Online Memory Research Articles

Head in the clouds: A Deleuzoguattarian analysis of the environmental impacts of digital memory

Digital memory in the twenty-first century is a complex combination of human and nonhuman elements that interact in various combinations. In an era where environmental devastation is inundating the planet, it is vital to address the digital’s role in this degradation. This article explores various facets of the environmental implications of the digital storage of memory objects using Deleuze and Guattari’s assemblage theory. I identify the ways in which the digital realm is constructed, or ‘territorialised’ in Deleuzoguattarian terms. Beyond identifying the most common myths related to digital memory’s environmental impact, I expand on the work of information theorists such as Clements, Floridi, and Ess by forwarding three ways in which digital memory practices can be deterritorialised. These approaches include (1) knowledge acquisition, (2) taking response-ability, and (3) anti-redundancy movements. I argue that it is vital for cloud storage users, especially those who self-identify as environmentally conscious, to align their online memory practices with accurate understandings of the environmental impacts of the digital.

Data from “A Registered Report Testing the Effect of Sleep on DRM False Memory: Greater Lure and Veridical Recall but Fewer Intrusions After Sleep”

This paper describes a rich dataset from a registered report investigating sleep’s effect on false memory in the Deese-Roediger-McDermott (DRM) paradigm. 534 young adults completed free recall either shortly or 12 hours after studying lists of semantic associates (e.g., hospital, nurse). Collected online, our recall data showcase high data quality, replicating classic behavioural effects (e.g., serial position curve). The dataset contains raw recall data with original spelling and recall order, accompanied by demographic information (e.g., gender, time-of-day preference). Its versatility supports reuse in modelling memory decay and search processes, understanding lexical effects and individual differences, and benchmarking online memory studies.

NLOCL: Noise-Labeled Online Continual Learning

Continual learning (CL) from infinite data streams has become a challenge for neural network models in real-world scenarios. Catastrophic forgetting of previous knowledge occurs in this learning setting, and existing supervised CL methods rely excessively on accurately labeled samples. However, the real-world data labels are usually misled by noise, which influences the CL agents and aggravates forgetting. To address this problem, we propose a method named noise-labeled online continual learning (NLOCL), which implements the online CL model with noise-labeled data streams. NLOCL uses an empirical replay strategy to retain crucial examples, separates data streams by small-loss criteria, and includes semi-supervised fine-tuning for labeled and unlabeled samples. Besides, NLOCL combines small loss with class diversity measures and eliminates online memory partitioning. Furthermore, we optimized the experience replay stage to enhance the model performance by retaining significant clean-labeled examples and carefully selecting suitable samples. In the experiment, we designed noise-labeled data streams by injecting noisy labels into multiple datasets and partitioning tasks to simulate infinite data streams realistically. The experimental results demonstrate the superior performance and robust learning capabilities of our proposed method.

When Monument Battles Go Digital: Russian–Ukrainian Conflicts over Material Heritage on Telegram

In the context of increasing conflicts over material heritage around the world, this article examines the role digital media play in battles over monuments. The rise of digital media brought significant changes to the cultural dynamics of heritage conflicts, which have not been adequately addressed in existing literature. Bringing together work on monuments, (digital) memory conflicts, and digital activism, we identify three key dimensions of monument battles in which the impact of digital media is most clearly visible: (a) participation, democratization, and deterritorialization; (b) reframing and contestation; and (c) mobilization and the online-offline movement of heritage battles. We illustrate these arguments drawing on a critical discourse analysis of monument battles on the messaging application Telegram in the context of Russia’s full-scale invasion of Ukraine, using a sample of 940 posts from both pro-Ukrainian and pro-Russian channels. We demonstrate that despite potentially providing space for alternative memory interpretations, online memory contestations over heritage contributed to the construction of polarized and mutually exclusive worlds.

The Art of Dying in a Digital World: How Death is Represented in Digital Art

With the rise of computing, human rituals and cultural practices have increasingly interwoven with digital technologies. In this milieu, the cultural facets of death and art are particularly noteworthy. The realm of art has undergone a seismic shift due to computing, developing new artistic forms and enhancing the reach of older forms. Concurrently, aspects of death are now integrated into digital technologies, manifesting in unique ways such as the retention of data from the deceased, digital expressions of grief, online memorials, and the like. In light of this, our fundamental, qualitative, and exploratory study endeavors to map out and discuss digital art pieces that delve into themes of death, mourning, and digital afterlife. In doing so, we probe the intersection of art, death, and technology. Our findings delineate the primary methodologies and technologies employed in digital art centered around death, and the nuanced modalities through which this subject is portrayed in digital media.

