Brain Activity Tracks Population Information Sharing by Capturing Consensus Judgments of Value

Formal perspectives on shared interbrain activity in social communication: Insights from information and control theories

The mechanisms underlying a reorientation of neuroscience from a single-brain to a multi-brain frame of reference have long been with us. These revolve around the evolutionary exaptation of the inevitable second-law 'leakage' of crosstalk between co-resident cognitive phenomena. Crosstalk characterizes such processes as immune response, wound-healing, gene expression, as so on, up through and including far more rapid neural processes. It is not a great leap-of-faith to infer that similar phenomena affect/afflict social interactions between individuals within and across populations.

Computational meaningfulness as the source of beneficial cognitive biases

The human brain has evolved to solve the problems it encounters in multiple environments. In solving these challenges, it forms mental simulations about multidimensional information about the world. These processes produce context-dependent behaviors. The brain as overparameterized modeling organ is an evolutionary solution for producing behavior in a complex world. One of the most essential characteristics of living creatures is that they compute the values of information they receive from external and internal contexts. As a result of this computation, the creature can behave in optimal ways in each environment. Whereas most other living creatures compute almost exclusively biological values (e.g., how to get food), the human as a cultural creature computes meaningfulness from the perspective of one's activity. The computational meaningfulness means the process of the human brain, with the help of which an individual tries to make the respective situation comprehensible to herself to know how to behave optimally. This paper challenges the bias-centric approach of behavioral economics by exploring different possibilities opened up by computational meaningfulness with insight into wider perspectives. We concentrate on confirmation bias and framing effect as behavioral economics examples of cognitive biases. We conclude that from the computational meaningfulness perspective of the brain, the use of these biases are indispensable property of an optimally designed computational system of what the human brain is like. From this perspective, cognitive biases can be rational under some conditions. Whereas the bias-centric approach relies on small-scale interpretable models which include only a few explanatory variables, the computational meaningfulness perspective emphasizes the behavioral models, which allow multiple variables in these models. People are used to working in multidimensional and varying environments. The human brain is at its best in such an environment and scientific study should increasingly take place in such situations simulating the real environment. By using naturalistic stimuli (e.g., videos and VR) we can create more realistic, life-like contexts for research purposes and analyze resulting data using machine learning algorithms. In this manner, we can better explain, understand and predict human behavior and choice in different contexts.

Brain and behavior in health communication: The Canadian COVID-19 Experiences Project

Background

Methods

Discussion

Resting-state functional connectivity of the sensory/somatomotor network associated with sleep quality: evidence from 202 young male samples

Previous neuroimaging studies have demonstrated that sleep is associated with brain functional changes in some specific brain regions. However, few studies have examined the relationship between all possible functional connectivities (FCs) within the sensory/somatomotor network (SSN) and the sleep quality of young male samples. The SSN consists of two motor cortices and is known to play a critical role in sleep. Poor sleep quality may be associated with increased sensory/somatomotor functional connectivity during rest. Hence, 202 young male participants underwent a resting-state functional magnetic resonance imaging (fMRI) scan and completed the Pittsburgh Sleep Quality Index (PSQI). Results indicated that increased functional connectivity within the SSN was associated with poor sleep quality. Specifically, the total PSQI score was positively correlated with the increased functional connectivity of the left paracentral lobule (PCL), bilateral precentral gyrus (PreCG), supplementary motor area (SMA) and bilateral postcentral gyrus (PoCG). Additionally, our findings also exhibited that (a) the subjective sleep quality factor of PSQI was positively correlated with FC between the bilateral PoCG and the bilateral PCL as well as between the left PreCG and the right SMA; (b) the sleep latency factor of PSQI was positively correlated with FC between the left PoCG and the right precuneus (PCUN); (c) the sleep disturbances factor of PSQI was positively correlated with FC between the left PCL and the right PoCG, and (d) the daytime dysfunction factor of PSQI was positively correlated with FC between the bilateral PoCG and the left PCL as well as between the bilateral PreCG and the SMA. In short, our findings can be comprehensively understood as neural mechanisms of intrinsic SSN connectivity are associated with sleep quality of man. Meanwhile, it may expand our knowledge and provide new insight into a deeper understanding of the neurobiological mechanisms of sleep or sleep problems.

