Biology subjects

Finn, E. S.

Publications and source records attributed to Finn, E. S..

At least 19 recordsLinked to original sources

bioRxiv2026

Shifts in neural representations, not gaze, track how people reinterpret ambiguous social scenes

How do we come to "see" the same thing differently after hearing someone else's perspective? Revising an interpretation could reflect changes in how the brain represents the meaning of what we see, changes in how we visually sample the input, or both. To disentangle these mechanisms, we developed MindLibs, an adaptive fMRI and eye-tracking paradigm in which participants generate interpretations of ambiguous photographs and then view alternative interpretations chosen in real time to vary in semantic content from their own. Exposure to semantically distant alternatives revealed a strong link between reinterpretation as reported behaviorally and representational shifts in association cortex, but no systematic relationship between reinterpretation and changes in gaze. In particular, representational shifts in lateral default-mode network regions (DMN-B) predicted reinterpretation independently of shifts in frontoparietal, attention, and visual networks, and independently of whether gaze changed. In contrast, shifts in visual cortex tracked reinterpretation only when gaze also shifted, and contributed no unique variance beyond DMN-B. These findings identify higher-order conceptual reconfiguration, rather than perceptual resampling, as the primary account of how people revise the meaning of an ambiguous scene.

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bioRxiv2026

Subjective, not objective, socialness drives activity in the brain's third visual pathway

Recent work, including the third visual stream hypothesis, frames social perception as a largely automatic visual process. Simple animations of shapes engaged in "social" behaviors such as chasing have long been used to study social perception; however, both behavioral and neural responses are typically modeled against discrete, experimenter-assigned stimulus labels. This approach is insufficient, given that perceptions of such stimuli vary not only between people but also across trials. Here, we directly compared the neural responses to objective (stimulus-based) and subjective (response-based) socialness in an fMRI study in which the degree of chase between two agents was varied algorithmically. Participants (n=24) rated their perception of a chase on a continuous scale. Subjective ratings better predicted activity in visuo-social brain regions, predominantly in area MT, even after controlling for optic flow. Our findings underscore the visual nature of social cognition and highlight the importance of analyses sensitive to idiosyncrasies in perception.

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bioRxiv2025

View, engage, predict: enhancing brain-behavior mapping with naturalistic movie-watching fMRI

Most brain-behavior mapping studies rely on resting-state functional connectivity (FC), but this approach has known accuracy limits and can be outperformed by movie-watching FC. Here, we present a novel deep neural network framework to predict cognitive scores and sex from FC during naturalistic movie viewing, and examine how movie content and its ability to synchronize brain activity across individuals relate to prediction performance. We show that FC from movie-watching generally outperforms resting-state FC - even when compared to five times more temporal data - with sensory and higher-order brain networks emerging as the most important for prediction. Using both static and sliding-window dynamic FC approaches, we find that higher cognitive prediction accuracy is significantly associated with greater inter-subject synchrony and the duration of human faces and voices in the movies; these effects were not found for sex prediction. This work underscores the promise of naturalistic movie viewing as a powerful tool for probing individual differences in the brain and revealing neural underpinnings of human behavior.

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bioRxiv2025

Shared and individual tuning curves for social vision

Stimuli with light are clearly visual; stimuli with sound are clearly auditory. But what makes a stimulus "social", and how do socialness judgments differ across people? Here, we characterize group-level and individual thresholds for perceiving the presence and nature of a social interaction. We leverage the fact that humans see social interactions--e.g., chasing, playing, fighting--even in very un-lifelike stimuli like animations of geometric shapes. Unlike most prior work that used hand-crafted stimuli, we exploit these animations most advantageous property: their visual features are fully parameterizable. Using this property, we construct psychophysics-inspired "social tuning curves" for individuals. We find not only that simple visual features influence social perception, but also that the exact shape of the tuning curve is unique to and stable within each person. Further, individual differences in tuning curves are related to socio-affective traits. Our approach lays the foundation to study how social percepts emerge from interactions between features of a stimulus and features of an individual observer.

