Long-term Representations Research Articles

Long-term Outcomes Following Non-operative Management of Acute Appendicitis: A Population-based Analysis.

To describe long-term re-presentations and interventions following non-operative management (NOM) of acute appendicitis (AA). Trial data suggest NOM of AA carries a substantial risk of subsequent appendectomy, although NOM is increasingly offered to patients. Population-based data is required to understand the real-world long-term course of patients undergoing NOM of AA. This population-based cohort study included all adult patients in Ontario, Canada who presented to any emergency department (ED) with AA between 2004-2019. Patients who did not undergo a procedure on index ED presentation or hospital admission were defined as NOM and followed for five years. The cumulative incidence of composite re-presentation or intervention (ED re-presentation, re-admission, emergency, or scheduled appendicitis procedure) was calculated accounting for competing risk of death. Of 156,362 patients identified with AA, 13,200 underwent NOM. The cumulative incidence of composite re-presentation or intervention was 33% at 1-year (95%CI 32-33%) and 36% at 5-years (95%CI 36-37%). At 5-years, the incidence of appendicitis-specific ED re-presentation or hospital re-admission was 16% (95%CI 15-16%), the incidence of an emergency procedure for AA was 12% (95%CI 12-13%), and the incidence of scheduled surgery was 21% (95%CI 20-21%). In a subgroup of patients with uncomplicated AA, composite re-presentation or intervention was 28% at 1-year (95%CI 27-29%) and 32% at 5-years (95%CI 32-33%). Real-world estimates of emergency re-presentation with or without urgent surgery following NOM of AA were lower than previously described. Scheduled appendectomy made up an important proportion of long-term interventions following NOM.

When Jack isn't Jacques: Simultaneous opposite language-specific speech perceptual learning in French-English bilinguals.

Humans are remarkably good at understanding spoken language, despite the huge variability of the signal as a function of the talker, the situation, and the environment. This success relies on having access to stable representations based on years of speech input, coupled with the ability to adapt to short-term deviations from these norms, e.g. accented speech or speech altered by ambient noise. In the last two decades, there has been a robust research effort focused on a possible mechanism for adjusting to accented speech. In these studies, listeners typically hear 15 - 20 words in which a speech sound has been altered, creating a short-term deviation from its longer-term representation. After exposure to these items, listeners demonstrate "lexically driven phonetic recalibration"-they alter their categorization of speech sounds, expanding a speech category to take into account the recently heard deviations from their long-term representations. In the current study, we investigate such adjustments by bilingual listeners. French-English bilinguals were first exposed to nonstandard pronunciations of a sound (/s/ or /f/) in one language and tested for recalibration in both languages. Then, the exposure continued with both the original type of mispronunciation in the same language, plus mispronunciations in the other language, in the opposite direction. In a final test, we found simultaneous recalibration in opposite directions for the two languages-listeners shifted their French perception in one direction and their English in the other: Bilinguals can maintain separate adjustments, for the same sounds, when a talker's speech differs across two languages.

Perceptual anchoring: Children with dyslexia benefit less than controls from contextual repetitions in speech processing

ObjectivesIndividuals with dyslexia perceive and utilize statistical features in the auditory input deficiently. The present study investigates whether affected children also benefit less from repeating context tones as perceptual anchors for subsequent speech processing. MethodsIn an event-related potential study, eleven-year-old children with dyslexia (n = 21) and without dyslexia (n = 20) heard syllable pairs, with the first syllable either receiving a constant pitch (anchor) or variable pitch (no-anchor), while second syllables were identical across conditions. ResultsChildren with and without dyslexia showed smaller auditory P2 responses to constant-pitch versus variable-pitch first syllables, while only control children additionally showed smaller N1 and faster P1 responses. This suggests less automatic processing of anchor repetitions in dyslexia. For the second syllables, both groups showed faster P2 responses following anchor than no-anchor first syllables, but only controls additionally showed smaller P2 responses. ConclusionsChildren with and without dyslexia show differences in anchor effects. While both groups seem to allocate less attention to speech stimuli after contextual repetitions, children with dyslexia display less facilitation in speech processing from acoustic anchors. SignificanceAltered anchoring in the linguistic domain may contribute to the difficulties of individuals with dyslexia in establishing long-term representations of speech.

