Retrieval Stage Research Articles

The parallel stack loading problem considering multiple batches of storage plates

This paper considers a parallel stack loading problem with multiple batches of storage plates, a problem that occurs in the plate logistics of shipbuilding. The objective of this problem is to store batches of plates in a yard simultaneously so that the number of relocations during the retrieval stage is minimal. Instead of choosing the plates’ location in the yard alone, the storage order should also be decided, and the storage priority between plates in a batch should be maintained. Such a problem can be regarded as a variant of the capacitated vehicle routing problem. This paper analyzes the influence of stack number, stack capacity, and batch number on the relocation number; optimal conditions and upper bounds under different parameter ranges are provided. Four mathematical models and an ALNS-based algorithm are designed to solve the small-sized and large-sized instances separately, and the experiments on benchmarks are conducted to demonstrate the performance of the proposed methods. Finally, some management suggestions are provided based on the analysis of the problem and experiment results.

Desorption hysteresis in organic-rich formations: Implications for long-term carbon dioxide sequestration and hydrogen recovery factor

This study explores the potential of organic-rich formations for geo-storage of hydrogen and carbon dioxide using a novel molecular simulation workflow combing Grand Canonical Monte Carlo (GCMC) and Molecular Dynamics (MD) to delineate the interplay between pore structure, kerogen maturity, and the resulting adsorption–desorption behavior. The investigation utilizes two representative kerogen models – immature (II-A) and overmature (II-D) – to account for the inherent heterogeneity of organic-rich formations. The simulations demonstrate a direct correlation between kerogen maturity and gas sorption capacity. The overmature model, with its larger intermolecular spaces, exhibits a higher capacity for both H2 and CO2 compared to the immature one. Notably, CO2 displays a significantly stronger affinity towards the organic material, resulting in a much higher sorption capacity compared to H2 for both kerogen types. The key finding lies in the contrasting desorption profiles for H2 and CO2. H2 desorption exhibits minimal hysteresis, suggesting readily recoverable gas with minimal trapping within the kerogen structure. This signifies the potential for efficient H2 storage with minimal losses during retrieval stages. Conversely, CO2 desorption displays a pronounced hysteresis loop, indicating significant gas retention within the formation even at lower pressure. This behavior aligns perfectly with the objective of CO2 sequestration, where minimizing leakage risk is paramount. These contrasting effects of hysteresis on H2 storage and CO2 sequestration highlight the unique suitability of organic-rich formations for both applications. Additionally, the study suggests a potential link between kerogen maturity and the magnitude of the hysteresis loop. Such insights could be instrumental in selecting optimal formations for targeted geo-storage operations.

Remembering the blues: negative emotion during encoding improve memory recall in major depressive Disorder

ABSTRACT Substantial research indicates that individuals with Major Depressive Disorder (MDD) remember more negative information compared to neutral and positive information. This phenomenon is commonly attributed to attentional biases toward negative over neutral and positive information. A recent attentional resources model suggests that in MDD, negative cues not only capture attention, but also lead to deeper processing of subsequent information, irrespective of its content. This study aimed to replicate findings supporting this attentional resources model and go beyond it by investigating the effect of negative cues on encoding and retrieval processes. Forty-one participants with MDD and no comorbid diagnoses, and 42 healthy-controls completed the emotional recall task with negative or positive videos presented during encoding and retrieval stages of a neutral word-list memory test. During encoding, only the MDD group exhibited a difference between negative and positive videos, such that for negative videos memory recall was improved and for positive words it was reduced. Emotional videos had no effect when presented during retrieval. These results suggest that in MDD, encountering emotional cues not only biases retrieval processes toward recalling more negative content, but rather fundamentally alters the depth of information processing, while not leading to a broad-spectrum recruitment of cognitive resources.

