Mnemonic Research Articles

Active contextualization reduces traumatic memory intrusions via memory integration

Traumatic memory intrusions, the involuntary retrieval of unwanted memories, significantly impact mental health. The dual representation theory proposes that the origin of intrusion lies in the overactivated sensory memory not being integrated with the corresponding contextual memory, highlighting the crucial associations between memory contextualization and intrusion. To test this, our study investigated whether enhancing memory contextualization could effectively reduce intrusion. After experiencing analog trauma with the trauma film paradigm, 96 healthy participants were randomly allocated to three intervention groups: active contextualization (AC) in which participants actively retrieve and restructure film content, passive contextualization (PC) in which participants passively restudy content-matched pre-contextualized information, and working memory taxation (WM) in which participants performed a working memory dual-task. Diary recordings over the subsequent week revealed a significant reduction in intrusion frequency in the AC group compared to both the PC group and a no-intervention control group. Furthermore, comparing AC with WM, a well-established laboratory intervention on intrusion, established a superior efficacy of the AC intervention in reducing intrusions. Finally, analyses of the explicitly recollected film memories identified the critical element of active contextualization to be memory integration induced by active memory retrieval. Together, our findings suggest that active contextualization causally diminishes intrusions, providing novel insights into the regulation of the contextual memory system in intrusion intervention.

PANGeA: Procedural Artificial Narrative Using Generative AI for Turn-Based, Role-Playing Video Games

Large language models (LLMs) offer unprecedented flexibility in procedural generation, enabling the creation of dynamic video game storylines that evolve with user input. A critical aspect of realizing this potential is allowing players and developers to provide dynamic or free-form text to drive generation. Ingesting free-form text for a video game poses challenges, however, as it can prompt the LLM to generate content beyond the intended narrative scope. In response to this challenge, this research introduces Procedural Artificial Narrative using Generative AI (PANGeA) for leveraging LLMs to create narrative content for turn-based, role-playing games (RPGs). PANGeA is an approach comprised of components including a memory system, validation system, a Unity game engine plug-in, and a server with a RESTful interface that enables connecting PANGeA components with any game engine as well as accessing local and private LLMs. PANGeA procedurally generates level data like setting, key items, non-playable characters (NPCs)), and dialogue based on a set of configuration and design rules provided by the game designer. This process is supported by a novel validation system for handling free-form text input during game development and gameplay, which aligns LLM generation with the narrative. It does this by evoking the LLM's capabilities to dynamically evaluate the text input against game rules that reinforce the designer's initial criteria. To enrich player-NPC interactions, PANGeA uses the Big Five Personality model to shape NPC responses. To explore its broad application, PANGeA is evaluated across two studies. First, this research presents a narrative test scenario of the prototype game, Dark Shadows, which was developed using PANGeA within the Unity game engine. This is followed by an ablation study that tests PANGeA's performance across 10 different role-playing game scenarios–from western to science fiction–and across three model sizes: Llama-3 (8B), GPT-3.5, and GPT-4. These evaluations demonstrate that PANGeA's NPCs can hold dynamic, narrative-consistent conversations that, without the memory system, would exceed the LLM's context length. In addition, the results demonstrate PANGeA's validation system not only aligns LLM responses with the game narrative but also improves the performance of Llama-3 (8B), enabling it to perform comparably to large-scale foundational models like GPT-4. With the validation system, Llama-3 (8B)'s performance improved from 28% accuracy to 98%, and GPT-4's from 71% to 99%. These findings indicate PANGeA can help game designers generate narrative-consistent content while leveraging LLMs of different sizes, suitable for various devices.

OLM-SLAM: Online lifelong memory system for simultaneous localization and mapping

Abstract Simultaneous Localization and Mapping is a fundamental task for robots in unknown environments. However, the poor generalization ability of learning-based algorithms in unknown environments hinders their widespread adoption. Additionally, artificial neural networks are subject to catastrophic forgetting. We propose a lifelong SLAM framework called OLM-SLAM that effectively solves the neural network catastrophic forgetting problem. To ensure the generalization of the neural network, this paper proposes a method for the sensitivity analysis of the network weight parameters. Meanwhile, inspired by human memory storage mechanisms, we design a dual memory storages mechanism that retains dynamic memory and static memory. A novel memory filtering mechanism is proposed to maximize image diversity within a fixed-size memory storage area addressing the problem of limited storage capacity of embedded devices in real-world situations. We have extensively evaluated the model on a variety of real-world datasets. Compared with CL-SLAM, the overall translation error of the test sequence is improved by 44.9%. The translation and rotation errors of Retention Ability were improved by 111.6% and 66.7%, respectively. The results demonstrate that OLM-SLAM can outperform previous methods of the same type, and OLM-SLAM has high retention ability when facing different sequences of the same type of environment.

