Human and animal lesion studies have shown that behavior can be catastrophically impaired after bilateral lesions but that unilateral damage often produces little or no effect, even controlling for lesion extent. This pattern is found across many different sensory, motor, and memory domains. Despite these findings, there has been no systematic, computational explanation. We found that the same striking difference between unilateral and bilateral damage emerged in a distributed, recurrent attractor neural network. The difference persists in simple feedforward networks, where it can be understood in explicit quantitative terms. In essence, damage both distorts and reduces the magnitude of relevant activity in each hemisphere. Unilateral damage reduces the relative magnitude of the contribution to performance of the damaged side, allowing the intact side to dominate performance. In contrast, balanced bilateral damage distorts representations on both sides, which contribute equally, resulting in degraded performance. The model's ability to account for relevant patient data suggests that mechanisms similar to those in the model may operate in the brain.

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