Press "Enter" to skip to content

Science

Arousal coherence, uncertainty, and well-being: an active inference account Here we build on recent findings which show that greater alignment between our subjective experiences (how we feel) and physiological states (measurable changes in our body) plays a pivotal role in the overall psychological well-being. Specifically, we propose that the alignment or ‘coherence’ between affective arousal (e.g. how excited we ‘feel’) and autonomic arousal (e.g. heart rate or pupil dilation) may be key for maintaining up-to-date uncertainty representations in dynamic environments. Drawing on recent advances in interoceptive and affective inference, we also propose that arousal coherence reflects interoceptive integration, facilitates adaptive belief updating, and impacts our capacity to adapt to changes in uncertainty, with downstream consequences to well-being. We also highlight the role of meta-awareness of arousal, a third level of inference, which may permit conscious awareness, learning about, and intentional regulation of lower-order sources of arousal. Practices emphasizing meta-awareness of arousal (like meditation) may therefore elicit some of their known benefits via improved arousal coherence. We suggest that arousal coherence is also likely to be associated with markers of adaptive functioning (like emotional awareness and self-regulatory capacities) and discuss mind–body practices that may increase coherence.

Learning environment-specific learning rates People often have to switch back and forth between different environments that come with different problems and volatilities. While volatile environments require fast learning (i.e., high learning rates), stable environments call for lower learning rates. Previous studies have shown that people adapt their learning rates, but it remains unclear whether they can also learn about environment-specific learning rates, and instantaneously retrieve them when revisiting environments. Here, using optimality simulations and hierarchical Bayesian analyses across three experiments, we show that people can learn to use different learning rates when switching back and forth between two different environments. We even observe a signature of these environment-specific learning rates when the volatility of both environments is suddenly the same. We conclude that humans can flexibly adapt and learn to associate different learning rates to different environments, offering important insights for developing theories of meta-learning and context-specific control.

Social bonding in groups of humans selectively increases inter-status information exchange and prefrontal neural synchronization Social groups in various social species are organized with hierarchical structures that shape group dynamics and the nature of within-group interactions. In-group social bonding, exemplified by grooming behaviors among animals and collective rituals and team-building activities in human societies, is recognized as a practical adaptive strategy to foster group harmony and stabilize hierarchical structures in both human and nonhuman animal groups. However, the neurocognitive mechanisms underlying the effects of social bonding on hierarchical groups remain largely unexplored. Here, we conducted simultaneous neural recordings on human participants engaged in-group communications within small hierarchical groups (n = 528, organized into 176 three-person groups) to investigate how social bonding influenced hierarchical interactions and neural synchronizations. We differentiated interpersonal interactions between individuals of different (inter-status) or same (intra-status) social status and observed distinct effects of social bonding on inter-status and intra-status interactions. Specifically, social bonding selectively increased frequent and rapid information exchange and prefrontal neural synchronization for inter-status dyads but not intra-status dyads. Furthermore, social bonding facilitated unidirectional neural alignment from group leader to followers, enabling group leaders to predictively align their prefrontal activity with that of followers. These findings provide insights into how social bonding influences hierarchical dynamics and neural synchronization while highlighting the role of social status in shaping the strength and nature of social bonding experiences in human groups.

Gray and White Matter Metrics Demonstrate Distinct and Complementary Prediction of Differences in Cognitive Performance in Children: Findings from ABCD (N = 11,876) Individual differences in cognitive performance in childhood are a key predictor of significant life outcomes such as educational attainment and mental health. Differences in cognitive ability are governed in part by variations in brain structure. However, studies commonly focus on either gray or white matter metrics in humans, leaving open the key question as to whether gray or white matter microstructure plays distinct or complementary roles supporting cognitive performance. To compare the role of gray and white matter in supporting cognitive performance, we used regularized structural equation models to predict cognitive performance with gray and white matter measures. Specifically, we compared how gray matter (volume, cortical thickness, and surface area) and white matter measures (volume, fractional anisotropy, and mean diffusivity) predicted individual differences in cognitive performance. The models were tested in 11,876 children (ABCD Study; 5,680 female, 6,196 male) at 10 years old. We found that gray and white matter metrics bring partly nonoverlapping information to predict cognitive performance. The models with only gray or white matter explained respectively 15.4 and 12.4% of the variance in cognitive performance, while the combined model explained 19.0%. Zooming in, we additionally found that different metrics within gray and white matter had different predictive power and that the tracts/regions that were most predictive of cognitive performance differed across metrics. These results show that studies focusing on a single metric in either gray or white matter to study the link between brain structure and cognitive performance are missing a key part of the equation.

