Toward a better understanding on the role of prediction error on memory processes: From bench to clinic

Krawczyk, Maria del Carmen; Fernández, Rodrigo S.; Pedreira, Marı́a Eugenia; Boccia, Mariano Martín

doi:10.1016/j.nlm.2016.12.011

Cited by 39 publications

(49 citation statements)

References 117 publications

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“…Previous research has speculated that updating of knowledge structures such as schemas might rely on a mismatch between a prediction and the encountered information (Richter et al, 2019;van Kesteren et al, 2012), and similar processes have been observed in other memory systems (Greve et al, 2019;Henson & Gagnepain, 2010;Krawczyk, Fernández, Pedreira, & Boccia, 2017;Pine et al, 2018). However, due to the absence of sufficiently sensitive methods to measure trial-by-trial differences in schema updating in the laboratory prior to the development of continuous report memory tasks, it has only recently been possible to visualise adjustments to pre-existing schemas in the aftermath of encountering schemainconsistent information (Richter et al, 2019).…”

Section: Discussionmentioning

confidence: 67%

Prediction errors indexed by the P3 track the updating of complex long-term memory schemas

Richter

2019

Preprint

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Schemas are higher-level knowledge structures that store an abstraction of multiple previous experiences. They allow us to retain a multitude of information, but without the cost of storing every detail. Schemas are believed to be relatively stable, but occasionally have to be updated to remain useful in the face of changing environmental conditions. Once a schema is consolidated, schema updating has been proposed to be the result of a prediction error (PE) based learning mechanism, similar to the updating of less complex knowledge. However, for schema memory this hypothesis has so far been difficult to test since the tools to track small modifications to abstracted memory schemas have not been sensitive enough. Moreover, existing research on electrophysiological correlates of updating have focused on short-term belief updating tasks. Here I am using EEG and continuous memory measures recorded during the encoding of new schema consistent and inconsistent material to test the behavioural and neural correlates of schema updating. Schema updating was assessed in a memory test 24 hours after encountering inconsistent information, demonstrating the long-term effect of such PE-based learning. I observed a stronger relationship between behavioural PE and schema updating measures for inconsistent compared to consistent material, in line with the idea that more updating is required when a schema changes. Moreover, the P3 EEG signal tracked both the PE at the time of learning, as well as the updating of the memory schema one day later in the inconsistent condition. These results demonstrate that schema updating in the face of inconsistent information is indeed driven by PE-based mechanisms, and that similar neural mechanisms underlie the updating of consolidated long-term schemas and short-term belief structures.

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Section: Discussionmentioning

confidence: 67%

Prediction errors indexed by the P3 track the updating of complex long-term memory schemas

Richter

2019

Preprint

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“…Our study is based on the implicit assumption that the mechanism underlying schema updating is a prediction error (PE) signal when first encountering the inconsistent material. PE-based learning is heavily studied in investigations of 'belief updating' in the context of reinforcement learning and decision making research, but less evidence exists for PE-based learning in memory tasks (though some progress has been made in this field 14,15,31,32 . Recently, it has been shown that reward prediction errors (RPEs) are positively related to later memory in an incidental episodic encoding paradigm 33,34 .…”

Section: Discussionmentioning

confidence: 99%

“…Erroneous predictions due to inconsistent information (i.e., unexpected outcomes) may be one source of such learning 13 . Similar mechanisms have been postulated in the long-term memory literature: predictive coding models of memory are based on the idea that learning results from divergences between experience-based predictions and observed outcomes 14,15 . The effect of consolidation on the successful learning of inconsistent information is less well understood.…”

