The default mode network (DMN) is a complex network that plays a significant and active role during naturalistic stimulation. Previous studies that have used naturalistic stimuli, such as real-life stories or silent or sonorous films, have found that the information processing involved a complex hierarchical set of brain regions, including the DMN nodes. The DMN is not involved in low-level features and is only associated with high-level content-related incoming information. The human sexual experience involves a complex set of processes related to both external context and inner processes. Since the DMN plays an active role in the integration of naturalistic stimuli and aesthetic perception with beliefs, thoughts, and episodic autobiographical memories, we aimed at quantifying the involvement of the nodes of the DMN during visual sexual stimulation. After a systematic search in the principal electronic databases, we selected 83 fMRI studies, and an ALE meta-analysis was calculated. We performed conjunction analyses to assess differences in the DMN related to stimulus modalities, sex differences, and sexual orientation. The results show that sexual stimulation alters the topography of the DMN and highlights the DMN\'s active role in the integration of sexual stimuli with sexual schemas and beliefs.

We investigated the neural signatures of expert decision-making in the context of police training in a virtual reality-based shoot/don\'t shoot scenario. Police officers can use stopping force against a perpetrator, which may require using a firearm and each decision made by an officer to discharge their firearm or not has substantial implications. Therefore, it is important to understand the cognitive and underlying neurophysiological processes that lead to such a decision. We used virtual reality-based simulations to elicit ecologically valid behavior from authorized firearms officers (AFOs) in the UK and matched novices in a shoot/don\'t shoot task and recorded electroencephalography concurrently. We found that AFOs had consistently faster response times than novices, suggesting our task was sensitive to their expertise. To investigate differences in decision-making processes under varying levels of threat and expertise, we analyzed electrophysiological signals originating from the anterior cingulate cortex. In line with similar response inhibition tasks, we found greater increases in preresponse theta power when participants inhibited the response to shoot when under no threat as compared with shooting. Most importantly, we showed that when preparing against threat, theta power increase was greater for experts than novices, suggesting that differences in performance between experts and novices are due to their greater orientation toward threat. Additionally, shorter beta rebounds suggest that experts were \"ready for action\" sooner. More generally, we demonstrate that the investigation of expert decision-making should incorporate naturalistic stimuli and an appropriate control group to enhance validity.

The language network, comprised of brain regions in the left frontal and temporal cortex, responds robustly and reliably during language comprehension but shows little or no response during many nonlinguistic cognitive tasks (e.g., Fedorenko & Blank, 2020). However, one domain whose relationship with language remains debated is semantics-our conceptual knowledge of the world. Given that the language network responds strongly to meaningful linguistic stimuli, could some of this response be driven by the presence of rich conceptual representations encoded in linguistic inputs? In this study, we used a naturalistic cognition paradigm to test whether the cognitive and neural resources that are responsible for language processing are also recruited for processing semantically rich nonverbal stimuli. To do so, we measured BOLD responses to a set of ∼5-minute-long video and audio clips that consisted of meaningful event sequences but did not contain any linguistic content. We then used the intersubject correlation (ISC) approach (Hasson et al., 2004) to examine the extent to which the language network \"tracks\" these stimuli, that is, exhibits stimulus-related variation. Across all the regions of the language network, meaningful nonverbal stimuli elicited reliable ISCs. These ISCs were higher than the ISCs elicited by semantically impoverished nonverbal stimuli (e.g., a music clip), but substantially lower than the ISCs elicited by linguistic stimuli. Our results complement earlier findings from controlled experiments (e.g., Ivanova et al., 2021) in providing further evidence that the language network shows some sensitivity to semantic content in nonverbal stimuli.

Language neuroscience currently relies on two major experimental paradigms: controlled experiments using carefully hand-designed stimuli, and natural stimulus experiments. These approaches have complementary advantages which allow them to address distinct aspects of the neurobiology of language, but each approach also comes with drawbacks. Here we discuss a third paradigm-in silico experimentation using deep learning-based encoding models-that has been enabled by recent advances in cognitive computational neuroscience. This paradigm promises to combine the interpretability of controlled experiments with the generalizability and broad scope of natural stimulus experiments. We show four examples of simulating language neuroscience experiments in silico and then discuss both the advantages and caveats of this approach.

UNASSIGNED: We investigated the factors underlying naturalistic action recognition and understanding, as well as the errors occurring during recognition failures.
UNASSIGNED: Participants saw full-light stimuli of ten different whole-body actions presented in three different conditions: as normal videos, as videos with the temporal order of the frames scrambled, and as single static representative frames. After each stimulus presentation participants completed one of two tasks-a forced choice task where they were given the ten potential action labels as options, or a free description task, where they could describe the action performed in each stimulus in their own words.
UNASSIGNED: While generally, a combination of form, motion, and temporal information led to the highest action understanding, for some actions form information was sufficient and adding motion and temporal information did not increase recognition accuracy. We also analyzed errors in action recognition and found primarily two different types.
UNASSIGNED: One type of error was on the semantic level, while the other consisted of reverting to the kinematic level of body part processing without any attribution of semantics. We elaborate on these results in the context of naturalistic action perception.

