Models Of Recognition Memory Research Articles

Explainable artificial intelligence in web phishing classification on secure IoT with cloud-based cyber-physical systems

IoT is the technology that aids the interconnection of all kinds of devices over the internet to replace data, monitor devices and enhance actuators to create outcomes. Cyber-physical systems (CPS) contain control and computation components, which are compactly assured with physical procedures. The internet plays a prominent role in modern lives, and the cybersecurity challenge caused by phishing attacks is significant. This research presents a novel approach to address this problem using machine learning (ML) methods for phishing website classification. Leveraging feature extraction and innovative algorithms, the projected method aims to distinguish between malicious and legitimate websites by features of phishing attempts and analyzing inherent patterns. Phishing is a significant threat that causes extensive financial losses for internet users yearly. This fraudulent act includes identity hackers utilizing clever approaches to deceive individuals into revealing sensitive data. Generally, phishers use strategies such as advanced phishing software and fake emails to illegally acquire confidential details like usernames and passwords from financial accounts. This article develops an Explainable Artificial Intelligence with Aquila Optimization Algorithm in Web Phishing Classification (XAIAOA-WPC) approach on secure Cyber-Physical Systems. The developed XAIAOA-WPC approach mainly emphasizes the effectual classification and recognition of web phishing based on CPS. In the first phase, preprocessing is carried out on three levels: data cleaning, text preprocessing, and standardization. Furthermore, the Harris' Hawks optimization-based feature selection (HHO-FS) method is applied to derive feature subsets. The XAIAOA-WPC method utilizes a multi-head attention-based long short-term memory (MHA-LSTM) model for web phishing recognition. Besides, the detection outcomes of the MHA-LSTM approach are enhanced by using the Aquila optimization algorithm (AOA) model. At last, the XAIAOA-WPC method incorporates the XAI model LIME for superior perception and explainability of the black-box process for precise identification of intrusions. The simulation outcome of the XAIAOA-WPC method is examined on a benchmark database. The experimental validation of the XAIAOA-WPC method exhibited a superior accuracy value of 99.29 % over existing techniques.

Enhanced Recognition Memory through Dual Modulation of Brain Carbonic Anhydrases and Cholinesterases.

This study introduces a novel multitargeting strategy that combines carbonic anhydrase (CA) activators and cholinesterase (ChE) inhibitors to enhance cognitive functions. A series of tacrine-based derivatives with amine/amino acid moieties were synthesized and evaluated for their dual activity on brain CA isoforms and ChEs (AChE and BChE). Several derivatives, notably compounds 26, 30, 34, and 40, demonstrated potent CA activation, particularly of hCA II and VII, and strong ChE inhibition with subnanomolar to low nanomolar IC50 values. In vivo studies using a mouse model of social recognition memory showed that these derivatives significantly improved memory consolidation at doses 10-100 times lower than the reference compounds (either alone or in combination). Molecular modeling and ADMET predictions elucidated the compound binding modes and confirmed favorable pharmacokinetic and safety profiles. The findings suggest that dual modulation of CA and ChE activities is a promising strategy for treating cognitive deficits associated with neurodegenerative and psychiatric disorders.

Information perseveration in recognition memory: Examining the scope of sequential dependencies.

Models of recognition memory often assume that decisions are made independently from each other. Yet there is growing evidence that consecutive recognition responses show sequential dependencies, whereby making one response increases the probability of repeating that response from one trial to the next trial. Across six experiments, we replicated this response-related carryover effect using word and nonword stimuli and further demonstrated that the content of the previous trial-both perceptual and conceptual-can also bias the response to the current test probe, with both perceptual (orthographic) and conceptual (semantic) similarity boosting the probability of consecutive "old" responses. Finally, a manipulation of attentional engagement in Experiments 3a and 3b provided little evidence these carryover effects on recognition decisions are merely a product of lapses in attention. Taken together, the current study reinforces prior findings that recognition decisions are not made independently, and that multiple forms of information perseverate across consecutive trials.