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confidence: 82%

Flexible updating of dynamic knowledge structures

Richter¹,

Bays²,

Jeyarathnarajah³

et al. 2018

Preprint

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Schemas are knowledge structures that allow us to make efficient judgments about the world without the cost of memorizing every detail of previous experiences. It has long been known that schemas can enhance long-term memory for related information. The usefulness of schemas, however, critically depends on their adaptability: how flexibly a schema can be updated according to changing environmental conditions. Prior consolidation of a schema supports new learning of schema-consistent information. Yet, the effect of consolidation on inconsistent information, and how schemas may be subsequently updated, are not well understood. It is difficult to track the dynamic updating of knowledge structures with traditional memory measures. Here, using a continuous-report paradigm, we were able to show that schematization increases incrementally with consolidation and that the strength with which schemas are initially established predicts schema-guided responding in a later test. Critically, schema updating in response to inconsistent information was more pronounced in a group which was given time to consolidate compared to a group that was not given time to consolidate. Importantly, the later group reverted back to the no longer relevant schema, indicating that systematic bias towards old information, rather than increased forgetting underlies decreased memory for schema-inconsistent information.

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“…Exton- McGuinness et al (2015) reviewed the role of prediction errors in reconsolidation studies and summarized their position by stating, "We propose that a prediction error signal…is necessary for destabilisation and subsequent reconsolidation of a memory" (p. 375). Krawczyk et al (2017), reviewing the functional role of prediction error in the neurobiology of learning and memory, stated, "Prediction error induces updating of consolidated memories in strength or content by memory reconsolidation" (p. 13) and "When our predictions or understandings of the world do not fit with the current experience, the detection of this incongruence triggers the destabilization-reconsolidation process, which allows us to adjust our internal models." (p. 15).…”

Section: How Destabilization Of Memory Occursmentioning

confidence: 99%

“…Currently, at the end of the second decade of laboratory research into reconsolidation, researchers' attention is extending to considerations of clinical translation at a rapidly accelerating pace (e.g., Beckers and Kindt, 2017;Dunbar and Taylor, 2016;Elsey and Kindt, 2017a;Krawczyk et al, 2017;Kroes et al, 2015;Nader et al, 2014;Treanor et al, 2017). Those authors have consistently called for a two-way flow of knowledge between researchers and clinicians in order to achieve the fullest clinical utilization of memory reconsolidation.…”

Section: Introductionmentioning

confidence: 99%

Clinical Translation of Memory Reconsolidation Research: Therapeutic Methodology for Transformational Change by Erasing Implicit Emotional Learnings Driving Symptom Production

Ecker¹

2017

Preprint

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After 20 years of laboratory study of memory reconsolidation, the translation of research findings into clinical application has recently been the topic of a rapidly growing number of review articles. The present article identifies previously unrecognized possibilities for effective clinical translation by examining research findings from the experience-oriented viewpoint of the clinician. It is well established that destabilization of a target learning and its erasure (robust functional disappearance) by behavioral updating are experience-driven processes. By interpreting the research in terms of internal experiences required by the brain, rather than in terms of external laboratory procedures, a clinical methodology of updating and erasure unambiguously emerges, with promising properties: It is applicable for any symptom generated by emotional learning and memory, it is readily adapted to the unique target material of each therapy client, and it has extensive corroboration in existing clinical literature, including cessation of a wide range of symptoms and verification of erasure using the same markers relied upon by laboratory researchers. Two case vignettes illustrate clinical implementation and show erasure of lifelong, complex, intense emotional learnings and full, lasting cessation of major long-term symptoms. The experience-oriented framework also provides a new interpretation of the laboratory erasure procedure known as post-retrieval extinction, indicating limited clinical applicability and explaining for the first time why, even with reversal of the protocol (postextinction retrieval), reconsolidation and erasure still occur. Also discussed are significant ramifications for the clinical field's "corrective experiences" paradigm, for psychotherapy integration, and for establishing that specific factors can produce extreme therapeutic effectiveness.

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Toward a better understanding on the role of prediction error on memory processes: From bench to clinic

Cited by 39 publications

References 117 publications

Prediction errors indexed by the P3 track the updating of complex long-term memory schemas

Prediction errors indexed by the P3 track the updating of complex long-term memory schemas

Flexible updating of dynamic knowledge structures

Clinical Translation of Memory Reconsolidation Research: Therapeutic Methodology for Transformational Change by Erasing Implicit Emotional Learnings Driving Symptom Production

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