False Recognition Research Articles

Conceptual metaphors influence memory automatically: Evidence from a divided attention false memory task.

Previous research has found that reading a list of ostensibly unrelated expressions based on the same underlying conceptual metaphor can evoke false recognition on a memory task for new expressions that use the same metaphor, the so-called conceptual metaphor false memory effect. We examined the automaticity of this effect by dividing participants' attention with a concurrent task. In Study 1, attention was manipulated while participants read the lists of expressions, whereas in Study 2, attention was manipulated both when they read the lists and when they were engaged in the later recognition memory test. Across both studies, the conceptual metaphor false memory effect was observed when conscious processing was limited by dividing attention. These data support the argument that conceptual metaphors are automatically activated when metaphorical expressions are read.

The dark side of corrective feedback: Controlled and automatic influences of retrieval practice.

Corrective feedback is often touted as a critical benefit to learning, boosting testing effects when retrieval is poor and reducing negative testing effects. Here, we explore the dark side of corrective feedback. In three experiments, we found that corrective feedback on multiple-choice (MC) practice questions is later endorsed as the answer to related second-test questions, even though it is no longer correct. We describe this effect as an automatic influence of memory for feedback that participants fail to control. We explored how this influence is affected by the depth of retrieval during practice by successively increasing the retrieval demands of the MC practice test across the three experiments: Experiment 1 used a standard (select a single favorite option) format; Experiment 2 used ranking (rank order the options); and Experiment 3 used elimination testing (provide reasons for rejecting unchosen options). Increasing retrieval depth enhanced controlled influences on a cued-recall second test, evidenced by better accuracy on related versus new questions. However, it did not reduce the automatic influence on accuracy when the second test was MC, partly because repeating the options between practice and test likely led to false recognition of related questions. Together, the results suggest that MC practice tests produce both automatic and controlled memory influences on related second-test questions, with retrieval depth at practice being an important determinant of the controlled influence. However, whether that controlled influence will override the automatic influence of memory for the corrective feedback also depends on the second test format. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

The effect of feedback and recollection rejection instructions on the development of memory monitoring and accuracy

Recollection rejection (a form of memory monitoring) involves rejecting false details on the basis of remembering true details (recall to reject), thereby increasing memory accuracy. This study examined how recollection rejection instructions and feedback affect memory accuracy and false recognition in 5-year-olds, 6- and 7-year-olds, 8- and 9-year-olds, and adults. Participants (N = 336) completed three study–test phases. Instructions and item-level feedback were manipulated during the first two phases, with the third phase including a test containing no instructions or feedback to evaluate learning effects. As predicted, in the younger children, as compared with the older children and adults, we found reduced accuracy scores (hits to studied items minus false alarms to related lures), reduced recollection rejection to related lures, and increased false recognition scores. We also found that, in the third phase, prior feedback reduced false recognition scores, potentially by improving monitoring, and typical developmental differences in false recognition were eliminated. However, there were mixed findings of instructions and feedback, and in some conditions these interventions harmed memory. These findings provide initial evidence that combining instructions and feedback with repeated task practice may improve monitoring effectiveness, but additional work is needed on how these factors improve and sometimes harm performance in young children.

Visual recognition of cherry tomatoes in plant factory based on improved deep instance segmentation