Long Short-Term Memory (LSTM) model for Indian sign language recognition

The human-computer interaction process is a vital task in attaining artificial intelligence, especially for a person suffering from hearing or speaking disabilities. Recognizing actions more traditionally known as sign language is a common way for them to interact. Computer vision and Deep learning models are capable of understanding these actions and can simulate them to build up a sustainable learning process. This sign language mechanism will be helpful for both the persons with disabilities and the machines to unbound the gap to achieve intelligence. Therefore, in the proposed work, a real-time sign language system is introduced that is capable of identifying numbers ranging from 0 to 9. The database is acquired from the 8 different subjects respectively and processed to achieve approximately 200k amount of data. Further, a deep learning model named LSTM is used for sign recognition. The results were compared with different approaches and on distinct databases proving the supremacy of the proposed work with 91.50% accuracy. Collection of daily life useful signs and further improving the efficiency of the LSTM model is the research direction for future work. The code and data will be available at https://github.com/rahuln2002/Sign-Language-Recognition-using-LSTM-model.

Hybrid densenet with long short-term memory model for multi-modal emotion recognition from physiological signals

Hybrid densenet with long short-term memory model for multi-modal emotion recognition from physiological signals

Relational and lexical similarity in analogical reasoning and recognition memory: Behavioral evidence and computational evaluation

We examined the role of different types of similarity in both analogical reasoning and recognition memory. On recognition tasks, people more often falsely report having seen a recombined word pair (e.g., flower: garden) if it instantiates the same semantic relation (e.g., is a part of) as a studied word pair (e.g., house: town). This phenomenon, termed relational luring, has been interpreted as evidence that explicit relation representations—known to play a central role in analogical reasoning—also impact episodic memory. We replicate and extend previous studies, showing that relation-based false alarms in recognition memory occur after participants encode word pairs either by making relatedness judgments about individual words presented sequentially, or by evaluating analogies between pairs of word pairs. To test alternative explanations of relational luring, we implemented an established model of recognition memory, the Generalized Context Model (GCM). Within this basic framework, we compared representations of word pairs based on similarities derived either from explicit relations or from lexical semantics (i.e., individual word meanings). In two experiments on recognition memory, best-fitting values of GCM parameters enabled both similarity models (even the model based solely on lexical semantics) to predict relational luring with comparable accuracy. However, the model based on explicit relations proved more robust to parameter variations than that based on lexical similarity. We found this same pattern of modeling results when applying GCM to an independent set of data reported by Popov, Hristova, and Anders (2017). In accord with previous work, we also found that explicit relation representations are necessary for modeling analogical reasoning. Our findings support the possibility that explicit relations, which are central to analogical reasoning, also play an important role in episodic memory.

Recovering population-level model frequency for recognition memory models

Recovering population-level model frequency for recognition memory models

A Qualitative and Quantitative Analysis Strategy for Continuous Turbulent Gas Mixture Monitoring

Electronic noses are one of the predominant technological choices for gas mixture detection, but their application in real-world atmospheric environments still leaves several issues to be resolved. The key bottleneck is the effect of turbulence caused by the diffusion of gases in the atmosphere on the quantitative and qualitative analytical performance of the electronic nose. In light of this, this paper presents a quantitative and qualitative analysis strategy for gas mixture monitoring. This strategy adopts baseline manipulation of the raw sensor data to reduce drift interference, and then performs feature extraction on the multidimensional response signals of the MOS gas sensor array using principal component analysis (PCA). In order to improve gas mixture recognition accuracy, the whale optimization algorithm (WOA) is used to optimize the network structure of the long short-term memory (LSTM) model for turbulent gas mixture composition recognition. The least squares support vector machine (LSSVM) algorithm is adopted to implement turbulent gas mixture concentration prediction. This paper focuses on two aspects of hyper-parameter optimization for the development of an LSSVM with particle swarm optimization (PSO) and for improved training sample selection for the LSSVM which should subsequently increase the accuracy of concentration estimation. The effectiveness of the proposed strategy is evaluated with a dataset from a chemical sensor array exposed to turbulent gas mixtures. Experimental results revealed that the proposed strategy for turbulent gas mixtures has satisfactory outcomes for both qualitative gas composition recognition and quantitative gas concentration prediction.

Does variability in recognition memory scale with mean memory strength or encoding variability in the UVSD model?

The unequal variance signal detection (UVSD) model of recognition memory assumes that the variance of old item memory strength (σo) is typically greater than that of new items. It has been suggested that this old item variance effect can be explained by the encoding variability hypothesis. However, Spanton and Berry (2020) failed to find evidence for this account, suggesting that σo may simply scale with mean memory strength (d) in the UVSD model. Experiments 1 and 2 examined the effects of encoding variability and strength scaling on old item variance by creating conditions in which mean memory strength and variability in item characteristics was either low or high in 2 × 2 factorial designs. In Experiment 1, overall strength determined estimates of σo, with no effect of item characteristic variability. The same effect of overall strength was found in Experiment 2; there was also a significant effect of item characteristic variability, although this manipulation also had some effect on d and was therefore partially confounded. Experiment 3 similarly found a simultaneous increase in old item variance and memory strength in a design using mixed item characteristic variability conditions in a single-study/test block. We conclude that old item variance increases with mean memory strength in the UVSD model, with uncertainty about the effects of encoding variability, and that future explanations of the old item variance effect should bear this in mind.

