Everyday amnesia: Residual memory for high confidence misses and implications for decision models of recognition.

Despite studying a list of items only minutes earlier, when reencountered in a recognition memory test, undergraduate participants often say with total confidence that they have not studied some of the items before. Such high confidence miss (HCM) responses have been taken as evidence of rapid and complete forgetting and of everyday amnesia (Roediger & Tekin, 2020). We investigated (a) if memory for HCMs is completely lost or whether a residual memory effect exists and (b) whether dominant decision models predict the effect. Participants studied faces (Experiments 1a, 2, and 3) or words (Experiment 1b), then completed a single-item recognition memory task, followed by either (a) a two-alternative forced-choice recognition task, in which the studied and nonstudied alternatives on each trial were matched for their previous old/new decision and confidence rating (Experiments 1 and 2) or (b) a second single-item recognition task in which the targets and foils were HCMs and high confidence correct rejections, respectively (Experiment 3). In each experiment, participants reliably distinguished HCMs from high-confidence correct rejections. The unequal variance signal detection and dual-process signal detection models were fit to the single-item recognition data, and the parameter estimates were used to predict the memory effect for HCMs. The dual-process signal detection model predicted the residual memory effect (as did another popular model, the mixture signal detection theory model). However, the unequal variance signal detection model incorrectly predicted a negative, or no, effect, invalidating this model. The residual memory effect for HCMs demonstrates that everyday amnesia is not associated with complete memory loss and distinguishes between decision models. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Pattern Mining-Based Pig Behavior Analysis for Health and Welfare Monitoring.

The increasing popularity of pigs has prompted farmers to increase pig production to meet the growing demand. However, while the number of pigs is increasing, that of farm workers has been declining, making it challenging to perform various farm tasks, the most important among them being managing the pigs' health and welfare. This study proposes a pattern mining-based pig behavior analysis system to provide visualized information and behavioral patterns, assisting farmers in effectively monitoring and assessing pigs' health and welfare. The system consists of four modules: (1) data acquisition module for collecting pigs video; (2) detection and tracking module for localizing and uniquely identifying pigs, using tracking information to crop pig images; (3) pig behavior recognition module for recognizing pig behaviors from sequences of cropped images; and (4) pig behavior analysis module for providing visualized information and behavioral patterns to effectively help farmers understand and manage pigs. In the second module, we utilize ByteTrack, which comprises YOLOx as the detector and the BYTE algorithm as the tracker, while MnasNet and LSTM serve as appearance features and temporal information extractors in the third module. The experimental results show that the system achieved a multi-object tracking accuracy of 0.971 for tracking and an F1 score of 0.931 for behavior recognition, while also highlighting the effectiveness of visualization and pattern mining in helping farmers comprehend and manage pigs' health and welfare.

Plant disease recognition using residual convolutional enlightened Swin transformer networks.

Agriculture plays a pivotal role in the economic development of a nation, but, growth of agriculture is affected badly by the many factors one such is plant diseases. Early stage prediction of these disease is crucial role for global health and even for game changers the farmer's life. Recently, adoption of modern technologies, such as the Internet of Things (IoT) and deep learning concepts has given the brighter light of inventing the intelligent machines to predict the plant diseases before it is deep-rooted in the farmlands. But, precise prediction of plant diseases is a complex job due to the presence of noise, changes in the intensities, similar resemblance between healthy and diseased plants and finally dimension of plant leaves. To tackle this problem, high-accurate and intelligently tuned deep learning algorithms are mandatorily needed. In this research article, novel ensemble of Swin transformers and residual convolutional networks are proposed. Swin transformers (ST) are hierarchical structures with linearly scalable computing complexity that offer performance and flexibility at various scales. In order to extract the best deep key-point features, the Swin transformers and residual networks has been combined, followed by Feed forward networks for better prediction. Extended experimentation is conducted using Plant Village Kaggle datasets, and performance metrics, including accuracy, precision, recall, specificity, and F1-rating, are evaluated and analysed. Existing structure along with FCN-8s, CED-Net, SegNet, DeepLabv3, Dense nets, and Central nets are used to demonstrate the superiority of the suggested version. The experimental results show that in terms of accuracy, precision, recall, and F1-rating, the introduced version shown better performances than the other state-of-art hybrid learning models.

