Retrieval Phase Research Articles

Pattern separation during encoding and Subsequent Memory Effect

Memory retrieval has been extensively studied in relation to the encoding processes that precede access to stored information. Event related potentials (ERP) research has compared brain potentials elicited during the study phase of successful and unsuccessful retrieval, finding greater activation for the subsequent retrieval information. In this work we were interested in exploring the neural markers associated to subsequent recognition when similar memories are subsequently encoded. We used a Subsequent Memory paradigm in which we manipulated the number of similar items within a category (2 or 6) that participants encoded. Manipulating the number of similar encoded items within a category allowed us to test whether encoding markers of subsequent recognition depend solely on memory trace strength or, on the contrary, successful recognition is influenced by subsequently presented similar memories, and consequently may not be reflected in higher activation in such cases. After a 20-minute period, participants performed a recognition task providing one of a three-option judgement: “old”, “similar” and “new”, which allowed us to test if the amplitude of ERP waveforms varied based on the similarity judgement of the unrecognized encoded item. We did not observe a significant parietal subsequent memory effect, however, old hits and similar false alarms were both significantly different from similar correct rejections and old false alarms in ERP retrieval. These findings suggest that differences in brain responses between conditions are specifically related to the retrieval process and not the encoding process, indicating potential differential effects on memory during retrieval. Moreover, it is also possible that differences in brain responses develop or change over the rest time between phases, influencing how these conditions manifest across different stages of information processing.

8734 Absent FSH Signaling Rescues Spatial and Recognition Memory Impairments in Female 3xTg Mice

Abstract Disclosure: S. Sims: None. F. Sen: None. F. Sultana: None. A. Liu: None. V. Laurencin: None. A. Gumerova: None. O. Barak: None. S. Rojekar: None. A.R. Pallapati: None. L. Cullen: None. R. Chen: None. K. Goosens: None. V. Ryu: None. T. Yuen: None. M. Zaidi: None. T. Frolinger: None. F. Korkmaz: None. Alzheimer’s disease (AD) is a major progressive neurodegenerative disorder of the aging population, accounting for more than 60-80% of dementia cases. High serum level of the pituitary gonadotropin, follicle-stimulating hormone (FSH), is strongly associated with the onset of AD. We recently showed that FSH activates hippocampal Fshr to drive AD–like pathology and cognitive impairment in AD mice, that FSH blockade in these mice abrogates the AD-like phenotype by inhibiting the neuronal C/EBPβ–δ-secretase pathway, and that FSH and ApoE4, the most prevalent genetic risk factor of AD, jointly trigger AD-like pathogenesis by activating C/EBPβ-δ-secretase signaling. To further confirm the role of FSH in AD cognitive decline, we used female 3xTg;Fshr+/+, 3xTg;Fshr+/– and 3xTg;Fshr–/– mice that underwent sham surgery or ovariectomy (OVX) at 8 weeks of age. Sham-operated 3xTg;Fshr–/– mice were implanted with 17β-estradiol pellets to normalize E2 levels. Morris Water Maze (MWM) and Novel object recognition (NOR) tests were perform in these mice to assess spatial and recognition memory, respectively. 3xTg;Fshr+/+ mice displayed impaired spatial memory at 5-month of age in the MWM test. This impairment, in both the learning and retrieval phases, is significantly ameliorated in 3xTg;Fshr–/– mice and to a lesser extent in 3xTg;Fshr+/– mice, suggesting that Fshr expression has a negative impact on memory. At 5 and 10 months of age, sham-operated 3xTg;Fshr–/– mice show better memory performance in the MWM test during the learning phase when compared to their wild-type littermates, suggesting a rescue of spatial memory and progression with age. However, this rescue was not seen when mice were ovariectomized. At 5 months of age, sham-operated 3xTg;Fshr–/– mice displayed better memory retrieval compared with 3xTg;Fshr+/+ mice. This effect was not observed when the mice were 10-month-old, suggesting that the beneficial effect of the absence of FSH signaling on memory consolidation could be masked by disease severity and older age. OVX 3xTg;Fshr+/+, 3xTg;Fshr+/– mice displayed a decline in memory consolidation at 10-months of age compared with 5-month of age, while the 3xTg;Fshr–/– mice showed a significantly slower decline. Using the NOR test, we found impaired recognition memory in 10-month-old sham-operated or OVX 3xTg;Fshr+/+ and 3xTg;Fshr+/– mice; this effect was prevented in OVX 3xTg;Fshr–/– mice. Finally, 3xTg;Fshr–/– mice displayed lower Aβ40 and Aβ42 levels in the brain (ELISA). Altogether, our results confirm a protective effect of absent Fshr signaling on spatial learning, consolidation, memory retrieval, recognition memory and reduction of brain Aβ40 and Aβ42. Presentation: 6/1/2024