Second-language phoneme learning positively relates to voice recognition abilities in the native language: Evidence from behavior and brain potentials.

Previous studies suggest a relationship between second-language learning and voice recognition processes, but the nature of such relation remains poorly understood. The present study investigates whether phoneme learning relates to voice recognition. A group of bilinguals that varied in their discrimination of a second-language phoneme contrast participated in this study. We assessed participants’ voice recognition skills in their native language at the behavioral and brain electrophysiological levels during a voice-avatar learning paradigm. Second-language phoneme discrimination positively correlated with behavioral and brain measures of voice recognition. At the electrophysiological level, correlations were present at two time windows and are interpreted within the dual-process model of recognition memory. The results are relevant to understanding the processes involved in language learning as they show a common variability for second-language phoneme and voice recognition processes.

Low‐Cost Data Glove Based on Deep‐Learning‐Enhanced Flexible Multiwalled Carbon Nanotube Sensors for Real‐Time Gesture Recognition

With advancements in artificial intelligence, wearable motion recognition systems based on flexible nanomaterial sensors exhibit excellent potential for harmonious human–machine interaction. However, the sensing stability and demand of large‐scale arrays limit the application of flexible nanomaterial sensors. Herein, a data glove system based on simple multiwalled carbon nanotube (MWCNT) sensors and a lightweight deep‐learning algorithm to achieve accurate gesture recognition is proposed. A regional‐crack mechanism is introduced through the microspine structure to enhance the strain sensitivity. Moreover, an efficient signal processing strategy based on an adaptive wavelet threshold function to improve the robustness and anti‐interference of signals obtained from MWCNT sensors, which exhibit strong generalization and can be used in other nanomaterial strain sensor. Based on the depth‐wise separable convolution, a novel hybrid convolutional neural network (CNN) long short‐term memory (LSTM) model for gesture recognition is constructed. The proposed model achieves an average accuracy of 97.5% and recognition accuracy of 30 gestures with an average recognition time of 2.173 ms based on only five sensors. The fabricated data glove is a promising platform for low‐cost and wearable human–machine interaction that can be directly interfaced in applications such as robotic hands, smart cars, and first‐person shooting games.

Recognition-memory models and ranking tasks: The importance of auxiliary assumptions for tests of the two-high-threshold model

The question of whether recognition memory should be measured assuming continuous memory strength (signal detection theory) or discrete memory states (threshold theory) has become a prominent point of discussion. In light of limitations associated with receiver operating characteristics, comparisons of the rival models based on simple qualitative predictions derived from their core properties were proposed. In particular, K-alternative ranking tasks (KARTs) yield a conditional probability of targets being assigned Rank 2, given that they were not assigned Rank 1, which is higher for strong than for weak targets. This finding has been argued to be incompatible with the two-high-threshold (2HT) model (Kellen & Klauer, 2014). However, we show that the incompatibility only holds under the auxiliary assumption that the probability of detecting lures is invariant under target-strength manipulations. We tested this assumption in two different ways: by developing new model versions of 2HT theory tailored to KARTs and by employing novel forced-choice-then-ranking tasks. Our results show that 2HT models can explain increases in the conditional probability of targets being assigned Rank 2 with target strength. This effect is due to larger 2HT lure-detection probabilities in test displays in which lures are ranked jointly with strong (as compared to weak) targets. We conclude that lure-detection probabilities vary with target strength and recommend that 2HT models should allow for this variation. As such models are compatible with KART performance, our work highlights the importance of carefully adapting measurement models to new paradigms.

Dopamine activity on the perceptual salience for recognition memory.

