Music can express people’s thoughts and emotions. Music therapy is to stimulate and hypnotize the human brain by using various forms of music activities, such as listening, singing, playing and rhythm. With the empowerment of artificial intelligence, music therapy technology has made innovative development in the whole process of “diagnosis, treatment and evaluation.” It is necessary to make use of the advantages of artificial intelligence technology to innovate music therapy methods, ensure the accuracy of treatment schemes, and provide more paths (...) for the development of the medical field. This paper proposes an long short-term memory -based generation and classification algorithm for multi-voice music data. A Multi-Voice Music Generation system called MVMG based on the algorithm is developed. MVMG contains two main steps. At first, the music data are modeled to the MDPI and text sequence data by using an autoencoder model, including music features extraction and music clip representation. And then an LSTM-based music generation and classification model is developed for generating and analyzing music in specific treatment scenario. MVMG is evaluated based on the datasets collected by us: the single-melody MIDI files and the Chinese classical music dataset. The experiment shows that the highest accuracy of the autoencoder-based feature extractor can achieve 95.3%. And the average F1-score of LSTM is 95.68%, which is much higher than the DNN-based classification model. (shrink)

This study mainly focuses on the emotion analysis method in the application of psychoanalysis based on sentiment recognition. The method is applied to the sentiment recognition module in the server, and the sentiment recognition function is effectively realized through the improved convolutional neural network and bidirectional long short-term memory model. First, the implementation difficulties of the C-BiL model and specific sentiment classification design are described. Then, the specific design process of the C-BiL model is introduced, and the (...) innovation of the C-BiL model is indicated. Finally, the experimental results of the models are compared and analyzed. Among the deep learning models, the accuracy of the C-BiL model designed in this study is relatively high irrespective of the binary classification, the three classification, or the five classification, with an average improvement of 2.47% in Diary data set, 2.16% in Weibo data set, and 2.08% in Fudan data set. Therefore, the C-BiL model designed in this study can not only successfully classify texts but also effectively improve the accuracy of text sentiment recognition. (shrink)

Through the analysis of emotional tendency in online public opinion, governments and enterprises can stabilize people’s emotion more effectively and maintain social stability. The problem studied in this paper is how to analyze the emotional tendency of online public opinion efficiently, and finally, this paper chooses deep learning algorithm to perform fast analysis of emotional tendency of online public opinion. This paper briefly introduced the structure of the basic model used for emotional tendency analysis of online public opinion and the (...) convolutional neural network model used for text emotion classification. Then, the CNN model was improved by long short-term memory. A simulation experiment was carried out on MATLAB for the improved text emotion classification model to verify the influence of activation function type on the improved model and the performance difference between the improved model and support vector machine and traditional CNN models. The results showed that the improved classification model that adopted the sigmoid activation function had higher accuracy and was less affected by language than the relu and tanh activation functions; the improved classification model had the highest accuracy, recall rate, and F-value in classifying emotional tendency of web texts, followed by the traditional CNN model and the SVM model. (shrink)

We challenge Ruchkin et al.'s claim in reducing short-term memory (STM) to the active part of long-term memory (LTM), by showing that their data cannot rule out the possibility that activation of posterior brain regions could also reflect the contribution of a verbal STM buffer.

The view that short-term memory should be conceived of as being a process based on the activation of long-term memory is inconsistent with neuropsychological evidence. Data from brain-damaged patients, showing specific patterns of impairment, are compatible with a vision of memory as a multiple-component system, whose different aspects, in neurologically unimpaired subjects, show a high degree of interaction.

ABSTRACTWhile some research has now started to suggest that there are long-term memory deficits in alexithymia, short-term memory in alexithymia remained largely unexplored. This study...