Astrocytes contribute to the functional differentiation of the hippocampal longitudinal axis during reward and aversion processing in the adult male rat

This study aims to investigate whether glial cells, in particular putative astrocytes, contribute to functional distinctions between the dorsal (DH), intermediate (IH), and ventral (VH) hippocampus. To evaluate this, we performed three different behavioral tasks (i.e., Morris water maze; MWM, Passive avoidance; PA, T-maze place preference; TPP) to determine whether the DH, IH, and VH are necessary for each task. Sensitivity of behavioral tasks was confirmed using lidocaine (2 %, 1 μl) reversible inactivation. Subsequently, we examined the effects of silencing astrocytes, using fluorocitrate (FC, 1 mM/1 μl), into the DH, IH, and VH on these tasks. The effects of drugs were examined separately. We observed that injection of FC into the DH resulted in a significant impairment in MWM performance. In contrast, while FC injections into the IH or VH did not prevent platform localization during the acquisition phase, rats showed difficulty recalling the target zone during the retrieval phase. In the PA test, FC injection into the VH impaired task learning and memory. During the acquisition phase, FC injection into the DH or IH did not differ from the control in the number of shocks; however, during retrieval, there was a significant decrease in the latency before entering the dark chamber. The TPP test performance was impaired by FC injection in the IH. In sum, we show that glial cells, especially astrocytes in specific functional regions of the hippocampus, play distinct roles in processing aversive and rewarding experiences and contribute to the functional organization of the hippocampal longitudinal axis.

Differential Mnemonic Contributions of Cortical Representations during Encoding and Retrieval.

Several recent fMRI studies of episodic and working memory representations converge on the finding that visual information is most strongly represented in occipito-temporal cortex during the encoding phase but in parietal regions during the retrieval phase. It has been suggested that this location shift reflects a change in the content of representations, from predominantly visual during encoding to primarily semantic during retrieval. Yet, direct evidence on the nature of encoding and retrieval representations is lacking. It is also unclear how the representations mediating the encoding-retrieval shift contribute to memory performance. To investigate these two issues, in the current fMRI study, participants encoded pictures (e.g., picture of a cardinal) and later performed a word recognition test (e.g., word "cardinal"). Representational similarity analyses examined how visual (e.g., red color) and semantic representations (e.g., what cardinals eat) support successful encoding and retrieval. These analyses revealed two novel findings. First, successful memory was associated with representational changes in cortical location (from occipito-temporal at encoding to parietal at retrieval) but not with changes in representational content (visual vs. semantic). Thus, the representational encoding-retrieval shift cannot be easily attributed to a change in the nature of representations. Second, in parietal regions, stronger representations predicted encoding failure but retrieval success. This encoding-retrieval "flip" in representations mimics the one previously reported in univariate activation studies. In summary, by answering important questions regarding the content and contributions to the performance of the representations mediating the encoding-retrieval shift, our findings clarify the neural mechanisms of this intriguing phenomenon.