The Costs (and Benefits?) of Effortful Listening for Older Adults: Insights from Simultaneous Electrophysiology, Pupillometry, and Memory.

Although the impact of acoustic challenge on speech processing and memory increases as a person ages, older adults may engage in strategies that help them compensate for these demands. In the current preregistered study, older adults (n = 48) listened to sentences-presented in quiet or in noise-that were high constraint with either expected or unexpected endings or were low constraint with unexpected endings. Pupillometry and EEG were simultaneously recorded, and subsequent sentence recognition and word recall were measured. Like young adults in prior work, we found that noise led to increases in pupil size, delayed and reduced ERP responses, and decreased recall for unexpected words. However, in contrast to prior work in young adults where a larger pupillary response predicted a recovery of the N400 at the cost of poorer memory performance in noise, older adults did not show an associated recovery of the N400 despite decreased memory performance. Instead, we found that in quiet, increases in pupil size were associated with delays in N400 onset latencies and increased recognition memory performance. In conclusion, we found that transient variation in pupil-linked arousal predicted trade-offs between real-time lexical processing and memory that emerged at lower levels of task demand in aging. Moreover, with increased acoustic challenge, older adults still exhibited costs associated with transient increases in arousal without the corresponding benefits.

A critical overview of emotion processing assessment in non-affective and affective psychoses.

AIMS: Patients with affective and non-affective psychoses show impairments in both the identification and discrimination of facial affect, which can significantly reduce their quality of life. The aim of this commentary is to present the strengths and weaknesses of the available instruments for a more careful evaluation of different stages of emotion processing in clinical and experimental studies on patients with non-affective and affective psychoses. METHODS: We reviewed the existing literature to identify different tests used to assess the ability to recognise (e.g. Ekman 60-Faces Test, Facial Emotion Identification Test and Penn Emotion Recognition Test) and to discriminate emotions (e.g. Face Emotion Discrimination Test and Emotion Differentiation Task). RESULTS: The current literature revealed that few studies combine instruments to differentiate between different levels of emotion processing disorders. The lack of comprehensive instruments that integrate emotion recognition and discrimination assessments prevents a full understanding of patients' conditions. CONCLUSIONS: This commentary underlines the need for a detailed evaluation of emotion processing ability in patients with non-affective and affective psychoses, to characterise the disorder at early phases from the onset of the disease and to design rehabilitation treatments.

Unfamiliarity generates costly aggression in interspecific avian dominance hierarchies.

Dominance hierarchies often form between species, especially at common feeding locations. Yet, relative to work focused on the factors that maintain stable dominance hierarchies within species, large-scale analyses of interspecific dominance hierarchies have been comparatively rare. Given that interspecific behavioral interference mediates access to resources, these dominance hierarchies likely play an important and understudied role in community assembly and behavioral evolution. To test alternative hypotheses about the formation and maintenance of interspecific dominance hierarchies, we employ an large, participatory science generated dataset of displacements observed at feeders in North America in the non-breeding season. Consistent with the hypothesis that agonistic interference can be an adaptive response to exploitative competition, we find that species with similar niches are more likely to engage in costly aggression over resources. Among interacting species, we find broad support for the hypothesis that familiarity (measured as fine-scale habitat overlap) predicts adherence to the structure of the dominance hierarchy and reduces aggression between species. Our findings suggest that the previously documented agonistic hierarchy in North American birds emerges from species-level adaptations and learned behaviors that result in the avoidance of costly aggression.

Verbal and non-verbal recognition memory assessment: validation of a computerized version of the Recognition Memory Test.