The trait and state negative affect can be separately predicted by stable and variable resting-state functional connectivity

BACKGROUND

Many emotional experiences such as anxiety and depression are influenced by negative affect (NA). NA has both trait and state features, which play different roles in physiological and mental health. Attending to NA common to various emotional experiences and their trait-state features might help deepen the understanding of the shared foundation of related emotional disorders.

METHODS

The principal component of five measures was calculated to indicate individuals' NA level. Applying the connectivity-based correlation analysis, we first identified resting-state functional connectives (FCs) relating to NA in sample 1 (n = 367), which were validated through an independent sample (n = 232; sample 2). Next, based on the variability of FCs across large timescale, we further divided the NA-related FCs into high- and low-variability groups. Finally, FCs in different variability groups were separately applied to predict individuals' neuroticism level (which is assumed to be the core trait-related factor underlying NA), and the change of NA level (which represents the state-related fluctuation of NA).

RESULTS

The low-variability FCs were primarily within the default mode network (DMN) and between the DMN and dorsal attention network/sensory system and significantly predicted trait rather than state NA. The high-variability FCs were primarily between the DMN and ventral attention network, the fronto-parietal network and DMN/sensory system, and significantly predicted the change of NA level.

CONCLUSIONS

The trait and state NA can be separately predicted by stable and variable spontaneous FCs with different attentional processes and emotion regulatory mechanisms, which could deepen our understanding of NA.

Being the Gatekeeper: How Thinking about Sharing Affects Neural Encoding of Information

Information transmission in a society depends on individuals' intention to share or not. Yet, little is known about whether being the gatekeeper shapes the brain's processing of incoming information. Here, we examine how thinking about sharing affects neural encoding of information, and whether this effect is moderated by the person's real-life social network position. In an functional magnetic resonance imaging study, participants rated abstracts of news articles on how much they wanted to read for themselves (read) or-as information gatekeepers-to share with a specific other (narrowcast) or to post on their social media feed (broadcast). In all conditions, consistent spatial blood oxygen level-dependent patterns associated with news articles were observed across participants in brain regions involved in perceptual and language processing as well as higher-order processes. However, when thinking about sharing, encoding consistency decreased in higher-order processing areas (e.g., default mode network), suggesting that the gatekeeper role involves more individualized processing in the brain, that is, person- and context-specific. Moreover, participants whose social networks had high ego-betweenness centrality (i.e., more likely to be information gatekeeper in real life) showed more individualized encoding when thinking about broadcasting. This study reveals how gatekeeping shapes our brain's processing of incoming information.

Health news sharing is reflected in distributed reward-related brain activity

Neuroimaging has identified individual brain regions, but not yet whole-brain patterns, that correlate with the population impact of health messaging. We used neuroimaging to measure whole-brain responses to health news articles across two studies. Beyond activity in core reward value-related regions (ventral striatum, ventromedial prefrontal cortex), our approach leveraged whole-brain responses to each article, quantifying expression of a distributed pattern meta-analytically associated with reward valuation. The results indicated that expression of this whole-brain pattern was associated with population-level sharing of these articles beyond previously identified brain regions and self-report variables. Further, the efficacy of the meta-analytic pattern was not reducible to patterns within core reward value-related regions but rather depended on larger-scale patterns. Overall, this work shows that a reward-related pattern of whole-brain activity is related to health information sharing, advancing neuroscience models of the mechanisms underlying the spread of health information through a population.

Media content sharing as a value-based decision

Exposure to media content (e.g. persuasive campaigns) affects daily behaviors, but these effects are partially determined by whether and how people who are exposed to the content share it with their peers. To decide whether to share, potential sharers need to compare and integrate diverse sources of information including characteristics of the media content and various social influences. What are the mechanisms that enable sharers to make such complex decisions quickly and effortlessly? We review evidence that sharing is preceded by a value-based decision-making process supported by three key characteristics of the so-called neural valuation system (domain-generality, value integration, and context-dependence). Finally, we describe theoretical and methodological advances that can be gained from conceptualizing sharing as a value-based decision-making process.