Beyond fixed sets: boundary conditions for obtaining SNARC-like effects with continuous semantic magnitudes.

Previous research has demonstrated the presence of an effect (i.e., the spatial-numerical association of response codes or SNARC) in both numerical parity and magnitude judgment tasks in which smaller numerical magnitudes are manually responded to faster on the left side and larger numerical magnitudes on the right side. Such a result has typically been attributed to a spatially based representation of numerical magnitude in long-term memory, the format of which has recently been postulated to be positional in line with learning of a canonically ordered number sequence. As a test of this view, in the current research, participants made classification judgments involving either the size (N = 88) or the living-nonliving status (N = 114) corresponding to the names of animals/objects etc. to which no learned canonical ordering of size exists. Names were taken from a very large set of 400 animals/objects etc. and each name was presented only once in an experimental session. Responses were made using left and right manual keypresses. In this work, the relation between response time and the relative sizes of the animals/objects did not differ across the left-right side of response indicating that SNARC-like effects did not occur. As such, the results suggest that space is not an inherent aspect of the long-term representation of magnitude in the brain and that some form of positional coding of magnitude is necessary for SNARC-like effects to occur.

The visual familiarity effect on attentional working memory maintenance.

Attentional refreshing has been described as an attention-based, domain-general maintenance mechanism in working memory. It is thought to operate via focusing executive attention on information held in working memory, protecting it from temporal decay and interference. Although attentional refreshing has attracted a lot of research, its functioning is still debated. At least one conception of refreshing supposes that it relies on semantic long-term memory representations to reconstruct working memory traces. Although investigations in the verbal domain found evidence against this hypothesis, a different pattern could emerge in visuospatial working memory in which absence of refreshing evidence has been observed for stimuli with minimal associated long-term knowledge. In a series of four experiments, the current study investigated the hypothesis of an involvement of semantic long-term representations in the functioning of attentional refreshing in the visuospatial domain. Both cognitive and memory load effects have been proposed as indexes of attentional refreshing. Therefore, we investigated the interaction between the effects of visual familiarity (a long-term memory effect) and cognitive load on recall performance (Experiments 1A and 1B), as well as the interaction between the effects of visual familiarity and memory load on the response times in a concurrent processing task (Experiments 2A and 2B). Results were consistent across experiments and go against the hypothesis of the involvement of semantic long-term memory in the functioning of attentional refreshing in visuospatial working memory. As such, this study corroborates the results found in the verbal domain. Implications for attentional refreshing and working memory are discussed.

Cognitive and sensory expectations independently shape musical expectancy and pleasure.

Expectation is crucial for our enjoyment of music, yet the underlying generative mechanisms remain unclear. While sensory models derive predictions based on local acoustic information in the auditory signal, cognitive models assume abstract knowledge of music structure acquired over the long term. To evaluate these two contrasting mechanisms, we compared simulations from four computational models of musical expectancy against subjective expectancy and pleasantness ratings of over 1000 chords sampled from 739 US Billboard pop songs. Bayesian model comparison revealed that listeners' expectancy and pleasantness ratings were predicted by the independent, non-overlapping, contributions of cognitive and sensory expectations. Furthermore, cognitive expectations explained over twice the variance in listeners' perceived surprise compared to sensory expectations, suggesting a larger relative importance of long-term representations of music structure over short-term sensory-acoustic information in musical expectancy. Our results thus emphasize the distinct, albeit complementary, roles of cognitive and sensory expectations in shaping musical pleasure, and suggest that this expectancy-driven mechanism depends on musical information represented at different levels of abstraction along the neural hierarchy. This article is part of the theme issue 'Art, aesthetics and predictive processing: theoretical and empirical perspectives'.

GCRec: Graph-Augmented Capsule Network for Next-Item Recommendation.

