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Bloxham A, Horton CL. Enhancing and advancing the understanding and study of dreaming and memory consolidation: Reflections, challenges, theoretical clarity, and methodological considerations. Conscious Cogn 2024; 123:103719. [PMID: 38941924 DOI: 10.1016/j.concog.2024.103719] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2023] [Revised: 04/24/2024] [Accepted: 06/12/2024] [Indexed: 06/30/2024]
Abstract
Empirical investigations that search for a link between dreaming and sleep-dependent memory consolidation have focused on testing for an association between dreaming of what was learned, and improved memory performance for learned material. Empirical support for this is mixed, perhaps owing to the inherent challenges presented by the nature of dreams, and methodological inconsistencies. The purpose of this paper is to address critically prevalent assumptions and practices, with the aim of clarifying and enhancing research on this topic, chiefly by providing a theoretical synthesis of existing models and evidence. Also, it recommends the method of Targeted Memory Reactivation (TMR) as a means for investigating if dream content can be linked to specific cued activations. Other recommendations to enhance research practice and enquiry on this subject are also provided, focusing on the HOW and WHY we search for memory sources in dreams, and what purpose (if any) they might serve.
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Affiliation(s)
- Anthony Bloxham
- Nottingham Trent University, Nottingham, NG1 4FQ, United Kingdom.
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Bowman H, Collins DJ, Nayak AK, Cruse D. Is predictive coding falsifiable? Neurosci Biobehav Rev 2023; 154:105404. [PMID: 37748661 DOI: 10.1016/j.neubiorev.2023.105404] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2023] [Revised: 09/16/2023] [Accepted: 09/21/2023] [Indexed: 09/27/2023]
Abstract
Predictive-coding has justifiably become a highly influential theory in Neuroscience. However, the possibility of its unfalsifiability has been raised. We argue that if predictive-coding were unfalsifiable, it would be a problem, but there are patterns of behavioural and neuroimaging data that would stand against predictive-coding. Contra (vanilla) predictive patterns are those in which the more expected stimulus generates the largest evoked-response. However, basic formulations of predictive-coding mandate that an expected stimulus should generate little, if any, prediction error and thus little, if any, evoked-response. It has, though, been argued that contra (vanilla) predictive patterns can be obtained if precision is higher for expected stimuli. Certainly, using precision, one can increase the amplitude of an evoked-response, turning a predictive into a contra (vanilla) predictive pattern. We demonstrate that, while this is true, it does not present an absolute barrier to falsification. This is because increasing precision also reduces latency and increases the frequency of the response. These properties can be used to determine whether precision-weighting in predictive-coding justifiably explains a contra (vanilla) predictive pattern, ensuring that predictive-coding is falsifiable.
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Affiliation(s)
- H Bowman
- School of Computing, University of Kent, UK; School of Psychology, University of Birmingham, UK; Wellcome Centre for Human Neuroimaging, UCL, UK.
| | | | - A K Nayak
- School of Psychology, University of Birmingham, UK
| | - D Cruse
- School of Psychology, University of Birmingham, UK
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Kolibius LD, Roux F, Parish G, Ter Wal M, Van Der Plas M, Chelvarajah R, Sawlani V, Rollings DT, Lang JD, Gollwitzer S, Walther K, Hopfengärtner R, Kreiselmeyer G, Hamer H, Staresina BP, Wimber M, Bowman H, Hanslmayr S. Hippocampal neurons code individual episodic memories in humans. Nat Hum Behav 2023; 7:1968-1979. [PMID: 37798368 PMCID: PMC10663153 DOI: 10.1038/s41562-023-01706-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Accepted: 08/23/2023] [Indexed: 10/07/2023]
Abstract
The hippocampus is an essential hub for episodic memory processing. However, how human hippocampal single neurons code multi-element associations remains unknown. In particular, it is debated whether each hippocampal neuron represents an invariant element within an episode or whether single neurons bind together all the elements of a discrete episodic memory. Here we provide evidence for the latter hypothesis. Using single-neuron recordings from a total of 30 participants, we show that individual neurons, which we term episode-specific neurons, code discrete episodic memories using either a rate code or a temporal firing code. These neurons were observed exclusively in the hippocampus. Importantly, these episode-specific neurons do not reflect the coding of a particular element in the episode (that is, concept or time). Instead, they code for the conjunction of the different elements that make up the episode.
