Hippocampal-neocortical interactions sharpen over time for predictive actions

Prediction error and event segmentation in episodic memory

Organizing the continuous flow of experiences into meaningful events is a crucial prerequisite for episodic memory. Prediction error and event segmentation both play important roles in supporting the genesis of meaningful mnemonic representations of events. We review theoretical contributions discussing the relationship between prediction error and event segmentation, as well as literature on episodic memory related to prediction error and event segmentation. We discuss the extent of overlap of mechanisms underlying memory emergence through prediction error and event segmentation, with a specific focus on attention and working memory. Finally, we identify areas in research that are currently developing and suggest future directions. We provide an overview of mechanisms underlying memory formation through predictions, violations of predictions, and event segmentation.

Predictions transform memories: How expected versus unexpected events are integrated or separated in memory

Our brains constantly generate predictions about the environment based on prior knowledge. Many of the events we experience are consistent with these predictions, while others might be inconsistent with prior knowledge and thus violate our predictions. To guide future behavior, the memory system must be able to strengthen, transform, or add to existing knowledge based on the accuracy of our predictions. We synthesize recent evidence suggesting that when an event is consistent with our predictions, it leads to neural integration between related memories, which is associated with enhanced associative memory, as well as memory biases. Prediction errors, in turn, can promote both neural integration and separation, and lead to multiple mnemonic outcomes. We review these findings and how they interact with factors such as memory reactivation, prediction error strength, and task goals, to offer insight into what determines memory for events that violate our predictions. In doing so, this review brings together recent neural and behavioral research to advance our understanding of how predictions shape memory, and why.

Context-dependent attentional spotlight in pulvinar-V1 interaction

Spatial attention is often described as a mental spotlight that enhances information processing at the attended location. Using fMRI, we investigated background connectivity between the pulvinar and V1 in relation to focused versus diffused attention allocation, in weak and strong crowding contexts. Our findings revealed that focused attention led to enhanced correlations between the pulvinar and V1. Notably, this modulation was initiated by the pulvinar, and the strength of the modulation was dependent on the saliency of the target. These findings suggest that the pulvinar initiates information reweighting to V1, which underlies attentional selection in cluttered scenes.

Integration of landmark and saccade target signals in macaque frontal cortex visual responses

Visual landmarks influence spatial cognition and behavior, but their influence on visual codes for action is poorly understood. Here, we test landmark influence on the visual response to saccade targets recorded from 312 frontal and 256 supplementary eye field neurons in rhesus macaques. Visual response fields are characterized by recording neural responses to various target-landmark combinations, and then we test against several candidate spatial models. Overall, frontal/supplementary eye fields response fields preferentially code either saccade targets (40%/40%) or landmarks (30%/4.5%) in gaze fixation-centered coordinates, but most cells show multiplexed target-landmark coding within intermediate reference frames (between fixation-centered and landmark-centered). Further, these coding schemes interact: neurons with near-equal target and landmark coding show the biggest shift from fixation-centered toward landmark-centered target coding. These data show that landmark information is preserved and influences target coding in prefrontal visual responses, likely to stabilize movement goals in the presence of noisy egocentric signals.

Updating perceptual expectations as certainty diminishes

Forming expectations about what we are likely to perceive often facilitates perception. We forge such expectations on the basis of strong statistical relationships between events in our environment. However, due to our ever-changing world these relationships often subsequently degrade or even disappear, yet it is unclear how these altered statistics influence perceptual expectations. We examined this question across two studies by training participants in perfect relationships between actions (index or little finger abductions) and outcomes (clockwise or counter-clockwise gratings), before degrading the predictive relationship in a test phase - such that 'expected' events followed actions on 50-75% of trials and 'unexpected' events ensued on the remainder. Perceptual decisions about outcomes were faster and less error prone on expected than unexpected trials when predictive relationships remained high and reduced as the relationship diminished. Drift diffusion modelling indicated that these effects are explained by shifting the starting point in the evidence accumulation process as well as biasing the rate of evidence accumulation - with the former reflecting biases from statistics within the training session and the latter those of the test session. These findings demonstrate how perceptual expectations are updated as statistical certainty diminishes, with interacting influences speculatively dependent upon learning consolidation. We discuss how underlying mechanisms optimise the interaction between learning and perception - allowing our experiences to reflect a nuanced, ever-changing environment.