Digital mourning in tweets: Multimodal analysis of image-based grieving practices among gang-affiliated Black youth

In the 21st century, social media has been utilized to connect youth to peers who share their experiences and provide support. Research shows how gang-affiliated youth use social media to present bravado and express feelings of grief. However, the research focusing on how gang-affiliated youth utilize photos online to memorialize the deceased and convey feelings and experiences of grief and loss is sparse. This qualitative study utilizes a methodological concept that includes theories regarding racial residential segregation and grief and a multimodal analytic technique called Critical Technocultural Discourse Analysis. The methodological concept was used to understand the digital grieving practices of gang-affiliated Black youth and how this population utilizes social media content, photos, and emojis (multimodal tweets) in their online memorialization practices. Findings illuminate how this population utilizes multimodal tweets to speak to those who have passed away.

Attention-Driven Memory Network for Online Visual Tracking.

A memory mechanism has attracted growing popularity in tracking tasks due to the ability of learning long-term-dependent information. However, it is very challenging for existing memory modules to provide the intrinsic attribute information of the target to the tracker in complex scenes. In this article, by considering the biological visual memory mechanisms, we propose the novel online tracking method via an attention-driven memory network, which can mine discriminative memory information and enhance the robustness and reliability of the tracker. First, to reinforce effectiveness of memory content, we design a novel attention-driven memory network. In the network, the long memory module gains property-level memory information by focusing on the state of the target at both the channel and spatial levels. Meanwhile, in reciprocity, we add a short-term memory module to maintain good adaptability when confronting drastic deformation of the target. The attention-driven memory network can adaptively adjust the contribution of short-term and long-term memories to tracking results under the weighted gradient harmonized loss. On this basis, to avoid model performance degradation, an online memory updater (MU) is further proposed. It is designed to mining for target information in tracking results through the Mixer layer and the online head network together. By evaluating the confidence of the tracking results, the memory updater can accurately judge the time of updating the model, which guarantees the effectiveness of online memory updates. Finally, the proposed method performs favorably and has been extensively validated on several benchmark datasets, including object tracking benchmark-50/100 (OTB-50/100), temple color-128 (TC-128), unmanned aerial vehicles-123 (UAV-123), generic object tracking -10k (GOT-10k), visual object tracking-2016 (VOT-2016), and VOT-2018 against several advanced methods.

Virtual Eulogies, Rituals, and Burials: Experiences of and Adaptations to Death and Mourning During the COVID-19 Pandemic among Transnational Families

This paper is based on in-depth qualitative data from key informants (i.e., village heads, and employees of burial societies) and dyads from five families interviewed between March 2020 and December 2022 in Tsholotsho and Johannesburg to explore the experiences of and adaptations to death within transnational families during the COVID-19 pandemic. This follows the extraordinary autocratic governance and weaponisation of COVID-19 and the resulting travel restrictions between Zimbabwe and South Africa. The paper finds that amid mobility constraints, families experienced sociocultural changes in relation to death, causing them to experience death and mourning in unorthodox ways. For example, they participated in online memorial, funeral, and burial services and eulogies, thus embracing transnationalism by virtually participating in funerary processes at home in Zimbabwe while based in South Africa. It concludes that these virtual rituals, while giving members an appreciated sense of togetherness, did not sufficiently satisfy families’ social and psychological needs nor regard their heterogeneous cultural values, beliefs, and norms concerning death. The paper acknowledges the evolving nature and complexity of the post-colonial family and its death practices. However, it stresses how the COVID-19 epidemic transformed relationships and the sense of belonging of transnational families usually displayed in death situations.

Developing a memory studies program: Lessons and challenges

The article results from the creation and implementation of a 1-year online Memory Studies Certificate Program at UCLouvain (Belgium). It describes the program designed by a team of researchers with different disciplinary backgrounds (i.e. sociology, political science, psychology, philosophy) and with different pedagogical styles. After this description, the article highlights key lessons informed by data from lecturers’ internal discussions and a survey involving the pioneer student cohort enrolled during the 2021–2022 academic year. This experience shows the importance of teaching as a way of contributing to field of Memory studies. As a counterpoint to the relentless publish-or-perish ethos, which often favors expedited dissemination of fragmented knowledge, creating a study program necessitates deep understanding and cohesive dialogue.

Dissociable online integration processes in visual working memory.