An integrated blockchain and fractional DCT based highly secured framework for storage and retrieval of retinal images

The digital image capability of storing large amount of data has resulted in it’s increased popularity. Images are used to transmit large amount of information across different geographical locations using different cloud services. Securing these digitally stored images has remained a challenging task for researchers as they are prone to cyber- attacks. A potential solution to this problem can be blockchain, which provide secure and unchangeable storage. However, securing images on blockchain has another challenge as the image size increases the associated cost involved in blockchain. Fractional Discrete Cosine Transform(fctDCT) has the capability to minimizes the amount of data necessary for expressing an image in a secure way. This paper presents a novel framework for securely storing and retrieving medical images by extracting feature maps from medical images by fctDCT, followed by encoding and storing the feature map on decentralized cloud and linking them on blockchain. The integration has been implemented by using four different α angles which are stored on blockchain and are needed to be same at storage and retrieval stage as only the authentic user would have access to unique α angles and number of coefficients that have been used in storing their medical images. The performance of proposed framework has been evaluated by employing image quality metric such as Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM) and multi-SSIM by comparing it with correct and incorrect α values on four different values of α.

A Human Retrieval System based on Human Attribute Ontology and Deep Multi-task Neural Network

The goal of this research is to enhance the capabilityof image retrieval systems to understand images moreeffectively. We present a model designed for searching humanobjects (such as pedestrians or persons) within expansiveimage datasets. Our unique approach involves developing animage retrieval system that incorporates attribute learningand the Human Attribute Ontology (HAO). This researchoffers several key contributions: (1) The development of theHuman Attribute Ontology (HAO) which serves as a repositoryfor storing prior knowledge about images. Thanks toits hierarchical structure, this ontology facilitates the reuse ofprior knowledge, optimizing the subsequent stages of attributelearning and image retrieval; (2) The implementation of aConvolutional Neural Network (CNN) to spearhead attributelearning, leveraging the HAO to enhance accuracy; (3) Thecreation of a Human Image Retrieval system that utilizes bothattribute learning and the HAO. Our system delves deeperby understanding images at the attribute level, highlightingthe advantages of harnessing the ontology to reuse existingknowledge. The efficacy of our methodology is validatedthrough experiments on benchmark datasets like PETA andPa100k achieving state-of-the-art results.

Assessing Relative Linguistic Impairment With Model-Based Item Selection.

A picture naming test is presented that reveals impairment to specific mechanisms involved in the naming process, using accuracy scores on curated item sets. A series of psychometric validation experiments are reported. Using a computational model that enables estimation of item difficulty at the lexical and sublexical stages of word retrieval, two complimentary sets of items were constructed that challenge the respective psycholinguistic levels of representation. The difference in accuracy between these item sets yields the relative linguistic impairment (RLI) score. In a cohort of 91 people with chronic left-hemisphere stroke who enrolled in a clinical trial for anomia, we assessed psychometric properties of the RLI score and then used the new scale to make predictions about other language behaviors, lesion distributions, and functional activation during naming. RLI scores had adequate psychometric properties for clinical significance. RLI scores contained predictive information about spontaneous speech fluency, over and above accuracy. A dissociation was observed between performance on the RLI item sets and performance on the subtests of an independent language battery. Sublexical RLI was significantly associated with apraxia of speech and with lesions encompassing perisylvian regions, while lexical RLI was associated with lesions to deep white matter. The RLI construct was reflected in functional brain activity during naming, independent of overall accuracy, with a respective shift of activation between dorsal and ventral networks responsible for different aspects of word retrieval. The RLI assessment satisfies the psychometric requirements to serve as a useful clinical measure.

Frontoparietal Brain Network Plays a Crucial Role in Working Memory Capacity during Complex Cognitive Task.

Recent neurophysiological studies provide inconsistent results of frontoparietal network (FPN) stimulation for altering working memory (WM) capacity. This study aimed to boost WM capacity by manipulating the activity of the FPN via dual-site high-definition transcranial direct current stimulation. Forty-eight participants were randomly assigned to three stimulation groups, receiving either simultaneous anodal stimulation of the frontal and parietal areas (double stimulation), or stimulation of the frontal area only (single stimulation), or the placebo stimulation (sham) to frontal and parietal areas. After the stimulation, we used an operation span task to test memory accuracy, mathematical accuracy, time of calculation and memorizing, and recall response time across the three groups. The results revealed an enhancement of memory accuracy and a reduction of time of calculation in the double stimulation group compared with that in others. In addition, recall response time was significantly decreased in the double and single stimulation groups compared with that in sham. No differences in mathematical accuracy were observed. Our results confirm the pivotal role of the FPN in WM and suggest its functional dissociation, with the frontal component more implicated in the retrieval stage and the parietal component in the processing and retention stages.