Sensory Over-responsivity: A Feature of Childhood Psychiatric Illness Associated With Altered Functional Connectivity of Sensory Networks

BACKGROUND

Sensory over-responsivity (SOR) is recognized as a common feature of autism spectrum disorder. However, SOR is also common among typically developing children, in whom it is associated with elevated levels of psychiatric symptoms. The clinical significance and neurocognitive bases of SOR in these children remain poorly understood and actively debated.

METHODS

This study used linear mixed-effects models to identify psychiatric symptoms and network-level functional connectivity (FC) differences associated with parent-reported SOR in the Adolescent Brain Cognitive Development (ABCD) Study, a large community sample (9 to 12 years of age) (N = 11,210).

RESULTS

Children with SOR constituted 18% of the overall sample but comprised more than half of the children with internalizing or externalizing scores in the clinical range. Controlling for autistic traits, both mild and severe SOR were associated with greater concurrent symptoms of depression, anxiety, obsessive-compulsive disorder, and attention-deficit/hyperactivity disorder. Controlling for psychiatric symptoms and autistic traits, SOR predicted increased anxiety, attention-deficit/hyperactivity disorder, and prodromal psychosis symptoms 1 year later and was associated with FC differences in brain networks supporting sensory and salience processing in datasets collected 2 years apart. Differences included reduced FC within and between sensorimotor networks, enhanced sensorimotor-salience FC, and altered FC between sensory networks and bilateral hippocampi.

CONCLUSIONS

SOR is a common, clinically relevant feature of childhood psychiatric illness that provides unique predictive information about risk. It is associated with differences in brain networks that subserve tactile processing, implicating a neural basis for sensory differences in affected children.

Brain-inspired Predictive Coding Improves the Performance of Machine Challenging Tasks

Backpropagation has been regarded as the most favorable algorithm for training artificial neural networks. However, it has been criticized for its biological implausibility because its learning mechanism contradicts the human brain. Although backpropagation has achieved super-human performance in various machine learning applications, it often shows limited performance in specific tasks. We collectively referred to such tasks as machine-challenging tasks (MCTs) and aimed to investigate methods to enhance machine learning for MCTs. Specifically, we start with a natural question: Can a learning mechanism that mimics the human brain lead to the improvement of MCT performances? We hypothesized that a learning mechanism replicating the human brain is effective for tasks where machine intelligence is difficult. Multiple experiments corresponding to specific types of MCTs where machine intelligence has room to improve performance were performed using predictive coding, a more biologically plausible learning algorithm than backpropagation. This study regarded incremental learning, long-tailed, and few-shot recognition as representative MCTs. With extensive experiments, we examined the effectiveness of predictive coding that robustly outperformed backpropagation-trained networks for the MCTs. We demonstrated that predictive coding-based incremental learning alleviates the effect of catastrophic forgetting. Next, predictive coding-based learning mitigates the classification bias in long-tailed recognition. Finally, we verified that the network trained with predictive coding could correctly predict corresponding targets with few samples. We analyzed the experimental result by drawing analogies between the properties of predictive coding networks and those of the human brain and discussing the potential of predictive coding networks in general machine learning.

Hippocampal representations switch from errors to predictions during acquisition of predictive associations

We constantly exploit the statistical regularities in our environment to help guide our perception. The hippocampus has been suggested to play a pivotal role in both learning environmental statistics, as well as exploiting them to generate perceptual predictions. However, it is unclear how the hippocampus balances encoding new predictive associations with the retrieval of existing ones. Here, we present the results of two high resolution human fMRI studies (N = 24 for both experiments) directly investigating this. Participants were exposed to auditory cues that predicted the identity of an upcoming visual shape (with 75% validity). Using multivoxel decoding analysis, we find that the hippocampus initially preferentially represents unexpected shapes (i.e., those that violate the cue regularities), but later switches to representing the cue-predicted shape regardless of which was actually presented. These findings demonstrate that the hippocampus is involved both acquiring and exploiting predictive associations, and is dominated by either errors or predictions depending on whether learning is ongoing or complete.