Next-item recommendation has been a hot research, which aims at predicting the next action by modeling users' behavior sequences. While previous efforts toward this task have been made in capturing complex item transition patterns, we argue that they still suffer from three limitations: 1) they have difficulty in explicitly capturing the impact of inherent order of item transition patterns; 2) only a simple and crude embedding is insufficient to yield satisfactory long-term users' representations from limited training sequences; and 3) they are incapable of dynamically integrating long-term and short-term user interest modeling. In this work, we propose a novel solution named graph-augmented capsule network (GCRec), which exploits sequential user behaviors in a more fine-grained manner. Specifically, we employ a linear graph convolution module to learn informative long-term representations of users. Furthermore, we devise a user-specific capsule module and a position-aware gating module, which are sensitive to the relative sequential order of the recently interacted items, to capture sequential patterns at union-level and point-level. To aggregate the long-term and short-term user interests as a representative vector, we design a dual-gating mechanism, which could decide the contribution ratio of each module given different contextual information. Through extensive experiments on four benchmarks, we validate the rationality and effectiveness of GCRec on the next-item recommendation task.

TADA: Temporal-aware Adversarial Domain Adaptation for patient outcomes forecasting

Patient outcomes forecasting (POF) has been shown to be an effective diagnostic assistant that can be used to predict disease progression and patient status in advance. In practice, the variation in patient progress and physical symptoms poses significant challenges for POF model training and testing. These differences result in POF models trained on one set of patients (source domain) not being efficiently self-adapted to the disease prediction task of another set of patients (target domain). Domain adaptive approaches can balance the overall distribution differences, named long-term divergence between patient training data and test data. However, the complex time series dependence and multivariate distribution properties of patient clinical data lead to the inability of existing domain adaptation methods to guarantee consistent distribution of patient clinical data at a finer granularity, named short-term divergence, e.g., clinical data distribution at different time intervals. In this paper, we propose a novel POF approach (TADA) based on clinical time series data using Temporal-aware Adversarial Domain Adaptation. In TADA, the traditional binary domain labels are generalized to act as the supervision signals to adversarially bridge the domain gap considering both long-term and short-term clinical information. TADA not only preserves the long-term representation of the clinical data but also captures the short-term data distribution at each period. The experimental results, based on both the publicly available and private clinical time series datasets, demonstrate that TADA can provide more reliable forecasting than state-of-the-art approaches.

Personal familiarity of faces, animals, objects, and scenes: Distinct perceptual and overlapping conceptual representations

While face, object, and scene recognition are often studied at a basic categorization level (e.g. “a face”, “a car”, “a kitchen”), we frequently recognise individual items of these categories as unique entities (e.g. “my mother”, “my car”, “my kitchen”). This recognition of individual identity is essential to appropriate behaviour in our world. However, relatively little is known about how we recognise individually familiar visual stimuli. Using event-related brain potentials, the present study examined whether and to what extent the underlying neural representations of personally familiar items are similar or different across different categories. In three experiments, we examined the recognition of personally highly familiar faces, animals, indoor scenes, and objects. We observed relatively distinct familiarity effects in an early time window (200-400 ms), with a clearly right-lateralized occipito-temporal scalp distribution for human faces and more bilateral and posterior distributions for other stimulus categories, presumably reflecting access to at least partly discrete visual long-term representations. In contrast, we found clearly overlapping familiarity effects in a later time window (starting 400 to 500 ms after stimulus onset), again with a mainly right occipito-temporal scalp distribution, for all stimulus categories. These later effects appear to reflect the sustained activation of conceptual properties relevant to any potential interaction. We conclude that familiarity for items from the various visual stimulus categories tested here is represented differently at the perceptual level, while relatively overlapping conceptual mechanisms allow for the preparation of impending potential interaction with the environment.

Cellular mechanisms of contextual fear memory reconsolidation: Role of hippocampal SFKs, TrkB receptors and GluN2B-containing NMDA receptors.

