Summary Value-based decision making often involves integration of reward outcomes over time, but this becomes considerably more challenging if reward assignments on alternative options are probabilistic and non-stationary. Despite the existence of various models for optimally integrating reward under uncertainty, the underlying neural mechanisms are still unknown. Here we propose that reward-dependent metaplasticity (RDMP) can provide a plausible mechanism for both integration of reward under uncertainty and estimation of uncertainty itself. We show that a model based on RDMP can robustly perform the probabilistic reversal learning task via dynamic adjustment of learning based on reward feedback, while changes in its activity signal unexpected uncertainty. The model predicts time-dependent and choice-specific learning rates which strongly depend on reward history. Key predictions from this model were confirmed with behavioral data from non-human primates. Overall, our results suggest that metaplasticity can provide a neural substrate for adaptive learning and choice under uncertainty.

Decision making often requires simultaneously learning about and combining evidence from various sources of information. However, when making inferences from these sources, humans show systematic biases that are often attributed to heuristics or limitations in cognitive processes. Here we use a combination of experimental and modelling approaches to reveal neural substrates of probabilistic inference and corresponding biases. We find systematic deviations from normative accounts of inference when alternative options are not equally rewarding; subjects' choice behaviour is biased towards the more rewarding option, whereas their inferences about individual cues show the opposite bias. Moreover, inference bias about combinations of cues depends on the number of cues. Using a biophysically plausible model, we link these biases to synaptic plasticity mechanisms modulated by reward expectation and attention. We demonstrate that inference relies on direct estimation of posteriors, not on combination of likelihoods and prior. Our work reveals novel mechanisms underlying cognitive biases and contributions of interactions between reward-dependent learning, decision making and attention to high-level reasoning.

Learning from reward feedback in a changing environment requires a high degree of adaptability, yet the precise estimation of reward information demands slow updates. We show that this tradeoff between adaptability and precision, which is present in standard reinforcementlearning models, can be substantially overcome via reward-dependent metaplasticity (rewarddependent synaptic changes that do not always alter synaptic efficacy). Metaplastic synapses achieve both adaptability and precision by forming two separate sets of meta-states: reservoirs and buffers. Synapses in reservoir meta-states do not change their efficacy upon reward feedback, whereas those in buffer meta-states can change their efficacy. Rapid changes in efficacy are limited to synapses occupying buffers, creating a bottleneck that reduces noise without significantly decreasing adaptability. In contrast, more-populated reservoirs can generate a strong signal without manifesting any observable plasticity. We suggest that ubiquitous unreliability of synaptic changes evinces metaplasticity that can provide a robust mechanism for adaptive learning.peer-reviewed)

In order to deal with a large amount of information carried by visual inputs entering the brain at any given point in time, the brain swiftly uses the same inputs to enhance processing in one part of visual field at the expense of the others. These processes, collectively called bottom-up attentional selection, are assumed to solely rely on feedforward processing of the external inputs, as it is implied by the nomenclature. Nevertheless, evidence from recent experimental and modeling studies points to the role of feedback in bottom-up attention. Here, we review behavioral and neural evidence that feedback inputs are important for the formation of signals that could guide attentional selection based on exogenous inputs. Moreover, we review results from a modeling study elucidating mechanisms underlying the emergence of these signals in successive layers of neural populations and how they depend on feedback from higher visual areas. We use these results to interpret and discuss more recent findings that can further unravel feedforward and feedback neural mechanisms underlying bottom-up attention. We argue that while it is descriptively useful to separate feedforward and feedback processes underlying bottom-up attention, these processes cannot be mechanistically separated into two successive stages as they occur at almost the same time and affect neural activity within the same brain areas using similar neural mechanisms. Therefore, understanding the interaction and integration of feedforward and feedback inputs is crucial for better understanding of bottom-up attention.