Goal-seeking compresses neural codes for space in the human hippocampus and orbitofrontal cortex.

Humans can navigate flexibly to meet their goals. Here, we asked how the neural representation of allocentric space is distorted by goal-directed behavior. Participants navigated an agent to two successive goal locations in a grid world environment comprising four interlinked rooms, with a contextual cue indicating the conditional dependence of one goal location on another. Examining the neural geometry by which room and context were encoded in fMRI signals, we found that map-like representations of the environment emerged in both hippocampus and neocortex. Cognitive maps in hippocampus and orbitofrontal cortices were compressed so that locations cued as goals were coded together in neural state space, and these distortions predicted successful learning. This effect was captured by a computational model in which current and prospective locations are jointly encoded in a place code, providing a theory of how goals warp the neural representation of space in macroscopic neural signals.

Replay in minds and machines.

Experience-related brain activity patterns reactivate during sleep, wakeful rest, and brief pauses from active behavior. In parallel, machine learning research has found that experience replay can lead to substantial performance improvements in artificial agents. Together, these lines of research suggest that replay has a variety of computational benefits for decision-making and learning. Here, we provide an overview of putative computational functions of replay as suggested by machine learning and neuroscientific research. We show that replay can lead to faster learning, less forgetting, reorganization or augmentation of experiences, and support planning and generalization. In addition, we highlight the benefits of reactivating abstracted internal representations rather than veridical memories, and discuss how replay could provide a mechanism to build internal representations that improve learning and decision-making.

Interaction of Instrumental and Goal-Directed Learning Modulates Prediction Error Representations in the Ventral Striatum.

Goal-directed and instrumental learning are both important controllers of human behavior. Learning about which stimulus event occurs in the environment and the reward associated with them allows humans to seek out the most valuable stimulus and move through the environment in a goal-directed manner. Stimulus-response associations are characteristic of instrumental learning, whereas response-outcome associations are the hallmark of goal-directed learning. Here we provide behavioral, computational, and neuroimaging results from a novel task in which stimulus-response and response-outcome associations are learned simultaneously but dominate behavior at different stages of the experiment. We found that prediction error representations in the ventral striatum depend on which type of learning dominates. Furthermore, the amygdala tracks the time-dependent weighting of stimulus-response versus response-outcome learning. Our findings suggest that the goal-directed and instrumental controllers dynamically engage the ventral striatum in representing prediction errors whenever one of them is dominating choice behavior. SIGNIFICANCE STATEMENT: Converging evidence in human neuroimaging studies has shown that the reward prediction errors are correlated with activity in the ventral striatum. Our results demonstrate that this region is simultaneously correlated with a stimulus prediction error. Furthermore, the learning system that is currently dominating behavioral choice dynamically engages the ventral striatum for computing its prediction errors. This demonstrates that the prediction error representations are highly dynamic and influenced by various experimental context. This finding points to a general role of the ventral striatum in detecting expectancy violations and encoding error signals regardless of the specific nature of the reinforcer itself.

Congruence of Inherent and Acquired Values Facilitates Reward-Based Decision-Making.

UNLABELLED: Most real-life cues exhibit certain inherent values that may interfere with or facilitate the acquisition of new expected values during associative learning. In particular, when inherent and acquired values are congruent, learning may progress more rapidly. Here we investigated such an influence through a 2 × 2 factorial design, using attractiveness (high/low) of the facial picture as a proxy for the inherent value of the cue and its reward probability (high/low) as a surrogate for the acquired value. Each picture was paired with a monetary win or loss either congruently or incongruently. Behavioral results from 32 human participants indicated both faster response time and faster learning rate for value-congruent cue-outcome pairings. Model-based fMRI analysis revealed a fractionation of reinforcement learning (RL) signals in the ventral striatum, including a strong and novel correlation between the cue-specific decaying learning rate and BOLD activity in the ventral caudate. Additionally, we detected a functional link between neural signals of both learning rate and reward prediction error in the ventral striatum, and the signal of expected value in the ventromedial prefrontal cortex, showing a novel confirmation of the mathematical RL model via functional connectivity. SIGNIFICANCE STATEMENT: Most real-world decisions require the integration of inherent value and sensitivity to outcomes to facilitate adaptive learning. Inherent value is drawing increasing interest from decision scientists because it influences decisions in contexts ranging from advertising to investing. This study provides novel insight into how inherent value influences the acquisition of new expected value during associative learning. Specifically, we find that the congruence between the inherent value and the acquired reward influences the neural coding of learning rate. We also show for the first time that neuroimaging signals coding the learning rate, prediction error, and acquired value follow the multiplicative Rescorla-Wagner learning rule, a finding predicted by reinforcement learning theory.

Homophila: human disease gene cognates in Drosophila.

Although many human genes have been associated with genetic diseases, knowing which mutations result in disease phenotypes often does not explain the etiology of a specific disease. Drosophila melanogaster provides a powerful system in which to use genetic and molecular approaches to investigate human genetic diseases. Homophila is an intergenomic resource linking the human and fly genomes in order to stimulate functional genomic investigations in Drosophila that address questions about genetic disease in humans. Homophila provides a comprehensive linkage between the disease genes compiled in Online Mendelian Inheritance in Man (OMIM) and the complete Drosophila genomic sequence. Homophila is a relational database that allows searching based on human disease descriptions, OMIM number, human or fly gene names, and sequence similarity, and can be accessed at http://homophila.sdsc.edu.