Forgetting dynamics for items of different categories

Georgiou, Antonios; Katkov, Mikhail; Tsodyks, Misha

doi:10.1101/lm.053713.122

Cited by 2 publications

(2 citation statements)

References 24 publications

(39 reference statements)

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“…Our findings were based on LLMs, which recently demonstrated strong resemblance with the brain's language representation (26)(27)(28)(29)(30) and similar behavioral performance during memory recall tasks (31). In conjunction with the findings of our study, this notable similarity offers potentially new insights into generalization mechanisms in the brain, namely that prior knowledge of the learning system can be leveraged to perform new tasks without plasticity.…”

Section: Discussionsupporting

confidence: 67%

Computation or Weight Adaptation? Rethinking the Role of Plasticity in Learning

Lior,

Shalev,

Stanovsky

et al. 2024

Preprint

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The human brain is an adaptive learning system that can generalize to new tasks and unfamiliar environments. The traditional view is that such adaptive behavior requires a structural change of the learning system (e.g., via neural plasticity). In this work, we use artificial neural networks, specifically large language models (LLMs), to challenge the traditional view about the role of plasticity in learning and suggest that such an adaptive behavior can be achieved solely through computation if the learning system is sufficiently trained. We focus on statistical learning paradigms. These require identifying underlying regularities in seemingly arbitrary word sequences and are largely considered to require neural plasticity. LLMs can capture arbitrary structures without weight adaptation despite the divergence from their natural language training data. Our work provides novel insights into the role of plasticity in learning, showing that sufficiently trained learning systems are highly flexible, adapting to new tasks and environments solely through computation, much more than previously acknowledged. Furthermore, our work opens the door for future research to use deep learning models to conjure hypotheses about the brain.

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Section: Discussionsupporting

confidence: 67%

Computation or Weight Adaptation? Rethinking the Role of Plasticity in Learning

Lior,

Shalev,

Stanovsky

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Moreover, this relation follows from a simple deterministic model where words are retrieved one by one according to a random symmetric matrix of 'similarities' reflecting their long-term encoding in memory, until the process enters a cycle and no more words can be recalled. The number of remembered words M , itself can be predicted by the retrograde interference model that assumes that each new word erases some of the previously presented words according to the 'valence' or 'importance' of each word [Georgiou et al, 2021[Georgiou et al, , 2023].…”

Section: Introductionmentioning

confidence: 99%

Using large language models to study human memory for meaningful narratives

Georgiou,

Can,

Katkov

et al. 2023

Preprint

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One of the most impressive achievements of the AI revolution is the development of large language models that can generate meaningful text and respond to instructions in plain English with no additional training necessary. Here we show that language models can be used as a scientific instrument for studying human memory for meaningful material. We developed a pipeline for designing large scale memory experiments and analyzing the obtained results. We performed online memory experiments with a large number of participants and collected recognition and recall data for narratives of different lengths. We found that both recall and recognition performance scale linearly with narrative length. Furthermore, in order to investigate the role of narrative comprehension in memory, we repeated these experiments using scrambled versions of the presented stories. We found that even though recall performance declined significantly, recognition remained largely unaffected. Interestingly, recalls in this condition seem to follow the original narrative order rather than the scrambled presentation, pointing to a contextual reconstruction of the story in memory.

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Forgetting dynamics for items of different categories

Cited by 2 publications

References 24 publications

Computation or Weight Adaptation? Rethinking the Role of Plasticity in Learning

Computation or Weight Adaptation? Rethinking the Role of Plasticity in Learning

Using large language models to study human memory for meaningful narratives

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