Optimum Integration Procedure for Connectionist and Dynamic Field Equations.

Connectionist and dynamic field models consist of a set of coupled first-order differential equations describing the evolution in time of different units. We compare three numerical methods for the integration of these equations: the Euler method, and two methods we have developed and present here: a modified version of the fourth-order Runge Kutta method, and one semi-analytical method. We apply them to solve a well-known nonlinear connectionist model of retrieval in single-digit multiplication, and show that, in many regimes, the semi-analytical and modified Runge Kutta methods outperform the Euler method, in some regimes by more than three orders of magnitude. Given the outstanding difference in execution time of the methods, and that the EM is widely used, we conclude that the researchers in the field can greatly benefit from our analysis and developed methods.

Masks and distancing during COVID-19: a causal framework for imputing value to public-health interventions.

During the COVID-19 pandemic, the scientific community developed predictive models to evaluate potential governmental interventions. However, the analysis of the effects these interventions had is less advanced. Here, we propose a data-driven framework to assess these effects retrospectively. We use a regularized regression to find a parsimonious model that fits the data with the least changes in the [Formula: see text] parameter. Then, we postulate each jump in [Formula: see text] as the effect of an intervention. Following the do-operator prescriptions, we simulate the counterfactual case by forcing [Formula: see text] to stay at the pre-jump value. We then attribute a value to the intervention from the difference between true evolution and simulated counterfactual. We show that the recommendation to use facemasks for all activities would reduce the number of cases by 200,000 ([Formula: see text] CI 190,000-210,000) in Connecticut, Massachusetts, and New York State. The framework presented here might be used in any case where cause and effects are sparse in time.

Explicit and implicit monitoring in neurodegeneration and stroke.

Monitoring is a complex multidimensional neurocognitive phenomenon. Patients with fronto-insular stroke (FIS), behavioural variant frontotemporal dementia (bvFTD) and Alzheimer's disease (AD) show a lack of self-awareness, insight, and self-monitoring, which translate into anosognosia and daily behavioural impairments. Notably, they also present damage in key monitoring areas. While neuroscientific research on this domain has accrued in recent years, no previous study has compared monitoring performance across these brain diseases and none has applied a multiple lesion model approach combined with neuroimaging analysis. Here, we evaluated explicit and implicit monitoring in patients with focal stoke (FIS) and two types of dementia (bvFTD and AD) presenting damage in key monitoring areas. Participants performed a visual perception task and provided two types of report: confidence (explicit judgment of trust about their performance) and wagering (implicit reports which consisted in betting on their accuracy in the perceptual task). Then, damaged areas were analyzed via structural MRI to identify associations with potential behavioral deficits. In AD, inadequate confidence judgments were accompanied by poor wagering performance, demonstrating explicit and implicit monitoring impairments. By contrast, disorders of implicit monitoring in FIS and bvFTD patients occurred in the context of accurate confidence reports, suggesting a reduced ability to turn self-knowledge into appropriate wagering conducts. MRI analysis showed that ventromedial compromise was related to overconfidence, whereas fronto-temporo-insular damage was associated with excessive wagering. Therefore, joint assessment of explicit and implicit monitoring could favor a better differentiation of neurological profiles (frontal damage vs AD) and eventually contribute to delineating clinical interventions.

Maintaining trust when agents can engage in self-deception.

The coexistence of cooperation and selfish instincts is a remarkable characteristic of humans. Psychological research has unveiled the cognitive mechanisms behind self-deception. Two important findings are that a higher ambiguity about others' social preferences leads to a higher likelihood of acting selfishly and that agents acting selfishly will increase their belief that others are also selfish. In this work, we posit a mathematical model of these mechanisms and explain their impact on the undermining of a global cooperative society. We simulate the behavior of agents playing a prisoner's dilemma game in a random network of contacts. We endow each agent with these two self-deception mechanisms which bias her toward thinking that the other agent will defect. We study behavior when a fraction of agents with the "always defect" strategy is introduced in the network. Depending on the magnitude of the biases the players could start a cascade of defection or isolate the defectors. We find that there are thresholds above which the system approaches a state of complete distrust.