Topic: prediction error

How can the hippocampal system transition from episodic one-shot learning to a multi-shot learning regime and what is the utility of the resultant neural representations? This talk will explore the role of the dentate gyrus (DG) anatomy in this context. The canonical DG model suggests it performs pattern separation. More recent experimental results challenge this standard model, suggesting DG function is more complex and also supports the precise binding of objects and events to space and the integration of information across episodes. Very recent studies attribute pattern separation and patte

Through the process of reconsolidation, consolidated memories can be reactivated and temporarily labialized, allowing them to be updated in strength and content. In the framework of reconsolidation, a prediction error (PE) is proposed as the key mechanism that triggers the process. Generally speaking, a PE is a mismatch between expected (based on prior experience) and current events. Previous associative memory reconsolidation studies show that incomplete reminders (IR) are a subtype of prediction error that strengthen memory trace. These reminders consist in an incomplete trial that ends abru

Beliefs influence the intensity of felt pain. We previously applied regression models to two independent pain-cueing datasets (https://osf.io/5r6z7/) and found that pain intensity reports were a function of the error between participants’ belief, as determined by cues that informed participants about the level of forthcoming noxious stimulation, and the actual stimulation intensity, such that greater error decreased the influence of prior belief. Although this result appeared to present a challenge to established models of perception, our analysis did not formally compare the result against es

Memory reconsolidation is the mechanism by which consolidated memories are updated in strength and/or content. Thus, memory reactivation can enhance the retention of reactivated items at long delays. An underlying hypothesis to these findings suggests that reconsolidation would be “reactivation specific”, implying that only the reactivated elements after consolidation would be susceptible to modifications. However, episodic memories are intrinsically associative, which implies that they are integrated into interconnected- networks between its elements and the acquisition context. Reactivation

When making decision under risk, people often exhibit behaviours that classical economic theories cannot explain. Newer models that attempt to account for these ‘irrational’ behaviours often lack neuroscience bases and require the introduction of subjective and problem-specific constructs. Here, we present a decision-making model inspired by the prediction error signals and introspective neuronal replay reported in the brain. In the model, decisions are chosen based on ‘anticipated surprise’, defined by a nonlinear average of the differences between individual outcomes and a reference point. T

Sensory experience often depends on one’s own actions, including self-motion. Theories of predictive coding postulate that actions are regulated by calculating prediction error, which is the difference between sensory experience and expectation based on self-generated actions. Signals consistent with prediction error have been reported in mouse visual cortex (V1) when visual flow coupled to running was unexpectedly stopped. Here, we show such signals can be elicited by visual stimuli uncoupled to animal’s running. We recorded V1 neurons while presenting drifting gratings that unexpectedly stop

The brain is a prediction machine. Yet the world is never entirely predictable, for any animal. Unexpected events are surprising and this typically evokes prediction error signatures in animal brains. In humans such mismatched expectations are often associated with an emotional response as well. Appropriate emotional responses are understood to be important for memory consolidation, suggesting that valence cues more generally constitute an ancient mechanism designed to potently refine and generalize internal models of the world and thereby minimize prediction errors. On the other hand, abolish

The ability to robustly recognize strings of letters, a cornerstone of reading, was observed in Baboons and Pigeons despite their lack of phonological and semantic knowledge. Here, we apply a comparative modeling approach to investigate the neuro-cognitive basis of Human, Baboon, and Pigeon orthographic decision behavior, addressing whether phylogenetic relatedness entails similar underlying neuro-cognitive phenotypes. We use the highly transparent Speechless Reader Model (SLR), which assumes letter string recognition based on widely accepted computational principles of predictive coding so th