The objective of this proof-of-concept study was to test the utility of NeuroTargeted Training (NTT), a new method using functional Near-Infrared Spectroscopy (fNIRS) to measure and enhance cognitive performance during simulator training. Traditional simulator training is limited to behavioral evaluations, without capturing the trainee's internal cognitive processes. NTT addresses this gap by comparing traineesâ brain activation patterns to those of experts, allowing for precise identification and remediation of cognitive performance gaps. Three studies were conducted with five participants. Expert neural benchmarks were established from a man overboard simulation. Novices were evaluated against these benchmarks using a Expert Reference Index (ERI), quantifying deviations from expert performance, and the NeuroTargeted Training methodology was compared with conventional evaluations. Personalized training, based on identified gaps, was conducted to align novice neural patterns with expert benchmarks. Significant differences were observed, particularly in the anterior insula and inferior frontal gyrus, with an ERI of 4.84. Cohenâs Kappa (.69) indicated moderate inter-rater reliability. Subsequent targeted training reduced the ERI by 27%, aligning novice neural patterns with experts. Without intervention, the ERI rose by 79%, indicating increased cognitive strain. These findings highlight NTTâs potential to enhance learning outcomes in high-stake exercises by providing insights into cognitive processes.
Introduction: Cryptocurrency investment and trading are rapidly growing activities due to the development of applications and platforms that offer fast, continuous, and easy entry into the cryptocurrency world. To understand decision making in cryptocurrency holders, we assessed temporal discounting, that is, whether Bitcoin holders disregard rewards if they are distant in time and overvalue rewards if they are more immediate. Further, we compared performance between short-term investors (i.e., day-traders) vs. long-term investors. Methods: Using an online survey, we invited 144 Bitcoin holders to answer temporal discounting questionnaires dealing with money ("Which do you prefer, that you get right now 20 USD in cash or 100 USD in a month?") and Bitcoin ("Which do you prefer, that you get right now 0.1 or 1 Bitcoin in a month?"). Results: Analysis demonstrated no significant differences between temporal discounting for money and Bitcoin. However, and critically, higher temporal discounting for both money and Bitcoin was observed in short-term investors compared with long-term investors. In a similar vein, significant positive correlations were observed between day trading and temporal discounting for both money and Bitcoin. Discussion: These findings demonstrate how Bitcoin holders with short-term time horizons tend to prioritize immediate rewards over larger but delayed rewards. Future research can assess the neural basis of temporal discounting for cryptocurrencies.