Cheng Xue writes that cognitive capacity limitations and reduced performance under uncertain conditions stem from feature interference, where irrelevant features become strongly represented or entangled with relevant ones in neural representations. By combining monkey electrophysiology, human psychophysics, and artificial neural network modeling, researchers demonstrated that both humans and monkeys exhibit decreased perceptual accuracy when task rules are uncertain compared to optimized networks. This phenomenon is driven by the induction of stronger-than-normal representations for irrelevant features during periods of uncertainty or after nonrewarded trials.
- The study found that unrewarded trials lead to higher feature interference in neural populations
- Monkey choice models trained on behavioral data replicated suboptimal performance seen in live subjects, whereas "correct" networks did not
- Feature axes become less orthogonal (more entangled) in the monkey choice network following nonrewards
- Task certainty can be continuously measured by tracking task output activity in recurrent neural networks
- The study found that unrewarded trials lead to higher feature interference in neural populations
- Monkey choice models trained on behavioral data replicated suboptimal performance seen in live subjects, whereas "correct" networks did not
- Feature axes become less orthogonal (more entangled) in the monkey choice network following nonrewards
- Task certainty can be continuously measured by tracking task output activity in recurrent neural networks
