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A detailed whole-body model of the fruit fly, developed using a physics-based simulation and deep reinforcement learning, accurately replicates real fly behaviour.

Humans and other mammals are prodigious learners, partly because they also ‘learn how to learn’. Wang and colleagues present a new theory showing how learning to learn may arise from interactions between prefrontal cortex and the dopamine system.

Analyses of single-cell recordings from mouse ventral tegmental area are consistent with a model of reinforcement learning in which the brain represents possible future rewards not as a single mean of stochastic outcomes, as in the canonical model, but instead as a probability distribution.

McNamee et al. develop a theory of entorhinal–hippocampal processing. Distributed entorhinal input drives hippocampal activity between distinct statistical and dynamical regimes of activity, thereby unifying several empirical observations.

We built an artificial neural network to control a biomechanically realistic virtual rodent, which, when trained to imitate real rats, predicts neural activity and variability across natural behaviours.

Mental effort is traditionally a subject of psychological research. Kool and Botvinick discuss how recent attempts to study mental effort using concepts from behavioural economics have allowed researchers to better understand how costs and benefits drive when people invest mental effort.

McKee et al. show that deep reinforcement learning can be used to learn a new and effective strategy for encouraging mutually beneficial cooperation in a network game.

Koster, Balaguer et al. show that an AI mechanism is able to learn to produce a redistribution policy which is preferred to alternatives by humans in an incentivized game.

Although the hippocampus has long been linked to planning, it has not been shown to be necessary for planning behavior. Using computational modeling and a new rat task that allows the quantification of planning behavior across many repeated trials, the authors report the first evidence that hippocampal inactivation impairs planning.

A sophisticated study of error-related brain potentials in patients with prefrontal lesions addresses how we monitor performance and adjust cognitive control based on task demands.

Piloto et al. introduce a deep-learning system which is able to learn basic rules of the physical world, such as object solidity and persistence.

Little is known about the brain’s computations that enable the recognition of faces. Here, the authors use unsupervised deep learning to show that the brain disentangles faces into semantically meaningful factors, like age or the presence of a smile, at the single neuron level.

Decision-making involves parallel information processing regarding what stimulus dimension to pay attention to and what action to take. Here, the authors show that vmPFC tracks the value of the attended attribute while dACC tracks the degree to which it is attended.

Using intracranial recordings in humans, the authors found decision conflict-related effects on firing rate in the dorsal anterior cingulate cortex (dACC), on spike-phase coupling in the dACC, and on spike-field coherence in the dorsolateral prefrontal cortex.

Research on event perception has focused on transient elevations in predictive uncertainty or surprise as the primary signal driving event segmentation. Here the authors report behavioral and neuroimaging evidence that suggests that event representations can emerge even in the absence of such cues. They propose that this learning occurs in a manner analogous to the learning of semantic categories.

Previous work decoding linguistic meaning from imaging data has generally been limited to a small number of semantic categories. Here, authors show that a decoder trained on neuroimaging data of single concepts sampling the semantic space can robustly decode meanings of semantically diverse new sentences with topics not encountered during training.

The authors show how predictive representations are useful for maximizing future reward, particularly in spatial domains. They develop a predictive-map model of hippocampal place cells and entorhinal grid cells that captures a wide variety of effects from human and rodent literature.

While changes in dACC activity have traditionally been associated with variability in decision difficulty, a recent high-profile study has suggested that dACC instead encodes the value of foraging. In this study, the authors challenge this previous finding by showing that, when foraging value and decision difficulty are effectively dissociated, dACC activity corresponds to changes in choice difficulty.

To study cognition, researchers have traditionally used laboratory-based experiments, but games offer a valuable alternative: they are intuitive and enjoyable. In this Perspective, Schulz et al. discuss the advantages and drawbacks of games and give recommendations for researchers.

One of the ambitions of computational neuroscience is that we will continue to make improvements in the field of artificial intelligence that will be informed by advances in our understanding of how the brains of various species evolved to process information. To that end, here the authors propose an expanded version of the Turing test that involves embodied sensorimotor interactions with the world as a new framework for accelerating progress in artificial intelligence.

The authors propose that dorsal anterior cingulate cortex (dACC) performs a cost/benefit analysis to specify how best to allocate cognitive control. They describe why this theory accounts well for dACC’s role in decision-making, motivation and cognitive control, including its observed role in foraging choice settings.

Recent research in motor neuroscience has focused on optimal feedback control of single, simple tasks while robotics and AI are making progress towards flexible movement control in complex environments employing hierarchical control strategies. Here, the authors argue for a return to hierarchical models of motor control in neuroscience.