Nicotine-related beliefs induce dose-dependent responses in the human brain.

Beliefs have a powerful influence on our behavior, yet their neural mechanisms remain elusive. Here we investigate whether beliefs could impact brain activities in a way akin to pharmacological dose-dependent effects. Nicotine-dependent humans were told that nicotine strength in an electronic cigarette was either 'low', 'medium' or 'high', while nicotine content was held constant. After vaping, participants underwent functional neuroimaging and performed a decision-making task known to engage neural circuits affected by nicotine. Beliefs about nicotine strength induced dose-dependent responses in the thalamus, a key binding site for nicotine, but not in other brain regions such as the striatum. Nicotine-related beliefs also parametrically modulated the connectivity between the thalamus and ventromedial prefrontal cortex, a region important for decision-making. These findings reveal a high level of precision in the way beliefs influence the brain, offering mechanistic insights into humans' heterogeneous responses to drugs and a pivotal role of beliefs in addiction.

The power of multivariate approach in identifying EEG correlates of interlimb coupling.

Interlimb coupling refers to the interaction between movements of one limb and movements of other limbs. Understanding mechanisms underlying this effect is important to real life because it reflects the level of interdependence between the limbs that plays a role in daily activities including tool use, cooking, or playing musical instruments. Interlimb coupling involves multiple brain regions working together, including coordination of neural activity in sensory and motor regions across the two hemispheres. Traditional neuroscience research took a univariate approach to identify neural features that correspond to behavioural coupling measures. Yet, this approach reduces the complexity of the neural activity during interlimb tasks to one value. In this brief research report, we argue that identifying neural correlates of interlimb coupling would benefit from a multivariate approach in which full patterns from multiple sources are used to predict behavioural coupling. We demonstrate the feasibility of this approach in an exploratory EEG study where participants (n = 10) completed 240 trials of a well-established drawing paradigm that involves interlimb coupling. Using artificial neural network (ANN), we show that multivariate representation of the EEG signal significantly captures the interlimb coupling during bimanual drawing whereas univariate analyses failed to identify such correlates. Our findings demonstrate that analysing distributed patterns of multiple EEG channels is more sensitive than single-value techniques in uncovering subtle differences between multiple neural signals. Using such techniques can improve identification of neural correlates of complex motor behaviours.

Lie to my face: An electromyography approach to the study of deceptive behavior.

BACKGROUND: Deception is present in all walks of life, from social interactions to matters of homeland security. Nevertheless, reliable indicators of deceptive behavior in real-life scenarios remain elusive. METHODS: By integrating electrophysiological and communicative approaches, we demonstrate a new and objective detection approach to identify participant-specific indicators of deceptive behavior in an interactive scenario of a two-person deception task. We recorded participants' facial muscle activity using novel dry screen-printed electrode arrays and applied machine-learning algorithms to identify lies based on brief facial responses. RESULTS: With an average accuracy of 73%, we identified two groups of participants: Those who revealed their lies by activating their cheek muscles and those who activated their eyebrows. We found that the participants lied more often with time, with some switching their telltale muscle groups. Moreover, while the automated classifier, reported here, outperformed untrained human detectors, their performance was correlated, suggesting reliance on shared features. CONCLUSIONS: Our findings demonstrate the feasibility of using wearable electrode arrays in detecting human lies in a social setting and set the stage for future research on individual differences in deception expression.

Emotional adaptation during a crisis: decline in anxiety and depression after the initial weeks of COVID-19 in the United States.

Crises such as the COVID-19 pandemic are known to exacerbate depression and anxiety, though their temporal trajectories remain under-investigated. The present study aims to investigate fluctuations in depression and anxiety using the COVID-19 pandemic as a model crisis. A total of 1512 adults living in the United States enrolled in this online study beginning April 2, 2020 and were assessed weekly for 10 weeks (until June 4, 2020). We measured depression and anxiety using the Zung Self-Rating Depression scale and State-Trait Anxiety Inventory (state subscale), respectively, along with demographic and COVID-related surveys. Linear mixed-effects models were used to examine factors contributing to longitudinal changes in depression and anxiety. We found that depression and anxiety levels were high in early April, but declined over time. Being female, younger age, lower-income, and previous psychiatric diagnosis correlated with higher overall levels of anxiety and depression; being married additionally correlated with lower overall levels of depression, but not anxiety. Importantly, worsening of COVID-related economic impact and increase in projected pandemic duration exacerbated both depression and anxiety over time. Finally, increasing levels of informedness correlated with decreasing levels of depression, while increased COVID-19 severity (i.e., 7-day change in cases) and social media use were positively associated with anxiety over time. These findings not only provide evidence for overall emotional adaptation during the initial weeks of the pandemic, but also provide insight into overlapping, yet distinct, factors contributing to depression and anxiety throughout the first wave of the pandemic.