DSCEH: Dual-Stream Correlation-Enhanced Deep Hashing for Image Retrieval

Deep Hashing is widely used for large-scale image-retrieval tasks to speed up the retrieval process. Current deep hashing methods are mainly based on the Convolutional Neural Network (CNN) or Vision Transformer (VIT). They only use the local or global features for low-dimensional mapping and only use the similarity loss function to optimize the correlation between pairwise or triplet images. Therefore, the effectiveness of deep hashing methods is limited. In this paper, we propose a dual-stream correlation-enhanced deep hashing framework (DSCEH), which uses the local and global features of the image for low-dimensional mapping and optimizes the correlation of images from the model architecture. DSCEH consists of two main steps: model training and deep-hash-based retrieval. During the training phase, a dual-network structure comprising CNN and VIT is employed for feature extraction. Subsequently, feature fusion is achieved through a concatenation operation, followed by similarity evaluation based on the class token acquired from VIT to establish edge relationships. The Graph Convolutional Network is then utilized to enhance correlation optimization between images, resulting in the generation of high-quality hash codes. This stage facilitates the development of an optimized hash model for image retrieval. In the retrieval stage, all images within the database and the to-be-retrieved images are initially mapped to hash codes using the aforementioned hash model. The retrieval results are subsequently determined based on the Hamming distance between the hash codes. We conduct experiments on three datasets: CIFAR-10, MSCOCO, and NUSWIDE. Experimental results show the superior performance of DSCEH, which helps with fast and accurate image retrieval.

SKYPER: Legal case retrieval via skeleton-aware hypergraph embedding in the hyperbolic space

Legal Case Retrieval (LCR) plays a significant role in ensuring judicial justice in various legal systems and has recently received increasing attention in Information Retrieval (IR) research. Existing LCR methods typically encode documents into low-dimensional vectors by using either bag of keywords or pretrained language models. However, such approaches generally overlook the interactions among numerous cases and the hierarchy skeleton between event and event type. In this paper, we propose to construct an event-aware knowledge hypergraph for representing legal cases. And we put forward a SKeleton-aware hYPERgraph representation framework named SKYPER, to learn the case embeddings. Concretely, a global skeleton aggregates coarse-grained type information and roughly locates a case. A local skeleton learns the fine-grained information and precisely locates a case. And SKYPER also uses hyperbolic space strengthens the hierarchy between event and event type and avoids vocabulary mismatch. At the retrieval stage, the SKYPER learned case embedding is leveraged for further case similarity matching with a few annotated similarity ranking data. Extensive experiments demonstrate that SKYPER outperforms existing models on the public LCR datasets LeCard and COLIEE20-T1 in both supervised and unsupervised settings. Especially, the retrieval efficiency is improved by a large margin.

Metacognitive processes accompanying the first stages of autobiographical retrieval in the self-memory system

ABSTRACT According to Conway’s view, Autobiographical memory (AM) construction is accompanied by control processes. These processes range from filtering out relevant memories according to the current context, to generating or elaborating appropriate retrieval cues. These processes can be conceptualised as metacognition, the ability to control and monitor cognitive processes. Experimentally, little has been carried out to support the idea that metacognition is involved in AM. To assess this, we designed a task, the Feeling of Retrieval. Participants had to predict whether cue words would facilitate AM access (i.e., fluent access cues) or not (i.e., limited access cues) in a limited time (either 1 (Exp. 2) or 2 (Exp. 1) s). Later, they retrieved memories in response to both types of cues. Results show that cues judged as fluent access led to better AM generation, as illustrated by AM retrieval latency and a subjective measure of the ease with which the AMs were retrieved. These rapid predictions may rely on epistemic feelings and / or other mnemonic cues such as a partial retrieval of information. This metacognitive access to the earliest stages of AM retrieval illustrates the ability to monitor AM processes as proposed by Conway (2005).

