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

Doré, Bruce; Scholz, Christin; Baek, Elisa C.; Garcia, Javier O.; O’Donnell, Matthew Brook; Bassett, Dani S.; Vettel, Jean M.

doi:10.1093/cercor/bhy176

Cited by 22 publications

(20 citation statements)

References 35 publications

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“…Interestingly, this brain-as-predictor relationship was significant for emotional responses measured on different days in different contexts. More generally, our findings further add to the growing literature that psychological studies broaden the scope beyond the laboratory for extended validity and generalizability to real-world outcomes (Kingstone et al, 2003; Doré et al, 2018; Schmälzle et al, 2017).…”

Section: Discussionsupporting

confidence: 69%

“…In addition to these predictions for within-session physiological responses, our core 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 HRV). For the behavioral responses, we expected that emotional reactivity would be heightened during the immersive experience (Parsons, 2015); however, the literature is mixed regarding how well behavior in stationary, laboratory tasks predicts task performance when the stimuli have features more typical of real-world environments (Smilek et al, 2008; Hasson et al, 2010; Gramann et al, 2011; Schmälzle et al, 2017; Doré et al, 2018; Wasylyshyn et al, 2018). For the physiological measurements, some recent work has suggested that a stress response can rapidly couple brain and heart responses (Sakaki et al, 2016), but it was unclear whether this relationship would be observed between laboratory and simulation environments, especially with a temporal delay across days.…”

Section: Introductionmentioning

confidence: 99%

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

Roy

Wasylyshyn

Spangler

et al. 2019

Front. Hum. Neurosci.

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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.

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Section: Discussionsupporting

confidence: 69%

Section: Introductionmentioning

confidence: 99%

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

Roy

Wasylyshyn

Spangler

et al. 2019

Front. Hum. Neurosci.

View full text Add to dashboard Cite

show abstract

“…To distinguish between the trade-off and parallel-processes hypotheses (Table 1), we conducted a within-subject experiment in which participants were exposed to 80 New York Times (NYTimes) articles in different conditions, which asked participants to consider either broad-or narrowcasting with others, while we monitored their neural activity using fMRI. We have reported on orthogonal analyses of the same neural data elsewhere to understand the neural correlates of individual and population-level sharing (Scholz et al, 2017;Doré et al, 2018), averaging across (and thereby ignoring differences between) narrow-and broadcasting situations. Here, for the first time, we distinguish between the processes involved in decisions to share when offered the opportunity to narrow-and broadcast.…”

Section: Methodsmentioning

confidence: 99%

Decision-making about broad- and narrowcasting: a neuroscientific perspective

2019

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What differentiates sharing with few, well-defined others (narrowcasting) from sharing with loosely defined crowds (broadcasting)? One possibility involves a trade-off where broadcasting is selffocused and self-serving, and narrowcasting is based on otheroriented, altruistic motives. We present neuroimaging data consistent with a second, parallel-processes perspective. According to this account, both narrow-and broadcasting simultaneously involve self-related and social motives since these concepts are strongly intertwined both on a psychological and neural level. We recorded brain activity within regions that are meta-analytically associated with self-related and social cognition while participants made decisions to narrow-or broadcast New York Times articles on social media. Results show increased involvement of brain regions associated with both self-related and social processing in narrowand broadcasting, compared to a control condition. However, both processes were involved with higher intensity during narrowcasting, compared to broadcasting. These data help to disambiguate a theoretical discussion in communication science and clarify the neuropsychological mechanisms that drive sharing decisions in different contexts. Specifically, we highlight that narrow-and broadcasting afford differing intensities of two psychological processes that are crucial to persuasion and population-level content virality.

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“…And perhaps -with a bit of courage -we may even begin to provide an empirical constitution to the deeper philosophical questions that humans have wrestled with for millennia: What makes us unique and different from non-human animals 240,302 ? How do we represent abstract concepts such as value to ourselves 303 and others 304 ? How are representations transmitted throughout the brain or reconfigured based on new knowledge 305 ?…”

Section: Conclusion and Future Directions In The Neurophysics Of Bramentioning

confidence: 99%

The physics of brain network structure, function and control

2019

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The brain is a complex organ characterized by heterogeneous patterns of structural connections supporting unparalleled feats of cognition and a wide range of behaviors. New noninvasive imaging techniques now allow these patterns to be carefully and comprehensively mapped in individual humans and animals. Yet, it remains a fundamental challenge to understand how the brain's structural wiring supports cognitive processes, with major implications for the personalized treatment of mental health disorders. Here, we review recent efforts to meet this challenge that draw on intuitions, models, and theories from physics, spanning the domains of statistical mechanics, information theory, and dynamical systems and control. We begin by considering the organizing principles of brain network architecture instantiated in structural wiring under constraints of symmetry, spatial embedding, and energy minimization. We next consider models of brain network function that stipulate how neural activity propagates along these structural connections, producing the long-range interactions and collective dynamics that support a rich repertoire of system functions. Finally, we consider perturbative experiments and models for brain network control, which leverage the physics of signal transmission along structural wires to infer intrinsic control processes that support goal-directed behavior and to inform stimulation-based therapies for neurological disease and psychiatric disorders. Throughout, we highlight several open questions in the physics of brain network structure, function, and control that will require creative efforts from physicists willing to brave the complexities of living matter. resents 36 , whether it be a brain, a granular material 306 , or a quantum lattice 307 . By far the simplest network model is represented by a binary undirected graph in which identical nodes represent system components and identical edges indicate relations or connections between pairs of nodes (see the figure). Such a network can be encoded in an adjacency matrix A, where each element A ij indicates the strength of connectivity between nodes i and j. When all edge strengths are unity, the network is said to be binary. When edges have a range of weights, the network represented by the adjacency matrix is said to be weighted. When A = A , the network is undirected; otherwise, the network is directed.One can extend this simple encoding to study multilayer, multislice, and multiplex networks 308 ; dynamic or temporal networks 127, 309 ; annotated networks 310 ; hypergraphs 311 ; and simplicial complexes 230 . One can also calculate various statistics to quantify the architecture of a network and to infer the function thereof (see figure). Intuitively, these statistics range from measures of the local structure in the network, which depend solely on the links directly emanating from a given node (e.g., degree and clustering), to measures of the network's global structure, which depend on the complex pattern of interconnections between all nodes (e.g.,...

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

Cited by 22 publications

References 35 publications

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

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

Decision-making about broad- and narrowcasting: a neuroscientific perspective

The physics of brain network structure, function and control

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