Electrochemical biosensors for dopamine.

Dopamine (DA), a key catecholamine, plays a pivotal role in the regulation of human cognition and emotions. It has profound effects on the hormonal, memory, and cardiovascular systems. Anomalies like Alzheimer's, Parkinson's, schizophrenia, and senile dementia are linked to abnormal DA levels. Consequently, the precise determination of DA levels in biological systems is critical for the accurate diagnosis and treatment of these disorders. Among all analytical techniques, electrochemical studies provide the most selective and highly sensitive methods for detecting DA in biological samples. Ascorbic acid and uric acid are two examples of small biomolecules that can obstruct the detection of DA in biological fluids. To address this issue, numerous attempts have been made to modify bare electrodes to separate the signals of these substances and enhance the electrocatalytic activity towards DA. Various surface modifiers, including coatings, conducting polymers, ionic liquids, nanomaterials, and inorganic complexes, have been employed in the modification process. Despite the reported success in DA detection using electrochemical sensors, many of these approaches are deemed too complex and costly for real-world applications. Therefore, this review aims to provide an overview of DA electrochemical biosensors that are practical for real-world applications.

A Social Group Chatbot System by Multiple Topics Tracking and Atkinson‐Shiffrin Memory Model Using AI Agents Collaboration

ABSTRACTThe widespread use of Internet has accelerated the explosive growth of data, which in turn leads to information overload and information confusion. This makes it difficult for us to communicate effectively in social groups, thereby intensifying the demands for emotional companionship. Therefore, we propose a novel social group chatting framework based on Large Language Model (LLM) powered multiple autonomous agents collaboration in this article. Specifically, BERTopic is used to extract topics from history chatting content for each social group everyday, and then multiple topics tracking is realised through multi‐level association by adaptive time sliding‐window mechanism and optimal matching. Furthermore, we use topic tracking architecture and prompts to design and implement an AI Chatbot system with different characters that can conduct natural language conversations with users in online social group. LLM, as the controller and coordinator of the whole AI Chatbot for sub‐tasks, allows different AI Agents to autonomously decide whether to participate in current topic, how to generate response, and whether to propose a new topic. Each AI Agent has their own multi‐store memory system based on the Atkinson‐Shiffrin model. Finally, we construct a verification environment based on online game that is consistent with real society. Subjective and objective evaluation methods were deployed to perform qualitative and quantitative analyses to demonstrate the performance of our AI Chatbot system.

Transactive Memory Systems for Improving the Innovation Capability of Hotel Establishments and Their Online Reputation

Transactive Memory Systems for Improving the Innovation Capability of Hotel Establishments and Their Online Reputation

Transactive Memory in Caregiver Networks Using Artificial Intelligence

As the population ages and an increasing number of adults want to age in place in their homes, they will rely on a network of family, friends, and other caregivers to provide various forms of assistance. Coordination across this loosely connected network is a common challenge, requiring information sharing, schedule alignment and task coordination. Here, we propose that artificial intelligence (AI) may be used to develop tools to help loosely connected care networks develop better collective cognition. Specifically, we focus on helping members of care networks develop a transactive memory system, or a shared system for storing and retrieving knowledge that expands the capacity of a group to effectively use information. In this paper, we describe the motivation for our study, and our planned research program based on the use of an online experimental platform facilitating human-AI collaboration to develop and test tools to enhance collective cognition in care networks.

Enhancing Communication Security in Drones Using QRNG in Frequency Hopping Spread Spectrum

