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An intrinsic part of seeing objects is seeing how similar or different they are relative to one another. This experience requires that objects be mentally represented in a common format over which such comparisons can be carried out. What is that representational format? Objects could be compared in terms of their superficial features (e.g., degree of pixel-by-pixel overlap), but a more intriguing possibility is that they are compared on the basis of a deeper structure. One especially promising candidate that has enjoyed success in the computer vision literature is the shape skeleton-a geometric transformation that represents objects according to their inferred underlying organization. Despite several hints that shape skeletons are computed in human vision, it remains unclear how much they actually matter for subsequent performance. Here, we explore the possibility that shape skeletons help mediate the ability to extract visual similarity. Observers completed a same/different task in which two shapes could vary either in their skeletal structure (without changing superficial features such as size, orientation, and internal angular separation) or in large surface-level ways (without changing overall skeletal organization). Discrimination was better for skeletally dissimilar shapes: observers had difficulty appreciating even surprisingly large differences when those differences did not reorganize the underlying skeletons. This pattern also generalized beyond line drawings to 3-D volumes whose skeletons were less readily inferable from the shapes' visible contours. These results show how shape skeletons may influence the perception of similarity-and more generally, how they have important consequences for downstream visual processing.

Learning about rewards and punishments is critical for survival. Classical studies have demonstrated an impressive correspondence between the firing of dopamine neurons in the mammalian midbrain and the reward prediction errors of reinforcement learning algorithms, which express the difference between actual reward and predicted mean reward. However, it may be advantageous to learn not only the mean but also the complete distribution of potential rewards. Recent advances in machine learning have revealed a biologically plausible set of algorithms for reconstructing this reward distribution from experience. Here, we review the mathematical foundations of these algorithms as well as initial evidence for their neurobiological implementation. We conclude by highlighting outstanding questions regarding the circuit computation and behavioral readout of these distributional codes. Biological and Artificial Intelligence HighlightsA large family of distributional RL algorithms emerges from a simple modification to traditional RL and dramatically improves performance of artificial agents on AI benchmark tasks. These algorithms operate using biologically plausible representations and learning rules. Dopamine neurons show substantial diversity in reward prediction error coding. Distributional RL provides a normative framework for interpreting such heterogeneity.Emerging evidence suggests that the combined activity of dopamine neurons in the VTA encodes not just the mean but rather the complete distribution of reward via an expectile code.

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After opening, the Shaker voltage-gated potassium (K V ) channel rapidly inactivates when one of its four N-termini enters and occludes the channel pore. Although it is known that the tip of the N-terminus reaches deep into the central cavity, the conformation adopted by this domain during inactivation and the nature of its interactions with the rest of the channel remain unclear. Here, we use molecular dynamics simulations coupled with electrophysiology experiments to reveal the atomic-scale mechanisms of inactivation. We find that the first six amino acids of the N-terminus spontaneously enter the central cavity in an extended conformation, establishing hydrophobic contacts with residues lining the pore. A second portion of the N-terminus, consisting of a long 24 amino acid a-helix, forms numerous polar contacts with residues in the intracellular entryway of the T1 domain. Double mutant cycle analysis revealed a strong relationship between predicted interatomic distances and empirically observed thermodynamic coupling, establishing a plausible model of the transition of K V channels to the inactivated state.