Performance on working memory (WM) tasks may partially be supported by long-term memory (LTM) processing. Hence, brain activation recently being implicated in WM may actually have been driven by (incidental) LTM formation. We examined which brain regions actually support successful WM processing, rather than being confounded by LTM processes, during the maintenance and probe phase of a WM task. We administered a four-pair (faces and houses) associative delayed-match-to-sample (WM) task using event-related fMRI and a subsequent associative recognition LTM (...) task, using the same stimuli. This enabled us to analyze subsequent memory effects for both the WM and the LTM test by contrasting correctly recognized pairs with incorrect pairs for either task. Critically, with respect to the subsequent WM effect, we computed this analysis exclusively for trials that were forgotten in the subsequent LTM recognition task. Hence, brain activity associated with successful WM processing was less likely to be confounded by incidental LTM formation. The subsequent LTM effect, in contrast, was analyzed exclusively for pairs that previously had been correctly recognized in the WM task, disclosing brain regions involved in successful LTM formation after successful WM processing. Results for the subsequent WM effect showed no significantly activated brain areas for WM maintenance, possibly due to an insensitivity of fMRI to mechanisms underlying active WM maintenance. In contrast, a correct decision at WM probe was linked to activation in the “retrieval success network” (anterior and posterior midline brain structures). The subsequent LTM analyses revealed greater activation in left dorsolateral prefrontal cortex and posterior parietal cortex in the early phase of the maintenance stage. No supra-threshold activation was found during the WM probe. Together, we obtained clearer insights in which brain regions support successful WM and LTM without the potential confound of the respective memory system. (shrink)

Miller (1956) summarized evidence that people can remember about seven chunks in short-term memory (STM) tasks. However, that number was meant more as a rough estimate and a rhetorical device than as a real capacity limit. Others have since suggested that there is a more precise capacity limit, but that it is only three to five chunks. The present target article brings together a wide variety of data on capacity limits suggesting that the smaller capacity limit is real. (...) Capacity limits will be useful in analyses of information processing only if the boundary conditions for observing them can be carefully described. Four basic conditions in which chunks can be identified and capacity limits can accordingly be observed are: (1) when information overload limits chunks to individual stimulus items, (2) when other steps are taken specifically to block the recoding of stimulus items into larger chunks, (3) in performance discontinuities caused by the capacity limit, and (4) in various indirect effects of the capacity limit. Under these conditions, rehearsal and long-term memory cannot be used to combine stimulus items into chunks of an unknown size; nor can storage mechanisms that are not capacity-limited, such as sensory memory, allow the capacity-limited storage mechanism to be refilled during recall. A single, central capacity limit averaging about four chunks is implicated along with other, noncapacity-limited sources. The pure STM capacity limit expressed in chunks is distinguished from compound STM limits obtained when the number of separately held chunks is unclear. Reasons why pure capacity estimates fall within a narrow range are discussed and a capacity limit for the focus of attention is proposed. Key Words: attention; enumeration; information chunks; memory capacity; processing capacity; processing channels; serial recall; short-term memory; storage capacity; verbal recall; working memory capacity. (shrink)

Emotional design is an important development trend of interaction design. Emotional design in products plays a key role in enhancing user experience and inducing user emotional resonance. In recent years, based on the user's emotional experience, the design concept of strengthening product emotional design has become a new direction for most designers to improve their design thinking. In the emotional interaction design, the machine needs to capture the user's key information in real time, recognize the user's emotional state, and use (...) a variety of clues to finally determine the appropriate user model. Based on this background, this research uses a deep learning mechanism for more accurate and effective emotion recognition, thereby optimizing the design of the interactive system and improving the user experience. First of all, this research discusses how to use user characteristics such as speech, facial expression, video, heartbeat, etc., to make machines more accurately recognize human emotions. Through the analysis of various characteristics, the speech is selected as the experimental material. Second, a speech-based emotion recognition method is proposed. The mel-Frequency cepstral coefficient of the speech signal is used as the input of the improved long and short-term memory network. To ensure the integrity of the information and the accuracy of the output at the next moment, ILSTM makes peephole connections in the forget gate and input gate of LSTM, and adds the unit state as input data to the threshold layer. The emotional features obtained by ILSTM are input into the attention layer, and the self-attention mechanism is used to calculate the weight of each frame of speech signal. The speech features with higher weights are used to distinguish different emotions and complete the emotion recognition of the speech signal. Experiments on the EMO-DB and CASIA datasets verify the effectiveness of the model for emotion recognition. Finally, the feasibility of emotional interaction system design is discussed. (shrink)

