When an infant’s perceptual system expects continuity or synchrony and that pattern is broken, the brain registers prediction error. Those surprise signals can drive attention and learning without requiring preformed, abstract concepts. This perspective reframes classic surprise experiments: some “violations” may reflect mismatches in perceptual prediction rather than conflicts with mature conceptual knowledge. The account also connects to known constraints of visual input and neural processing, offering a mechanistic route to both early successes and the uneven patterns children show as capacities develop.
For people interested in human potential, this shift matters because it emphasizes how attention and perception together scaffold later reasoning and social understanding. Thinking in terms of selection and prediction highlights pathways for inclusive early experiences that shape which information children receive and learn from. Follow the full article to see how these ideas map onto experiments and what they imply for nurturing flexible, equitable cognitive growth.
For decades, core knowledge accounts have proposed that early cognition is organized by innate, domain-specific conceptual or concept-like representations of objects, physical events, number, and agents. I propose a different starting point: infants’ behaviors may arise from domain-general attentional priors—evolutionarily shaped selection biases—interacting with perceptual mechanisms that organize information and support perceptual prediction, rather than from abstract, postperceptual representations. Attentional priors favor ecologically consequential information, including continuity, contact, and synchrony. When such information is disrupted, perceptual prediction error—rather than conceptual contradiction—may produce violation-of-expectation effects. Grounded in constraints on visual input and neural processing, this framework accounts for early successes and systematic failures, suggesting that development begins with perceptual-attentional mechanisms that shape selection, organization, prediction, and learning.