This paper presents a novel approach to enhancing the security and reliability of drone communications through the integration of Quantum Random Number Generators (QRNG) in Frequency Hopping Spread Spectrum (FHSS) systems. We propose a multi-drone framework that leverages QRNG technology to generate truly random frequency hopping sequences, significantly improving resistance against jamming and interception attempts. Our method introduces a concurrent access protocol for multiple drones to share a QRNG device efficiently, incorporating robust error handling and a shared memory system for random number distribution. The implementation includes secure communication protocols, ensuring data integrity and confidentiality through encryption and Hash-based Message Authentication Code (HMAC) verification. We demonstrate the system’s effectiveness through comprehensive simulations and statistical analyses, including spectral density, frequency distribution, and autocorrelation studies of the generated frequency sequences. The results show a significant enhancement in the unpredictability and uniformity of frequency distributions compared to traditional pseudo-random number generator-based approaches. Specifically, the frequency distributions of the drones exhibited a relatively uniform spread across the available spectrum, with minimal discernible patterns in the frequency sequences, indicating high unpredictability. Autocorrelation analyses revealed a sharp peak at zero lag and linear decrease to zero values for other lags, confirming a general absence of periodicity or predictability in the sequences, which enhances resistance to predictive attacks. Spectral analysis confirmed a relatively flat power spectral density across frequencies, characteristic of truly random sequences, thereby minimizing vulnerabilities to spectral-based jamming. Statistical tests, including Chi-squared and Kolmogorov-Smirnov, further confirm the unpredictability of the frequency sequences generated by QRNG, supporting enhanced security measures against predictive attacks. While some short-term correlations were observed, suggesting areas for improvement in QRNG technology, the overall findings confirm the potential of QRNG-based FHSS systems in significantly improving the security and reliability of drone communications. This work contributes to the growing field of quantum-enhanced wireless communications, offering substantial advancements in security and reliability for drone operations. The proposed system has potential applications in military, emergency response, and secure commercial drone operations, where enhanced communication security is paramount.

Dysgraphia and Memory: Insights into the Cognitive Mechanisms, Neural Correlates, and Intervention Strategies.

Studies regarding dysgraphia, an impairment in writing, have been receiving more attention in recent research. Most studies have broadly discussed the multiple cognitive mechanisms involved in writing and its disruption leading to dysgraphia. However, little attention has been paid to the involvement of different memory systems integral to writing and its disruption in individuals with dysgraphia. Orthographic long-term memory and orthographic working memory are the two memory systems predominantly involved in the production of written expressions, and the subsequent interruption of these memory systems often leads to varied deficit profiles of dysgraphia. These disruptions have resulted from damage in the brain caused by neural injuries, neurological disorders, or epigenetic factors. The existing studies did not probe into the nuances of the disruptions of these two memory systems in dysgraphia and associated neural pathways. In order to fill this gap, the review attempts to provide a comprehensive account of dysgraphia and its association with orthographic long-term memory and orthographic working memory by comparing and contrasting their workings and patterns of disruption in the deficit profiles of dysgraphia by probing into the underlying neural correlates. Such a detailed account brings insights into pertinent intervention strategies for improving memory systems and dysgraphia. It also helps identify the limitations of the existing intervention methods like CART, ACT, or Spell-Study-Spell, leading to the proposal of improvised neuro-targeted interventions for dysgraphia.

The Restoring Force Triangle: A Mnemonic Device for Polymer Mechanochemistry.

In polymer mechanochemistry, mechanophores are specific molecular units within the macromolecular backbone that are particularly sensitive to tension. To facilitate understanding of this selective responsiveness, we introduce the restoring force triangle (RFT). The RFT is a mnemonic device intended to provide intuitive insight into how external tensile forces (i.e., stretching) can selectively activate scissile bonds, thereby initiating mechanically driven chemical reactions. The RFT utilizes two easily computable parameters: the effective bond stiffness constant, which measures a bond's resistance to elongation, and the bond dissociation energy, which is the energy required to break a bond. These parameters help categorize reactivity into thermal and mechanical domains, providing a useful framework for developing new mechanophores that are responsive to force but thermally stable. The RFT helps chemists intuitively understand how tensile force contributes to the activation of a putative mechanophore, facilitating the development of mechanochemical reactions and mechano-responsive materials.

Autonomous and ubiquitous in-node learning algorithms of active directed graphs and its storage behavior

The brain’s memory system is extraordinarily complex, evidenced by the multitude of neurons involved and the intricate electrochemical activities within them, as well as the complex interactions among neurons. Memory research spans various levels, from cellular and molecular to cognitive behavioral studies, each with its own focus, making it challenging to fully describe the memory mechanism. Many details of how biological neuronal networks encode, store, and retrieve information remain unknown. In this study, we model biological neuronal networks as active directed graphs, where each node is self-adaptive and relies on local information for decision-making. To explore how these networks implement memory mechanisms, we propose a parallel distributed information access algorithm based on the node scale of the active directed graph. Here, subgraphs are seen as the physical realization of the information stored in the active directed graph. Unlike traditional algorithms with global perspectives, our algorithm emphasizes global node collaboration in resource utilization through local perspectives. While it may not achieve the global optimum like a global-view algorithm, it offers superior robustness, concurrency, decentralization, and biological feasibility. We also tested network capacity, fault tolerance, and robustness, finding that the algorithm performs better in sparser network structures.

