Abstract for: How the Human Mind Thinks and Learns about Feedback Loops
This paper considers how concepts and approaches from cognitive and learning sciences might shed light on how humans think and learn about feedback loops. Cognitive biases that might interfere with one's ability to accurately form individual causal links include tendencies to perceive covariation that is direct rather than inverse, to overweight information about presence rather than absence, to weight a plausible mechanism over empirical evidence, and to fail to account with what other influences might matter. Cognitive limitations that might compromise one's ability to merge individual links into a closed loop include working memory limitations, tendency to see sequences as linear chains, tendency to look for explanations of phenomena at the level of components, and difficulty comprehending exponential growth. On the more optimistic side, cognitive affordances that make feedback loop thinking possible include analogical reasoning, language and categorization, ability to create runnable mental models, and distributed cognition. The paper concludes that realm of how humans think and learn with and about feedback loops is ripe for further cognitive, learning science, and neuroscience research, and suggests research questions that have the potential to both advance systems education practice and elucidate under-researched capabilities of the mind.