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bioRxiv2025

Narrative 'twist' shifts within-individual neural representations of dissociable story features

Given the same input, understanding of that input can differ depending on context. How does the brain represent the latent mental frameworks that support different interpretations of the same sensory information? In this study, participants listened to the same auditory narrative twice; the narrative had a plot twist in the middle that dramatically shifted interpretations of the story. Using a within-subject design that held both the stimulus and the individual constant, we leveraged reinterpretation-driven shifts in neural activity between the two listens to identify where representations of different narrative elements are updated under a new interpretative framework. We separated the narrative into three interrelated levels--the overall narrative model, episodes, and characters--to determine where reinterpretation-driven updates to each element were reflected in brain activity. Neural activity patterns associated with interpretations and reinterpretations of these three elements were observed in overlapping but partially distinct sets of temporal, parietal, and prefrontal regions. Results suggest that heteromodal cortex represents specific narrative elements according not to their surface features, but to latent conceptual frameworks for understanding those elements, which can shift under a new narrative interpretation. Significance statementInformation is never experienced the same way twice. Here, we investigated where and how interpretations are updated in the brain when sensory input remains identical but contextual knowledge changes how we interpret that input. We used a unique narrative stimulus with a mid-story plot twist, which participants listened to twice. We found that sets of relatively dissociable regions across the brain represent three distinct narrative elements--narrative models, episodes, and characters. These findings reveal the neural basis for how identical information is processed differently through the mental frameworks we bring to understanding it.

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bioRxiv2023

Neural representations of concrete concepts enable identification of individuals during naturalistic story listening

Different people listening to the same story may converge upon a largely shared interpretation while still developing idiosyncratic experiences atop that shared foundation. What semantic properties support this individualized experience of natural language? Here, we investigate how the "concreteness" of word meanings -- i.e., the extent to which a concept is derived from sensory experience -- relates to variability in the neural representations of language. Leveraging a large dataset of participants who each listened to four auditory stories while undergoing functional MRI, we demonstrate that an individuals neural representations of concrete concepts are reliable across stories and unique to the individual. In contrast, we find that neural representations of abstract concepts are variable both within individuals and across the population. Using natural language processing tools, we show that concrete words exhibit similar neural signatures despite spanning larger distances within a high-dimensional semantic space, which potentially reflects an underlying signature of sensory experience -- namely, imageability -- shared by concrete words but absent from abstract words. Our findings situate the concrete-abstract semantic axis as a core dimension that supports reliable yet individualized representations of natural language.

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bioRxiv2023

Getting personal: brain decoding of spontaneous thought using personal narratives

The contents of spontaneous thought and their dynamics are important factors for ones personality traits and mental health. However, they are difficult to assess because spontaneous thought occurs voluntarily without conscious constraints. Here, we aimed to decode two important content dimensions of spontaneous thought--self-relevance and valence--directly from functional Magnetic Resonance Imaging (fMRI) signals. To train brain decoders, we induced a wide range of levels of self-relevance and emotional valence using individually generated personal stories as well as stories written by others to mimic narrative-like spontaneous thoughts (n = 49). We then tested the brain decoders on two resting-state fMRI datasets (n = 49 and 90) with and without intermittent thought sampling, achieving significant predictions. The default mode and ventral attention networks were important contributors to the predictions. Overall, this study paves the way for the brain decoding of spontaneous thought and its use for clinical applications.

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bioRxiv2023

Negative Affect homogenizes and Positive Affect diversifies Social Memory Consolidation across people

We are often surprised when an interaction we remember positively is recalled by a peer negatively. What colors social memories with positive versus negative hues? We show that when resting after a social experience, individuals showing similar default network responding subsequently remember more negative information, while individuals showing idiosyncratic default network responding remember more positive information. Results were specific to rest after the social experience (as opposed to before or during the social experience, or rest after a nonsocial experience). The results provide novel neural evidence in support of the "broaden and build" theory of positive emotion, which posits that while negative affect confines, positive affect broadens idiosyncrasy in cognitive processing. For the first time, we identified post-encoding rest as a key moment and the default network as a key brain system in which negative affect homogenizes, whereas positive affect diversifies social memories.