Memories are stored into long-term representations through a process that depends on protein synthesis. However, a consolidated memory is not static and inflexible and can be reactivated under certain circumstances, the retrieval is able to reactivate memories and destabilize them engaging a process of restabilization known as reconsolidation. Although the molecular mechanisms that mediate fear memory reconsolidation are not entirely known, so here we investigated the molecular mechanisms in the hippocampus involved in contextual fear conditioning memory (CFC) reconsolidation in male Wistar rats. We demonstrated that the blockade of Src family kinases (SFKs), GluN2B-containing NMDA receptors and TrkB receptors (TrkBR) in the CA1 region of the hippocampus immediately after the reactivation session impaired contextual fear memory reconsolidation. These impairments were blocked by the neurotrophin BDNF and the NMDAR agonist, D-Serine. Considering that the study of the link between synaptic proteins is crucial for understanding memory processes, targeting the reconsolidation process may provide new ways of disrupting maladaptive memories, such as those seen in post-traumatic stress disorder. Here we provide new insights into the cellular mechanisms involved in contextual fear memory reconsolidation, demonstrating that SFKs, GluN2B-containing NMDAR, and TrkBR are necessary for the reconsolidation process. Our findings suggest a link between BDNF and SFKs and GluN2B-containing NMDAR as well as a link between NMDAR and SFKs and TrkBR in fear memory reconsolidation. These preliminary pharmacological findings provide new evidence of the mechanisms involved in the reconsolidation of fear memory and have the potential to contribute to the development of treatments for psychiatric disorders involving maladaptive memories.

Memory capacity and prioritization in female mice

Our brain’s capacity for memory storage may be vast but is still finite. Given that we cannot remember the entirety of our experiences, how does our brain select what to remember and what to forget? Much like the triage of a hospital’s emergency room, where urgent cases are prioritized and less critical patients receive delayed or even no care, the brain is believed to go through a similar process of memory triage. Recent salient memories are prioritized for consolidation, which helps create stable, long-term representations in the brain; less salient memories receive a lower priority, and are eventually forgotten if not sufficiently consolidated (Stickgold and Walker in Nat Neurosci 16(2):139–145, 2013). While rodents are a primary model for studying memory consolidation, common behavioral tests typically rely on a limited number of items or contexts, well within the memory capacity of the subject. A memory test allowing us to exceed an animal’s memory capacity is key to investigating how memories are selectively strengthened or forgotten. Here we report a new serial novel object recognition task designed to measure memory capacity and prioritization, which we test and validate using female mice.

Long-term representations systematically bias ongoing perception

Long-term representations systematically bias ongoing perception

Enhancing mango disease diagnosis through eco-informatics: A deep learning approach

The mango is one of the most popular and economically important fruits in the world, but it is vulnerable to various diseases that can significantly reduce the quality and yield of the fruit. In the field of ecological informatics, tackling these challenges usually necessitate integrating cutting-edge technologies and ecological insights for effective disease management. Deep learning (DL) has been showing great promise in developing automated systems for leaf disease detection. This work proposes a novel DL approach that integrates the power of DL with ecological information management for the automated detection of mango leaf diseases. Our methodology introduces a visual modulation network that can innovatively learn the visual representations of leaf diseases along spatial and channel dimensions through simple but effective linear layers. An overlapped patching embedding is presented to tackle the discontinuity of non-overlapped patching in vision transformers (ViTs). Then, a succession of visual modulator blocks is presented to learn compact and long-term representations of disease in mango leaves without the inclusion of attention operation. Experimental results on the MangoLeafDB dataset show that the proposed visual modulation network can accurately detect various mango leaf diseases, demonstrating its potential as a cost-effective and efficient tool for disease management in mango cultivation.

Long-term phonemic representations become audiovisual by mid-childhood

In earlier work with adults, we showed that long-term phonemic representations are audiovisual, meaning that they contain information on typical mouth shape during articulation. Many aspects of audiovisual processing have a prolonged developmental course, often not reaching maturity until late adolescence. In this study, we examined the status of phonemic representations in two groups of children – 8-9-year-olds and 11-12-year-olds. We used the same audiovisual oddball paradigm as in the earlier study with adults (Kaganovich and Christ, 2021). On each trial, participants saw a face and heard one of two vowels. One vowel occurred frequently (standard), while another occurred rarely (deviant). In one condition (neutral), the face had a closed, non-articulating mouth. In the other condition (audiovisual violation), the mouth shape matched the frequent vowel. Although stimuli were audiovisual in both conditions, we hypothesized that identical auditory changes would be perceived differently by participants. Namely, in the neutral condition, deviants violated only the audiovisual pattern specific to each experimental block. By contrast, in the audiovisual violation condition, deviants additionally violated long-term representations for how a speaker's mouth looks during articulation. We compared the amplitude of MMN and P3 components elicited by deviants in the two conditions. In the 11-12-year-old group, the pattern of neural responses was similar to that in adults – namely, they had a larger MMN component in the audiovisual compared to neutral condition, with no major difference in the P3 amplitude. In contrast, in the 8-9-year-old group, we saw a posterior MMN in the neutral condition only and a larger P3 in the audiovisual violation compared to the neutral condition. The larger P3 in the audiovisual violation condition suggests that younger children did perceive deviants as being more attention-grabbing when they violated the typical combination of sound and mouth shape. Yet, at this age, the earlier, more automatic stages of phonemic processing indexed by the MMN component may not yet encode visual speech elements the same way they do in older children and adults. We conclude that phonemic representations do not become audiovisual until 11–12 years of age.