Linking Emotional Reactivity Between Laboratory Tasks and Immersive Environments Using Behavior and Physiology

An event or experience can induce different emotional responses between individuals, including strong variability based on task parameters or environmental context. Physiological correlates of emotional reactivity, as well as related constructs of stress and anxiety, have been found across many physiological metrics, including heart rate and brain activity. However, the interdependances and interactions across contexts and between physiological systems are not well understood. Here, we recruited military and law enforcement to complete two experimental sessions across two different days. In the laboratory session, participants viewed high-arousal negative images while brain activity electroencephalogram (EEG) was recorded from the scalp, and functional connectivity was computed during the task and used as a predictor of emotional response during the other experimental session. In an immersive simulation session, participants performed a shoot-don't-shoot scenario while heart rate electrocardiography (ECG) was recorded. Our analysis examined the relationship between the sessions, including behavioral responses (emotional intensity ratings, task performance, and self-report anxiety) and physiology from different modalities [brain connectivity and heart rate variability (HRV)]. Results replicated previous research and found that behavioral performance was modulated within-session based on varying levels of emotional intensity in the laboratory session (t ₍₂₄₎ = 4.062, p < 0.0005) and stress level in the simulation session (Z = 2.45, corrected p-value = 0.0142). Both behavior and physiology demonstrated cross-session relationships. Behaviorally, higher intensity ratings in the laboratory was related to higher self-report anxiety in the immersive simulation during low-stress (r = 0.465, N = 25, p = 0.019) and high-stress (r = 0.400, N = 25, p = 0.047) conditions. Physiologically, brain connectivity in the theta band during the laboratory session significantly predicted low-frequency HRV in the simulation session (p < 0.05); furthermore, a frontoparietal connection accounted for emotional intensity ratings during the attend laboratory condition (r = 0.486, p = 0.011) and self-report anxiety after the high-stress simulation condition (r = 0.389, p = 0.035). Interestingly, the predictive power of the brain activity occurred only for the conditions where participants had higher levels of emotional reactivity, stress, or anxiety. Taken together, our findings describe an integrated behavioral and physiological characterization of emotional reactivity.

Neural Mechanisms of Emotion Regulation Moderate the Predictive Value of Affective and Value-Related Brain Responses to Persuasive Messages

Emotionally evocative messages can be an effective way to change behavior, but the neural pathways that translate messages into effects on individuals and populations are not fully understood. We used a human functional neuroimaging approach to ask how affect-, value-, and regulation-related brain systems interact to predict effects of graphic anti-smoking messages for individual smokers (both males and females) and within a population-level messaging campaign. Results indicated that increased activity in the amygdala, a region involved in affective reactivity, predicted both personal quit intentions and population-level information-seeking and this was mediated by activity in ventromedial prefrontal cortex (vmPFC), a region involved in computing an integrative value signal. Further, the predictive value of these regions was moderated by expression of a meta-analytically defined brain pattern indexing emotion regulation. That is, amygdala and vmPFC activity strongly tracked with population behavior only when participants showed low recruitment of this brain pattern, which consists of regions involved in goal-driven regulation of affective responses. Overall, these findings suggest that affective and value-related brain responses can predict the success of persuasive messages and that neural mechanisms of emotion regulation can shape these responses, moderating the extent to which they track with population-level message impact.SIGNIFICANCE STATEMENT People and organizations often appeal to our emotions to persuade us, but how these appeals engage the brain to drive behavior is not fully understood. We present an fMRI-based model that integrates affect-, control-, and value-related brain responses to predict the impact of graphic anti-smoking stimuli within a small group of smokers and a larger-scale public messaging campaign. This model indicated that amygdala activity predicted the impact of the anti-smoking messages, but that this relationship was mediated by ventromedial prefrontal cortex and moderated by expression of a distributed brain pattern associated with regulating emotion. These results suggest that neural mechanisms of emotion regulation can shape the extent to which affect and value-related brain responses track with population behavioral effects.