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bioRxiv2022

Individual variability in neural event segmentation reflects stimulus content and interpretation

Event segmentation is a spontaneous part of perception, important for processing continuous information and organizing it into memory. While neural and behavioral event segmentation show a degree of inter-subject consistency, meaningful individual variability exists atop these shared patterns. Here we characterized individual differences in the location of neural event boundaries across four short movies that evoked variable interpretations. Event boundary alignment across subjects followed a posterior-to-anterior gradient that was tightly correlated with the rate of segmentation: slower-segmenting regions that integrate information over longer time periods showed more individual variability in boundary locations. This relationship held irrespective of the stimulus, but the degree to which boundaries in particular regions were shared versus idiosyncratic depended on certain aspects of movie content. Furthermore, this variability was behaviorally significant in that similarity of neural boundary locations during movie-watching predicted similarity in how the movie was ultimately remembered and appraised. In particular, we identified a subset of regions in which neural boundary locations are both aligned with behavioral boundaries during encoding and predictive of stimulus interpretation, suggesting that event segmentation may be a mechanism by which narratives generate variable memories and appraisals of stimuli.

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bioRxiv2022

Bringing language to dynamic brain states: the default network dominates neural responses to evolving movie stories

Naturalistic neuroscience opened the door to new insights into neural circuits that serve real-world dynamic perception. Such studies have often neglected the rich texture of the movie narrative itself, but semantic content can be used to contextualize the induced neural responses. Here, we translated natural language processing tools from machine learning to characterize brain states estimated from hidden Markov models. Our analytical strategy allowed pitting shallow unimodal against the deep associative brain network layers in explaining how semantic content of the movie links to observed neural activity. Pooling information across >53,000 brain image time points watching Forrest Gump, we could show that distinct dynamic brain states capture unique semantic facets along the unfolding movie narrative. The spatiotemporal dynamics of brain states explicitly captured subject-level responses throughout the brain network hierarchy. Across all analyses, the default network was most intimately linked to semantic information integration, and this neural system switched online for longest durations during movie watching. Further, we identified and described two mechanisms of how the default network liaises dynamically with microanatomically defined subregion partners: the amygdala and the hippocampus. Our study thus unlocks the potential of natural language processing to explore neural processes in everyday life situations that engage key aspects of conscious awareness.

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bioRxiv2022

Seeing social: A neural signature for conscious perception of social interactions

Percepts of ambiguous information are subjective and depend on observers traits and mental states. Social information is some of the most ambiguous content we encounter in our daily lives, yet in experimental contexts, percepts of social interactions--i.e., whether an interaction is present and if so, the nature of that interaction--are often dichotomized as correct or incorrect based on experimenter-assigned labels. Here, we investigated the behavioral and neural correlates of conscious social perception using a large dataset in which neurotypical individuals viewed animations of geometric shapes during fMRI and indicated whether they perceived a social interaction or random motion. Critically, rather than experimenter-assigned labels, we used observers own reports of "Social" or "Non-social" to classify percepts and characterize brain activity, including leveraging a particularly ambiguous animation perceived as "Social" by some observers but "Non-social" by others to control for visual input. Observers were biased toward perceiving information as social (versus non-social), and activity across much of the brain was higher during animations ultimately perceived as social. Using "Unsure" reports, we identified several regions that responded parametrically to perceived socialness. Neural responses to social versus nonsocial content diverged early both in time and in the cortical hierarchy. Lastly, individuals with higher internalizing trait scores showed both a higher response bias towards social and an inverse relationship with activity in default-mode and limbic regions while scanning for social information. Findings underscore the subjective nature of social perception and the importance of using observer reports to study percepts of social interactions. Significance StatementSimple animations involving two or more geometric shapes have been used as a gold standard to understand social cognition and impairments thereof. Yet experimenter-assigned labels of what is social versus non-social are frequently used as a ground truth, despite the fact that percepts of such ambiguous social stimuli are highly subjective. Here, we used behavioral and fMRI data from a large sample of neurotypical individuals to show that participants responses reveal subtle behavioral biases, help us study neural responses to social content more precisely, and covary with internalizing trait scores. Our findings underscore the subjective nature of social perception and the importance of considering observer reports in studying its behavioral and neural dynamics.

