Purpose: To assess the role of task-relevance and explicit memory in implicit colour memory. Methods: At study, participants (N=112) reported either the colour or the category of 26 coloured line drawings. At test, black-and-white versions of objects (26 old, 26 new) were shown and participants reported the colour of the line drawing seen at study (explicit memory). Subsequently on each trial, participants were shown coloured versions of the same objects (half the same colour as during study and half colour transformed) and reported colour, category or identity (implicit memory). Results: When the colour and category of the items were correlated at study, those participants who reported category at study (N=48) evidenced reaction times for colour transformed items that were longer than those for non-transformed objects in all test conditions. Participants who reported colour at study (N=32) evidenced no influence of colour transformation on implicit memory. Furthermore, when colour and category were not correlated at study, participants who reported category at study (N=32) evidenced no influence of colour transformation on implicit memory. However, in all conditions, repetition priming was absent when explicit recall was incorrect. Conclusions: Colour appears to be encoded even when colour is irrelevant to the task at study (i.e. report category). Repetition priming occurred regardless of whether colour was relevant (i.e. report category or colour) or irrelevant to the decision at test (i.e. report identity). However, implicit recall was not independent of explicit recall in any of the conditions in these experiments.

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Memory$^3$: Language Modeling with Explicit Memory

Hongkang Yang, Zehao Lin, Wenjin Wang et al. · Journal of Machine Learning · 2024

The training and inference of large language models (LLMs) are together a costly process that transports knowledge from raw data to meaningful computation. Inspired by the memory hierarchy of the human brain, we reduce this cost by equipping LLMs with explicit memory, a memory format cheaper than model parameters and text retrieval-augmented generation (RAG). Conceptually, with most of its knowledge externalized to explicit memories, the LLM can enjoy a smaller parameter size, training cost, and inference cost, all proportional to the amount of remaining “abstract knowledge”. As a preliminary proof of concept, we train from scratch a 2.4 B LLM, which achieves better performance than much larger LLMs as well as RAG models, and maintains higher decoding speed than RAG. The model is named ${\rm Memory}^3$, since explicit memory is the third form of memory in LLMs after implicit memory (model parameters) and working memory (context key-values). We introduce a memory circuitry theory to support the externalization of knowledge, and present novel techniques including a memory sparsification mechanism that makes storage tractable and a two-stage pretraining scheme that facilitates memory formation.

Cited by 4Open access

Memory management with explicit regions

David Gay, Alex Aiken · 1998

Much research has been devoted to studies of and algorithms for memory management based on garbage collection or explicit allocation and deallocation. An alternative approach, region-based memory management, has been known for decades, but has not been well-studied. In a region-based system each allocation specifies a region, and memory is reclaimed by destroying a region, freeing all the storage allocated therein. We show that on a suite of allocation-intensive C programs, regions are competitive with malloc/free and sometimes substantially faster. We also show that regions support safe memory management with low overhead. Experience with our benchmarks suggests that modifying many existing programs to use regions is not difficult.

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Implicit and explicit poetic memory

Raphael Lyne · Cambridge University Press eBooks · 2016

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Explicit memory in anxiety disorders.

Eni S. Becker, Walton T. Roth, Matthias Andrich et al. · Journal of Abnormal Psychology · 1999

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Morsels: Explicit Virtual Memory Objects

Alexander Halbuer, Christian Dietrich, Florian Rommel et al. · 2023

The tremendous growth of RAM capacity - now exceeding multiple terabytes - necessitates a reevaluation of traditional memory-management methods, which were developed when resources were scarce. Current virtual-memory subsystems handle address-space regions as sets of individual 4-KiB pages with demand paging and copy-on-write, resulting in significant management overhead. Although huge pages reduce the number of managed entities, they induce internal fragmentation and have a coarse copy granularity.

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Modeling Implicit and Explicit Memory.

Raaijmakers, J.G.W., Ohta, N., Izawa, C. · UvA-DARE (University of Amsterdam) · 2005

Mathematical models of memory are useful for describing basic processes of memory in a way that enables generalization across a number of experimental paradigms. Models that have these characteristics do not just engage in empirical curve-fitting, but may also provide explanations for puzzling phenomena and may lead to new discoveries. This chapter provides a number of examples, taken from previous research with the SAM model. Although previous research has focused exclusively on the explanation of episodic memory, recent research within the SAM/REM approach has extended this model to implicit and semantic memory phenomena. This review provides some speculations on how this approach may be extended to deal with a number of basic data in implicit memory. It is emphasized that constructing a model for implicit memory necessitates the development of detailed models of lexical-semantic processing.

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Explicit Memory-Based Viewing Effects.

Allison E. Nickel (819772), Katharina Henke (715479), Deborah E. Hannula (819773) · Figshare · 2015

<p>A) Proportion of total viewing time directed to faces of interest collapsed across the duration of the entire test trial for the experimental group (top) and the control group (bottom). More time was spent viewing selected faces, and participants looked disproportionately at the associates of scene cues whether explicit recognition responses were correct or not. B) Proportion of total viewing time directed to faces of interest broken down into 250ms time bins starting with 3-face display onset. Selected faces were prioritized, and memory-based viewing that distinguished correctly identified associates from faces selected in error were evident for both groups shortly after display onset. Error bars indicate standard errors of the mean.</p>

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