Skating-Mixer: Long-Term Sport Audio-Visual Modeling with MLPs

Figure skating scoring is challenging because it requires judging players’ technical moves as well as coordination with the background music. Most learning-based methods struggle for two reasons: 1) each move in figure skating changes quickly, hence simply applying traditional frame sampling will lose a lot of valuable information, especially in 3 to 5 minutes lasting videos; 2) prior methods rarely considered the critical audio-visual relationship in their models. Due to these reasons, we introduce a novel architecture, named Skating-Mixer. It extends the MLP framework into a multimodal fashion and effectively learns long-term representations through our designed memory recurrent unit (MRU). Aside from the model, we collected a high-quality audio-visual FS1000 dataset, which contains over 1000 videos on 8 types of programs with 7 different rating metrics, overtaking other datasets in both quantity and diversity. Experiments show the proposed method achieves SOTAs over all major metrics on the public Fis-V and our FS1000 dataset. In addition, we include an analysis applying our method to the recent competitions in Beijing 2022 Winter Olympic Games, proving our method has strong applicability.

Estimation of SoH and internal resistances of Lithium ion battery based on LSTM network

State of Health (SoH) and internal resistances, including the solid electrolyte interphase (SEI) resistance and charge transfer resistance, are important parameters that change in the long-term representation of the aging state of Lithium-ion batteries. Using long short-term memory (LSTM) network, a neural network with the ability to remember long-term data features, this paper presents a method for estimating SoH and internal resistances of Lithium-ion batteries using LSTM network with deep learning mechanism. Based on experimental data including voltage, current, temperature with 03 charge/discharge scenarios and measuring impedance, input/output data structure is set up to reflect aging features used for estimating SoH and internal resistances by LSTM. The first LSTM network is designed to estimate SoH, then the data including current, voltage, temperature and estimated SoH will be used to estimate the SEI resistance and charge transfer resistance by the second LSTM network. With this structure, the estimation of internal resistances in practice will become simpler as it does not require measuring capacity and impedance spectroscopy. Comparing the estimation errors using LSTM and FNN with 03 performance metrics including mean absolute percentage error (MAPE), mean percentage error (MPE) and root mean square error (RMSE) shows that the estimation results of SoH and internal resistances of the cell by LSTM have higher accuracy than the estimation by Feedforward Neural Network (FNN).

Data integration reveals dynamic and systematic patterns of breeding habitat use by a threatened shorebird

Incorporating species distributions into conservation planning has traditionally involved long-term representations of habitat use where temporal variation is averaged to reveal habitats that are most suitable across time. Advances in remote sensing and analytical tools have allowed for the integration of dynamic processes into species distribution modeling. Our objective was to develop a spatiotemporal model of breeding habitat use for a federally threatened shorebird (piping plover, Charadrius melodus). Piping plovers are an ideal candidate species for dynamic habitat models because they depend on habitat created and maintained by variable hydrological processes and disturbance. We integrated a 20-year (2000–2019) nesting dataset with volunteer-collected sightings (eBird) using point process modeling. Our analysis incorporated spatiotemporal autocorrelation, differential observation processes within data streams, and dynamic environmental covariates. We evaluated the transferability of this model in space and time and the contribution of the eBird dataset. eBird data provided more complete spatial coverage in our study system than nest monitoring data. Patterns of observed breeding density depended on both dynamic (e.g., surface water levels) and long-term (e.g., proximity to permanent wetland basins) environmental processes. Our study provides a framework for quantifying dynamic spatiotemporal patterns of breeding density. This assessment can be iteratively updated with additional data to improve conservation and management efforts, because reducing temporal variability to average patterns of use may cause a loss in precision for such actions.

