Two pathways to resolve relational inconsistencies
This research addresses a puzzle in cognitive science about expectation adaptation, with implications for understanding learning mechanisms in both humans and AI, though it is incremental in applying known neural network models to this domain.
The study investigated how individuals and artificial neural networks adjust expectations when faced with contradictory observations, finding that small violations lead to expectation adjustments, while extreme violations cause individuals to maintain prior expectations, a phenomenon also observed in neural networks as a natural learning dynamic.
When individuals encounter observations that violate their expectations, when will they adjust their expectations and when will they maintain them despite these observations? For example, when individuals expect objects of type A to be smaller than objects B, but observe the opposite, when will they adjust their expectation about the relationship between the two objects (to A being larger than B)? Naively, one would predict that the larger the violation, the greater the adaptation. However, experiments reveal that when violations are extreme, individuals are more likely to hold on to their prior expectations rather than adjust them. To address this puzzle, we tested the adaptation of artificial neural networks (ANNs) capable of relational learning and found a similar phenomenon: Standard learning dynamics dictates that small violations would lead to adjustments of expected relations while larger ones would be resolved using a different mechanism -- a change in object representation that bypasses the need for adaptation of the relational expectations. These results suggest that the experimentally-observed stability of prior expectations when facing large expectation violations is a natural consequence of learning dynamics and does not require any additional mechanisms. We conclude by discussing the effect of intermediate adaptation steps on this stability.