Modern physics suggest that time may be symmetric, thus allowing for backward in time effects, also referred to as retrocausality. Likewise, there is experimental work consistent with the notion that information about a future event, unknowable through inference alone, could be obtained before the event actually occurs. Despite this body of work, there has yet to be an experimental paradigm that has convinced the scientific community at large that retrocausality can influence human behavior. The particular paradigm we will be presenting stands apart from other work on this topic through its potential to demonstrate tangible, realworld applications based on the effect (e.g., successful prediction of the spin of a roulette wheel (black vs. red) or the up/down fluctuations of the market). In this experiment subjects see four different shapes (Shape A, B, C, and D) that randomly appear one at a time in the center of the computer screen. In phase 1, all subjects are simply told to press a button if they see Shape A or Shape B, otherwise they should not respond. Therefore, in phase 1, all subjects respond to both Shape A and Shape B. In phase 2, subjects are randomly divided into two groups. One group only responds to Shape A, while another group only responds to Shape B. In phase 2, therefore, subjects are getting practice with either Shape A or Shape B. Here we can test whether performance in phase 1, where all subjects are doing the exact same task, responding to both Shape A and B, is influenced by future practice with just one of the two shapes. The data from nearly 800 subjects collected at the University of Michigan and UCSB shows that there are reliable effects found in the paradigm, where future practice with a given shape (i.e., in phase 2) significantly affects prior performance (i.e., in phase 1; p = 0.0002). Ultimately, we realized that the most convincing demonstration of this phenomenon would be to show tangible effects applied in real-world settings. Importantly, this particular paradigm offers a way to test for retrocausal effects in an applied manner because what these results actually show is that performance in phase 1 gives a better than chance prediction of an unknown random binary event (i.e., whether the subject will be assigned Shape A or Shape B in phase 2). Therefore, this same logic can be used to predict other random binary events (e.g., a coin flip) at greater than chance levels. We will present work done
thus far in which we have been succesful at predicting the outcome of a roulette spin (black vs. red) better than chance (n=204, hit rate 57%, p<.05).