Accurate recognition of cherry tomatoes is a key issue for the automatic picking system in plant factories, which helps to improve picking efficiency and reduce production costs. By using the depth information and considering the prior adjacent constraint between the fruit and the stem of cherry tomatoes, this paper proposes an improved Mask R-CNN for visual recognition of cherry tomatoes. Firstly, the input layer of the network is modified to achieve dual-mode data fusion of RGB and depth images. Secondly, by constructing the corresponding region generation network to indicate the integral constraint between the fruit and the stem, false recognition of branches is reduced. Thirdly, a multi-class prediction subnetwork is used to decouple the pixel-level category predictions of fruit and stem. Meanwhile, multi-task loss balance and adaptive feature pooling are adopted to overcome the limitation caused by the size difference between fruit and stem. The experimental results show that the improved Mask R-CNN achieved an accuracy of 93.76% for fruit recognition, which is 11.53% and 15.5% higher than that of the standard Mask R-CNN and YOLACT, and it achieves an accuracy of 89.34% for stem recognition, which is 13.91% and 19.7% higher than that of the standard Mask R-CNN and YOLACT, respectively. Besides, the recall rate of the proposed method for stem recognition is 94.47%, which is 11.53% and 8.3% higher than that of YOLACT and Mask R-CNN, respectively. In addition, the proposed method takes only 0.04 s to process a single image, providing an efficient approach for automatically picking cherry tomatoes in plant factories.

Application of an Improved YOLOv5 Algorithm in Real-Time Detection of Foreign Objects by Ground Penetrating Radar

Ground penetrating radar (GPR) detection is a popular technology in civil engineering. Because of its advantages of non-destructive testing (NDT) and high work efficiency, GPR is widely used to detect hard foreign objects in soil. However, the interpretation of GPR images relies heavily on the work experience of researchers, which may lead to problems of low detection efficiency and a high false recognition rate. Therefore, this paper proposes a real-time detection technology of GPR based on deep learning for the application of soil foreign object detection. In this study, the GPR image signal is obtained in real time by the GPR instrument and software, and the image signals are preprocessed to improve the signal-to-noise ratio of the GPR image signals and improve the image quality. Then, in view of the problem that YOLOv5 poorly detects small targets, this study improves the problems of false detection and missed detection in real-time GPR detection by improving the network structure of YOLOv5, adding an attention mechanism, data enhancement, and other means. Finally, by establishing a regression equation for the position information of the ground penetrating radar, the precise localization of the foreign matter in the underground soil is realized.

A coarse-to-fine full attention guided capsule network for medical image segmentation

As an important material in clinical diagnoses, medical images are widely used by the doctors to discover the diseases and make therapeutic schedules. Accurately locating the lesions and correctly delineating their severities based on the medical images can significantly help to improve the detection rate and diagnosis accuracy of the diseases. In this paper, we design an effective attention guided capsule network, named HR-CapsSegNet, for segmenting medical targets from medical images. First, by forming a capsule-based high-resolution network architecture assisted by cross-branch multiscale feature augmentation, the HR-CapsSegNet performs advantageously in providing multiscale feature representations with high-level strong semantics. Second, by designing a capsule-based full attention mechanism, the feature encoding quality at each scale can be significantly promoted by sticking up the informative feature semantics from a cross-channel global perspective. In addition, by employing a hierarchical coarse-to-fine segmentation strategy, the feature distractions causing false recognitions can be progressively removed to provide an accurate segmentation map. Intensive quantitative assessments, visual examinations, and comparative analyses on four challenging datasets prove the promising applicability and competitive superiority of the HR-CapsSegNet for medical image segmentation applications.

Fast Speckle Noise Suppression Algorithm in Breast Ultrasound Image Using Three-Dimensional Deep Learning.

The rapid development of ultrasound medical imaging technology has greatly broadened the scope of application of ultrasound, which has been widely used in the screening, diagnosis of breast diseases and so on. However, the presence of excessive speckle noise in breast ultrasound images can greatly reduce the image resolution and affect the observation and judgment of patients’ condition. Therefore, it is particularly important to investigate image speckle noise suppression. In the paper, we propose fast speckle noise suppression algorithm in breast ultrasound image using three-dimensional (3D) deep learning. Firstly, according to the gray value of the breast ultrasound image, the input breast ultrasound image contrast is enhanced using logarithmic and exponential transforms, and guided filter algorithm was used to enhance the details of glandular ultrasound image, and spatial high-pass filtering algorithm was used to suppress the excessive sharpening of breast ultrasound image to complete the pre-processing of breast ultrasound image and improve the image clarity; Secondly, the pre-processed breast ultrasound images were input into the 3D convolutional cloud neural network image speckle noise suppression model; Finally, the edge sensitive terms were introduced into the 3D convolutional cloud neural network to suppress the speckle noise of breast ultrasound images while retaining image edge information. The experiments demonstrate that the mean square error and false recognition rate all reduced to below 1.2% at the 100th iteration of training, and the 3D convolutional cloud neural network is well trained, and the signal-to-noise ratio of ultrasound image speckle noise suppression is greater than 60 dB, the peak signal-to-noise ratio is greater than 65 dB, the edge preservation index value exceeds the experimental threshold of 0.45, the speckle noise suppression time is low, the edge information is well preserved, and the image details are clearly visible. The speckle noise suppression time is low, the edge information is well preserved, and the image details are clearly visible, which can be applied to the field of breast ultrasound diagnosis.

