Task state representations in vmPFC mediate relevant and irrelevant value signals and their behavioral influence

Prediction, inference, and generalization in orbitofrontal cortex

Our understanding of the orbitofrontal cortex (OFC) has significantly evolved over the past few decades. This prefrontal region has been associated with a wide range of cognitive functions, including a popular view that it primarily signals the expected value of each possible option, allowing downstream areas to use these value signals for decision-making. However, the discovery of rich, task-related information within the OFC and its essential role in inference-based behaviors has shifted our perspective and led to the proposal that the OFC holds a cognitive map used by both humans and animals for making predictions and inferences. Recent studies have further shown that these cognitive maps can be abstracted and generalized, serving both immediate and future needs. In this review, we trace the research journey leading to these evolving insights, discuss the potential neural mechanisms supporting the OFC's roles in prediction, inference, and generalization, and compare the OFC with the hippocampus, another critical region for cognitive mapping, while also exploring the interactions between these two areas.

Schemas, reinforcement learning and the medial prefrontal cortex

Schemas are rich and complex knowledge structures about the typical unfolding of events in a context; for example, a schema of a dinner at a restaurant. In this Perspective, we suggest that reinforcement learning (RL), a computational theory of learning the structure of the world and relevant goal-oriented behaviour, underlies schema learning. We synthesize literature about schemas and RL to offer that three RL principles might govern the learning of schemas: learning via prediction errors, constructing hierarchical knowledge using hierarchical RL, and dimensionality reduction through learning a simplified and abstract representation of the world. We then suggest that the orbitomedial prefrontal cortex is involved in both schemas and RL due to its involvement in dimensionality reduction and in guiding memory reactivation through interactions with posterior brain regions. Last, we hypothesize that the amount of dimensionality reduction might underlie gradients of involvement along the ventral-dorsal and posterior-anterior axes of the orbitomedial prefrontal cortex. More specific and detailed representations might engage the ventral and posterior parts, whereas abstraction might shift representations towards the dorsal and anterior parts of the medial prefrontal cortex.

Striatal arbitration between choice strategies guides few-shot adaptation

Animals often exhibit rapid action changes in context-switching environments. This study hypothesized that, compared to the expected outcome, an unexpected outcome leads to distinctly different action-selection strategies to guide rapid adaptation. We designed behavioral measures differentiating between trial-by-trial dynamics after expected and unexpected events. In various reversal learning data with different rodent species and task complexities, conventional learning models failed to replicate the choice behavior following an unexpected outcome. This discrepancy was resolved by the proposed model with two different decision variables contingent on outcome expectation: the support-stay and conflict-shift bias. Electrophysiological data analyses revealed that striatal neurons encode our model's key variables. Furthermore, the inactivation of striatal direct and indirect pathways neutralizes the effect of past expected and unexpected outcomes, respectively, on the action-selection strategy following an unexpected outcome. Our study suggests unique roles of the striatum in arbitrating between different action selection strategies for few-shot adaptation.

Single-neuron spiking variability in hippocampus dynamically tracks sensory content during memory formation in humans

During memory formation, the hippocampus is presumed to represent the content of stimuli, but how it does so is unknown. Using computational modelling and human single-neuron recordings, we show that the more precisely hippocampal spiking variability tracks the composite features of each individual stimulus, the better those stimuli are later remembered. We propose that moment-to-moment spiking variability may provide a new window into how the hippocampus constructs memories from the building blocks of our sensory world.

Subregions in the ventromedial prefrontal cortex integrate threat and protective information to meta-represent safety

Pivotal to self-preservation is the ability to identify when we are safe and when we are in danger. Previous studies have focused on safety estimations based on the features of external threats and do not consider how the brain integrates other key factors, including estimates about our ability to protect ourselves. Here, we examine the neural systems underlying the online dynamic encoding of safety. The current preregistered study used 2 novel tasks to test 4 facets of safety estimation: Safety Prediction, Meta-representation, Recognition, and Value Updating. We experimentally manipulated safety estimation changing both levels of external threats and self-protection. Data were collected in 2 independent samples (behavioral N = 100; MRI N = 30). We found consistent evidence of subjective changes in the sensitivity to safety conferred through protection. Neural responses in the ventromedial prefrontal cortex (vmPFC) tracked increases in safety during all safety estimation facets, with specific tuning to protection. Further, informational connectivity analyses revealed distinct hubs of safety coding in the posterior and anterior vmPFC for external threats and protection, respectively. These findings reveal a central role of the vmPFC for coding safety.

Context changes retrieval of prospective outcomes during decision deliberation

Foreseeing the future outcomes is the art of decision-making. Substantial evidence shows that, during choice deliberation, the brain can retrieve prospective decision outcomes. However, decisions are seldom made in a vacuum. Context carries information that can radically affect the outcomes of a choice. Nevertheless, most investigations of retrieval processes examined decisions in isolation, disregarding the context in which they occur. Here, we studied how context shapes prospective outcome retrieval during deliberation. We designed a decision-making task where participants were presented with object-context pairs and made decisions which led to a certain outcome. We show during deliberation, likely outcomes were retrieved in transient patterns of neural activity, as early as 3 s before participants decided. The strength of prospective outcome retrieval explains participants' behavioral efficiency, but only when context affects the decision outcome. Our results suggest context imparts strong constraints on retrieval processes and how neural representations are shaped during decision-making.