Modeling and control of perturbation torques and mass distribution impact on a tethered system for a 12U CubeSat in sun-synchronous orbit

Tethered CubeSat systems are characterized by momentum exchange and gravity gradient stabilization, and encounter stability challenges, especially with shorter tethers in highly inclined and eccentric orbits. This study investigates the libration dynamics of a 12U CubeSat in its stowed configuration, which separates into two satellites connected via tether, throughout its deployment, station-keeping, and retrieval phases. Two deployed configurations, symmetrical 6U-6U, and asymmetrical 8U-4U connected by a non-conductive tether with a maximum length of 100m are analyzed. The study accounts for perturbations including Earth’s oblateness, atmospheric drag, solar radiation pressure, and lunisolar gravitation, modeling the tether as a rigid, extendable rod and the satellites as lumped masses. The translational and rotational dynamics are decoupled, assuming the system’s center of mass follows an unperturbed Keplerian Sun-synchronous orbit at altitudes between 400 and 600km. A tension control strategy based on Lyapunov’s direct method and supplemented by external actuation torques is explored. Results show high orbit eccentricity significantly affects the maximum in-plane libration angle. The 6U-6U system experiences smaller disturbance torques but greater tether tension variations than the 8U-4U system. Both configurations exhibit larger in-plane oscillations compared to near-zero out-of-plane oscillations, nonetheless, eclipse passages exacerbate the out-of-plane libration of the 8U-4U system. Relative stability is maintained during deployment, however, retrieval is chaotic, with notable oscillations in libration angles and tether tension. The tension control strategy effectively dampens oscillations during retrieval but loses effectiveness as tether tension approaches zero at retrieval’s conclusion. External actuation torques, ranging from 1-2mNm for deployment to 15mNm for retrieval, complement tension control.

Binary ant colony optimization algorithm in learning random satisfiability logic for discrete hopfield neural network

This study introduced a novel ant colony optimization algorithm that implements the population selection strategy of the Estimation of Distribution Algorithm and a new pheromone updating formula. It aimed to optimize the performance of G-type random high-order satisfiability logic structures embedded in Discrete Hopfield Neural Networks, thereby enhancing the efficiency of the Hopfield Neural Network learning algorithm. Through comparative analysis with other metaheuristic algorithms, our model demonstrated superior performance in terms of global convergence, time complexity, and algorithm complexity. Additionally, we evaluated the learning phase, retrieval phase, and similarity analysis using various ratios of literals and clauses. It was shown that our proposed model exhibits stronger search ability compared to other metaheuristic algorithms and Exhaustive Search. Our model enhanced the efficiency of the learning phase, resulting in the number of global solutions accounting for 100 %, and significantly improved the global solution diversity. These advancements contributed to the efficiency of the model in convergence, rendering it applicable to a wide range of nonlinear classification and prediction problems.

Using imagination and the contents of memory to create new scene and object representations: A functional MRI study

Humans can use the contents of memory to construct scenarios and events that they have not encountered before, a process colloquially known as imagination. Much of our current understanding of the neural mechanisms mediating imagination is limited by paradigms that rely on participants' subjective reports of imagined content. Here, we used a novel behavioral paradigm that was designed to systematically evaluate the contents of an individual's imagination. Participants first learned the layout of four distinct rooms containing five wall segments with differing geometrical characteristics, each associated with a unique object. During functional MRI, participants were then shown two different wall segments or objects on each trial and asked to first, retrieve the associated objects or walls, respectively (retrieval phase) and then second, imagine the two objects side-by-side or combine the two wall segments (imagination phase). Importantly, the contents of each participant's imagination were interrogated by having them make a same/different judgment about the properties of the imagined objects or scenes. Using univariate and multivariate analyses, we observed widespread activity across occipito-temporal cortex for the retrieval of objects and for the imaginative creation of scenes. Interestingly, a classifier, whether trained on the imagination or retrieval data, was able to successfully differentiate the neural patterns associated with the imagination of scenes from that of objects. Our results reveal neural differences in the cued retrieval of object and scene memoranda, demonstrate that different representations underlie the creation and/or imagination of scene and object content, and highlight a novel behavioral paradigm that can be used to systematically evaluate the contents of an individual's imagination.

