Can sleep help us unlearn biases and make our memories more positive?

Implicit biases are unconscious stereotypes that influence our judgments and decisions - like assuming a particular gender for a specific job role. But what if we could change these biases?

In this episode, we explore how manipulations of sleep might help reshape our implicit attitudes. We speak with Professor Xiaoqing Hu, a leading researcher in the use of Targeted Memory Reactivation (TMR) during sleep to alter implicit bias and make memories more positive. Xiaoqing shares his journey of applying Implicit Association Tasks (IATs) to sleep research, and how conflict biases, congruent vs. incongruent data, and task design play a role in measuring and modifying bias.

We also dive into the nitty gritty of memory consolidation in sleep — examining how REM and NREM stages contribute to emotional memory consolidation, and how recency vs. saliency affects which memories get updated.

Prof. Hu discusses his groundbreaking study demonstrating the ability to update unwanted emotional memories during sleep, and we explore the potential for applying this research to clinical populations. We also consider how individual schemas might influence the effectiveness of TMR across different people.

If you would like to find out more about Prof. Hu's work, please see the lab website here. You can find relevant papers below:

• Unlearning implicit social biases during sleep, 2015
• Updating memories of unwanted emotions during human sleep, 2022
• Targeted memory reactivation during sleep influences social bias as a function of slow-oscillation phase and delta power, 2023
• Reactivating cue approached positive personality traits during sleep promotes positive self-referential processing, 2024
• Aversive memories can be weakened during human sleep via the reactivation of positive interfering memories. 2024
• Disarming emotional memories using Targeted Memory Reactivation during Rapid Eye Movement sleep, 2024
• Targeted memory reactivation in REM but not SWS selectively reduces arousal responses, 2021

Glossary

IAT - Implicit Association Task - a test used to measure the strength of automatic associations between our concepts that we may not be consciously aware of.

TMR - Targeted Memory Reactivation - A technique used to modify memory processing, through the use of presenting cues (audio or smell) that were associated with learning, whilst a person sleeps. These cues can modify select memories and in recent research is being used in emotional memory.

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Host Professor Penny Lewis

Produced by Sophie Smith

Check out our NaPS website to find out more about the podcast, our research and events.

This recording is the property of the Sleep Science Podcast and not for resale.

We're joined by Hongi Ngo, who developed a revolutionary technique for manipulating sleep oscillations during his PhD. Closed-loop Auditory Stimulation (CLAS), which uses bursts of pink noise which are carefully timed to a particular phase of an existing brain oscillation to either boost or dampen the target oscillation. It has been used to selectively strengthen or weaken memories, improve the immune response, reduce epileptic seizures, and even potentially to slow down age related cognitive decline.

Hongi tells us how he got got the idea for CLAS, explains how it works, and tells us about some of his existing studies. He talks about combining CLAS with Targeted Memory Reactivation (TMR) and discusses the ideas for future of brain modulation methods with techniques such as transcranial magnetic stimulation (TMS).

Some of the key papers discussed are:

Auditory closed-loop stimulation of the sleep slow oscillation enhances memory (2013)

Auditory closed-loop stimulation of EEG slow oscillations strengthens sleep and signs of its immune-supportive function (2017)

Thalamic spindles promote memory formation during sleep thrugh triple phase-locking of cortical, thalamic and hippocampal rhythms (2017)

Examining the optimal timing for closed-loop auditory stimulation of slow-wave sleep in young and older adults (2019)

Auditory stimulation during sleep suppresses spike activity in benign epilepsy with centrotemporal spikes (2021)

Shaping overnight consolidation via slow-oscillation closed-loop targeted memory reactivation (2022)

Check out our NaPS website to find out more about the podcast, our research and events.

This recording is the property of the Sleep Science Podcast and not for resale.

How are memories represented in the brain and how can we model this?

Professor Anna Schapiro from University of Pennsylvania as we discuss her work on computational modelling of sleep and memory. In this episode, we discuss several kinds of Neural Network models as well as diving into the history of Jay McClleland's Complementary Learning Systems theory and Anna's recent work on combining neural network modeling with EEG.

Find out more about Anna Schapiro and her research group here.

Links to articles discussed in this episode:

• Why there are Complementary Learning Systems in the hippocampus and neocortex
• Methods for reducing interference in the Complementary Learning Systems model
• Self-recovery of memory via generative replay
• A unifying account of reply as context-driven memory reactivation

Host: Penny Lewis
Editor: Sophie Smith

Check out our NaPS website to find out more about the podcast, our research and events.

This recording is the property of the Sleep Science Podcast and not for resale.