ShieldCXL: A Practical Obliviousness Support with Sealed CXL Memory

The CXL (Compute Express Link) technology is an emerging memory interface with high-level commands. Recent studies applied the CXL memory expanding technique to mitigate the capacity limitation of the conventional DDRx memory. Unlike the prior studies to use the CXL memory as the capacity expander, this study proposes to use the CXL-based memory as a secure main memory device, while removing the conventional memory. In the conventional DDRx memory, to provide confidentiality, integrity, replay protection, and obliviousness, costly mechanisms such as counter-based integrity trees and location shuffling by ORAM (Oblivious RAM) are used. Such mechanisms incur significant performance degradation in the current DDR-based memory systems, and their costs increase as the capacity of the memory increases. To mitigate the performance degradation, the prior work proposed an obfuscated channel for a secure memory module enclosing its controller in the package. Based on the approach, we propose a secure CXL-only memory architecture called ShieldCXL . It uses the channel encryption and integrity protection mechanism of the CXL interface to provide a practical ORAM while supporting confidentiality, integrity, and replay protection from physical attacks and rowhammers. To protect the PCIe-connected memory expanding board, this study proposes to use the standard physical sealing technique to detect physical intrusion. To mitigate the increased latency with the sealed CXL memory module, the study further optimizes performance by adopting an in-package DRAM cache. In addition, this study investigates destination obfuscation when a CXL switch is used to route among multiple hosts and memory devices. The evaluation shows that ShieldCXL provides 9.16x performance improvements over the prior ORAM technique.

A High Scalability Memory NoC with Shared-Inside Hierarchical-Groupings for Triplet-Based Many-Core Architecture

Innovative processor architecture designs are shifting towards Many-Core Architectures (MCAs) to meet the future demands of high-performance computing as the limits of Moore’s Law have almost been reached. Many-core processors utilize shared memory hierarchies to achieve high-speed memory systems, improving memory access efficiency. However, as the number of cores multiplies, the scalability of this system is significantly constrained by the increased proportion of long-distance and Non-Uniform Memory Access (NUMA). Improving the scalability of MCAs is crucial for achieving large/super-scale general-purpose many-core processors. This work proposes a high scalability memory Network-on-Chip (NoC) for Triplet-Based Many-Core Architecture (TriBA), named TriBA-mNoC. TriBA-mNoC maintains a consistent core-to-core spacing as the network scale increases, effectively preventing increased long-distance memory access latency. Moreover, it leverages an inherent advantage of shared-inside hierarchical-groupings, alleviating common NUMA issues in the NoC design. Evaluations of static network characteristics show that TriBA-mNoC outperforms most classical NoCs in network diameter, average distance, and cost. TriBA-mNoC can be integrated with TriBA in the same silicon die with a tile-like floorplan, forming a novel NoC called TriBA-NoC, which can combine the strengths of both networks to maximize the architecture performance. We evaluated the memory access performance and scalability of TriBA-NoC using the mathematical evaluation models and actual simulations with real traffic (PARSEC 3.0 and SPLASH-2) at different network scales. The mathematical evaluation results indicate that TriBA-NoC achieves an aggregate speedup of approximately 3x compared with 2D-Mesh for a similar number of cores. Furthermore, TriBA-NoC’s single-core speedup efficiency remains stable as the number of cores increases under the same cache hit ratio, while 2D-Mesh experiences a rapid decline, highlighting TriBA-NoC’s exceptional scalability. Finally, the actual traffic simulation results show that TriBA-NoC achieves an average memory access latency and time reduction of 25.90% − 40.50% and 5.61% − 31.69% respectively, compared with 2D-Mesh.

Decoupling Between Brain Activity and Cerebrospinal Fluid Movement in Neurological Disorders.

Recent research has identified a link between the global mean signal of resting-state functional MRI (fMRI) and macro-scale cerebrospinal fluid movement, indicating the potential link between this resting-state dynamic and brain waste clearance. Consistent with this notion, the strength of this coupling has been associated with multiple neurodegenerative disease pathologies, especially the build-up of toxic proteins. This article aimed to review the latest advancements in this research area, emphasizing studies on spontaneous global brain activity that is tightly linked to the global mean resting-state fMRI signal, and aimed to discuss potential mechanisms through which this activity and associated physiological modulations might affect brain waste clearance. The available evidence supports the presence of a highly organized global brain activity that is linked to arousal and memory systems. This global brain dynamic, along with its associated physiological modulations, has the potential to influence brain waste clearance through multiple pathways through multiple pathways. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 3.