The neural basis of attentional selection in goal-directed memory retrieval

Goal-directed memory reactivation involves retrieving the most relevant information for the current behavioral goal. Previous research has linked this process to activations in the fronto-parietal network, but the underlying neurocognitive mechanism remains poorly understood. The current electroencephalogram (EEG) study explores attentional selection as a possible mechanism supporting goal-directed retrieval. We designed a long-term memory experiment containing three phases. First, participants learned associations between objects and two screen locations. In a following phase, we changed the relevance of some locations (selective cue condition) to simulate goal-directed retrieval. We also introduced a control condition, in which the original associations remained unchanged (neutral cue condition). Behavior performance measured during the final retrieval phase revealed faster and more confident responses in the selective vs. neutral condition. At the EEG level, we found significant differences in decoding accuracy, with above-chance effects in the selective cue condition but not in the neutral cue condition. Additionally, we observed a stronger posterior contralateral negativity and lateralized alpha power in the selective cue condition. Overall, these results suggest that attentional selection enhances task-relevant information accessibility, emphasizing its role in goal-directed memory retrieval.

APPS: Authentication-enabled privacy protection scheme for secure data transfer in Internet of Things

The Internet of Things (IoT) is an emerging field that encompasses several heterogeneous devices and smart objects that are integrated with the network. In open platforms, these objects are deployed to present advanced services in numerous applications. Innumerable security-sensitive data is generated by the IoT device and therefore, the security of these devices is an important task. This work formulates a secure data transfer technique in IoT, named Authentication enabled Privacy Protection (APPS) scheme for resource-constrained IoT devices. The proposed scheme demonstrates resilience against various attacks; such as resisting reply attacks, device anonymity, untracebility, session key establishment, quantum attacks and resisting MITM attacks. For the privacy protection scheme, the secured data transfer is initiated between the entities, like IoT devices, servers, and registration centers, by using various phases, namely registration phase, key generation phase, data encryption, authentication, verification, and data retrieval phase. Here, a mathematical model is designed for protecting data privacy using hashing, encryption, secret keys, etc. Finally, performance of proposed APPS model is analyzed; wherein the outcomes reveal that the proposed APPS model attained the maximum detection rate of 0.85, minimal memory usage of 0.497MB, and minimal computational time of 112. 79 sec and minimal turnaround time 131.91 sec.

Dual optimization approach in discrete Hopfield neural network

Having effective learning and retrieval phases of satisfiability logic in Discrete Hopfield Neural Network models ensures optimal synaptic weight management, which consequently leads to the production of optimal final neuron states. However, the problem with this model is that different initial states can affect the biasedness of the retrieval phase since the model memorizes final states without generating new ones and produces suboptimal final neuron states. To date, there is no recent research that solves this issue by improving both phases in the Discrete Hopfield Neural Network that involves first-order satisfiability logic. Therefore, this research contributes to the improvement of the learning and retrieval phases by integrating the Hybrid Differential Evolution Algorithm and Swarm Mutation respectively. This research utilizes Y-Type Random 2 Satisfiability, which combines first and second-order clauses to expand the storage capacity of DHNN models, facilitating the retrieval of optimal final neuron states. To evaluate the effectiveness of the Hybrid Differential Evolution Algorithm and Swarm Mutation in the learning and retrieval phases, several performance metrics are employed in terms of synaptic weight management, learning errors, testing errors, energy profiles, solution variations, and similarity for 10 different cases. Quantitative evaluations show that the proposed model successfully enhances the optimization of both phases, ranking first compared to 10 recent algorithms for all metrics. In terms of convergence analysis, the proposed model progressed fast towards the optimal solution with only one iteration for all cases. Additionally, the proposed model can generate a 100 % global minima ratio when dealing with a high number of neurons for Case 5.