BACKGROUND: The use of computerized devices for neuropsychological assessment (CNADs) as an effective alternative to the traditional pencil-and-paper modality has recently increased exponentially, both in clinical practice and research, especially due to the pandemic. However, several authors underline that the computerized modality requires the same psychometric validity as "in-presence" tests. The current study aimed at building and validating a computerized version of the verbal and non-verbal recognition memory test (RMT) for words, unknown faces and buildings. METHODS: Seventy-two healthy Italian participants, with medium-high education and ability to proficiently use computerized systems, were enrolled. The sample was subdivided into six groups, one for each age decade. Twelve neurological patients with mixed aetiology, age and educational level were also recruited. Both the computerized and the paper-and-pencil versions of the RMT were administered in two separate sessions. RESULTS: In healthy participants, the computerized and the paper-and-pencil versions of the RMT showed statistical equivalence for words, unknown faces and buildings. In the neurological patients, no statistical difference was found between the performance at the two versions of the RMT. A moderate-to-good inter-rater reliability between the two versions was also found in both samples. Finally, the computerized version of the RMT was perceived as acceptable by both healthy participants and neurological patients at System Usability Scale (SUS). CONCLUSION: The computerized version of the RMT can be used as a reliable alternative to the traditional version.

Validation of Cross-Individual Pain Assessment with Individual Recognition Model from Electroencephalogram.

Cross-individual pain assessment models based on electroencephalography (EEG) allow pain assessment in individuals who cannot report pain (e.g., unresponsive patients). The main obstacle to the generalisation of pain assessment models is the individual variation of brain responses to pain. Hence, we took the individual variation into account in cross-individual model development. We developed two convolutional neural networks (CNN) sharing an encoder architecture. One CNN predicts pain, while the other predicts the identity of an individual. We performed a leave-one-out (LOO) test with the exclusion of each subject and applied evidence accumulation to it for validating the pain prediction model's performance, where the binary classifier involves the states of pain (Hot) and resting state (Eyes-open). The mean accuracy produced by the LOO tests was 57.81% (max: 73.33%), and the mean accuracy of evidence accumulation achieved 69.75% (max: 100.00%). The individual recognition model achieved an accuracy of 99.63%. Nevertheless, when we acquired the most similar subject to a novel subject using the individual recognition model, where the most similar subject was used to train a subject-wise pain prediction model. The accuracy of predicting the pain-related conditions of the novel subject by the subject-wise model was only 53.73% (max: 79.50%). Therefore, the approach to utilising the features related to individual variation extracted by the CNN model needs more investigation for improving cross-individual pain assessment.Clinical relevance- This model can be applied to assess pain from EEG signals at the bedside with future improvement, which can help caretakers of unresponsive patients.

Assessment of memory recognition using a smartphone-based test system: A pilot study.

BACKGROUND: Elucidation of the detailed nature of age-related memory decline requires analysis of memory performance in large populations of various ages. To promote large-scale studies, we developed a smartphone-based self-test for memory recognition. We examined whether this test could detect age-related memory decline and the effects of aerobic exercise on memory. METHODS: Seventy-eight younger and 42 older participants were randomly divided into active and passive groups. Both groups took a memory recognition test (consisting of 2 sessions separated by a 48-hour interval) conducted on smartphones. The participants answered the positive and negative affect schedule questionnaire at the beginning and end of each session. In the first session, the participants performed cognitive tasks on 90 photographs displayed on a smartphone screen. Immediately after the cognitive tasks, the active group performed a bout of light aerobic exercise for 10 minutes, while the passive group remained calm for 10 minutes. In the second session, the participants were tested on the recognition of 90 previously observed photographs and 90 distractor photographs. RESULTS: Passive older participants had ~40% to ~50% lower recognition scores (RSs) than passive younger participants did. Moreover, the aerobic exercise used in this study improved the RSs of active younger participants by up to ~40% compared with those of passive younger participants, while such an improvement was not observed in older participants. The RS did not depend on the affect levels evaluated using positive and negative affect schedule questionnaire. CONCLUSIONS: These results demonstrated that the smartphone-based test could detect age-related decline and could promote behavior modification that may lead to memory enhancement, as reported in previous studies using conventional laboratory tests. The results of the smartphone-based test were not influenced by the subjects affect. This indicates the possibility of large-scale memory studies and healthcare for memory performance by using personal mobile devices.

Recognition subtests of the Repeatable Battery for the Assessment of Neuropsychological Status: evidence for a cortical vs. subcortical distinction.