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Affiliation(s)
- Luca D Kolibius
- Department of Biomedical Engineering, Columbia University, New York, NY, USA.
- Centre for Cognitive Neuroimaging, School of Psychology and Neuroscience, University of Glasgow, Glasgow, UK.
- Centre for Human Brain Health, School of Psychology, University of Birmingham, Birmingham, UK.
| | - Frederic Roux
- Centre for Human Brain Health, School of Psychology, University of Birmingham, Birmingham, UK
| | - George Parish
- Centre for Human Brain Health, School of Psychology, University of Birmingham, Birmingham, UK
| | - Marije Ter Wal
- Centre for Human Brain Health, School of Psychology, University of Birmingham, Birmingham, UK
| | - Mircea Van Der Plas
- Centre for Cognitive Neuroimaging, School of Psychology and Neuroscience, University of Glasgow, Glasgow, UK
- Centre for Human Brain Health, School of Psychology, University of Birmingham, Birmingham, UK
| | - Ramesh Chelvarajah
- Centre for Human Brain Health, School of Psychology, University of Birmingham, Birmingham, UK
- Complex Epilepsy and Surgery Service, Neurosciences Centre, Queen Elizabeth Hospital Birmingham, Birmingham, UK
| | - Vijay Sawlani
- Complex Epilepsy and Surgery Service, Neurosciences Centre, Queen Elizabeth Hospital Birmingham, Birmingham, UK
| | - David T Rollings
- Complex Epilepsy and Surgery Service, Neurosciences Centre, Queen Elizabeth Hospital Birmingham, Birmingham, UK
| | - Johannes D Lang
- Epilepsy Center, Department of Neurology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Stephanie Gollwitzer
- Epilepsy Center, Department of Neurology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Katrin Walther
- Epilepsy Center, Department of Neurology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Rüdiger Hopfengärtner
- Epilepsy Center, Department of Neurology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Gernot Kreiselmeyer
- Epilepsy Center, Department of Neurology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Hajo Hamer
- Epilepsy Center, Department of Neurology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Bernhard P Staresina
- Department of Experimental Psychology, University of Oxford, Oxford, UK
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford, UK
| | - Maria Wimber
- Centre for Cognitive Neuroimaging, School of Psychology and Neuroscience, University of Glasgow, Glasgow, UK
- Centre for Human Brain Health, School of Psychology, University of Birmingham, Birmingham, UK
| | - Howard Bowman
- Centre for Human Brain Health, School of Psychology, University of Birmingham, Birmingham, UK
- Centre for Cognitive Neuroscience and Cognitive Systems and the School of Computing, University of Kent, Canterbury, UK
| | - Simon Hanslmayr
- Centre for Cognitive Neuroimaging, School of Psychology and Neuroscience, University of Glasgow, Glasgow, UK.
- Centre for Human Brain Health, School of Psychology, University of Birmingham, Birmingham, UK.
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Spectral Pattern Similarity Analysis: Tutorial and Application in Developmental Cognitive Neuroscience. Dev Cogn Neurosci 2022; 54:101071. [PMID: 35063811 PMCID: PMC8784303 DOI: 10.1016/j.dcn.2022.101071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Revised: 12/06/2021] [Accepted: 01/14/2022] [Indexed: 11/23/2022] Open
Abstract
The human brain encodes information in neural activation patterns. While standard approaches to analyzing neural data focus on brain (de-)activation (e.g., regarding the location, timing, or magnitude of neural responses), multivariate neural pattern similarity analyses target the informational content represented by neural activity. In adults, a number of representational properties have been identified that are linked to cognitive performance, in particular the stability, distinctiveness, and specificity of neural patterns. However, although growing cognitive abilities across childhood suggest advancements in representational quality, developmental studies still rarely utilize information-based pattern similarity approaches, especially in electroencephalography (EEG) research. Here, we provide a comprehensive methodological introduction and step-by-step tutorial for pattern similarity analysis of spectral (frequency-resolved) EEG data including a publicly available pipeline and sample dataset with data from children and adults. We discuss computation of single-subject pattern similarities and their statistical comparison at the within-person to the between-group level as well as the illustration and interpretation of the results. This tutorial targets both novice and more experienced EEG researchers and aims to facilitate the usage of spectral pattern similarity analyses, making these methodologies more readily accessible for (developmental) cognitive neuroscientists.
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