Neural Modularity Helps Organisms Evolve to Learn New Skills without Forgetting Old Skills
Fig 9
P&CC networks significantly outperform PA networks in both learning and retention.
P&CC individuals learn significantly more associations, whether counting only when the associations for both seasons are known (“Perfect” knowledge) or separately counting knowledge of either season’s association (total “Known”). P&CC networks also forget fewer associations, defined as associations known in one season and then forgotten in the next, which is significant when looking at the percent of known associations forgotten (“% Forgotten”). P&CC networks also retain significantly more associations, meaning they did not forget one season’s association when learning the next season’s association. See text for more information about the “Perfect”, “Known”, “Forgotten,” and “Retained” metrics. During all performance measurements, learning was disabled to prevent such measurements from changing an individual’s known associations (Methods). Bars show median performance, whiskers show the 95% bootstrapped confidence interval of the median. Two asterisks indicate p < 0.01, three asterisks indicate p < 0.001.