Visual working memory has severe capacity limits, creating a bottleneck for active processing. A key way of mitigating this limitation is by chunking, i.e. compressing several pieces of information into one visual working memory representation. However, despite decades of research, chunking efficiency remains debated because of mixed evidence. We propose that there are actually 2 integration mechanisms: Grouping combines several objects to one representation, and object-unification merges the parts of a single object. Critically, we argue that the fundamental distinction between the 2 processes is their differential use of the pointer system, the indexing process connecting visual working memory representations with perception. In grouping, the objects that are represented together still maintain independent pointers, making integration costly but highly flexible. Conversely, object-unification fuses the pointers as well as the representations, with the single pointer producing highly efficient integration but blocking direct access to individual parts. We manipulated integration cues via task-irrelevant movement, and monitored visual working memory's online electrophysiological marker. Uniquely colored objects were flexibly grouped and ungrouped via independent pointers (experiment 1). If objects turned uniformly black, object-integration could not be undone (experiment 2), requiring visual working memory to reset before re-individuation. This demonstrates 2 integration levels (representational-merging versus pointer-compression) and establishes the dissociation between visual working memory representations and their underlying pointers.

Hippocampal ensemble dynamics and memory performance are modulated by respiration during encoding

During offline brain states, such as sleep and memory consolidation, respiration coordinates hippocampal activity. However, the role of breathing during online memory traces remains unclear. Here, we show that respiration can be recruited during online memory encoding. Optogenetic manipulation was used to control activation of the primary inspiratory rhythm generator PreBötzinger complex (PreBötC) in transgenic mice. When intermittent PreBötC-induced apnea covered the object exploration time during encoding, novel object detection was impaired. Moreover, the mice did not exhibit freezing behavior during presentation of fear-conditioned stimuli (CS+) when PreBötC-induced apnea occurred at the exact time of encoding. This apnea did not evoke changes in CA3 cell ensembles between presentations of CS+ and conditioned inhibition (CS−), whereas in normal breathing, CS+ presentations produced dynamic changes. Our findings demonstrate that components of central respiratory activity (e.g., frequency) during online encoding strongly contribute to shaping hippocampal ensemble dynamics and memory performance.

Memory for artwork is predictable

Viewing art is often seen as a highly personal and subjective experience. However, are there universal factors that make a work of art memorable? We conducted three experiments, where we recorded online memory performance for 4,021 paintings from the Art Institute of Chicago, tested in-person memory after an unconstrained visit to the Art Institute, and obtained abstract attribute measures such as beauty and emotional valence for these pieces. Participants showed significant agreement in their memories both online and in-person, suggesting that pieces have an intrinsic "memorability" based solely on their visual properties that is predictive of memory in a naturalistic museum setting. Importantly, ResMem, a deep learning neural network designed to estimate image memorability, could significantly predict memory both online and in-person based on the images alone, and these predictions could not be explained by other low- or high-level attributes like color, content type, aesthetics, and emotion. A regression comprising ResMem and other stimulus factors could predict as much as half of the variance of in-person memory performance. Further, ResMem could predict the fame of a piece, despite having no cultural or historical knowledge. These results suggest that perceptual features of a painting play a major role in influencing its success, both in memory for a museum visit and in cultural memory over generations.

The Writing on the Wall: Affective Politicization of the Sewŏl Disaster on Facebook

Abstract: In 2014, the South Korean ferry, Sewŏl, sank. In sinking, it took with it 304 lives, as well as initiated broad public dissent that lasted several years. In this study, we investigate how online social media users employed and manipulated the emotions toward this disaster for political purposes. By the utilization of digital humanities methodologies (i.e., topic modeling and frequencies) on the data collected from selected Facebook accounts, we compared different strategies of online engagement with the intersection between mass death commemoration and political activism. In particular, we explored the delegitimization of President Pak Kŭn-hye, her impeachment, and the resultant elections in 2017. Quantitative and qualitative analysis revealed that different social actors manipulated their audience's emotions through posts on their Facebook walls in a manner that politicized the more personal mournful discourse. These social actors associated the disaster with the president's ineptness, especially after her corruption was revealed in the November 2016 scandal. From that point onward, the online memorialization of the Sewŏl's victims became a weapon in the broader efforts to oust the president and to change the political system.

Distinguishing guesses from fuzzy memories: Further evidence for item limits in visual working memory.