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bioRxiv2020

Neural signatures of attentional engagement during narratives and its consequences for event memory

As we comprehend narratives, our attentional engagement fluctuates over time. Despite theoretical conceptions of narrative engagement as emotion-laden attention, little empirical work has characterized the cognitive and neural processes that comprise subjective engagement in naturalistic contexts or its consequences for memory. Here, we relate fluctuations in narrative engagement to patterns of brain coactivation, and test whether neural signatures of engagement predict later recall. In behavioral studies, participants continuously rated how engaged they were as they watched a television episode or listened to a story. Self-reported engagement was synchronized across individuals and driven by the emotional content of the narratives. During fMRI, we observed highly synchronized activity in the default mode network when people were, on average, more engaged in the same narratives. Models based on time-varying whole-brain functional connectivity predicted evolving states of engagement across participants and even across different datasets. The same functional connections also predicted post-scan event recall, suggesting that engagement during encoding impacts subsequent memory. Finally, group-average engagement was related to fluctuations of an independent functional connectivity index of sustained attention. Together, our findings characterize the neural signatures of engagement dynamics and elucidate relationships between narrative engagement, sustained attention, and event memory.

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bioRxiv2020

Movie-watching outperforms rest for functional connectivity-based prediction of behavior

A major goal of human neuroscience is to relate differences in brain function to differences in behavior across people. Recent work has established that whole-brain functional connectivity patterns are relatively stable within individuals and unique across individuals, and that features of these patterns predict various traits. However, while functional connectivity is most often measured at rest, certain tasks may enhance individual signals and improve sensitivity to behavioral differences. Here, we show that compared to the resting state, functional connectivity measured during naturalistic viewing--i.e., movie watching--yields more accurate predictions of trait-like phenotypes in the both cognitive and emotional domains. Traits could be predicted using less than three minutes of data from single video clips, and clips with highly social content gave the most accurate predictions. Results suggest that naturalistic stimuli amplify individual differences in behaviorally relevant brain networks.

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bioRxiv2019

Functional connectivity predicts changes in attention over minutes, days, and months

The ability to sustain attention differs across people and changes within a single person over time. Although recent work has demonstrated that patterns of functional brain connectivity predict individual differences in sustained attention, whether these same patterns capture fluctuations in attention in single individuals remains unclear. Here, across five independent studies, we demonstrate that the sustained attention connectome-based predictive model (CPM), a validated model of sustained attention function, generalizes to predict attention changes across minutes, days, weeks, and months. Furthermore, the sustained attention CPM is sensitive to within-subject state changes induced by propofol as well as sevoflurane, such that individuals show functional connectivity signatures of stronger attentional states when awake than when under deep sedation and light anesthesia. Together these results demonstrate that fluctuations in attentional state reflect variability in the same functional connectivity patterns that predict individual differences in sustained attention.

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bioRxiv2019

Untangling the Relatedness among Correlations, Part III: Inter-Subject Correlation Analysis through Bayesian Multilevel Modeling for Naturalistic Scanning

While inter-subject correlation (ISC) analysis is a powerful tool for naturalistic scanning data, drawing appropriate statistical inferences is difficult due to the daunting task of accounting for the intricate relatedness in data structure as well as the intricacy of handling the multiple testing issue. In our previous work we proposed nonparametric approaches to performing group ISC analysis through bootstrapping for one group of subjects and permutation testing for two groups (Chen et al., 2016). A more flexible methodology is to parametrically build a linear mixed-effects (LME) model that captures the relatedness embedded in the data (Chen et al., 2017); in addition, the LME approach also has the capability of incorporating explanatory variables such as subject-grouping factors and quantitative covariates. However, the whole-brain LME modeling methodology still faces some challenges. When an LME model becomes sophisticated, it becomes difficult or even impossible to assign accurate degrees of freedom for each testing statistic. In addition, the typical correction methods for multiple testing through spatial extent tend to be over-penalizing, and dichotomous decisions through thresholding under null hypothesis significance testing (NHST) are controversial in general and equally problematic in neuroimaging as well. For instance, the popular practice of only reporting \"statistically significant\" results in neuroimaging not only wastes data information, but also distorts the full results as well as perpetuates the reproducibility crisis because of the fact that the difference between a \"significant\" result and a \"non-significant\" one is not necessarily significant.\n\nHere we propose a Bayesian multilevel (BML) framework for ISC data analysis that integrates all the spatial elements (i.e., regions of interest) into one model. By loosely constraining the regions through a weakly informative prior, BML conservatively pools the effect of each region toward the center, and improves collective fitting and overall model performance. The BML paradigm leverages the commonality or similarity among brain regions and the information across multiple levels embedded in the hierarchical data structure instead of leveraging the spatial extent adopted in the conventional correction method for multiple testing. In addition to potentially achieving a higher inference efficiency than the conventional LME approach, BML improves spatial specificity and easily allows the investigator to adopt a philosophy of full results reporting (instead of dichotomizing into \"significant\" and \"non-significant\" results), thus minimizing loss of information while enhancing reproducibility. A dataset of naturalistic scanning is utilized to illustrate the modeling approach with 268 parcels and to showcase the modeling capability, flexibility and advantages in reports reporting. The associated program will be available as part of the AFNI suite for general use.