Goal Shifts Structure Memories and Prioritize Event-defining Information in Memory.

Every day, we encounter far more information than we could possibly remember. Thus, our memory systems must organize and prioritize the details from an experience that can adaptively guide the storage and retrieval of specific episodic events. Prior work has shown that shifts in internal goal states can function as event boundaries, chunking experiences into distinct and memorable episodes. In addition, at short delays, memory for contextual information at boundaries has been shown to be enhanced compared with items within each event. However, it remains unclear if these memory enhancements are limited to features that signal a meaningful transition between events. To determine how changes in dynamic goal states influence the organization and content of long-term memory, we designed a 2-day experiment in which participants viewed a series of black-and-white objects surrounded by a color border on a two-by-two grid. The location of the object on the grid determined which of two tasks participants performed on a given trial. To examine if distinct types of goal shifts modulate the effects of event segmentation, we changed the border color, the task, or both after every four items in a sequence. We found that goal shifts influenced temporal memory in a manner consistent with the formation of distinct events. However, for subjective memory representations in particular, these effects differed by the type of event boundary. Furthermore, to examine if goal shifts lead to the prioritization of goal-relevant features in longer lasting memories, we tested source memory for each object's color and grid location both immediately and after a 24-hr delay. On the immediate test, boundaries enhanced the memory for all concurrent source features compared with nonboundary items, but only if those boundaries involved a goal shift. In contrast, after a delay, the source memory was selectively enhanced for the feature relevant to the goal shift. These findings suggest that goals can adaptively structure memories by prioritizing contextual features that define a unique episode in memory.

Tuning the structural, morphological, optical, dielectric, and magnetic properties of PVA via embedding with CoFe2O4:CuO nanoparticles

Polyvinyl alcohol (PVA) embedded CoFe2O4: CuO (CC) nanocomposites (NC) were prepared by casting technique. The prepared CoFe2O4 (CFO) sample have crystallite size in the range of 20.10-29.08 nm, whereas the crystallite sizes of CuO were found in the range of 20.29-23.68 nm, respectively. TEM images revealed the spherical shape morphology of CC nanocomposite with an average particle size distribution of 15 nm. The Urbach energy of the PCC films increased from 0.44 to 2.590 eV, whereas the direct optical band gaps decreased from 5.584 to 4.345 eV, respectively. The refractive indices of the PCC films were found to be in the range of 1.9 -2.6, depending on the different models. Room Temperature-dependent saturation magnetization and coercive field for the prepared nanocomposites were found to be 0.52-1.296 (emu/g) and 1210-1340 (Oe), respectively. The uniaxial magnetic anisotropic constant of PCC films was remarkably enhanced from 0.654x103 to 3.826x103 (erg/cm3) for PCC2, PCC3, and PCC4, correspondingly. The enhanced values of dielectric and magnetic parameters of the prepared nanocomposites suggest their suitability for magnetic recording and memory systems.

Aquaporin-4 antibody-positive neuromyelitis optica spectrum disorder triggered by herpes zoster: a case report and literature review.

This paper presents the case of a 45-year-old woman who developed aquaporin-4 (AQP4) antibody-positive neuromyelitis optica spectrum disorder (NMOSD) approximately 6 weeks after a herpes zoster infection. Her initial symptoms included area postrema syndrome, which was marked by persistent nausea, vomiting, and belching. This report also provides a summary of the demographic and clinical features, disease progression, magnetic resonance imaging findings, cerebrospinal fluid analysis, treatment plans, and recovery outcomes of 11 patients-including this case and 10 others reported in the literature since 2008-who developed AQP4 antibody-positive NMOSD following herpes zoster infection. This review aims to improve clinicians' understanding of the characteristics, treatment, and prognosis of this disease. It also highlights the importance for pain management specialists to consider central pain and use the MIDNIGHTS or VINDψCATE mnemonic devices to systematically consider the differential diagnoses of a patient's new pain symptoms.