There is consistent debate over whether capacity in working memory (WM) is subject to an item limit, or whether an unlimited number of items can be held in this online memory system. The item limit hypothesis clearly predicts guessing responses when capacity is exceeded, and proponents of this view have highlighted evidence for guessing in visual working memory tasks. Nevertheless, various models that deny item limits can explain the same empirical patterns by asserting extremely low fidelity representations that cannot be distinguished from guesses. To address this ambiguity, we employed a task for which guess responses elicited a qualitatively distinct pattern from low fidelity memories. Inspired by work from Rouder et al. (2014), we employed an orientation WM task that required subjects to recall the precise orientation of each of six memoranda presented 1 s earlier. The orientation stimuli were created by rotating the position of a "clock hand" inside a circular region that was demarcated by four colored quadrants. Critically, when observers guess with these stimuli, the distribution of responses is biased towards the center of these quadrants, creating a "banded" pattern that cannot be explained by a low precision memory. We confirmed the presence of this guessing pattern using formal model comparisons, and we show that the prevalence of this pattern matches observers' own reports of when they thought they were guessing. Thus, these findings provide further evidence for guessing behaviors predicted by item limit models of WM capacity.

An online continual object detector on VHR remote sensing images with class imbalance

It is a great challenge for traditional offline detectors to learn from continuous data streams, remember previous tasks and adapt to new-coming tasks in dynamic environments. To meet the challenge, online continual learning has recently attracted increasing attention, while the overwhelming majority of works focus only on classification with a balanced class distribution assumption. In this paper, we propose a replay-based approach called an online continual object detector (OCOD) for very-high-resolution (VHR) remote sensing images. First, we find that rehearsal imbalance is ubiquitous, and has more important impact on experimental results than class imbalance, which is contrary to the situation of offline learning (due to the limited memory). Here, rehearsal imbalance refers to significant difference among the number of images pertaining to various classes. Second, entropy is used to measure the degree of rehearsal imbalance in the memory, and an entropy reservoir sampling (ERS) strategy is proposed to maintain rehearsal balance in the online memory. Finally, a rehearsal-balancing priority assignment network (RBPAN) is proposed to adaptively select images from the memory for a rehearsal-balancing replay procedure. The experimental results obtained on three publicly available VHR satellite images from the NWPU VHR-10, DIOR and DOTA datasets, highlight the effectiveness and practicality of developed method.

Correctness and response time distributions in the MemTrax continuous recognition task: Analysis of strategies and a reverse-exponential model

A critical issue in addressing medical conditions is measurement. Memory measurement is difficult, especially episodic memory, which is disrupted by many conditions. On-line computer testing can precisely measure and assess several memory functions. This study analyzed memory performances from a large group of anonymous, on-line participants using a continuous recognition task (CRT) implemented at https://memtrax.com. These analyses estimated ranges of acceptable performance and average response time (RT). For 344,165 presumed unique individuals completing the CRT a total of 602,272 times, data were stored on a server, including each correct response (HIT), Correct Rejection, and RT to the thousandth of a second. Responses were analyzed, distributions and relationships of these parameters were ascertained, and mean RTs were determined for each participant across the population. From 322,996 valid first tests, analysis of correctness showed that 63% of these tests achieved at least 45 correct (90%), 92% scored at or above 40 correct (80%), and 3% scored 35 correct (70%) or less. The distribution of RTs was skewed with 1% faster than 0.62 s, a median at 0.890 s, and 1% slower than 1.57 s. The RT distribution was best explained by a novel model, the reverse-exponential (RevEx) function. Increased RT speed was most closely associated with increased HIT accuracy. The MemTrax on-line memory test readily provides valid and reliable metrics for assessing individual episodic memory function that could have practical clinical utility for precise assessment of memory dysfunction in many conditions, including improvement or deterioration over time.

The effects of wakeful rest on memory consolidation in an online memory study.

Memory consolidation is the process in which memory traces are strengthened over time for later retrieval. Although some theories hold that consolidation can only occur during sleep, accumulating evidence suggests that brief periods of wakeful rest may also facilitate consolidation. Interestingly, however, Varma and colleagues reported that a demanding 2-back task following encoding produced a similar performance to a wakeful reset condition. We tested whether participants' recall would be best following a wakeful rest condition as compared to other distractor conditions, consistent with the extant wakeful rest literature, or whether we would replicate the finding by Varma and colleagues such that participants' memory benefitted from both a rest and a 2-back task following encoding. Across two experiments, we used similar (Experiment 1) and the same (Experiment 2) encoding material as used the one by Varma and colleagues, employed a wakeful rest condition adapted for online testing, and compared participants' recall across post-encoding conditions. In the first experiment, we used a between-subjects design and compared participants' cued recall performance following a period of wakeful rest, a 2-back task, or a rest + sounds condition. The second experiment more closely replicated the experimental design used by Varma and colleagues using a within-subjects manipulation. Ultimately, our findings more consistently aligned with the canonical wakeful rest finding, such that recall was better following the rest condition than all other post-encoding conditions. These results support the notion that wakeful rest may allow for consolidation by protecting recently encoded information from interference, thereby improving memory performance.