Iris image retrieval using partial matching of image blocks

AbstractThe problem of human identification through recognition of patterns in iris images captured in unconstrained environments results in image artefacts such as image occlusion and specular reflection, in which iris tissue is observable to extremely low content. To overcome this problem, this paper presents a novel method for iris image retrieval and recognition based on partial pattern matching. The main contribution of the proposed method relies on an image partitioning schema in which the iris is divided into non‐overlapping blocks with varying dimensions, facilitating the identification and removal of image regions impaired by eyelids, eyelashes, and specular reflections. In fact, the blocks that contain artefacts are completely ignored and those blocks are preserved that include useful patterns for identification. In addition, a multi‐feature similarity followed by a score fusion technique is employed for ranking the retrieval results. The remarkable results of the classification stage include an accuracy of 100%, 98.75%, and 99.94% on three benchmark databases, including UPOL, UBIRIS.V2, and CASIA‐Iris‐Interval.v3, respectively. Additionally, in the retrieval stage, the proposed method achieves a precision of 100% on all three benchmark databases, a recall of 83.33%, and a F1‐measure of 90.91 on the CASIA‐Iris‐Interval.v3 dataset.

Learning hierarchical embedding space for image-text matching

There are two mainstream strategies for image-text matching at present. The one, termed as joint embedding learning, aims to model the semantic information of both image and sentence in a shared feature subspace, which facilitates the measurement of semantic similarity but only focuses on global alignment relationship. To explore the local semantic relationship more fully, the other one, termed as metric learning, aims to learn a complex similarity function to directly output score of each image-text pair. However, it significantly suffers from more computation burden at retrieval stage. In this paper, we propose a hierarchically joint embedding model to incorporate the local semantic relationship into a joint embedding learning framework. The proposed method learns the shared local and global embedding spaces simultaneously, and models the joint local embedding space with respect to specific local similarity labels which are easy to access from the lexical information of corpus. Unlike the methods based on metric learning, we can prepare the fixed representations of both images and sentences by concatenating the normalized local and global representations, which makes it feasible to perform the efficient retrieval. And experiments show that the proposed model can achieve competitive performance when compared to the existing joint embedding learning models on two publicly available datasets Flickr30k and MS-COCO.

Sex differences in hippocampal β-adrenergic receptor subtypes drive retrieval, retention, and learning of cocaine-associated memories.

Drug seeking behavior occurs in response to environmental contexts and drug-associated cues. The presence of these pervasive stimuli impedes abstinence success. β-adrenergic receptors (β-ARs) have a long-standing historical implication in driving processes associated with contextual memories, including drug-associated memories in substance use disorders. However, sex differences in the role of β-adrenergic receptors in drug memories remain unknown. Prior reports indicate a selective role for β2-ARs in retrieval and retention of contextual drug memories in males, and substantial sex differences exist in the expression of β-ARs of male and female rats. Therefore, we hypothesized that there are sex differences in selective recruitment of β-ARs during different stages of memory encoding and retrieval. The role of β-ARs in driving retrieval and learning of contextual cocaine memories was investigated using cocaine conditioned place preference (CPP) in adult male and female Sprague-Dawley rats. Rats were infused directly to the dorsal hippocampus with Propranolol (β1 and β2) or ICI-118,551 (β1) and/or Betaxolol (β2), immediately prior to testing (retrieval), or paired to each cocaine (10 mg/kp, IP) conditioning session (learning). In males, administration of either β1, β2, or combined β1 and β2-ARs before the initial CPP testing reduced the expression of a CPP compared to vehicle administration. In females, β2-ARs transiently decreased CPP memories, whereas β1 had long lasting but not immediate effects to decrease CPP memories. Additionally, β1 and combined β1 and β2-ARs had immediate and persistent effects to decrease CPP memory expression. DG Fos + neurons predicted cocaine CPP expression in males, whereas CA1 and CA3 Fos + neurons predicted cocaine CPP expression in females. There are significant sex differences in the role of dorsal hippocampus β-ARs in the encoding and expression of cocaine conditioned place preference. Furthermore, sub regions of the dorsal hippocampus appear to activate differently between male and female rats during CPP. Therefore DG, CA3, and CA1 may have separate region- and sex-specific impacts on driving drug- associated, or context-associated cues.

Hypergraph-Based Multi-Modal Representation for Open-Set 3D Object Retrieval.

