Ambiguity and conflict: Dissecting uncertainty in decision-making.

Making decisions is fundamental to how we navigate, survive, and thrive in our environment. The quality of information used to support decisions is rarely perfect. Many decisions are made under conditions of uncertainty, arising from ambiguous or conflicting information. Conflict and ambiguity, though conceptually distinct, both generate uncertainty, a commonality that has led to overlapping and inconsistent terminology in the literature. Evidence from human and animal research suggests a behavioral dissociation in responding to conflict and ambiguity. This dissociation can be studied through the implementation of spatial or operant tasks in rodents which find close parallels in gambling tasks in humans. Pharmacological manipulations in rodents and fMRI studies in humans further suggest a dissociation in the neural processing of conflict and ambiguity such that fronto-striato-parietal circuits may be most important for interpreting ambiguous information, while the ventral striatum and ventral hippocampus are critical for resolving conflicting information. Overall, the neural representation and resolution of conflict and ambiguity remain relatively understudied despite the fundamental importance of these processes to understanding decision-making. We highlight the need for further research to differentiate these related yet distinct processes through implementation of carefully designed behavioral tasks with neural circuit-dissection techniques and the potential to pursue translational research between rodents and humans. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Ventral Hippocampal Afferents to Nucleus Accumbens Encode Both Latent Vulnerability and Stress-Induced Susceptibility.

BACKGROUND: Stress is a major risk factor for depression, but not everyone responds to stress in the same way. Identifying why certain individuals are more susceptible is essential for targeted treatment and prevention. In rodents, nucleus accumbens (NAc) afferents from the ventral hippocampus (vHIP) are implicated in stress-induced susceptibility, but little is known about how this pathway might encode future vulnerability or specific behavioral phenotypes. METHODS: We used fiber photometry to record in vivo activity in vHIP-NAc afferents during tests of depressive- and anxiety-like behavior in male and female mice, both before and after a sex-specific chronic variable stress protocol, to probe relationships between prestress neural activity and behavior and potential predictors of poststress behavioral adaptation. Furthermore, we examined chronic variable stress-induced alterations in vHIP-NAc activity in vivo and used ex vivo slice electrophysiology to identify the mechanism of this change. RESULTS: We identified behavioral specificity of the vHIP-NAc pathway to anxiety-like and social interaction behavior. We also showed that this activity is broadly predictive of stress-induced susceptibility in both sexes, while prestress behavior is predictive only of anxiety-like behavior. We observed a stress-induced increase in in vivo vHIP-NAc activity coincident with an increase in spontaneous excitatory postsynaptic current frequency. CONCLUSIONS: We implicate vHIP-NAc in social interaction and anxiety-like behavior and identify markers of vulnerability in this neural signal, with elevated prestress vHIP-NAc activity predicting increased susceptibility across behavioral domains. Our findings indicate that individual differences in neural activity and behavior play a role in predetermining susceptibility to later stress, providing insight into mechanisms of vulnerability.

Probing relationships between reinforcement learning and simple behavioral strategies to understand probabilistic reward learning.

BACKGROUND: Reinforcement learning (RL) and win stay/lose shift model accounts of decision making are both widely used to describe how individuals learn about and interact with rewarding environments. Though mutually informative, these accounts are often conceptualized as independent processes and so the potential relationships between win stay/lose shift tendencies and RL parameters have not been explored. NEW METHOD: We introduce a methodology to directly relate RL parameters to behavioral strategy. Specifically, by calculating a truncated multivariate normal distribution of RL parameters given win stay/lose shift tendencies from simulating these tendencies across the parameter space, we maximize the normal distribution for a given set of win stay/lose shift tendencies to approximate reinforcement learning parameters. RESULTS: We demonstrate novel relationships between win stay/lose shift tendencies and RL parameters that challenge conventional interpretations of lose shift as a metric of loss sensitivity. Further, we demonstrate in both simulated and empirical data that this method of parameter approximation yields reliable parameter recovery. COMPARISON WITH EXISTING METHOD: We compare this method against the conventionally used maximum likelihood estimation method for parameter approximation in simulated noisy and empirical data. For simulated noisy data, we show that this method performs similarly to maximum likelihood estimation. For empirical data, however, this method provides a more reliable approximation of reinforcement learning parameters than maximum likelihood estimation. CONCLUSIONS: We demonstrate the existence of relationships between win stay/lose shift tendencies and RL parameters and introduce a method that leverages these relationships to enable recovery of RL parameters exclusively from win stay/lose shift tendencies.