Machine Learning for the Prediction of Lymph Node Metastasis in Patients with Prostate Cancer

<h3>Purpose/Objective(s)</h3> Many tools are used to predict the probability of lymph node involvement (LNI) in patients with prostate cancer before radical prostatectomy (RP) and to select patients for lymph node dissection. Our goal was to externally validate common prediction tools and to create a machine learning model for the prediction of LNI in patients with prostate cancer referred for RP. <h3>Materials/Methods</h3> Patients treated at two academic institutions between 1990 and 2020 with RP followed by pelvic lymph node dissection were included. We trained a gradient boosted tree on data from one institution and the model was validated on the other dataset. Variables incorporated into the model are patient's age, percentage of cores positive on biopsy, Gleason scores, preoperative PSA, and clinical T-stage (cT). The area under curve of the receiver operator characteristic curve (AUC) served as the performance metric. We further compared our model's performance to that of established nomograms, namely the Roach formula, the online Memorial Sloan Kettering Center (MSKCC) nomogram, and the 2012 Briganti nomogram. <h3>Results</h3> The combined dataset consisted of 19,090 patients with median age of 65, median PSA at diagnosis of 7.60 ng/mL, and median percentage of positive cores at biopsy of 44%. 14,252 patients (74.6%) had a Gleason score (GS) of 7, and 4,841 (25.4%) had a GS of ≥ 8. Most patients (18,570 or 97.3%) had cT-stage ≤cT2 disease, and 523 (2.7%) had cT3-4 disease. 3,058 patients (16%) were found to have lymph node involvement. The external validation dataset had 1,093 patients, with 10.7% having lymph node positive disease. After training and on external validation, the AUC of our model was 0.79 (CI: 0.7465-0.83), compared to 0.69 (0.64-0.7455), 0.73 (0.6813-0.7742), and 0.74 (0.6931 - 0.7914) when using the Roach formula, MSKCC nomogram, and Briganti 2012 nomogram, respectively. Furthermore, our model's improved performance was statistically significant, with p-value <0.05 when comparing its AUC to that of all other models. <h3>Conclusion</h3> Our machine learning model can be used to estimate an individual patient's risk of LNI and serves as an additional tool with improved performance compared to established prediction models.

Sustained upregulation of widespread hippocampal-neocortical coupling following memory encoding.

Systems consolidation of new experiences into lasting episodic memories involves hippocampal-neocortical interactions. Evidence of this process is already observed during early post-encoding rest periods, both as increased hippocampal coupling with task-relevant perceptual regions and reactivation of stimulus-specific patterns following intensive encoding tasks. We investigate the spatial and temporal characteristics of these hippocampally anchored post-encoding neocortical modulations. Eighty-nine adults participated in an experiment consisting of interleaved memory task- and resting-state periods. We observed increased post-encoding functional connectivity between hippocampus and individually localized neocortical regions responsive to stimuli encountered during memory encoding. Post-encoding modulations were manifested as a nearly system-wide upregulation in hippocampal coupling with all major functional networks. The configuration of these extensive modulations resembled hippocampal-neocortical interaction patterns estimated from active encoding operations, suggesting hippocampal post-encoding involvement exceeds perceptual aspects. Reinstatement of encoding patterns was not observed in resting-state scans collected 12h later, nor when using other candidate seed regions. The similarity in hippocampal functional coupling between online memory encoding and offline post-encoding rest suggests reactivation in humans involves a spectrum of cognitive processes engaged during the experience of an event. There were no age effects, suggesting that upregulation of hippocampal-neocortical connectivity represents a general phenomenon seen across the adult lifespan.

"COVID-19, I Hate You!": Framing Death and Dying in COVID-19 Online Memorials.

The COVID-19 pandemic has reshaped the way Americans deal with death and dying. Social distancing restrictions prevented or delayed end of life services for families who lost loved ones to COVID-19. Alternatively, some took to the internet to express their grief through online memorialization. Using content analysis, this study analyzes 200 randomly selected online memorials from the COVID Memorial website, a website created to share the stories of those lost to COVID-19 on social media. Our findings offer insight into memorial author demographics, how memorial authors framed and understood death due to COVID-19, and the COVID-19 pandemic through online memorialization. Two major themes emerged during data analysis, "references to COVID-19 death" and "COVID-specific directives to readers." Theformer, details how memorial authors framed and understood the death of a loved one to COVID-19. The latter details "advice" or "directives" authors gave to readers about social distancing guidelines and other guidelines meant to prevent the spread of COVID-19.