Simulation Evaluation of Calligraphy Art Style Relying on Fuzzy Support Vector Machine Scientific Calculation Algorithm

With the comprehensive follow-up of the information age and the rise of digital technology, the collection of calligraphy art can be preserved in the form of image data. As a testimony to the Chinese 5,000-year history, calligraphy has formed its own genre in the long river of time and its style prevailed in different periods. Nowadays, the recognition technology of characters has become advanced, but there is no better method for the recognition and classification of the artistic style of calligraphy. In addition, there are more and more counterfeiting methods, and computers alone cannot completely replace human identification; therefore, the two need to be integrated and cooperated in the identification work. This article proposes an improved fuzzy support vector machine algorithm based on the aggregation algorithm, which uses the skeleton extraction of calligraphic fonts as the main method. It uses the extracted font style and font morphological characteristics to identify the style of the work and whether it is genuine. In the article, the improvement strategy of the traditional fuzzy support vector machine is proposed, and its detection accuracy is compared with that of the traditional fuzzy support vector machine. The experimental results of this article show that the discrimination rate of authenticity cannot reach 100% and there is a certain degree of error. Through repeated experiments for many times, the value has a certain change, and when the step size is 0.1, calculating the average value can get the value of the false rejection rate and the false recognition rate.

Small-area Fingerprint Recognition Based on Improved ORB Algorithm in Embedded Environment

Most of the fingerprint matching algorithms were proposed for large area fingerprints, which can hardly work effectively in small-area fingerprints. In this work, an improved ORB algorithm is proposed for small-area fingerprint matching in embedded mobile devices. In feature descriptor design, we analyzed the characters of the fingerprint in the embedded mobile devices and discard the multi-scale feature process to reduce the amount of operations. Moreover, we proposed a fusion descriptor combing LBP and rBRIEF descriptor. In the key point matching process, we proposed a two-step (coarse and fine) matching method by using Hamming distance and cosine similarity, respectively. The experimental results show that the proposed method has a rejection rate of 6.4%, a false recognition rate of 0.1%, and an average matching time of 58ms. It can effectively improve the performance of small-area fingerprint matching and meet the application requirements of embedded mobile device authentication.

English Pronunciation Calibration Model Based on Multimodal Acoustic Sensor

In recent years, with the increasing frequency of international exchanges, people have gradually realized that language is a tool of communication and communication, and language learning should attach importance to oral teaching. However, in traditional classrooms, one of the problems faced by oral teaching is the mismatch of the teacher-student ratio: a teacher has to deal with dozens of students, one-on-one oral teaching and pronunciation guidance is impossible, and it is also affected by the teachers and the environment constraints. Therefore, the research on how to efficiently automate pronunciation training is becoming more and more popular. Many phonemes in English have different facial visual features, especially vowels. Almost all of them can be distinguished by the roundness and tightness of the lips in appearance. In order to give full play to the role of lip features in oral pronunciation error detection, this paper proposes a multimodal feature fusion model based on lip angle features. The model interpolates the lip features constructed based on the opening and closing angles and combines audio and video in time series. Feature alignment and fusion and feature learning and classification are realized through the two-way LSTM SOFTMAX layer, and finally, end-to-end pronunciation error detection is realized through CTC. It is verified on the GRID audio and video corpus after phoneme conversion and the self-built multimodal test set. The experimental results show that the model has a higher false pronunciation recognition rate than the traditional single-modal acoustic error detection model. The increase in error detection rate is more obvious. Verification by the audio and video corpus with white noise was added, and the proposed model has better noise immunity than the traditional acoustic model.

