How minds differ
Looser expectations, louder senses
In shortSome people give more weight to their senses than to what they expect: they notice more, and the world is less predictable.
The idea
Some people give less weight to what they expect and more to what their senses report, moment by moment. They notice details others miss, hear the fridge, see the crooked picture frame and remember exactly how something was. The cost is a less predictable world: each detail is new information, small variations feel like new situations, and grouping things into broader categories takes more experience.
What to try
- Reduce competing detail when something has to be learned or done: one voice, one task, fewer things in view.
- Help build categories out loud: ‘they are all different, and they are all dogs’.
- Value the detail: it is often a strength in maths, music, nature, building and science.
The common mistake
Calling it ‘oversensitivity’ and trying to train it away, instead of adjusting the setting and making use of the detail.
Want to know more?
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Short, plain pieces by the researchers and good writers.
Books
How the predictive brain shapes what we see, feel and do, with many everyday examples.
See on Amazon ›Experience as a ‘controlled hallucination’: the body and world perceived from predictions.
See on Amazon ›Amazon links are affiliate links: if you buy, Bounded Uncertainty receives a small commission at no cost to you.
The science behind itHow strong the evidence is, the model, videos and references.
How strong the evidence is
Promising Good studies, but little tested with children at home or school, or the step to daily life is our interpretation.
Pellicano and Burr (2012) proposed ‘hypo-priors’ in autism: broader priors, so perception stays closer to the sensory input. Lawson, Rees and Friston (2014) reframed this as atypical precision, typically higher sensory precision relative to priors. Van de Cruys et al. (2014) proposed HIPPEA (high, inflexible precision of prediction errors in autism): errors are weighted highly regardless of context, so the system cannot tell which errors are worth learning from and tends to fit noise. The consequence is a model that is not coarse-grained, close to particular instances and generalising less (see ‘Learning the detail, missing the pattern’). Evidence is mixed and domain-specific (see the topic on Bayesian brains). Constant et al. (2020) add an ecological view: precise minds do well in predictable, well-structured niches.
References
- Pellicano, E., & Burr, D. (2012). When the world becomes ‘too real’: A Bayesian explanation of autistic perception. Trends in Cognitive Sciences, 16(10), 504–510.
- Lawson, R. P., Rees, G., & Friston, K. J. (2014). An aberrant precision account of autism. Frontiers in Human Neuroscience, 8, 302.
- Van de Cruys, S., Evers, K., Van der Hallen, R., Van Eylen, L., Boets, B., de-Wit, L., & Wagemans, J. (2014). Precise minds in uncertain worlds: Predictive coding in autism. Psychological Review, 121(4), 649–675.
- Constant, A., Bervoets, J., Hens, K., & Van de Cruys, S. (2020). Precise worlds for certain minds: An ecological perspective on the relational self in autism. Topoi, 39(3), 611–622.
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