The traditional 3D object retrieval (3DOR) task is under the close-set setting, which assumes the categories of objects in the retrieval stage are all seen in the training stage. Existing methods under this setting may tend to only lazily discriminate their categories, while not learning a generalized 3D object embedding. Under such circumstances, it is still a challenging and open problem in real-world applications due to the existence of various unseen categories. In this paper, we first introduce the open-set 3DOR task to expand the applications of the traditional 3DOR task. Then, we propose the Hypergraph-Based Multi-Modal Representation (HGM 2 R) framework to learn 3D object embeddings from multi-modal representations under the open-set setting. The proposed framework is composed of two modules, i.e., the Multi-Modal 3D Object Embedding (MM3DOE) module and the Structure-Aware and Invariant Knowledge Learning (SAIKL) module. By utilizing the collaborative information of modalities derived from the same 3D object, the MM3DOE module is able to overcome the distinction across different modality representations and generate unified 3D object embeddings. Then, the SAIKL module utilizes the constructed hypergraph structure to model the high-order correlation among 3D objects from both seen and unseen categories. The SAIKL module also includes a memory bank that stores typical representations of 3D objects. By aligning with those memory anchors in the memory bank, the aligned embeddings can integrate the invariant knowledge to exhibit a powerful generalized capacity toward unseen categories. We formally prove that hypergraph modeling has better representative capability on data correlation than graph modeling. We generate four multi-modal datasets for the open-set 3DOR task, i.e., OS-ESB-core, OS-NTU-core, OS-MN40-core, and OS-ABO-core, in which each 3D object contains three modality representations: multi-view, point clouds, and voxel. Experiments on these four datasets show that the proposed method can significantly outperform existing methods. In particular, the proposed method outperforms the state-of-the-art by 12.12%/12.88% in terms of mAP on the OS-MN40-core/OS-ABO-core dataset, respectively. Results and visualizations demonstrate that the proposed method can effectively extract the generalized 3D object embeddings on the open-set 3DOR task and achieve satisfactory performance.

Memory deficit in patients with cerebral small vessel disease: evidence from eye tracking technology.

Cerebral small vessel disease is the one of the most prevalent causes of vascular cognitive impairment. We aimed to find objective and process-based indicators related to memory function to assist in the detection of memory impairment in patients with cerebral small vessel disease. Thirty-nine cerebral small vessel disease patients and 22 healthy controls were invited to complete neurological examinations, neuropsychological assessments, and eye tracking tasks. Eye tracking indicators were recorded and analyzed in combination with imaging features. The cerebral small vessel disease patients scored lower on traditional memory task and performed worse on eye tracking memory task performance compared to the healthy controls. The cerebral small vessel disease patients exhibited longer visit duration and more visit count within areas of interest and targets and decreased percentage value of total visit duration on target images to total visit duration on areas of interest during decoding stage among all levels. Our results demonstrated the cerebral small vessel disease patients performed worse in memory scale and eye tracking memory task, potentially due to their heightened attentional allocation to nontarget images during the retrieval stage. The eye tracking memory task could provide process-based indicators to be a beneficial complement to memory assessment and new insights into mechanism of memory impairment in cerebral small vessel disease patients.

The effect of target detection on memory retrieval.

Attention and memory are fundamental cognitive processes that closely interact. In the attentional boost effect (ABE), the stimuli that co-occur with targets are remembered better than those that co-occur with distractors in target detection tasks performed during memory encoding. In target detection tasks performed during retrieval, the stimuli that co-occur with targets are recognized as 'old' more easily than the stimuli that co-occur with distractors. This study mainly explored the internal mechanism of the effect of target detection on recognition. In Experiment 1, the full attention (FA; where participants performed only the memory task) condition was used to compare with divided attention (DA; where participants performed target detection while performing memory retrieval) condition to explore the impact of target detection and distraction inhibition on recognition. In Experiment 2, the proportion of old and new words in the retrieval stage was adjusted to 1:1 to eliminate the possible reaction tendency caused by the high proportion of old words. In Experiment 3, the presentation time of words was extended to 1.5 s and 3 s to eliminate the possible impact of rapid processing. The results indicated that the effect of target detection on recognition was attributed to both target detection and distraction rejection and is not affected by the ratio of old and new words and the word presentation time. The effect of target detection on recognition may be owing to temporal yoking of the dual tasks, which is different from the effect of target detection on memory encoding.