Levels of specificity in episodic memory: Insights from response accuracy and subjective confidence ratings in older adults and in younger adults under full or divided attention.

We propose that the specificity with which associations in episodic memory can be remembered varies on a continuum. Older adults have been shown to forget highly specific information (Greene & Naveh-Benjamin, 2020b), and in Experiment 1, we provide further evidence that older adults' deficits in associative memory scale with the amount of specificity that needs to be retrieved. In Experiment 2, we address whether depleted attentional resources, simulated in young adults under divided attention at encoding, could account for older adults' associative memory specificity deficits. Participants studied face-scene pairs and later completed an associative recognition test, with test pairs that were old, highly similar or less similar to old pairs, or completely dissimilar. Participants rated their confidence in their decisions. False positive recognition responses increased with the amount of specificity needed to be retrieved. Whereas older adults' associative memory deficits scaled with how much specific information needed to be remembered, younger adults under divided attention had a more general deficit in associative memory. Confidence-accuracy analysis showed that participants were best able to calibrate their confidence when less specific information was needed to perform well. While divided attention young adults were generally prone to high-confidence errors, older adults' high-confidence errors were most apparent when highly specific information needed to be remembered. These results provide further evidence for levels of specificity in episodic memory. Access to the most specific levels is most vulnerable to forgetting, in line with a specificity principle of memory (Surprenant & Neath, 2009). Further, depleted attentional resources at encoding cannot entirely explain older adults' associative memory specificity deficits. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Aircraft Detection in Remote Sensing Imagery Based on Improved YOLOv4

Abstract A method is provided to resolve the issues of false recognition and lower recall of the you only look once version 4 (YOLOv4) algorithm in the aircraft targets prediction of remote sensing imagery. The method is based on the YOLOv4. According to the characteristics of small size and intensive arrangement of aircraft targets in remote sensing images, the backbone is mended to boost the prediction capacity of smaller targets. To strengthen the transmission and reuse of network features, the connection mode of the residual module in YOLOv4 is changed to the dense connection mode, and the five residual blocks in the backbone network are modified to the corresponding dense connection blocks to reduce the network layers and model redundancy. The experiment results reveal that compared with the YOLOv4, our method can enlarge targets features in smaller image regions, thereby availably increasing the prediction precision of aircraft targets.

Neural spatio-temporal patterns of information processing related to cognitive conflict and correct or false recognitions

Using a visual short-term memory task and employing a new methodological approach, we analyzed neural responses from the perspective of the conflict level and correctness/erroneous over a longer time window. Sixty-five participants performed the short-term memory task in the fMRI scanner. We explore neural spatio-temporal patterns of information processing in the context of correct or erroneous response and high or low level of cognitive conflict using classical fMRI analysis, surface-based cortical data, temporal analysis of interpolated mean activations, and machine learning classifiers. Our results provide evidence that information processing dynamics during the retrieval process vary depending on the correct or false recognition—for stimuli inducing a high level of cognitive conflict and erroneous response, information processing is prolonged. The observed phenomenon may be interpreted as the manifestation of the brain’s preparation for future goal-directed action.

