Interpreting Classification Images of the Self in the Context of Predictive Coding
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According to the predictive coding theory, the perception of the environment, others and ourselves is influenced by prior knowledge, also referred to as predictions. The objective sensory input is combined with these predictions into a weighted average, which forms our eventual subjective perception. These different internalized predictions explain interindividual differences within perception. The reverse correlation technique strives to visualize this implicit prediction also known as “prior” within an image. Within the first part of our research we strived to validate whether this technique is a valid tool to visualize the implicit self-image through a recognition task, which might eventually be used within therapeutic settings. Results show that participants can significantly recognize their implicit self-image, which eventually indicates that reverse correlation visualizes self-image. The second part of our research investigated whether this visualization represents the prior within the predictive coding theory. Our results suggest that this is indeed the case. Ultimately, we propose some adjustments within our research to even further validate this notion, but consider reverse correlation to be a valuable method to do research within the theory of predictive coding.