Impact of process interference on memory encoding and retrieval processes in dual-task situations

Dual-tasks at the memory encoding stage have been shown to decrease recall performance and impair concurrent task performance. In contrast, studies on the effect of dual-tasks at the memory retrieval stage observed mixed results. Which cognitive mechanisms are underlying this dual-task interference is still an unresolved question. In the present study, we investigated the influence of a concurrent reaction-time task on the performance in a long-term memory task in two experiments. In Experiment 1, participants performed an auditory-verbal free recall memory task and a visual-manual spatial Stroop task in a single or dual-task condition, either at the encoding or retrieval stage of the memory task. In Experiment 2, we examined the influence of processing conflicts in a concurrent RT task on memory encoding. Both experiments showed detrimental effects on recall accuracy and concurrent RT task performance in dual-task conditions at the encoding stage. Dual-task conditions at the retrieval stage led to a slowdown in recall latency and impaired concurrent RT task performance, but recall accuracy was maintained. In addition, we observed larger Stroop congruency effects in the dual-task conditions, indicating an increased processing conflict. However, in Experiment 2, we analyzed the effect of the processing conflict in a time-locked manner and could not find a significant influence on success of memory encoding. These findings suggest that processes in both tasks share the same limited capacity and are slowed down due to parallel processing, but we could not find evidence that this is further influenced by task-specific processing conflicts.

Exploring Retrieval Augmented Generation in Arabic

Recently, Retrieval Augmented Generation (RAG) has emerged as a powerful technique in natural language processing, combining the strengths of retrieval-based and generation-based models to enhance text generation tasks. However, the application of RAG in Arabic, a language with unique characteristics and resource constraints, remains underexplored. This paper presents a comprehensive case study on the implementation and evaluation of RAG for Arabic text. The work focuses on exploring various semantic embedding models in the retrieval stage and several LLMs in the generation stage, in order to investigate what works and what doesn't in the context of Arabic. The work also touches upon the issue of variations between document dialect and query dialect in the retrieval stage. Results show that existing semantic embedding models and LLMs can be effectively employed to build Arabic RAG pipelines.

Towards Fast and Accurate Image-Text Retrieval With Self-Supervised Fine-Grained Alignment

Image-text retrieval requires the system to bridge the heterogenous gap between vision and language for accurate retrieval while keeping the network lightweight-enough for efficient retrieval. Existing trade-off solutions mainly study from the view of incorporating cross-modal interactions with the independent-embedding framework or leveraging stronger pre-trained encoders, which still demand time-consuming similarity measurement or heavyweight model structure in the retrieval stage. In this work, we propose an image-text alignment module SelfAlign on top of the independent-embedding framework, which improves the retrieval accuracy while maintains the retrieval efficiency without extra supervision. SelfAlign contains two collaborative sub-modules that force image-text alignment at both the concept level and context level by self-supervised contrastive learning. It doesn't require cross-modal embedding interactions during training while maintaining independent image and text encoders during retrieval. With comparable time cost, SelfAlign consistently boosts the accuracy of state-of-the-art non-pre-training independent-embedding models respectively by 9.1%, 4.2%, and 6.6% in terms of R@sum score on Flickr30K, MS-COCO 1K and MS-COCO 5K datasets. The retrieval accuracy also outperforms most existing interactive-embedding models with orders of magnitude decrease in retrieval time. The source code is available at: https://github.com/Zjamie813/SelfAlign.

The relationship of aerobic fitness with verbal and spatial working memory: An ERP study.

Working memory (WM) plays an important role in daily life and is known to correlated with aerobic fitness. However, whether the relationship between aerobic fitness and WM is dependent on the stimulus modality or is associated with one or multiple subprocesses involved in WM remains unknown. Accordingly, this study utilized event-related potentials (ERPs) to comprehensively examine the encoding, preparation, and retrieval processes during verbal and spatial WM performance. Eighty-eight young adults aged 18-30years were recruited to participate in two laboratory visits on separate days. On day 1, aerobic fitness was assessed by maximum oxygen consumption (V˙O2max) during a treadmill-based graded exercise test. On day 2, participants completed verbal and spatial WM tasks while P2, contingent negative voltage (CNV), and P3 components of ERP were recorded during the encoding, preparatory, and retrieval stages of WM, respectively. Results of hierarchical regression analysis showed that V˙O2max was positively correlated with response accuracy during the high-demanding condition of spatial WM after controlling for age, sex, and self-reported physical activity. Additionally, a higher level of V˙O2max was associated with larger terminal CNV amplitude at the Cz electrode during the high-demanding condition of spatial WM. These findings suggest that aerobic fitness may have selective beneficial associations with the motor preparatory process and subsequent task performance requiring a greater amount of spatial information but not the encoding and retrieval stages nor the verbal modality of WM.