INTRODUCTION: Within clinical neuropsychology, a classic diagnostic distinction is made between cortical and subcortical disorders, especially based on their memory profiles. Typically, this is based on the comparison of recall and recognition trials, where individuals with cortical conditions do not tend to benefit (i.e., score well) on recognition trials and individuals with subcortical conditions do. Although the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) is a widely used brief cognitive battery, there is a lack of evidence to support this measure's utility in distinguishing between the memory profiles of these conditions. METHOD: Thirty-six mild Alzheimer's disease (AD), 55 Parkinson's disease (PD), and 105 essential tremor (ET) participants (N = 196) were administered the RBANS with additional Story and Figure Recognition subtests. Group differences on recall and recognition scores (Total Correct, Hits or True Positives, False Positive Errors, and discriminability index) were examined across the three groups, while controlling for the influence of age and gender. RESULTS: As expected, individuals with AD had poorer recognition scores compared to the other clinical groups across tasks (all p-values < .05), while the ET sample largely performed comparably to the PD sample. With the exception of comparable Figure Recognition and Recall in the PD sample, all groups exhibited significantly greater recognition Hit performance compared to Recall (all p-values < .05). CONCLUSIONS: The group differences in performance across RBANS recognition subtests suggest support for traditional "cortical" and "subcortical" profiles. However, all groups, including the mild AD sample, demonstrated a benefit from recognition cues compared to free recall. Overall, these findings support the inclusion of the newly developed Story and Figure Recognition subtests in future clinical practice and research endeavors.

Remote Health Monitoring Systems for Elderly People: A Survey.

This paper addresses the growing demand for healthcare systems, particularly among the elderly population. The need for these systems arises from the desire to enable patients and seniors to live independently in their homes without relying heavily on their families or caretakers. To achieve substantial improvements in healthcare, it is essential to ensure the continuous development and availability of information technologies tailored explicitly for patients and elderly individuals. The primary objective of this study is to comprehensively review the latest remote health monitoring systems, with a specific focus on those designed for older adults. To facilitate a comprehensive understanding, we categorize these remote monitoring systems and provide an overview of their general architectures. Additionally, we emphasize the standards utilized in their development and highlight the challenges encountered throughout the developmental processes. Moreover, this paper identifies several potential areas for future research, which promise further advancements in remote health monitoring systems. Addressing these research gaps can drive progress and innovation, ultimately enhancing the quality of healthcare services available to elderly individuals. This, in turn, empowers them to lead more independent and fulfilling lives while enjoying the comforts and familiarity of their own homes. By acknowledging the importance of healthcare systems for the elderly and recognizing the role of information technologies, we can address the evolving needs of this population. Through ongoing research and development, we can continue to enhance remote health monitoring systems, ensuring they remain effective, efficient, and responsive to the unique requirements of elderly individuals.

A brain-inspired algorithm that mitigates catastrophic forgetting of artificial and spiking neural networks with low computational cost.

Neuromodulators in the brain act globally at many forms of synaptic plasticity, represented as metaplasticity, which is rarely considered by existing spiking (SNNs) and nonspiking artificial neural networks (ANNs). Here, we report an efficient brain-inspired computing algorithm for SNNs and ANNs, referred to here as neuromodulation-assisted credit assignment (NACA), which uses expectation signals to induce defined levels of neuromodulators to selective synapses, whereby the long-term synaptic potentiation and depression are modified in a nonlinear manner depending on the neuromodulator level. The NACA algorithm achieved high recognition accuracy with substantially reduced computational cost in learning spatial and temporal classification tasks. Notably, NACA was also verified as efficient for learning five different class continuous learning tasks with varying degrees of complexity, exhibiting a markedly mitigated catastrophic forgetting at low computational cost. Mapping synaptic weight changes showed that these benefits could be explained by the sparse and targeted synaptic modifications attributed to expectation-based global neuromodulation.

Dual-sensory fusion self-powered triboelectric taste-sensing system towards effective and low-cost liquid identification.

