A predictive account of how novelty influences declarative memory

Quent, Jörn Alexander; Henson, Richard N.; Greve, Andrea

doi:10.1016/j.nlm.2021.107382

Cited by 57 publications

(66 citation statements)

References 157 publications

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“…While humans have the ability to retain and recall such information, potentially in isolation of the content that may have originally underpinned the time estimation [73], it is beyond the scope of our present modelling work to incorporate such influences. While of great interest, such work would require a much deeper understanding of the interaction of experience, language, and memory than we currently possess, though this is likely not outside the scope of an extended predictive processing approach [74].…”

Section: The Subjective Timescale Of Time Perception and Episodic Memorymentioning

confidence: 99%

A predictive processing model of episodic memory and time perception

Fountas

Sylaidi²,

Nikiforou

et al. 2020

Preprint

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Human perception and experience of time is strongly affected by environmental context. When paying close attention to time, time experience seems to expand; when distracted from time, experience of time seems to contract. Contrasts in experiences like these are common enough to be exemplified in sayings like "time flies when you're having fun". Similarly, experience of time depends on the content of perceptual experience -more rapidly changing or complex perceptual scenes seem longer in duration than less dynamic ones. The complexity of interactions among stimulation, attention, and memory that characterise time experience is likely the reason that a single overarching theory of time perception has been difficult to achieve. In the present study we propose a framework that reconciles these interactions within a single model, built using the principles of the predictive processing approach to perception. We designed a neural hierarchical Bayesian system, functionally similar to human perceptual processing, making use of hierarchical predictive coding, short-term plasticity, spatio-temporal attention, and episodic memory formation and recall. A large-scale experiment with ∼ 13, 000 human participants investigated the effects of memory, cognitive load, and stimulus content on duration reports of natural scenes up to ∼ 1 minute long. Model-based estimates matched human reports, replicating key qualitative biases including differences by cognitive load, scene type, and judgement (prospective or retrospective). Our approach provides an end-to-end model of duration perception from natural stimulus processing to estimation and from current experience to recalling the past, providing a new understanding of this central aspect of human experience. Predictive processing neural architecture for perceptionAccording to (Bayesian) predictive processing theories, the brain is constantly updating its internal representations of the state of the world with new sensory information x 0 t (at time t) through the process of hierarchical probabilistic inference 14,17,19,20 . The resulting 'top-down' generative model is used to predict new states and simulate the outcome of prospective actions 21 . Comparing predicted against actual sensory signals gives rise to prediction errors ξ n t which, in most instantiations of predictive processing, are assumed to flow in a 'bottom-up' direction from the sensory periphery (n = 0), towards higher hierarchical levels n ∈ {1, 2, ..}. Numerous connections with neurophysiology have been made over the years, including proposals viewing predictive processing as the fundamental function of canonical microcircuits in the cortex 22 , or as the result of the interplay between competing oscillatory bands 23, 24 , as well as providing theoretical explanations for a wide range of cognitive and perceptual effects such as hallucinations 25 , autism 26 and schizophrenia 27 . In addition, this framework is uniquely qualified for studying the relation between perceptual change and human perception of time 4 , as i...

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Section: The Subjective Timescale Of Time Perception and Episodic Memorymentioning

confidence: 99%

A predictive processing model of episodic memory and time perception

Fountas

Sylaidi²,

Nikiforou

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…When our 4 predictions are incorrect, we must update our mnemonic models of the world to support adaptive 5 behavior. Prediction error is a measure of the discrepancy between expectation and reality; this surprise signal is both evident in brain activity and related to learning success (1)(2)(3)(4)(5)(6). The brain dynamically reconstructs memories during recall, recreating and revising past experiences based on current information (7).…”

Section: Introductionmentioning

confidence: 99%

Prediction Errors Disrupt Hippocampal Representations and Update Episodic Memories

Sinclair

Manalili

Brunec

et al. 2020

Preprint

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The brain supports adaptive behavior by generating predictions, learning from errors, and updating memories. Prediction error, or surprise, is a known trigger for memory updating; however, the mechanisms that link prediction error, neural representations, and naturalistic memory updating remain unknown. In an fMRI study, we elicited prediction errors by interrupting familiar narrative videos immediately before an expected conclusion. We found that prediction errors reversed the effect of post-video univariate hippocampal activation on subsequent memory: hippocampal activation predicted false memories after prediction errors, but protected memories from distortion after expected events. Tracking second-by-second neural patterns revealed that prediction errors disrupted the temporal continuity of hippocampal representations. This disruption of signal history led to memory updating after prediction error. We conclude that prediction errors during memory reactivation prompt the hippocampus to abandon ongoing predictions and neural representations. Following prediction error, the hippocampus switches to an externally-oriented processing mode that supports memory updating.

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“…In Quent et al (2021 ), five types of novelty/surprise signals are outlined—item vs context novelty, item vs context surprise, and complete novelty, following the model of Henson and Gagnepain (2010 ). A model is being described of the interrelation between distributions of the subjective experience of novelty or surprise in terms of existence of prior memory and the current expectation.…”

Section: Learning Processes Revealed By Observing Eeg Activationsmentioning

confidence: 99%

“…By definition, the intensity of surprise is higher, than of novelty , due to the fact that in novelty no prior expectation is being formed as in surprise . In relation to surprise, Quent et al (2021) reported observed involvement of the hippocampus combined with increased motivation and exploration ( Kang et al, 2009 ; Gruber and Ranganath, 2019 )—psychological processes being largely involved in classroom learning.…”

Section: Learning Processes Revealed By Observing Eeg Activationsmentioning

confidence: 99%

A Review of Possible EEG Markers of Abstraction, Attentiveness, and Memorisation in Cyber-Physical Systems for Special Education

et al. 2021

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Cyber-physical systems (CPSs) for special education rely on effective mental and brain processing during the lesson, performed with the assistance of humanoid robots. The improved diagnostic ability of the CPS is a prerogative of the system for efficient technological support of the pedagogical process. The article focuses on the available knowledge of possible EEG markers of abstraction, attentiveness, and memorisation (in some cases combined with eye tracking) related to predicting effective mental and brain processing during the lesson. The role of processing abstraction is emphasised as the learning mechanism, which is given priority over the other mechanisms by the cognitive system. The main markers in focus are P1, N170, Novelty P3, RewP, N400, and P600. The description of the effects is accompanied by the analysis of some implications for the design of novel educational scenarios in inclusive classes.

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A predictive account of how novelty influences declarative memory

Cited by 57 publications

References 157 publications

A predictive processing model of episodic memory and time perception

A predictive processing model of episodic memory and time perception

Prediction Errors Disrupt Hippocampal Representations and Update Episodic Memories

A Review of Possible EEG Markers of Abstraction, Attentiveness, and Memorisation in Cyber-Physical Systems for Special Education

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