Future directions in precognition research: more research can bridge the gap between skeptics and proponents

Precognition of Known And Unknown Biothreats: A Risk-Based Approach

Data mining and artificial intelligence algorithms can estimate the probability of future occurrences with defined precision. Yet, the prediction of infectious disease outbreaks remains a complex and difficult task. This is demonstrated by the limited accuracy and sensitivity of current models in predicting the emergence of previously unknown pathogens such as Zika, Chikungunya, and SARS-CoV-2, and the resurgence of Mpox, along with their impacts on global health, trade, and security. Comprehensive analysis of infectious disease risk profiles, vulnerabilities, and mitigation capacities, along with their spatiotemporal dynamics at the international level, is essential for preventing their transnational propagation. However, annual indexes about the impact of infectious diseases provide a low level of granularity to allow stakeholders to craft better mitigation strategies. A quantitative risk assessment by analytical platforms requires billions of near real-time data points from heterogeneous sources, integrating and analyzing univariable or multivariable data with different levels of complexity and latency that, in most cases, overwhelm human cognitive capabilities. Autonomous biosurveillance can open the possibility for near real-time, risk- and evidence-based policymaking and operational decision support.

'Feeling' or 'sensing' the future? Testing for anomalous cognitions in clinical versus healthy populations

In the study and treatment of psychosis, emotional intelligence (EI) and thinking styles are important patient characteristics for successful outcomes in clinical intervention. Anticipation of unpredictable stimuli (AUS) may be understood as an anomalous perception and anomalous cognition in which an individual supposedly senses and recognizes future stimuli in an unexpected way, also referred to as “hunches or premonitions.” This examined the roles of EI and thinking styles in AUSs in convenience samples of healthy participants (n = 237) versus patients diagnosed with psychosis (n = 118). We adjusted several quadratic and exponential regression models according to the obtained functions. Group means were also compared to examine differences in EI scores for participants with psychosis compared to healthy participants. In the healthy group, EI predicted AUSs with a weight between 42% and 58%. Thinking styles were not correlated with AUSs. However, EI was not correlated with AUSs in the clinical group. Patients with psychosis tended to score higher on AUSs and lower on EI and thinking styles compared to participants in the healthy group. We discuss EI as a variable that can contextualize some anomalous perceptions which are otherwise difficult to classify or measure within the classic psychosis continuum model.

Predictive physiological anticipatory activity preceding seemingly unpredictable stimuli: An update of Mossbridge et al's meta-analysis

Background: This is an update of the Mossbridge et al’s meta-analysis related to the physiological anticipation preceding seemingly unpredictable stimuli which overall effect size was 0.21; 95% Confidence Intervals: 0.13 - 0.29

Methods: Nineteen new peer and non-peer reviewed studies completed from January 2008 to June 2018 were retrieved describing a total of 27 experiments and 36 associated effect sizes.

Results: The overall weighted effect size, estimated with a frequentist multilevel random model, was: 0.28; 95% Confidence Intervals: 0.18-0.38; the overall weighted effect size, estimated with a multilevel Bayesian model, was: 0.28; 95% Credible Intervals: 0.18-0.38.

The weighted mean estimate of the effect size of peer reviewed studies was higher than that of non-peer reviewed studies, but with overlapped confidence intervals: Peer reviewed: 0.36; 95% Confidence Intervals: 0.26-0.47; Non-Peer reviewed: 0.22; 95% Confidence Intervals: 0.05-0.39.

Similarly, the weighted mean estimate of the effect size of Preregistered studies was higher than that of Non-Preregistered studies: Preregistered: 0.31; 95% Confidence Intervals: 0.18-0.45; No-Preregistered: 0.24; 95% Confidence Intervals: 0.08-0.41.

The statistical estimation of the publication bias by using the Copas selection model suggest that the main findings are not contaminated by publication bias.

Conclusions: In summary, with this update, the main findings reported in Mossbridge et al’s meta-analysis, are confirmed.