Infusing human taste perception into smart sensing devices to mimic the processing ability of gustatory organs to perceive liquid substances remains challenging. Here we developed a self-powered droplet-tasting sensor system based on the dynamic morphological changes of droplets and liquid-solid contact electrification. The sensor system has achieved accuracies of liquid recognition higher than 90% in five different applications by combining triboelectric fingerprint signals and deep learning. Furthermore, an image sensor is integrated to extract the visual features of liquids, and the recognition capability of the liquid-sensing system is improved to up to 96.0%. The design of this dual-sensory fusion self-powered liquid-sensing system, along with the droplet-tasting sensor that can autonomously generate triboelectric signals, provides a promising technological approach for the development of effective and low-cost liquid sensing for liquid food safety identification and management.

A Joint Extraction System Based on Conditional Layer Normalization for Health Monitoring.

Natural language processing (NLP) technology has played a pivotal role in health monitoring as an important artificial intelligence method. As a key technology in NLP, relation triplet extraction is closely related to the performance of health monitoring. In this paper, a novel model is proposed for joint extraction of entities and relations, combining conditional layer normalization with the talking-head attention mechanism to strengthen the interaction between entity recognition and relation extraction. In addition, the proposed model utilizes position information to enhance the extraction accuracy of overlapping triplets. Experiments on the Baidu2019 and CHIP2020 datasets demonstrate that the proposed model can effectively extract overlapping triplets, which leads to significant performance improvements compared with baselines.

Hidden Semi-Markov Models-Based Visual Perceptual State Recognition for Pilots.

Pilots' loss of situational awareness is one of the human factors affecting aviation safety. Numerous studies have shown that pilot perception errors are one of the main reasons for a lack of situational awareness without a proper system to detect these errors. The main objective of this study is to examine the changes in pilots' eye movements during various flight tasks from the perspective of visual awareness. The pilot's gaze rule scanning strategy is mined through cSPADE, while a hidden semi-Markov model-based model is used to detect the pilot's visuoperceptual state, linking the correlation between the hidden state and time. The performance of the proposed algorithm is then compared with that of the hidden Markov model (HMM), and the more flexible hidden semi-Markov model (HSMM) is shown to have an accuracy of 93.55%.

An Assessment of In-the-Wild Datasets for Multimodal Emotion Recognition.

Multimodal emotion recognition implies the use of different resources and techniques for identifying and recognizing human emotions. A variety of data sources such as faces, speeches, voices, texts and others have to be processed simultaneously for this recognition task. However, most of the techniques, which are based mainly on Deep Learning, are trained using datasets designed and built in controlled conditions, making their applicability in real contexts with real conditions more difficult. For this reason, the aim of this work is to assess a set of in-the-wild datasets to show their strengths and weaknesses for multimodal emotion recognition. Four in-the-wild datasets are evaluated: AFEW, SFEW, MELD and AffWild2. A multimodal architecture previously designed is used to perform the evaluation and classical metrics such as accuracy and F1-Score are used to measure performance in training and to validate quantitative results. However, strengths and weaknesses of these datasets for various uses indicate that by themselves they are not appropriate for multimodal recognition due to their original purpose, e.g., face or speech recognition. Therefore, we recommend a combination of multiple datasets in order to obtain better results when new samples are being processed and a good balance in the number of samples by class.

Designing a neuroclinical assessment of empathy deficits in psychopathy based on the Zipper Model of Empathy.

The heterogeneity of the literature on empathy highlights its multidimensional and dynamic nature and affects unclear descriptions of empathy in the context of psychopathology. The Zipper Model of Empathy integrates current theories of empathy and proposes that empathy maturity is dependent on whether contextual and personal factors push affective and cognitive processes together or apart. This concept paper therefore proposes a comprehensive battery of physiological and behavioral measures to empirically assess empathy processing according to this model with an application for psychopathic personality. We propose using the following measures to assess each component of this model: (1) facial electromyography; (2) the Emotion Recognition Task; (3) the Empathy Accuracy task and physiological measures (e.g., heart rate); (4) a selection of Theory of Mind tasks and an adapted Dot Perspective Task, and; (5) an adjusted Charity Task. Ultimately, we hope this paper serves as a starting point for discussion and debate on defining and assessing empathy processing, to encourage research to falsify and update this model to improve our understanding of empathy.