Neural network model for recognition and classification of types of interactions in road traffic

The article presents neural network for recognition of driving strategies based on interactions between drivers in road traffic. It analyzes the architecture of the model implemented as a self-organizing map (SOM), consisting of a group of neural networks based on radial basis functions (RBF). It is a training model grounded in the biological foundations of artificial neural networks, in which the training set should consist exclusively of input vectors; wherein the network training algorithm adjusts itself the network's weights to obtain consistent output vectors (i.e. to make presenting sufficiently close input vectors result into the same outputs). The article presents the results of using a new generation of the neural network developed by us, which includes an adaptive learning algorithm to reduce the effect of re-training (overfitting) and false recognition, as well as to improve the determination of the boundaries between clusters. The aim of the research is to outline architecture and structure of the neural network model that allows recognizing strategical characteristics of driving and can identify strategies of interactions between vehicles (their drivers) in road traffic as well as identify behavioral patterns This paper considers driving strategies that characterize the interaction of dyads of vehicles (drivers) moving in road traffic. The research results show that the SOM RBF neural networks can recognize and classify types of interactions in road traffic based on modelling of the analysis of vehicle movement trajectories. Experimental results demonstrate the neural networks architecture and networks learning involving 400 iterations of streaming the training data representing 500 possible simulated interaction situations. This paper presents a novel neural network model for recognition of drivers' behaviour patterns and for classification of driving strategies into five general classes: (1) competition strategy, (2) contest strategy, (3) evasion strategy, (4) compromise strategy, and (5) active confrontation strategy. This neural network has demonstrated a high rate of recognition and concise clusterization of similar driving strategies. The key contribution of this paper: it proposes a neural network model based on Kohonen's Self-Organizing Map (SOM) for detecting drivers' behaviours from vehicle movement patterns - driving strategies - instead of monitoring driver's specific activities.

A memory-interference versus the "dud"-effect account of a DRM false memory result: Fewer related targets at test, higher critical-lure false recognition.

Memory interference theories hold that exposure to more similar information to a target item impairs memory of the target item. The dud effect refers to the finding in eyewitness lineup identification that fillers dissimilar to the suspect cause more false identification of the suspect than similar fillers, contrary to the interference concept. Previous studies on the Deese–Roediger–McDermott false memory typically showed a testing priming effect that a larger number of studied items presented at test leads to a higher level of false recognition of the critical lure (CL). In the present study, either all, or all but one studied item were replaced by unrelated distractors at test. Subjects made more false recognitions of the CL in the no- or only-one-studied item than in the multiple-studied-item condition, supporting the dud-effect account. The slower response time in the “dud” condition suggested a deliberate, monitoring-like approach taken by subjects in that condition.

False (or biased) memory: Emotion and working memory capacity effects in the DRM paradigm.

This study aimed to investigate the role of emotion and working memory capacity (WMC) on false memory by measuring memory sensitivity independently of response bias. We used the Deese-Roediger-McDermott (DRM) paradigm in which arousal levels were kept constant across positive, negative, and neutral word lists associated with unstudied critical lures. Participants' WMC was measured by the Operation Span Task. Although negative critical lures generated significantly more false recognition (i.e., false-alarm rates) compared to positive or neutral ones, Receiver operating characteristic (ROC) analysis based on signal detection theory showed that this effect could be ascribed to shifts in bias rather than actual memory sensitivity. Data revealed that the DRM effect is a robust illusion influenced by neither emotion nor WMC in terms of memory sensitivity. However, negative words led to a prominent increase in liberal bias to say "old" for both critical and noncritical lures. Furthermore, reaction time (RT) data suggested that mentally activated but actually unstudied critical lures were monitored as old words and that participants were faster to accept negative critical lures than positive or neutral ones, suggesting that the DRM illusion was clearly reflected on the RT data as well. These results were discussed emphasizing the role of negative emotion on response bias in recognition memory.

The refined determination of the ion pair lifetimes in ionic liquids

Assessing lifetimes of liquid’s local structures could bring impactful output. For example, the key transport properties of ionic liquids are governed by local structure dynamics to a large extent. Due to long-range Coulomb interactions, these systems show pronounced charge ordering. In this case, the local structure breaking rate could be described in terms of cation–anion pair lifetimes. Conventional pair lifetime analysis relies on geometrical criterion, defining the maximum distance from cation to anion in pair. In this study, we argue towards additional restriction to ensure pair stability. Based on vibrational properties, we introduce lower boundary for pair lifetime to exclude false pair recognition in the case of collision-like interaction. Also, we put forward a computational algorithm which allows obtaining both mean lifetime and lifetime distribution. The calculations of mean pair lifetimes in molten alkali halides support our reasoning.

