COMPLEXITY & RANDOMNESS Exploring the Limits of Pattern Perception

Abstract

Pattern perception is a core part of human cognition, however, our capacity to process patterns is limited. If a pattern is too complex to process, we no longer perceive it as a pattern but rather as noise, thus we hypothesize that there is a limit to human pattern perception that can be measured in terms of the complexity of the pattern. To test this, we apply Aksentijevic-Gibson complexity (AG), a sophisticated measurement for the perception of complexity developed by Aksentijevic et al. [Symmetry, 12(6), 948 (2020)], in a sequential comparison procedure task to test at what level of complexity participants can't differentiate between two randomly generated patterns. The result confirmed that an increase in the level of AG resulted in a decrease in performance and a significant difference between the lowest and highest level of AG. However, the hypothesized sharp decline in performance at the limits of our pattern perception was not present in the data. Despite not being able to answer the research question in detail, valuable insight into the implementation of AG in studies on visual perception was gained, as well as general insights on the implementation and application of AG and other measurements of complexity. By applying quantitative measures like AG in a wide variety of experiments, it would be possible to start outlining a general overview of the limits of human pattern perception and information processing in terms of complexity. This can provide an empirical basis for perception and information processing theories. In addition, since our perception shapes our assumptions about the world, improving our understanding of how we perceive complexity could have implications for what scientific inquiries we deem interesting and relevant and how we go about answering these questions.