Situated models of emotion hypothesize that emotions are optimized for the context at hand, but most neuroimaging approaches ignore context. For the first time, we applied Granger causality (GC) analysis to determine how an emotion is affected by a person’s cultural background and situation. Electroencephalographic (EEG) recordings were taken from mainland Chinese and US participants as they viewed and rated fearful and neutral images displaying either social or non-social contexts. Independent components analysis (ICA) and GC analysis was applied to determine the epoch of peak effect for each condition and to identify sources and sinks among brain regions of interest. We found that source-sink couplings differed across culture, situation, and culture x situation. Mainland Chinese participants alone showed preference for an early-onset source-sink pairing with the supramarginal gyrus as a causal source, suggesting that, relative to US participants, Chinese participants more strongly prioritized a scene’s social aspects in their response to fearful scenes. Our findings suggest that the neural representation of fear indeed varies according to both culture, situation, and their interaction in ways that are consistent with norms instilled by cultural background.

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Objective Using dynamic causal modeling (DCM), we examined how credibility and reliability affected the way brain regions exert causal influence over each other—effective connectivity (EC)—in the context of trust in automation. Background Multiple brain regions of the central executive network (CEN) and default mode network (DMN) have been implicated in trust judgment. However, the neural correlates of trust judgment are still relatively unexplored in terms of the directed information flow between brain regions. Method Sixteen participants observed the performance of four computer algorithms, which differed in credibility and reliability, of the system monitoring subtask of the Air Force Multi-Attribute Task Battery (AF-MATB). Using six brain regions of the CEN and DMN commonly identified to be activated in human trust, a total of 30 (forward, backward, and lateral) connection models were developed. Bayesian model averaging (BMA) was used to quantify the connectivity strength among the brain regions. Results Relative to the high trust condition, low trust showed unique presence of specific connections, greater connectivity strengths from the prefrontal cortex, and greater network complexity. High trust condition showed no backward connections. Conclusion Results indicated that trust and distrust can be two distinctive neural processes in human–automation interaction—distrust being a more complex network than trust, possibly due to the increased cognitive load. Application The causal architecture of distributed brain regions inferred using DCM can help not only in the design of a balanced human–automation interface design but also in the proper use of automation in real-life situations.

The only evidence that seeing in slow-motion exists comes from retrospective interviews. An ongoing debate is whether this phenomenon exists as a figment of memory or a true function of visual perception. Testing these speculations is difficult given slow-motion experience is often associated with intense, stressful, and even threatening situations that dramatically heighten arousal. Virtual reality systems might provide an opportunity to study the experience online, thus offering insights into the speculated mechanisms. This study explores the feasibility to induce heightened arousal and its possible implications on perceptual encoding of information. Participants were exposed to various situations designed to influence arousal as measured by heart rate, and an implicit memory task was used for each situation to test perceptual processing. This study did not reveal performance gains associated with increased physiological arousal.

A diagramming method called Propositional Constraint (PC) graphing was developed as an aid for tasks involving argumentation, planning, and design. Motivated by several AI models of defeasible (or non- monotonic) reasoning, PC graphs were designed to represent knowledge according to an analogical framework in which constraints (e.g., evidence, goals, system constraints) may elicit or deny possibilities (e.g., explanations, decisions, behaviors). In cases of underspecification, an absence of constraints yields uncertainty and competition among plausible outcomes. In cases of overspecification, no plausible outcome is yielded until one of the constraints is amended or forfeited. This framework shares features with theoretical models of reasoning and argumentation, but despite its intuitiveness and applicability, we know of no modeling language or graphical aid that explicitly depicts this defeasible constraint structure. We describe the syntax and semantics for PC graphing and then illustrate potential uses for it.