectations about material behaviors are met. These signals are followed by increased processing of objects displaying unexpected material behaviors.

neuroscience↗

bioRxiv2021

Prolonged and distributed processing of facial identity in the human brain

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 brains identity coding network while processing information of familiar faces.

neuroscience↗