Investigating the nature of speech representations by contrasting computational models against behavioral data

How do listeners interpret speech input when exposure does not contain information about the talker’s category representations? Competing accounts are rarely contrasted. We implement competing hypotheses within the same general computational framework (Bayesian inference). All models were trained on phonetic productions to predict perception, reflecting the hypothesis that listeners learn category representations from the speech input. We compare them against two experiments on the perception of L1-US English stop voicing (N = 24 and 122). Both experiments used minimal pairs (e.g., tin/din) varying in the primary cue (VOT) while keeping secondary cues (f0 and vowel duration) at expected correlations. VOT values occurred equally often and spanned the range observed in US English. We find that (1) models that integrated perceptual noise performed better in predicting categorization responses than those without; (2) models with multiple cues performed better than those with just VOT; (3) models trained on talker-normalized phonetic cues performed better than those trained on unnormalized cues; and, surprisingly, (4) models that also normalized the novel speech input during the experiment performed worse than those that did not. (3) and (4) suggest that listeners’ long-term representations are based on talker-normalized cues but require *labelled* input—contrary to most normalization accounts.

Self-Attention Pooling-Based Long-Term Temporal Network for Action Recognition

With the development of Internet of Things (IoT), self-driving technology has been successful. Yet safe driving faces challenges due to such cases as pedestrians crossing roads. How to sense their movements and identify their behaviors from video data is important. Most of the existing methods fail to: 1) capture long-term temporal relationship well due to their limited temporal coverage and 2) aggregate discriminative representation effectively, such as caused by little or even no attention paid to differences among representations. To address such issues, this work presents a new architecture called a self-attention pooling-based long-term temporal network (SP-LTN), which can learn long-term temporal representations and aggregate those discriminative representations in an end-to-end manner, and on the other hand, effectively conduct long-term representation learning on a given video by capturing spatial information and mining temporal patterns. Next, it develops a self-attention pooling method to predict the importance scores of obtained representations for distinguishing them from each other and then weights them together to highlight the contributions of those discriminative representations in action recognition. Finally, it designs a new loss function that combines a standard cross-entropy loss function with a regularization term to further focus on the discriminative representations while restraining the impact of distractive ones on activity classification. Experimental results on two data sets show that our SP-LTN, fed by only red–green–blue (RGB) frames, outperforms the state-of-the-art methods.

ЦІННІСНО-ЗМІСТОВИЙ ВИМІР “УКРАЇНСЬКОГО ЧАСУ” НАЦІОНАЛЬНОЇ ГРОМАДИ В РАДІОПРОСТОРІ КАНАДИ

The article examines the historical stages of formation, elaborates the current state and value-content support of radio initiatives of Ukrainian emigration using the example of the radio program "Ukrainian Time" (Montreal, Canada). Unique data from the first person was obtained during direct communication with the producer of the media initiative with more than thirty years of work experience, an active figure in the Ukrainian community S. Kuklevsky. The chronology of the continuous existence of the radio program from its foundation in 1963 by I. Oparyk and V. Gladun is traced; a meaningful contribution to its development by two generations of a creative team of talented authors headed by the current producer S. Kuklevsky. The names of the media activists involved in the creation of the radio program were included in the scientific media discourse: O. Onyshchenko, O. Shak (hosts), L. Gavrylov (information sector), N. Fedoseeva (journalist), priests of the Ukrainian Greek Catholic Church, Fr. I. Oschipko and the Ukrainian Orthodox Church Fr. I. Kutash, producer S. Kuklevsky. The orientation of its authors to the implementation of convergent tools of modern radio broadcasting is characterized, in particular, the broadcast of issues on the channel of the same name on "Youtube" and information support and a page on the social network "Facebook". The high role and importance of the radio program as an integrator of the community around national values and the Ukrainian cause and the belief that only a powerful united community can have a successful, high-quality, long-term representation in the media space are determined. And in order to make the community like this, you need to be included in its life, it is not necessary to organize event attractions, but to join them, listen to programs, speak Ukrainian with children, sing our songs.