An effective hybrid attention model for crop yield prediction using IoT-based three-phase prediction with an improved sailfish optimizer

Internet of Things (IoT)-based smart agriculture approaches have been adopted to achieve promising results in terms of food productivity. Moreover, high cost and technological complexity are major problems in existing agricultural management systems. Thus, a new IoT-based smart agriculture farming model is implemented in three phases namely crop detection, crop disease detection, and crop yield prediction. In the first phase, the plant leaf images are gathered from the Kaggle source PlantifyDr. The collected images are passed to the deep feature retrieval phase, where the Visual Geometry Group (VGG16) is utilized to calculate feature (f1). Then, a Hybrid Attention-based Crop Type Detection Network (HA-CTDecNet) is used for detecting the crop types, in which the parameters are optimized by Attack Power Improved Sailfish Optimizer (API-SFO). In the second step of crop disease detection phase, the input images are fed to Otsu Thresholding for Abnormality segmentation. The deep attributes from the segmented images are haul out using VGG16 and attain the feature (f2). The extracted features are forwarded to the Hybrid Attention-based Crop Disease Detection Network (HA-CDDecNet), where the constraints are optimized using API-SFO. In the third crop yield prediction phase, the data related to crop location are collected from the crop yield forecast dataset. Then, the collected data and the extracted features (f1) and (f2) are concatenated and fed to the Principle Component Analysis (PCA). Then, these features are given to the Hybrid Attention-based Crop Yield Prediction Network (HA-CYPreNet) with the parameter optimization of API-SFO. The investigation is accepted to verify the effectiveness of the suggested IoT-based smart agriculture model with the traditional approaches by considering distinct measures. The Mean Absolute Error (MAE) of the crop yield prediction model is improved with 35% than CNN, 27.5% than LSTM, 17.5% than GRU, and 7.5% than LSTM_GRU while considering the learning percentage value as 55.

Affective music during episodic memory recollection modulates subsequent false emotional memory traces: an fMRI study.

Music is a powerful medium that influences our emotions and memories. Neuroscience research has demonstrated music's ability to engage brain regions associated with emotion, reward, motivation, and autobiographical memory. While music's role in modulating emotions has been explored extensively, our study investigates whether music can alter the emotional content of memories. Building on the theory that memories can be updated upon retrieval, we tested whether introducing emotional music during memory recollection might introduce false emotional elements into the original memory trace. We developed a 3-day episodic memory task with separate encoding, recollection, and retrieval phases. Our primary hypothesis was that emotional music played during memory recollection would increase the likelihood of introducing novel emotional components into the original memory. Behavioral findings revealed two key outcomes: 1) participants exposed to music during memory recollection were more likely to incorporate novel emotional components congruent with the paired music valence, and 2) memories retrieved 1day later exhibited a stronger emotional tone than the original memory, congruent with the valence of the music paired during the previous day's recollection. Furthermore, fMRI results revealed altered neural engagement during story recollection with music, including the amygdala, anterior hippocampus, and inferior parietal lobule. Enhanced connectivity between the amygdala and other brain regions, including the frontal and visual cortex, was observed during recollection with music, potentially contributing to more emotionally charged story reconstructions. These findings illuminate the interplay between music, emotion, and memory, offering insights into the consequences of infusing emotional music into memory recollection processes.

Embedding-based retrieval: measures of threshold recall and precision to evaluate product search

Modern product retrieval systems are becoming increasingly complex due to the use of extra product representations, such as user behavior, language semantics and product images. However, adding new information and complicating machine learning models does not necessarily lead to an improvement in online and business search performance, since after retrieval the product list is ranked, which introduces its own bias. Nevertheless, the business performance of a product search will be worse from ranking an incomplete list of products than a complete one, and the relevance of search results will not improve from perfect sorting of products that do not match the search query. Therefore, the main quality indicators for the products retrieval phase remain Recall and Precision at the k threshold. This paper compares several architectures of product retrieval systems in product search for e-commerce. To do this, the concepts of threshold Recall and Precision for information retrieval are investigated and the dependence of these measures on the order of issuance is revealed. An automatic procedure has been developed for calculating R@k and P@k, which allows us to compare the effectiveness of information retrieval systems. The proposed automatic procedure has been tested on the WANDS public dataset for several key architectures. The obtained values R@1000 = 84% ± 9% and P@10 = 67% ± 17% are at the level of SOTA models.

