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Lefebvre et al. present behavioural and neural evidence showing that the ‘optimism bias’ is a manifestation of a general cognitive tendency for preferential learning from positive, compared with negative, outcomes.
A Bayesian theory of mind model is shown to infer and quantify the mental state and judgements of humans in decision-making scenarios. The model is a key step towards enabling machines to ‘intuit’ human thoughts and desires.
Chen et al. construct a model of the neural bases of semantic representation that unifies domain-specific (distinct systems represent different kinds of things) and domain-general (knowledge for all kinds is encoded in a single network) accounts.
Data obtained over twelve months from a large cohort of stroke patients shows that most recovery occurs within three months and is predicted by the severity of the initial deficit and patients’ education level.
Perceptual sensitivity in humans is shown to improve after training on a perceptual learning task owing to faster post-perceptual (late) decision-processing rather than faster sensory (early) processing.
By linking data from a 40-year birth cohort study with multiple administrative databases, the authors show that 20% of the population accounts for close to 80% of economic burden. Strikingly, this group can be predicted with high accuracy from as early as 3 years old.
Every time we make a choice, we maintain an explicit representation of our confidence in that choice. Using eye-tracking and behavioural measures, the authors show that tracking decision uncertainty is helpful in guiding future behaviour.