What is the prevalence of developmental prosopagnosia? An empirical assessment of different diagnostic cutoffs.

The prevalence of developmental prosopagnosia (DP), lifelong face recognition deficits, is widely reported to be 2-2.5%. However, DP has been diagnosed in different ways across studies, resulting in differing prevalence rates. In the current investigation, we estimated the range of DP prevalence by administering well-validated objective and subjective face recognition measures to an unselected web-based sample of 3116 18-55 year-olds and applying DP diagnostic cutoffs from the last 14 years. We found estimated prevalence rates ranged from .64-5.42% when using a z-score approach and .13-2.95% when using a percentile approach, with the most commonly used cutoffs by researchers having a prevalence rate of .93% (z-score, .45% when using percentiles). We next used multiple cluster analyses to examine whether there was a natural grouping of poorer face recognizers but failed to find consistent grouping beyond those with generally above versus below average face recognition. Lastly, we investigated whether DP studies with more relaxed diagnostic cutoffs were associated with better performance on the Cambridge Face Perception Test. In a sample of 43 studies, there was a weak nonsignificant association between greater diagnostic strictness and better DP face perception accuracy (Kendall's tau-b correlation, τb =.18 z-score; τb = .11 percentiles). Together, these results suggest that researchers have used more conservative DP diagnostic cutoffs than the widely reported 2-2.5% prevalence. We discuss the strengths and weaknesses of using more inclusive cutoffs, such as identifying mild and major forms of DP based on DSM-5.

Information accessibility, accounting manipulation, and sustainable development of digital enterprises: Based on double moderating effect model and panel PSM-DID method.

A theoretical mechanism was analyzed from the micro perspective of the enterprise to explore how information accessibility moderates the effect of accounting manipulation on the sustainable development of digital enterprises. Using data from 1200 listing digital enterprises in China and the DEA-Malmquist index method, the efficiency value of digital enterprises in 2007-2021 was estimated to represent the index of sustainable development of digital enterprises. The accounting manipulation was detected using the panel PSM-DID method based on the Administrative Measures for the Recognition of High-tech Enterprise's policy. The information accessibility value was estimated based on the MDA method. Empirical studies were conducted using text analysis, the panel PSM-DID method, and the double moderating effect model. The results showed that: (1) Accounting manipulation had a negative impact on the sustainable development of "true" digital enterprises and the "fake" digital enterprises. (2) Information accessibility directly and positively enhanced the technological progress and scale efficiency of digital enterprises, and its moderating effect was heterogeneous, with a significant moderating effect on the "true" digital enterprises and a negative effect on the "fake" ones.

Assessment of non-directed computer-use behaviours in the home can indicate early cognitive impairment: A proof of principle longitudinal study.

OBJECTIVES: Computer-use behaviours can provide useful information about an individual's cognitive and functional abilities. However, little research has evaluated unaided and non-directed home computer-use. In this proof of principle study, we explored whether computer-use behaviours recorded during routine home computer-use i) could discriminate between individuals with subjective cognitive decline (SCD) and individuals with mild cognitive impairment (MCI); ii) were associated with cognitive and functional scores; and iii) changed over time. METHODS: Thirty-two participants with SCD (n = 18) or MCI (n = 14) (mean age = 72.53 years; female n = 19) participated in a longitudinal study in which their in-home computer-use behaviour was passively recorded over 7-9 months. Cognitive and functional assessments were completed at three time points: baseline; mid-point (4.5 months); and end point (month 7 to 9). RESULTS: Individuals with MCI had significantly slower keystroke speed and spent less time on the computer than individuals with SCD. More time spent on the computer was associated with better task switching abilities. Faster keystroke speed was associated with better visual attention, recall, recognition, task inhibition, and task switching. No significant change in computer-use behaviour was detected over the study period. CONCLUSION: Passive monitoring of computer-use behaviour shows potential as an indicator of cognitive abilities, and can differentiate between people with SCD and MCI. Future studies should attempt to monitor computer-use behaviours over a longer time period to capture the onset of cognitive decline, and thus could inform timely therapeutic interventions.Supplemental data for this article can be accessed online at http://dx.doi.org/10.1080/13607863.2022.2036946.