To survive, animals must recognize relevant stimuli and distinguish them from inconspicuous information. Usually, the properties of the stimuli, such as intensity, duration, frequency, and novelty, among others, determine the salience of the stimulus. However, previously learned experiences also facilitate the perception and processing of information to establish their salience. Here, we propose “perceptual salience” to define how memory mediates the integration of inconspicuous stimuli into a relevant memory trace without apparently altering the recognition of the physical attributes or valence, enabling the detection of stimuli changes in future encounters. The sense of familiarity is essential for successful recognition memory; in general, familiarization allows the transition of labeling a stimulus from the novel (salient) to the familiar (non-salient). The novel object recognition (NOR) and object location recognition (OLRM) memory paradigms represent experimental models of recognition memory that allow us to study the neurobiological mechanisms involved in episodic memory. The catecholaminergic system has been of vital interest due to its role in several aspects of recognition memory. This review will discuss the evidence that indicates changes in dopaminergic activity during exposure to novel objects or places, promoting the consolidation and persistence of memory. We will discuss the relationship between dopaminergic activity and perceptual salience of stimuli enabling learning and consolidation processes necessary for the novel-familiar transition. Finally, we will describe the effect of dopaminergic deregulation observed in some pathologies and its impact on recognition memory.

Bayesian modeling of item heterogeneity in dichotomous recognition memory data and prospects for computerized adaptive testing

Most current models of recognition memory fail to separately model item and person heterogeneity which makes it difficult to assess ability at the latent construct level and prevents the administration of adaptive tests. Here we propose to employ a General Condorcet Model for Recognition (GCMR) in order to estimate ability, response bias and item difficulty in dichotomous recognition memory tasks. Using a Bayesian modeling framework and MCMC inference, we perform 3 separate validation studies comparing GCMR to the Rasch model from IRT and the 2-High-Threshold (2HT) recognition model. First, two simulations demonstrate that recovery of GCMR ability estimates with varying sparsity and test difficulty is more robust and that estimates improve from the two other models under common test scenarios. Then, using a real dataset, face validity is confirmed by replicating previous findings of general and domain-specific age effects (Güsten et al. in Cortex 137:138–148, https://doi.org/10.1016/j.cortex.2020.12.017, 2021). Using cross-validation we show better out-of-sample prediction for the GCMR as compared to Rasch and 2HT model. In addition, we present a hierarchical extension of the model that is able to estimate age- and domain-specific effects directly, without recurring to a two-stage procedure. Finally, an adaptive test using the GCMR is simulated, showing that the test length necessary to obtain reliable ability estimates can be significantly reduced compared to a non-adaptive procedure. The GCMR allows to model trial-by-trial performance and to increase the efficiency and reliability of recognition memory assessments.

Monotonicity of rank order probabilities in signal detection models of simultaneous detection and identification

We examine different models of recognition memory in a simultaneous detection and identification task, which features multiple simultaneously presented test stimuli. A common finding from eyewitness identification research investigating such tasks is that the more confident decision makers are about detecting the presence of a target, the higher the probability that they also correctly identify it. We demonstrate that for members of the signal detection theory (SDT) model framework, predicting such a relationship is — contrary to previous assertions — not entailed by a monotonic diagnosticity ratio. Instead, it can be shown that this prediction follows if latent memory signals’ rank order probabilities exhibit monotonicity under changes in the response criterion. For a selection of common SDT models, we prove that this monotonicity property holds in situations in which two test stimuli are presented simultaneously. Threshold models such as the two-high-threshold model (2HTM), however, do not necessarily possess this feature. Leveraging this fact, we show that in the presence of lures which resemble a target, the 2HTM is unable to make the same predictions as many reasonable SDT models with monotonic rank order probabilities. This enables us to construct a critical, distribution-free test between these models. An empirical investigation implementing this test reveals a clear failure of the 2HTM to account for the qualitative response patterns, which are consistent with the predictions of SDT models with monotonic rank order probabilities.

Testing the foundations of signal detection theory in recognition memory.

Signal detection theory (SDT) plays a central role in the characterization of human judgments in a wide range of domains, most prominently in recognition memory. But despite its success, many of its fundamental properties are often misunderstood, especially when it comes to its testability. The present work examines five main properties that are characteristic of existing SDT models of recognition memory: (a) random-scale representation, (b) latent-variable independence, (c) likelihood-ratio monotonicity, (d) ROC function asymmetry, and (e) nonthreshold representation. In each case, we establish testable consequences and test them against data collected in the appropriately designed recognition-memory experiment. We also discuss the connection between yes-no, forced-choice, and ranking judgments. This connection introduces additional behavioral constraints and yields an alternative method of reconstructing yes-no ROC functions. Overall, the reported results provide a strong empirical foundation for SDT modeling in recognition memory. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Improved Visual Recognition Memory Model Based on Grid Cells for Face Recognition.

