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.

Narratives are effective tools for evoking emotions, and physiological measurements provide a means of objectively assessing emotional reactions - making them a potentially powerful pair of tools for studying emotional processes. However, extent research combining emotional narratives and physiological measurement varies widely in design and application, making it challenging to identify previous work, consolidate findings, and design effective experiments. Our scoping review explores the use of auditory emotional narratives and physiological measures in research, examining paradigms, study populations, and represented emotions. Following the PRISMA-ScR Checklist, we searched five databases for peer-reviewed experimental studies that used spoken narratives to induce emotion and reported autonomic physiological measures. Among 3466 titles screened and 653 articles reviewed, 110 studies were included. Our exploration revealed a variety of applications and experimental paradigms; emotional narratives paired with physiological measures have been used to study diverse topics and populations, including neurotypical and clinical groups. Although incomparable designs and sometimes contradictory results precluded general recommendations as regards which physiological measures to use when designing new studies, as a whole, the body of work suggests that these tools can be valuable to study emotions. Our review offers an overview of research employing narratives and physiological measures for emotion study, and highlights weaknesses in reporting practices and gaps in our knowledge concerning the robustness and specificity of physiological measures as indices of emotion. We discuss study design considerations and transparent reporting, to facilitate future using emotional narratives and physiological measures in studying emotions.

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.

Visuospatial processing impairments are prevalent in individuals with cerebral visual impairment (CVI) and are typically ascribed to \"dorsal stream dysfunction\" (DSD). However, the contribution of other cortical regions, including early visual cortex (EVC), frontal cortex, or the ventral visual stream, to such impairments remains unknown. Thus, here, we examined fMRI activity in these regions, while individuals with CVI (and neurotypicals) performed a visual search task within a dynamic naturalistic scene. First, behavioral performance was measured with eye tracking. Participants were instructed to search and follow a walking human target. CVI participants took significantly longer to find the target, and their eye gaze patterns were less accurate and less precise. Second, we used the same task in the MRI scanner. Along the dorsal stream, activation was reduced in CVI participants, consistent with the proposed DSD in CVI. Intriguingly, however, visual areas along the ventral stream showed the complete opposite pattern, with greater activation in CVI participants. In contrast, we found no differences in either EVC or frontal cortex between groups. These results suggest that the impaired visuospatial processing abilities in CVI are associated with differential recruitment of the dorsal and ventral visual streams, likely resulting from impaired selective attention.

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.

Musical engagement can be conceptualized through various activities, modes of listening and listener states. Recent research has reported that a state of focused engagement can be indexed by the inter-subject correlation (ISC) of audience responses to a shared naturalistic stimulus. While statistically significant ISC has been reported during music listening, we lack insight into the temporal dynamics of engagement over the course of musical works-such as those composed in the Western classical style-which involve the formulation of expectations that are realized or derailed at subsequent points of arrival. Here, we use the ISC of electroencephalographic (EEG) and continuous behavioural (CB) responses to investigate the time-varying dynamics of engagement with functional tonal music. From a sample of adult musicians who listened to a complete cello concerto movement, we found that ISC varied throughout the excerpt for both measures. In particular, significant EEG ISC was observed during periods of musical tension that built to climactic highpoints, while significant CB ISC corresponded more to declarative entrances and points of arrival. Moreover, we found that a control stimulus retaining envelope characteristics of the intact music, but little other temporal structure, also elicited significantly correlated EEG and CB responses, though to lesser extents than the original version. In sum, these findings shed light on the temporal dynamics of engagement during music listening and clarify specific aspects of musical engagement that may be indexed by each measure.

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.

Cognitive neuroscience aims to develop computational models that can accurately predict and explain neural responses to sensory inputs in the cortex. Recent studies attempt to leverage the representation power of deep neural networks (DNNs) to predict the brain response and suggest a correspondence between artificial and biological neural networks in their feature representations. However, typical voxel-wise encoding models tend to rely on specific networks designed for computer vision tasks, leading to suboptimal brain-wide correspondence during cognitive tasks. To address this challenge, this work proposes a novel approach that upgrades voxel-wise encoding models through multi-level integration of features from DNNs and information from brain networks. Our approach combines DNN feature-level ensemble learning and brain atlas-level model integration, resulting in significant improvements in predicting whole-brain neural activity during naturalistic video perception. Furthermore, this multi-level integration framework enables a deeper understanding of the brain\'s neural representation mechanism, accurately predicting the neural response to complex visual concepts. We demonstrate that neural encoding models can be optimized by leveraging a framework that integrates both data-driven approaches and theoretical insights into the functional structure of the cortical networks.

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.