The hippocampus computes diverse information involving spatial memory, anxiety, or reward and directly projects to several brain areas. Are different computations transmitted to all downstream targets uniformly, or does the hippocampus selectively route information according to content and target region? By recording from ventral hippocampal CA1 neurons in rats during different behavioral tasks and determining axonal projections with optogenetics, we observed subsets of neurons changing firing at places of elevated anxiety or changing activity during goal approach. Anxiety-related firing was selectively increased in neurons projecting to the prefrontal cortex. Goal-directed firing was most prominent in neurons targeting the nucleus accumbens; and triple-projecting neurons, targeting the prefrontal cortex, amygdala, and nucleus accumbens, were most active during tasks and sharp wave/ripples. Thus, hippocampal neurons route distinct behavior-contingent information selectively to different target areas.

Abstract Recent years have witnessed a dramatic increase in the use of reinforcement learning (RL) models in social, cognitive and affective neuroscience. This approach, in combination with neuroimaging techniques such as functional magnetic resonance imaging, enables quantitative investigations into latent mechanistic processes. However, increased use of relatively complex computational approaches has led to potential misconceptions and imprecise interpretations. Here, we present a comprehensive framework for the examination of (social) decision-making with the simple Rescorla-Wagner RL model. We discuss common pitfalls in its application and provide practical suggestions. First, with simulation, we unpack the functional role of the learning rate and pinpoint what could easily go wrong when interpreting differences in the learning rate. Then, we discuss the inevitable collinearity between outcome and prediction error in RL models and provide suggestions of how to justify whether the observed neural activation is related to the prediction error rather than outcome valence. Finally, we suggest posterior predictive check is a crucial step after model comparison, and we articulate employing hierarchical modeling for parameter estimation. We aim to provide simple and scalable explanations and practical guidelines for employing RL models to assist both beginners and advanced users in better implementing and interpreting their model-based analyses.

Animals are predicted to selectively observe and learn from the conspecifics with whom they share social connections. Yet, hardly anything is known about the role of different connections in observation and learning. To address the relationships between social connections, observation and learning, we investigated transmission of information in two raven (Corvus corax) groups. First, we quantified social connections in each group by constructing networks on affiliative interactions, aggressive interactions and proximity. We then seeded novel information by training one group member on a novel task and allowing others to observe. In each group, an observation network based on who observed whose task-solving behaviour was strongly correlated with networks based on affiliative interactions and proximity. Ravens with high social centrality (strength, eigenvector, information centrality) in the affiliative interaction network were also central in the observation network, possibly as a result of solving the task sooner. Network-based diffusion analysis revealed that the order that ravens first solved the task was best predicted by connections in the affiliative interaction network in a group of subadult ravens, and by social rank and kinship (which influenced affiliative interactions) in a group of juvenile ravens. Our results demonstrate that not all social connections are equally effective at predicting the patterns of selective observation and information transmission.

Summary Neuronal signals in the prefrontal cortex have been reported to predict upcoming decisions. Such activity patterns are often coupled to perceptual cues indicating correct choices or values of different options. How does the prefrontal cortex signal future decisions, when no cues are present, but when decisions are made based on internal valuations of past experiences with stochastic outcomes? We trained rats to perform a two-arm bandit-task, successfully adjusting choices between certain-small, or possible-big rewards with changing long-term advantages. We discovered specialized prefrontal neurons, whose firing during the encounter of no-reward predicted the subsequent choice of animals, even for unlikely or uncertain decisions and several seconds before choice-execution. Optogenetic silencing of the prelimbic cortex exclusively timed to encounters of no-reward, provoked animals to excessive gambling for large rewards. Firing of prefrontal neurons during outcome evaluation signal subsequent choices during gambling and is essential for dynamically adjusting decisions based on internal valuations.