Representational spaces in orbitofrontal and ventromedial prefrontal cortex: task states, values, and beyond

The orbitofrontal cortex (OFC) and ventromedial-prefrontal cortex (vmPFC) play a key role in decision-making and encode task states in addition to expected value. We review evidence suggesting a connection between value and state representations and argue that OFC / vmPFC integrate stimulus, context, and outcome information. Comparable encoding principles emerge in late layers of deep reinforcement learning (RL) models, where single nodes exhibit similar forms of mixed-selectivity, which enables flexible readout of relevant variables by downstream neurons. Based on these lines of evidence, we suggest that outcome-maximization leads to complex representational spaces that are insufficiently characterized by linear value signals that have been the focus of most prior research on the topic. Major outstanding questions concern the role of OFC/ vmPFC in learning across tasks, in encoding of task-irrelevant aspects, and the role of hippocampus-PFC interactions.

Revisiting the role of computational neuroimaging in the era of integrative neuroscience

Computational models have become integral to human neuroimaging research, providing both mechanistic insights and predictive tools for human cognition and behavior. However, concerns persist regarding the ecological validity of lab-based neuroimaging studies and whether their spatiotemporal resolution is not sufficient for capturing neural dynamics. This review aims to re-examine the utility of computational neuroimaging, particularly in light of the growing prominence of alternative neuroscientific methods and the growing emphasis on more naturalistic behaviors and paradigms. Specifically, we will explore how computational modeling can both enhance the analysis of high-dimensional imaging datasets and, conversely, how neuroimaging, in conjunction with other data modalities, can inform computational models through the lens of neurobiological plausibility. Collectively, this evidence suggests that neuroimaging remains critical for human neuroscience research, and when enhanced by computational models, imaging can serve an important role in bridging levels of analysis and understanding. We conclude by proposing key directions for future research, emphasizing the development of standardized paradigms and the integrative use of computational modeling across neuroimaging techniques.

Shifts in attention drive context-dependent subspace encoding in anterior cingulate cortex in mice during decision making

Attention supports decision making by selecting the features that are relevant for decisions. Selective enhancement of the relevant features and inhibition of distractors has been proposed as potential neural mechanisms driving this selection process. Yet, how attention operates when relevance cannot be directly determined, and the attention signal needs to be internally constructed is less understood. Here we recorded from populations of neurons in the anterior cingulate cortex (ACC) of mice in an attention-shifting task where relevance of stimulus modalities changed across blocks of trials. In contrast with V1 recordings, decoding of the irrelevant modality gradually declined in ACC after an initial transient. Our analytical proof and a recurrent neural network model of the task revealed mutually inhibiting connections that produced context-gated suppression as observed in mice. Using this RNN model we predicted a correlation between contextual modulation of individual neurons and their stimulus drive, which we confirmed in ACC but not in V1.

Counterfactual thinking induces different neural patterns of memory modification in anxious individuals

Episodic counterfactual thinking (eCFT) is the process of mentally simulating alternate versions of experiences, which confers new phenomenological properties to the original memory and may be a useful therapeutic target for trait anxiety. However, it remains unclear how the neural representations of a memory change during eCFT. We hypothesized that eCFT-induced memory modification is associated with changes to the neural pattern of a memory primarily within the default mode network, moderated by dispositional anxiety levels. We tested this proposal by examining the representational dynamics of eCFT for 39 participants varying in trait anxiety. During eCFT, lateral parietal regions showed progressively more distinct activity patterns, whereas medial frontal neural activity patterns became more similar to those of the original memory. Neural pattern similarity in many default mode network regions was moderated by trait anxiety, where highly anxious individuals exhibited more generalized representations for upward eCFT (better counterfactual outcomes), but more distinct representations for downward eCFT (worse counterfactual outcomes). Our findings illustrate the efficacy of examining eCFT-based memory modification via neural pattern similarity, as well as the intricate interplay between trait anxiety and eCFT generation.

Grid-like entorhinal representation of an abstract value space during prospective decision making

How valuable a choice option is often changes over time, making the prediction of value changes an important challenge for decision making. Prior studies identified a cognitive map in the hippocampal-entorhinal system that encodes relationships between states and enables prediction of future states, but does not inherently convey value during prospective decision making. In this fMRI study, participants predicted changing values of choice options in a sequence, forming a trajectory through an abstract two-dimensional value space. During this task, the entorhinal cortex exhibited a grid-like representation with an orientation aligned to the axis through the value space most informative for choices. A network of brain regions, including ventromedial prefrontal cortex, tracked the prospective value difference between options. These findings suggest that the entorhinal grid system supports the prediction of future values by representing a cognitive map, which might be used to generate lower-dimensional value signals to guide prospective decision making.

Nicotine-related beliefs induce dose-dependent responses in the human brain

Beliefs have a powerful influence on our behavior, yet their neural mechanisms remain elusive. Here we investigate whether beliefs could impact brain activities in a way akin to pharmacological dose-dependent effects. Nicotine-dependent humans were told that nicotine strength in an electronic cigarette was either 'low', 'medium' or 'high', while nicotine content was held constant. After vaping, participants underwent functional neuroimaging and performed a decision-making task known to engage neural circuits affected by nicotine. Beliefs about nicotine strength induced dose-dependent responses in the thalamus, a key binding site for nicotine, but not in other brain regions such as the striatum. Nicotine-related beliefs also parametrically modulated the connectivity between the thalamus and ventromedial prefrontal cortex, a region important for decision-making. These findings reveal a high level of precision in the way beliefs influence the brain, offering mechanistic insights into humans' heterogeneous responses to drugs and a pivotal role of beliefs in addiction.