Decomposing the semantic processes underpinning veridical and false memories.

The exact semantic processes subserving the formation of false memories are still poorly understood. Here, we directly probed the semantic origins of false memories in a typical Deese-Roediger-McDermott (DRM) task, by predicting participants' performance in this task through data-driven distributional semantic models. Participants were required to study lists of words and then to perform a recognition task. Our findings indicate that the participants' performance is better accounted for by a local rather than a global strategy on the task at hand: the single lists composing the task activate specific semantic clusters that are responsible for the occurrence of false memories. In particular, memory performance followed a continuous semantic gradient, with higher false recognitions occurring for higher sematic similarity between the lures (i.e., the false memory items) and the words in the relative lists. Crucially, our findings also show that semantic memory is differently involved in veridical and false memories, with this pattern being consistent across two reanalyses of data from previous studies and being replicated in an independent experiment. We thus outline an empirically-driven theoretical framework to account for the semantic processes supporting veridical and false memories formation. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Self-Enhanced False Memory Across the Life Span.

The role of self in veridical memory has been extensively studied, but what is the role of self in false memory development across the life span? The current study examined the impact of self-reference on associative false memory in children, younger adults, and older adults, and further investigated possible mechanisms concerning how self-reference might affect false memory in different age groups. Combining a self-reference manipulation with the Deese/Roediger-McDermott (DRM) paradigm, children, younger adults, and older adults encoded DRM word lists as paired with their own name, another person's name, or a red square. Later their true and false recognition memory as well as recollection and familiarity were measured. A self-enhanced false memory effect was found in all age groups. That is, participants generated more false memories in the self-reference condition relative to the other-reference and neutral conditions. Furthermore, when examining its underlying memory mechanisms, we found that self-reference mainly increased false recollection in younger adults but facilitated familiarity of critical lures in older adults. Although self-reference increases false memory in both younger and older adults, the underlying mechanisms are different in that older adults have more self-relevant false familiarity while younger adults generate more self-relevant phantom recollection. The current study also has implications for eyewitness reports, suggesting that the self-relevance of memory may be one relevant factor to consider when evaluating potential risk factors of false memory.

Ошибки эпизодической вербальной памяти у больных шизофренией и в группе здоровых людей с разными профилями шизотипических черт

The aim of the study was to provide a fine grain analysis of errors generated on word list learning tasks by schizophrenia patients and healthy controls with different profiles of schizotypal traits, since it can shed light on the cognitive mechanisms of memory impairment in these cohorts. 511 patients (mean age 33.40±11.53 years; 57% women) and 307 healthy controls (mean age 25.20±7.64 years; 73% women) completed the Rey Auditory Verbal Learning Test (A. Rey, 1964). The healthy subjects were assigned to groups with low, high, positive and negative schizotypy based on the Schizotypal Personality Questionnaire adapted by A.G. Efremov and S.N. Enikolopov (2002). In patients, symptomatology was assessed with the Positive and Negative Syndrome Scale adapted by S.N. Mosolov (2001). We analyzed the number of extra-list intrusions, their perseverations, frequency and strength of the associations with stimuli, as well as phonetic intrusions, intrusions from interference trails, and false recognition of semantic and phonetic lures. The results showed that patients are characterized by an increase in the number of extra-list intrusions and their perseverations, proactive interference and recognition of semantic lures; similar trends regarding extra-list intrusions and false recognitions were observed in the high schizotypy group. The data can be interpreted in favor of the idea of impairment of reality monitoring and insufficient encoding of stimulus’s surface features as inherent deficits of information processing in schizophrenia.