Function of feedback information as positive or negative in observational motor learning: Toward a theoretical integration with individual motor learning Theories of individual and observational motor learning have been developed separately. Hence, while the former is a history of feedback manipulation, the latter discounts feedback and relies on the functions of mirror neurons. In response to this trend, in recent years, feedback from the experience of sensitive motor control by others, which can be useful for one’s own motor control, has been demonstrated using a jumping height adjustment task. The present study clarifies the similarities between the two learning theories by clarifying the role of knowledge of results (KR) and motor information obtained through observation, and by further incorporating the subjective-objective error perspective. When only KR is acquired, KR functions as positive feedback and moves away from ideal learning, whereas when KR and motor information, which is the process of producing the outcome, are acquired, they function as negative feedback and promote learning. These results can be explained by Thorndike’s trial-and-error learning (1898) and Schmidt’s schema theory (1975), which assume individual learning. Our findings provide insight into the integration of both theories, as well as suggest teachers and coaches on the importance of providing information about how the results were reached, not just formal knowledge, when transmitting motor tips.

Repetition Suppression Reveals Cue-specific Spatial Representations for Landmarks and Self­motion Cues in Human Retrosplenial Cortex The efficient use of various spatial cues within a setting is crucial for successful navigation. Two fundamental forms of spatial navigation, landmark-based and self-motion-based, engage distinct cognitive mechanisms. The question of whether these modes invoke shared or separate spatial representations in the brain remains unresolved. While non-human animal studies have yielded inconsistent results, human investigation is limited. In our previous work (Chen et al., 2019), we introduced a novel spatial navigation paradigm utilizing ultra-high field fMRI to explore neural coding of positional information. We found that different entorhinal subregions in the right hemisphere encode positional information for landmarks and self-motion cues. The present study tested the generalizability of our previous finding with a modified navigation paradigm. Although we did not replicate our previous finding in the entorhinal cortex, we identified adaptation-based allocentric positional codes for both cue types in the retrosplenial cortex, which were not confounded by the path to the spatial location. However, the multi-voxel patterns of these spatial codes differed between the cue types, suggesting cue-specific positional coding. The parahippocampal cortex exhibited positional coding for self-motion cues, which was not dissociable from path length. Finally, the brain regions involved in successful navigation differed from our previous study, indicating overall distinct neural mechanisms recruited in our two studies. Taken together, the current findings demonstrate cue-specific allocentric positional coding in the human retrosplenial cortex in the same navigation task for the first time, and that spatial representations in the brain are contingent on specific experimental conditions.

Frequency-dependent covariance reveals critical spatio-temporal patterns of synchronized activity in the human brain Recent analyses combining advanced theoretical techniques and high-quality data from thousands of simultaneously recorded neurons provide strong support for the hypothesis that neural dynamics operate near the edge of instability across regions in the brain. However, these analyses, as well as related studies, often fail to capture the intricate temporal structure of brain activity as they primarily rely on time-integrated measurements across neurons. In this study, we present a novel framework designed to explore signatures of criticality across diverse frequency bands and construct a much more comprehensive description of brain activity. Additionally, we introduce a method for projecting brain activity onto a basis of spatio-temporal patterns, facilitating time-dependent dimensionality reduction. Applying this framework to a magnetoencephalography dataset, we observe significant differences in both criticality signatures and spatio-temporal activity patterns between healthy subjects and individuals with Parkinson’s disease.

Dopamine lesions alter the striatal encoding of single-limb gait The striatum serves an important role in motor control, and neurons in this area encode the body’s initiation, cessation, and speed of locomotion. However, it remains unclear whether the same neurons also encode the step-by-step rhythmic motor patterns of individual limbs that characterize gait. By combining high-speed video tracking, electrophysiology, and optogenetic tagging, we found that a sizable population of both D1 and D2 receptor expressing medium spiny projection neurons (MSNs) were phase-locked to the gait cycle of individual limbs in mice. Healthy animals showed balanced limb phase-locking between D1 and D2 MSNs, while dopamine depletion led to stronger phase-locking in D2 MSNs. These findings indicate that striatal neurons represent gait on a single-limb and step basis, and suggest that elevated limb phase-locking of D2 MSNs may underlie some of the gait impairments associated with dopamine loss.

created by https://andyadkins.com