Containment of COVID-19: Simulating the impact of different policies and testing capacities for contact tracing, testing, and isolation.

Efficient contact tracing and testing are fundamental tools to contain the transmission of SARS-CoV-2. We used multi-agent simulations to estimate the daily testing capacity required to find and isolate a number of infected agents sufficient to break the chain of transmission of SARS-CoV-2, so decreasing the risk of new waves of infections. Depending on the non-pharmaceutical mitigation policies in place, the size of secondary infection clusters allowed or the percentage of asymptomatic and paucisymptomatic (i.e., subclinical) infections, we estimated that the daily testing capacity required to contain the disease varies between 0.7 and 9.1 tests per thousand agents in the population. However, we also found that if contact tracing and testing efficacy dropped below 60% (e.g. due to false negatives or reduced tracing capability), the number of new daily infections did not always decrease and could even increase exponentially, irrespective of the testing capacity. Under these conditions, we show that population-level information about geographical distribution and travel behaviour could inform sampling policies to aid a successful containment, while avoiding concerns about government-controlled mass surveillance.

Contribution of self- and other-regarding motives to (dis)honesty.

Why would people tell the truth when there is an obvious gain in lying and no risk of being caught? Previous work suggests the involvement of two motives, self-interest and regard for others. However, it remains unknown if these motives are related or distinctly contribute to (dis)honesty, and what are the neural instantiations of these motives. Using a modified Message Game task, in which a Sender sends a dishonest (yet profitable) or honest (less profitable) message to a Receiver, we found that these two motives contributed to dishonesty independently. Furthermore, the two motives involve distinct brain networks: the LPFC tracked potential value to self, whereas the rTPJ tracked potential losses to other, and individual differences in motives modulated these neural responses. Finally, activity in the vmPFC represented a balance of the two motives unique to each participant. Taken together, our results suggest that (dis)honest decisions incorporate at least two separate cognitive and neural processes-valuation of potential profits to self and valuation of potential harm to others.

Containment of future waves of COVID-19: simulating the impact of different policies and testing capacities for contact tracing, testing, and isolation.

We used multi-agent simulations to estimate the testing capacity required to find and isolate a number of infections sufficient to break the chain of transmission of SARS-CoV-2. Depending on the mitigation policies in place, a daily capacity between 0.7 to 3.6 tests per thousand was required to contain the disease. However, if contact tracing and testing efficacy dropped below 60% (e.g. due to false negatives or reduced tracing capability), the number of infections kept growing exponentially, irrespective of any testing capacity. Under these conditions, the population's geographical distribution and travel behaviour could inform sampling policies to aid a successful containment.

Objective Physiological Measurements but Not Subjective Reports Moderate the Effect of Hunger on Choice Behavior.

Hunger is a powerful driver of human behavior, and is therefore of great interest to the study of psychology, economics, and consumer behavior. Assessing hunger levels in experiments is often biased, when using self-report methods, or complex, when using blood tests. We propose a novel way of objectively measuring subjects' levels of hunger by identifying levels of alpha-amylase (AA) enzyme in their saliva samples. We used this measure to uncover the effect of hunger on different types of choice behaviors. We found that hunger increases risk-seeking behavior in a lottery-choice task, modifies levels of vindictiveness in a social decision-making task, but does not have a detectible effect on economic inconsistency in a budget-set choice task. Importantly, these findings were moderated by AA levels and not by self-report measures. We demonstrate the effects hunger has on choice behavior and the problematic nature of subjective measures of physiological states, and propose to use reliable and valid biologically based methods to overcome these problems.

Common Sense in Choice: The Effect of Sensory Modality on Neural Value Representations.

Although it is well established that the ventromedial prefrontal cortex (vmPFC) represents value using a common currency across categories of rewards, it is unknown whether the vmPFC represents value irrespective of the sensory modality in which alternatives are presented. In the current study, male and female human subjects completed a decision-making task while their neural activity was recorded using functional magnetic resonance imaging. On each trial, subjects chose between a safe alternative and a lottery, which was presented visually or aurally. A univariate conjunction analysis revealed that the anterior portion of the vmPFC tracks subjective value (SV) irrespective of the sensory modality. Using a novel cross-modality multivariate classifier, we were able to decode auditory value based on visual trials and vice versa. In addition, we found that the visual and auditory sensory cortices, which were identified using functional localizers, are also sensitive to the value of stimuli, albeit in a modality-specific manner. Whereas both primary and higher-order auditory cortices represented auditory SV (aSV), only a higher-order visual area represented visual SV (vSV). These findings expand our understanding of the common currency network of the brain and shed a new light on the interplay between sensory and value information processing.