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Memory is categorized into three major systems: sensory memory, short-term memory (STM), and long-term memory (LTM). These systems differ in their capacity and the duration for which they can hold information. Sensory memory captures raw sensory input from the environment, holding it for just a few seconds or less. For example, on hearing a brief, loud sound, like a car horn honking, the sound seems to linger in the mind for a moment even after it stops. This is an instance of sensory memory...

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Related Experiment Video

Updated: Jul 10, 2026

Improved Preparation and Preservation of Hippocampal Mouse Slices for a Very Stable and Reproducible Recording of Long-term Potentiation

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The lifetime of sequential memory traces.

Leonardo Pinto Arata^1,2,3, Carlos Ramisch^4,5, Arnaud Rey^6,4

¹Aix Marseille Univ, CNRS, CRPN, Marseille, France. leonardo.pinto-arata@univ-amu.fr.

Memory & Cognition

|July 8, 2026

Summary

This summary is machine-generated.

Implicit statistical learning occurs even with long delays between sequence repetitions. Learning rates follow a power law, and processing speed is affected by repetition distance.

Keywords:

Memory trace Sequence learning Statistical learning

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Area of Science:

Cognitive Psychology
Neuroscience
Computational Linguistics

Background:

Implicit statistical learning (ISL) is crucial for acquiring complex information.
Factors influencing ISL, such as repetition frequency and inter-repetition distance, are not fully understood.
Current models of ISL require further constraints from empirical data.

Purpose of the Study:

To investigate the impact of inter-repetition distance on implicit sequence learning.
To determine how processing speed is affected by varying distances between sequence repetitions.
To provide data for refining computational models of implicit statistical learning.

Main Methods:

A Hebbian lexical decision task was employed.
Participants identified words versus non-words, while implicitly learning a repeated three-word sequence.
The distance between sequence repetitions was manipulated using 4, 7, 10, 20, 30, or 60 filler items.

Main Results:

Implicit sequence learning was observed even with up to 60 filler items between repetitions.
The rate of learning best fitted a power law function.
Processing speed for words within the learned sequence was differentially affected by the inter-repetition distance.

Conclusions:

Implicit statistical learning can occur over substantial temporal lags.
The findings support a power-law relationship in sequence learning.
These results offer new insights into the mechanisms underlying implicit statistical learning and inform computational modeling efforts.