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bioRxiv2018

Sub-millimeter fMRI reveals multiple topographical digit representations that form action maps in human motor cortex

The human brain coordinates a wide variety of motor activities. On a large scale, the cortical motor system is topographically organized such that neighboring body parts are represented by neighboring brain areas. This homunculus-like somatotopic organization along the central sulcus has been observed using neuroimaging for large body parts such as the face, hands and feet. However, on a finer scale, invasive electrical stimulation studies show deviations from this somatotopic organization that suggest an organizing principle based on motor actions rather than body part moved. It has not been clear how the action-map organization principle of the motor cortex in the mesoscopic (sub-millimeter) regime integrates into a body map organization principle on a macroscopic scale (cm). Here we developed and applied advanced mesoscopic (sub-millimeter) fMRI and analysis methodology to non-invasively investigate the functional organization topography across columnar and laminar structures in humans. We find that individual fingers have multiple mirrored representations in the primary motor cortex depending on the movements they are involved in. We find that individual digits have cortical representations up to 3 mm apart from each other arranged in a column-like fashion. These representations are differentially engaged depending on whether the digits muscles are used for different motor actions such as flexion movements like grasping a ball or retraction movements like releasing a ball. This research provides a starting point for noninvasive investigation of mesoscale topography across layers and columns of the human cortex and bridges the gap between invasive electrophysiological investigations and large coverage non-invasive neuroimaging.

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bioRxiv2018

Layer-dependent activity in human prefrontal cortex during working memory

Working memory involves a series of functions: encoding a stimulus, maintaining or manipulating its representation over a delay, and finally making a behavioral response. While working memory engages dorsolateral prefrontal cortex (dlPFC), few studies have investigated whether these subfunctions are localized to different cortical depths in this region, and none have done so in humans. Here, we use high-resolution functional MRI to interrogate the layer specificity of neural activity during different epochs of a working memory task in dlPFC. We detect activity timecourses that follow the hypothesized patterns: superficial layers are preferentially active during the delay period, while deeper layers are preferentially active during the response. Results demonstrate that layer-specific fMRI can be used in higher-order brain regions to non-invasively map cognitive information processing along cortical circuitry in humans.

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bioRxiv2018

The functional brain organization of an individual predicts measures of social abilities in autism spectrum disorder

Autism Spectrum Disorder (ASD) is associated with multiple complex abnormalities in functional brain connectivity measured with functional magnetic resonance imaging (fMRI). Despite much research in this area, to date, neuroimaging-based models are not able to characterize individuals with ASD with sufficient sensitivity and specificity; this is likely due to the heterogeneity and complexity of this disorder. Here we apply a data-driven subject-level approach, connectome-based predictive modeling, to resting-state fMRI data from a set of individuals from the Autism Brain Imaging Data Exchange. Using leave-one-subject-out and split-half analyses, we define two functional connectivity networks that predict continuous scores on the Social Responsiveness Scale (SRS) and Autism Diagnostic Observation Schedule (ADOS) and confirm that these networks generalize to novel subjects. Notably, these networks were found to share minimal anatomical overlap. Further, our results generalize to individuals for whom SRS/ADOS scores are unavailable, predicting worse scores for ASD than typically developing individuals. In addition, predicted SRS scores for individuals with attention-deficit/hyperactivity disorder (ADHD) from the ADHD-200 Consortium are linked to ADHD symptoms, supporting the hypothesis that the functional brain organization changes relevant to ASD severity share a component associated with attention. Finally, we explore the membership of predictive connections within conventional (atlas-based) functional networks. In summary, our results suggest that an individuals functional connectivity profile contains information that supports dimensional, non-binary classification in ASD, aligning with the goals of precision medicine and individual-level diagnosis.

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