Prior semantic-embedding representation learning for on-the-fly FG-SBIR

In fine-grained sketch-based image retrieval (FG-SBIR) framework, sketch drawing is a time-consuming and skill-intensive process that greatly restricts its practical application. Our study aims to enhance the early-stage retrieval efficiency by utilizing partial sketches with a minimum number of strokes in the on-the-fly FG-SBIR framework. Nevertheless, partial sketches with few strokes usually lack global information and consist only of sparse local details. This lack of detail results in ambiguous semantic content and indistinct differences among images, which consequently impairs early-stage retrieval. To acquire an efficient representation for these sketches, we propose a multi-granularity feature representation model that utilizes prior knowledge embedding to deal with the sketch drawing process. In the first stage,we design a triplet network to learn the joint a coarse representation embedding space that is common between the complete sketch and the image. In the second stage, We enhanced the semantic representation of sketches by utilizing prior knowledge, which aids in supplementing missing fine-grained features. This was achieved by formalizing the prior knowledge and integrating it into the feature vector obtained in the initial stage, thereby acquiring the final representation of a subset of sketches. Experiments conducted on two public datasets indicate that our method outperforms the state-of-the-art baseline during the early retrieval phase. In practical applications, our approach has also achieved good results.

Discrete online cross-modal hashing with consistency preservation

Online cross-modal hashing has attracted widespread attention with the rapid expansion of large-scale streaming data, which can reduce storage requirements and enhance efficiency for online cross-modal retrieval. However, despite promising progress, existing methods still suffer from defective accuracy in a way, primarily attributed to two issues: insufficient semantic information exploitation and mismatched training-retrieval process. To address these challenges, we propose a novel supervised hashing method with dual consistency preservation, called Discrete Online Cross-Modal Hashing (DOCMH). On the one hand, we design more informative continuous semantic labels and fine-grained similarity graphs to preserve semantic consistency across different streaming data chunks and modality representations. On the other hand, we propose an effective modality deviation calibration mechanism for preserving learning process consistency between the training and retrieval phases. Extensive experiments on three widely used benchmark datasets demonstrate the superior performance of the proposed DOCMH under various scenarios.

The parallel stack loading problem of minimizing the exact number of relocations

This study addresses the parallel stack loading problem, a general optimization problem arising in storage facilities such as container yards, slab yards, and warehouses. In this problem, we load incoming items into parallel stacks in the loading phase to minimize the number of relocations in the subsequent retrieval phase. Because of difficulties in treating the nested problem structure originating from the mutual dependence of the two phases, the existing studies approximately minimized the number of relocations using surrogate objective functions. In contrast, this study considers the parallel stack loading problem aiming to minimize the exact number of relocations. We first provide an integer programming formulation and next develop a nested branch-and-bound algorithm. In a computational study, we verify the effectiveness of the proposed branch-and-bound algorithm and evaluate the known surrogate objective functions based on the exact minimization.

Neural mechanisms of the continued influence effect of misinformation: Analysis based on fMRI causal connectivity

The continued influence effect of misinformation (CIEM) can negatively affect individuals’ reasoning and judgment processes. This research aims to enhance the correction of misinformation and foster rational judgement by investigating the internal brain mechanisms involved in the processing of the CIEM through the use of task-based functional magnetic resonance imaging combined with Granger causality analysis. Our findings demonstrate notable effective interactions in varying directions between the left inferior frontal gyrus and middle temporal gyrus during the encoding phase, and between the right anterior cingulate gyrus and left inferior occipital gyrus in the retrieval phase. These insights elucidate the roles of mental model updating and retrieval failure in the processing of CIEM, offering more granular evidence to support the differentiation in processing phases.