How is the fundamental sense of one\'s body, a basic aspect of selfhood, incorporated into memories for events? Disrupting bodily self-awareness during encoding impairs functioning of the left posterior hippocampus during retrieval, which implies weakened encoding. However, how changes in bodily self-awareness influence neural encoding is unknown. We investigated how the sense of body ownership, a core aspect of the bodily self, impacts encoding in the left posterior hippocampus and additional core memory regions including the angular gyrus. Furthermore, we assessed the degree to which memories are reinstated according to body ownership during encoding and vividness during retrieval as a measure of memory strength. We immersed participants in naturalistic scenes where events unfolded while we manipulated feelings of body ownership with a full-body-illusion during functional magnetic resonance imaging scanning. One week later, participants retrieved memories for the videos during functional magnetic resonance imaging scanning. A whole brain analysis revealed that patterns of activity in regions including the right hippocampus and angular gyrus distinguished between events encoded with strong versus weak body ownership. A planned region-of-interest analysis showed that patterns of activity in the left posterior hippocampus specifically could predict body ownership during memory encoding. Using the wider network of regions sensitive to body ownership during encoding and the left posterior hippocampus as separate regions-of-interest, we observed that patterns of activity present at encoding were reinstated more during the retrieval of events encoded with strong body ownership and high memory vividness. Our results demonstrate how the sense of physical self is bound within an event during encoding, which facilitates reactivation of a memory trace during retrieval.

Participant-specific, functionally defined brain areas are usually mapped with functional localizers and estimated by making contrasts between responses to single categories of input. Naturalistic stimuli engage multiple brain systems in parallel, provide more ecologically plausible estimates of real-world statistics, and are friendly to special populations. The current study shows that cortical functional topographies in individual participants can be estimated with high fidelity from naturalistic stimuli. Importantly, we demonstrate that robust, individualized estimates can be obtained even when participants watched different movies, were scanned with different parameters/scanners, and were sampled from different institutes across the world. Our results create a foundation for future studies that allow researchers to estimate a broad range of functional topographies based on naturalistic movies and a normative database, making it possible to integrate high-level cognitive functions across datasets from laboratories worldwide.

Film functional magnetic resonance imaging (fMRI) has gained tremendous popularity in many areas of neuroscience. However, affective neuroscience remains somewhat behind in embracing this approach, even though films lend themselves to study how brain function gives rise to complex, dynamic and multivariate emotions. Here, we discuss the unique capabilities of film fMRI for emotion research, while providing a general guide of conducting such research. We first give a brief overview of emotion theories as these inform important design choices. Next, we discuss films as experimental paradigms for emotion elicitation and address the process of annotating them. We then situate film fMRI in the context of other fMRI approaches, and present an overview of results from extant studies so far with regard to advantages of film fMRI. We also give an overview of state-of-the-art analysis techniques including methods that probe neurodynamics. Finally, we convey limitations of using film fMRI to study emotion. In sum, this review offers a practitioners\' guide to the emerging field of film fMRI and underscores how it can advance affective neuroscience.

Storytelling-an ancient way for humans to share individual experiences with others-has been found to induce neural synchronization among listeners. In our exploration of the dynamic fluctuations in listener-listener (LL) coupling throughout stories, we uncover a significant correlation between LL and lag-speaker-listener (lag-SL) couplings over time. Using the analogy of neural pattern (dis)similarity as distances between participants, we term this phenomenon the \"herding effect\": like a shepherd guiding a group of sheep, the more closely listeners follow the speaker\'s prior brain activity patterns (higher lag-SL similarity), the more tightly they cluster together (higher LL similarity). This herding effect is particularly pronounced in brain regions where neural synchronization among listeners tracks with behavioral ratings of narrative engagement, highlighting the mediating role of narrative content in the observed multi-brain neural coupling dynamics. By integrating LL and SL neural couplings, this study illustrates how unfolding stories shape a dynamic multi-brain functional network and how the configuration of this network may be associated with moment-by-moment efficacy of communication.

Deep convolutional neural networks (DCNNs) trained for face identification can rival and even exceed human-level performance. The ways in which the internal face representations in DCNNs relate to human cognitive representations and brain activity are not well understood. Nearly all previous studies focused on static face image processing with rapid display times and ignored the processing of naturalistic, dynamic information. To address this gap, we developed the largest naturalistic dynamic face stimulus set in human neuroimaging research (700+ naturalistic video clips of unfamiliar faces). We used this naturalistic dataset to compare representational geometries estimated from DCNNs, behavioral responses, and brain responses. We found that DCNN representational geometries were consistent across architectures, cognitive representational geometries were consistent across raters in a behavioral arrangement task, and neural representational geometries in face areas were consistent across brains. Representational geometries in late, fully connected DCNN layers, which are optimized for individuation, were much more weakly correlated with cognitive and neural geometries than were geometries in late-intermediate layers. The late-intermediate face-DCNN layers successfully matched cognitive representational geometries, as measured with a behavioral arrangement task that primarily reflected categorical attributes, and correlated with neural representational geometries in known face-selective topographies. Our study suggests that current DCNNs successfully capture neural cognitive processes for categorical attributes of faces but less accurately capture individuation and dynamic features.