Hippocampal Contribution to Probabilistic Feedback Learning: Modeling Observation- and Reinforcement-based Processes

Simple probabilistic reinforcement learning is recognized as a striatum-based learning system, but in recent years, has also been associated with hippocampal involvement. This study examined whether such involvement may be attributed to observation-based learning (OL) processes, running in parallel to striatum-based reinforcement learning. A computational model of OL, mirroring classic models of reinforcement-based learning (RL), was constructed and applied to the neuroimaging data set of Palombo, Hayes, Reid, and Verfaellie (2019). Hippocampal contributions to value-based learning: Converging evidence from fMRI and amnesia. Cognitive, Affective & Behavioral Neuroscience, 19(3), 523-536. Results suggested that OL processes may indeed take place concomitantly to reinforcement learning and involve activation of the hippocampus and central orbitofrontal cortex. However, rather than independent mechanisms running in parallel, the brain correlates of the OL and RL prediction errors indicated collaboration between systems, with direct implication of the hippocampus in computations of the discrepancy between the expected and actual reinforcing values of actions. These findings are consistent with previous accounts of a role for the hippocampus in encoding the strength of observed stimulus-outcome associations, with updating of such associations through striatal reinforcement-based computations. In addition, enhanced negative RL prediction error signaling was found in the anterior insula with greater use of OL over RL processes. This result may suggest an additional mode of collaboration between the OL and RL systems, implicating the error monitoring network.

Spatial and Temporal Context Jointly Modulate the Sensory Response within the Ventral Visual Stream

Both spatial and temporal context play an important role in visual perception and behavior. Humans can extract statistical regularities from both forms of context to help process the present and to construct expectations about the future. Numerous studies have found reduced neural responses to expected stimuli compared with unexpected stimuli, for both spatial and temporal regularities. However, it is largely unclear whether and how these forms of context interact. In the current fMRI study, 33 human volunteers were exposed to pairs of object stimuli that could be expected or surprising in terms of their spatial and temporal context. We found reliable independent contributions of both spatial and temporal context in modulating the neural response. Specifically, neural responses to stimuli in expected compared with unexpected contexts were suppressed throughout the ventral visual stream. These results suggest that both spatial and temporal context may aid sensory processing in a similar fashion, providing evidence on how different types of context jointly modulate perceptual processing.

Prediction errors disrupt hippocampal representations and update episodic memories

The brain supports adaptive behavior by generating predictions, learning from errors, and updating memories to incorporate new information. Prediction error, or surprise, triggers learning when reality contradicts expectations. Prior studies have shown that the hippocampus signals prediction errors, but the hypothesized link to memory updating has not been demonstrated. In a human functional MRI study, we elicited mnemonic prediction errors by interrupting familiar narrative videos immediately before the expected endings. We found that prediction errors reversed the relationship between univariate hippocampal activation and memory: greater hippocampal activation predicted memory preservation after expected endings, but memory updating after surprising endings. In contrast to previous studies, we show that univariate activation was insufficient for understanding hippocampal prediction error signals. We explain this surprising finding by tracking both the evolution of hippocampal activation patterns and the connectivity between the hippocampus and neuromodulatory regions. We found that hippocampal activation patterns stabilized as each narrative episode unfolded, suggesting sustained episodic representations. Prediction errors disrupted these sustained representations and the degree of disruption predicted memory updating. The relationship between hippocampal activation and subsequent memory depended on concurrent basal forebrain activation, supporting the idea that cholinergic modulation regulates attention and memory. We conclude that prediction errors create conditions that favor memory updating, prompting the hippocampus to abandon ongoing predictions and make memories malleable.