Unstructured Data Fusion for Schema and Data Extraction

Recently, there has been significant interest in extracting actionable insights from the abundance of unstructured textual data. In this paper, we introduce a novel problem, which we term Semistructured Schema and Data Extraction (SDE). This task aims to enhance and complete tables using information discovered from textual repositories, given partial table specifications in the form of queries. To effectively solve SDE, several challenges must be overcome, which involve transforming the partial table specifications into effective queries, retrieving relevant documents, discerning values for partially specified attributes, inferring additional attributes, and constructing an enriched output table while mitigating the influence of false positives from the retrieval. We propose an end-to-end pipeline for SDE, which consists of a retrieval component and an augmentation component, to address each of the challenges. In the retrieval component, we serialize the partial table specifications into a query and employ a dense passage retrieval algorithm to extract the top-k relevant results from the text repository. Subsequently, the augmentation component ingests the output documents from the retrieval phase and generates an enriched table. We formulate this table enrichment task as a unique sequence-to-sequence task, distinct from traditional approaches, as it operates on multiple documents during generation. Utilizing an interpolation mechanism on the encoder output, our model maintains a nearly constant context length while automatically prioritizing the importance of documents during the generation. Due to the novelty of SDE, we establish a validation methodology, adapting and expanding existing benchmarks with the use of powerful large language models. Our extensive experiments show that our method achieves high accuracy in enriching query tables through multi-document fusion, while also surpassing baseline methods in both accuracy and computational efficiency.

Working memory processes and intrinsic motivation: An EEG study

Processes typically encompassed by working memory (WM) include encoding, retention, and retrieval of information. Previous research has demonstrated that motivation can influence WM performance, although the specific WM processes affected by motivation are not yet fully understood. In this study, we investigated the effects of motivation on different WM processes, examining how task difficulty modulates these effects. We hypothesized that motivation level and personality traits of the participants (N = 48, 32 females; mean age = 21) would modulate the parietal alpha and frontal theta electroencephalography (EEG) correlates of WM encoding, retention, and retrieval phases of the Sternberg task. This effect was expected to be more pronounced under conditions of very high task difficulty. We found that increasing difficulty led to reduced accuracy and increased response time, but no significant relationship was found between motivation and accuracy. However, EEG data revealed that motivation influenced WM processes, as indicated by changes in alpha and theta oscillations. Specifically, higher levels of the Resilience trait—associated with mental toughness, hardiness, self-efficacy, achievement motivation, and low anxiety—were related to increased alpha desynchronization during encoding and retrieval. Increased scores of Subjective Motivation to perform well in the task were related to enhanced frontal midline theta during retention. Additionally, these effects were significantly stronger under conditions of high difficulty. These findings provide insights into the specific WM processes that are influenced by motivation, and underscore the importance of considering both task difficulty and intrinsic motivation in WM research.

Modulation of hippocampal theta oscillations via deep brain stimulation of the parietal cortex depends on cognitive state

The angular gyrus (AG) and posterior cingulate cortex (PCC) demonstrate extensive structural and functional connectivity with the hippocampus and other core recollection network regions. Consequently, recent studies have explored neuromodulation targeting these and other regions as a potential strategy for restoring function in memory disorders such as Alzheimer's Disease. However, determining the optimal approach for neuromodulatory devices requires understanding how parameters like selected stimulation site, cognitive state during modulation, and stimulation duration influence the effects of deep brain stimulation (DBS) on electrophysiological features relevant to episodic memory. We report experimental data examining the effects of high-frequency stimulation delivered to the AG or PCC on hippocampal theta oscillations during the memory encoding (study) or retrieval (test) phases of an episodic memory task. Results showed selective enhancement of anterior hippocampal slow theta oscillations with stimulation of the AG preferentially during memory retrieval. Conversely, stimulation of the PCC attenuated slow theta oscillations. We did not observe significant behavioral effects in this (open-loop) stimulation experiment, suggesting that neuromodulation strategies targeting episodic memory performance may require more temporally precise stimulation approaches.