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The N400 evoked potential is a window to meaning in the brain, but it remains incompletely understood. The authors provide a unified explanation of the N400 in a neural network model that avoids the commitments of traditional approaches to meaning in language.
Rutherford et al. analyse temporal, network and hierarchical effects to uncover, understand and quantify competing mechanisms of constitutional change worldwide.
Nowak and colleagues present a game theoretic model that explains how behaviours like subtlety, modesty and anonymous good deeds can be maintained under the standard model of reputation building and indirect reciprocity.
Analytis et al. study social learning strategies for matters of taste and test their performance on a large-scale dataset. They show why a strategy’s success depends both on people’s level of experience and how their tastes relate to those of others.
Lindström and Tobler find that ostracism of individuals can emerge incidentally, based on initial group structure, and is propagated by a simple reinforcement learning mechanism. The same mechanism can be used to reduce incidental ostracism.
High arousal enables young people to better detect salient stimuli. In older people, arousal leads to increased processing of all stimuli. This difference can be explained by age-related changes in how the locus coeruleus–noradrenaline system interacts with cortical attention networks.
In a common-pool resource experiment, Koomen and Herrmann show that six-year-old children are collectively able to avoid collapsing a shared resource and use similar strategies to adults.
A model of minority–majority group interactions shows that minority cultural practices can be preserved from cultural homogenization where a group boundary allows free movement of minority members, but excludes members of the more powerful majority.
Using an imagery-perception paradigm, the authors find that imagined speech affects the perceived loudness of sound. They also show that early neural responses correlate with the loudness ratings, even without external stimulation.
Glaze et al. show that individual variability in learning from noisy evidence involves a bias–variance trade-off that is best explained by a model using a sampling algorithm that approximates optimal inference.
Waniek and colleagues show that individuals and communities can disguise themselves from detection online by standard social network analysis tools through simple changes to their social network connections.
Strimling and colleagues develop and empirically test a mathematical model of the 'civilizing process', that is, the tendency of social norms about violence and hygiene to become increasingly strict over time.
Steinbeis and colleagues show that chimpanzees and six-year-old children will pay a cost to see the punishment of an antisocial agent when it is deserved, suggesting that both are motivated to see just punishment enacted.
Intracranial recordings from epileptic patients during a number of different behavioural tasks reveal, in impressive spatiotemporal detail, that the human brain links perception and action through persistent neural activity in the prefrontal cortex and functionally linked brain regions.
The authors used graph signal processing to examine whether fMRI signals correspond to underlying anatomical networks. They found that alignment between functional signals and anatomical structure was associated with greater cognitive flexibility.
In a series of 11 experiments, the authors show that what has traditionally been considered 'pitch perception' is mediated by several different mechanisms.
Using magnetic resonance imaging and electroencephalography during a face-discrimination task, the authors show face-processing lateralization in infants in the first postnatal semester, despite a corpus callosum mature enough to transfer visual information across hemispheres.
Over four functional MRI experiments, Axelrod et al. show that several cognitive processes function simultaneously during self-generated mental activity.
Just et al. develop a highly accurate biological classification method for identifying suicidal ideators by applying machine learning to neural representations of death- and life-related concepts.