Rapid coordination of effective learning by the human hippocampus

Although the human hippocampus is necessary for long-term memory, controversial findings suggest that it may also support short-term memory in the service of guiding effective behaviors during learning. We tested the counterintuitive theory that the hippocampus contributes to long-term memory through remarkably short-term processing, as reflected in eye movements during scene encoding. While viewing scenes for the first time, short-term retrieval operative within the episode over only hundreds of milliseconds was indicated by a specific eye-movement pattern, which was effective in that it enhanced spatiotemporal memory formation. This viewing pattern was predicted by hippocampal theta oscillations recorded from depth electrodes and by shifts toward top-down influence of hippocampal theta on activity within visual perception and attention networks. The hippocampus thus supports short-term memory processing that coordinates behavior in the service of effective spatiotemporal learning.

Evaluating the evidence for expectation suppression in the visual system

Reports of expectation suppression have shaped the development of influential predictive coding-based theories of visual perception. However recent work has highlighted confounding factors that may mimic or inflate expectation suppression effects. In this review, we describe four confounds that are prevalent across experiments that tested for expectation suppression: effects of surprise, attention, stimulus repetition and adaptation, and stimulus novelty. With these confounds in mind we then critically review the evidence for expectation suppression across probabilistic cueing, statistical learning, oddball, action-outcome learning and apparent motion designs. We found evidence for expectation suppression within a specific subset of statistical learning designs that involved weeks of sequence learning prior to neural activity measurement. Across other experimental contexts, whereby stimulus appearance probabilities were learned within one or two testing sessions, there was inconsistent evidence for genuine expectation suppression. We discuss how an absence of expectation suppression could inform models of predictive processing, repetition suppression and perceptual decision-making. We also provide suggestions for designing experiments that may better test for expectation suppression in future work.

Sensorimotor predictions shape reported conscious visual experience in a breaking continuous flash suppression task

Accounts of predictive processing propose that conscious experience is influenced not only by passive predictions about the world, but also by predictions encompassing how the world changes in relation to our actions-that is, on predictions about sensorimotor contingencies. We tested whether valid sensorimotor predictions, in particular learned associations between stimuli and actions, shape reports about conscious visual experience. Two experiments used instrumental conditioning to build sensorimotor predictions linking different stimuli with distinct actions. Conditioning was followed by a breaking continuous flash suppression task, measuring the speed of reported breakthrough for different pairings between the stimuli and prepared actions, comparing those congruent and incongruent with the trained sensorimotor predictions. In Experiment 1, counterbalancing of the response actions within the breaking continuous flash suppression task was achieved by repeating the same action within each block but having them differ across the two blocks. Experiment 2 sought to increase the predictive salience of the actions by avoiding the repetition within blocks. In Experiment 1, breakthrough times were numerically shorter for congruent than incongruent pairings, but Bayesian analysis supported the null hypothesis of no influence from the sensorimotor predictions. In Experiment 2, reported conscious perception was significantly faster for congruent than for incongruent pairings. A meta-analytic Bayes factor combining the two experiments confirmed this effect. Altogether, we provide evidence for a key implication of the action-oriented predictive processing approach to conscious perception, namely that sensorimotor predictions shape our conscious experience of the world.

Prefrontal reinstatement of contextual task demand is predicted by separable hippocampal patterns

Goal-directed behavior requires the representation of a task-set that defines the task-relevance of stimuli and guides stimulus-action mappings. Past experience provides one source of knowledge about likely task demands in the present, with learning enabling future predictions about anticipated demands. We examine whether spatial contexts serve to cue retrieval of associated task demands (e.g., context A and B probabilistically cue retrieval of task demands X and Y, respectively), and the role of the hippocampus and dorsolateral prefrontal cortex (dlPFC) in mediating such retrieval. Using 3D virtual environments, we induce context-task demand probabilistic associations and find that learned associations affect goal-directed behavior. Concurrent fMRI data reveal that, upon entering a context, differences between hippocampal representations of contexts (i.e., neural pattern separability) predict proactive retrieval of the probabilistically dominant associated task demand, which is reinstated in dlPFC. These findings reveal how hippocampal-prefrontal interactions support memory-guided cognitive control and adaptive behavior.

Spatial contexts are often predictive of the tasks to be performed in them (e.g., a kitchen predicts cooking). Here the authors show that the retrieval of task demand when encountering a spatial context depends on hippocampal-prefrontal interactions.