Traditional facial recognition methods depend on a large number of training samples due to the massive turning of synaptic weights for low-level feature extractions. In prior work, a brain-inspired model of visual recognition memory suggested that grid cells encode translation saccadic eye movement vectors between salient stimulus features. With a small training set for each recognition type, the relative positions among the selected features for each image were represented using grid and feature label cells in Hebbian learning. However, this model is suitable only for the recognition of familiar faces, objects, and scenes. The model's performance for a given face with unfamiliar facial expressions was unsatisfactory. In this study, an improved computational model via grid cells for facial recognition was proposed. Here, the initial hypothesis about stimulus identity was obtained using the histograms of oriented gradients (HOG) algorithm. The HOG descriptors effectively captured the sample edge or gradient structure features. Thus, most test samples were successfully recognized within three saccades. Moreover, the probability of a false hypothesis and the average fixations for successful recognition were reduced. Compared with other neural network models, such as convolutional neural networks and deep belief networks, the proposed method shows the best performance with only one training sample for each face. Moreover, it is robust against image occlusion and size variance or scaling. Our results may give insight for efficient recognition with small training samples based on neural networks.

Functional Connectivity during Encoding Predicts Individual Differences in Long-Term Memory.

What is the neural basis of individual differences in the ability to hold information in long-term memory (LTM)? Here, we first characterize two whole-brain functional connectivity networks based on fMRI data acquired during an n-back task that robustly predict individual differences in two important forms of LTM, recognition and recollection. We then focus on the recognition memory model and contrast it with a working memory model. Although functional connectivity during the n-back task also predicts working memory performance and the two networks have some shared components, they are also largely distinct from each other: The recognition memory model performance remains robust when we control for working memory, and vice versa. Functional connectivity only within regions traditionally associated with LTM formation, such as the medial temporal lobe and those that show univariate subsequent memory effect, have little predictive power for both forms of LTM. Interestingly, the interactions between these regions and other brain regions play a more substantial role in predicting recollection memory than recognition memory. These results demonstrate that individual differences in LTM are dependent on the configuration of a whole-brain functional network including but not limited to regions associated with LTM during encoding and that such a network is separable from what supports the retention of information in working memory.

Functional Specialization of the Medial Temporal Lobes in Human Recognition Memory: Dissociating Effects of Hippocampal versus Parahippocampal Damage.

A central debate in the systems neuroscience of memory concerns whether different medial temporal lobe (MTL) structures support different processes in recognition memory. Using two recognition memory paradigms, we tested a rare patient (MH) with a perirhinal lesion that appeared to spare the hippocampus. Consistent with a similar previous case, MH showed impaired familiarity and preserved recollection. When compared with patients with hippocampal lesions appearing to spare perirhinal cortex, MH showed greater impairment on familiarity and less on recollection. Nevertheless, the hippocampal patients also showed impaired familiarity compared with healthy controls. However, when replacing this traditional categorization of patients with analyses relating memory performance to continuous measures of damage across patients, hippocampal volume uniquely predicted recollection, whereas parahippocampal, rather than perirhinal, volume uniquely predicted familiarity. We consider whether the familiarity impairment in MH and our patients with hippocampal lesions arises from “subthreshold” damage to parahippocampal cortex (PHC). Our data provide the most compelling neuropsychological support yet for dual-process models of recognition memory, whereby recollection and familiarity depend on different MTL structures, and may support a role for PHC in familiarity. Our study highlights the value of supplementing single-case studies with examinations of continuous brain–behavior relationships across larger patient groups.

Applying confidence accuracy characteristic plots to old/new recognition memory experiments

ABSTRACT Confidence-accuracy characteristic (CAC) plots were developed for use in eyewitness identification experiments, and previous findings show that high confidence indicates high accuracy in all studies of adults with an unbiased lineup. We apply CAC plots to standard old/new recognition memory data by calculating response-based and item-based accuracy, one using false alarms and the other using misses. We use both methods to examine the confidence-accuracy relationship for both correct old responses (hits) and new responses (correct rejections). We reanalysed three sets of published data using these methods and show that the method chosen, as well as the relation of lures to targets, determines the confidence-accuracy relation. Using response-based accuracy for hits, high confidence yields quite high accuracy, and this is generally true with the other methods, especially when lures are unrelated to targets. However, when analyzing correct rejections, the relationship between confidence and accuracy is less pronounced. When lures are semantically related to targets, the various CAC plots show different confidence-accuracy relations. The different methods of calculating CAC plots provide a useful tool in analyzing standard old/new recognition experiments. The results generally accord with unequal-variance signal detection models of recognition memory.