INCLUSIVE COMPETENCE AS A COMPONENT OF THE FUTURE MATHEMATICS TEACHER’S PROFESSIONAL COMPETENCE

The article deals with the concepts of «inclusive competence of a teacher» and «inclusive competence of a future mathematics teacher», with the latter being considered as a component of their professional competence. The aim of the article is to characterize inclusive competence as a component of professional competence and to analyze the content of its structural components. Research methods: analysis of scientific sources on the issues of inclusive education and the development of teachers’ inclusive competence to define the essence of the concept «inclusive competence» and its structure, as well as comparison, systematization, and generalization to characterize the content of the inclusive competence components in future mathematics teachers. The inclusive competence of future mathematics teachers is defined as an integral personal and professional characteristic and a component of their professional competence. The structural components of inclusive competence in future mathematics teachers have been identified and characterized: motivational-value, cognitive, activity-operational and reflective. The cognitive and activity-operational components of inclusive competence in subject teachers reflect the content and specifics of a particular subject or field of knowledge (such as physics, mathematics, physical education, etc.) and the specifics of teaching these subjects in an inclusive educational environment. components refer to the organization of an inclusive educational environment where children with special educational needs will study. Key skills include the ability to adapt learning materials to suit the students’ awareness, taking considering their special perception, memory and way of thinking, as well as offering engaging forms and methods of teaching. In this context, it is important to draw on the achievements of primary school mathematics teaching methods, such as the «Numicon» (method, Cuisenaire rods, Dienes’ method, edutainment technologies, eidetic techniques and mnemonic devices). The cognitive and activity-operational components of inclusive competence in future mathematics teachers are continuously developing, updated with information about new resources, methods, technologies and opportunities for organizing mixed and distance learning for children with special educational needs (such as online platforms). They are also enhanced by the teachers’ experiences. It has been proven that to ensure the efficiency of inclusive education and the development of mathematical competence in students with special educational needs, a comprehensive preparation of future mathematics teachers for professional activity in an inclusive environment is necessary. This should be based on the unity of theoretical and practical components at the general pedagogical, methodological and specialized levels. It is also important to create conditions and motivate future mathematics teachers to develop their inclusive competence through self-education and informal learning.

Decoding episodic autobiographical memory in naturalistic virtual reality

Episodic autobiographical memory (EAM) is a long-term memory system of personally experienced events with their context – what, where, when – and subjective elements, e.g., emotions, thoughts, or self-reference. EAM formation has rarely been studied in a controlled, real-life-like paradigm, and there is no predictive model of long-term retrieval from self-rated subjective experience at encoding. The present longitudinal study, with three surprise free recall memory tests immediately, one-week and one-month after encoding, investigated incidental encoding of EAM in an immersive virtual environment where 30 participants either interacted with or observed specific events of varying emotional valences with simultaneous physiological recordings. The predictive analyses highlight the temporal dynamics of the predictors of EAM from subjective ratings at encoding: common characteristics related to sense of remembering and infrequency of real-life encounter of the event were identified over time, but different variables become relevant at different time points, such as the emotion and mental imagery or prospective aspects. This dynamic and time-dependent role of memory predictors challenges traditional views of a uniform influence of encoding factors over time. Current evidence for the multiphasic nature of memory formation points to the role of different mechanisms at play during encoding but also consolidation and subsequent retrieval.

How Psychedelics Modulate Multiple Memory Mechanisms in Posttraumatic Stress Disorder.

Posttraumatic stress disorder (PTSD) is a psychiatric disorder with defining abnormalities in memory, and psychedelics may be promising candidates for the treatment of PTSD given their effects on multiple memory systems. Most PTSD and psychedelic research has investigated memory with fear conditioning and extinction. While fruitful, conditioning and extinction provide a limited model of the complexity of PTSD and phenomenology of psychedelics, thereby limiting the refinement of therapies. In this review, we discuss abnormalities in fear conditioning and extinction in PTSD and review 25 studies testing psychedelics on these forms of memory. Perhaps the most reliable effect is that the acute effects of psychedelics can enhance extinction learning, which is impaired in PTSD. However, the post-acute effects may also enhance extinction learning, and the acute effects can also enhance fear conditioning. We then discuss abnormalities in episodic and semantic memory in PTSD and review current knowledge on how psychedelics impact these memory systems. Although PTSD and psychedelics acutely impair the formation of hippocampal-dependent episodic memories, psychedelics may acutely enhance cortical-dependent learning of semantic memories that could facilitate the integration of trauma memories and disrupt maladaptive beliefs. More research is needed on the acute effects of psychedelics on episodic memory consolidation, retrieval, and reconsolidation and post-acute effects of psychedelics on all phases of episodic memory. We conclude by discussing how targeting multiple memory mechanisms could improve upon the current psychedelic therapy paradigm for PTSD, thereby necessitating a greater emphasis on assessing diverse measures of memory in translational PTSD and psychedelic research.