High temporal resolution event-related brain potential and electroencephalographic coherence studies of the neural substrate of short-term storage in working memory indicate that the sustained coactivation of both prefrontal cortex and the posterior cortical systems that participate in the initial perception and comprehension of the retained information are involved in its storage. These studies further show that short-term storage mechanisms involve an increase in neural synchrony between prefrontal cortex and posterior cortex and the enhanced activation of (...) class='Hi'>long-term memory representations of material held in short-term memory. This activation begins during the encoding/comprehension phase and evidently is prolonged into the retention phase by attentional drive from prefrontal cortex control systems. A parsimonious interpretation of these findings is that the long-term memory systems associated with the posterior cortical processors provide the necessary representational basis for working memory, with the property of short-term memory decay being primarily due to the posterior system. In this view, there is no reason to posit specialized neural systems whose functions are limited to those of short-term storage buffers. Prefrontal cortex provides the attentional pointer system for maintaining activation in the appropriate posterior processing systems. Short-term memory capacity and phenomena such as displacement of information in short-term memory are determined by limitations on the number of pointers that can be sustained by the prefrontal control systems. Key Words: coherence; event-related potentials; imaging; long-term memory; memory; short-term memory; working memory. (shrink)

Cowan assumes that chunk-based capacity limits are synonymous with the essence of a “specialized STM mechanism.” In a single experiment, we measured the capacity, or span, of long-term memory and found that it, too, corresponds roughly to the magical number 4. The results imply that a chunk-based capacity limit is not a signature characteristic of remembering over the short-term.

We focus on the functional specificity of theta and alpha oscillations and show that theta is related to working memory, whereas alpha is related to semantic long-term memory. Recent studies, however, indicate that alpha oscillations also play an important role during short-term memory retention and retrieval. This latter finding provides support for the basic hypothesis suggested by Ruchkin et al.

There has been a long-standing debate in philosophy and psychology about the role of representation in visual perception. Here, we argue on the basis of evidence from philosophy, psychology, and neuroscience that episodic and schematic memory representations are pivotal to the visual perception of objects and scenes. In the visual perception of objects and scenes, sensory information is initially matched with object and scene templates, or schemas, in long-term memory. The most relevant representations are then selected for (...) encoding in working memory. We furthermore argue that activations of episodic memory representations contribute to the fineness of grain of visual representations. The representational view of visual perception that emerges is what we call the "template tuning view." According to this view, prior information – specifically, long-term memories – shape the representational content of visual perception. In the final section of the chapter, we argue that unlike representational conceptions of visual perception, naïve and direct realist theories have difficulties accommodating these findings. (shrink)

Classification learning is a preeminent human ability within the animal kingdom but the key mechanisms of brain networks regulating learning remain mostly elusive. Recent neuroimaging advancements have depicted human brain as a complex graph machinery where brain regions are nodes and coherent activities among them represent the functional connections. While long-term motor memories have been found to alter functional connectivity in the resting human brain, a graph topological investigation of the short-time effects of learning are still not (...) widely investigated. For instance, classification learning is known to orchestrate rapid modulation of diverse memory systems like short-term and visual working memories but how the brain functional connectome accommodates such modulations is unclear. We used publicly available repositories (openfmri.org) selecting three experiments, two focused on short-term classification learning along two consecutive runs where learning was promoted by trial-by-trial feedback errors, while a further experiment was used as supplementary control. We analyzed the functional connectivity extracted from BOLD fMRI signals, and estimated the graph information processing in the cerebral networks. The information processing capability, characterized by complex network statistics, significantly improved over runs, together with the subject classification accuracy. Instead, null-learning experiments, where feedbacks came with poor consistency, did not provoke any significant change in the functional connectivity over runs. We propose that learning induces fast modifications in the overall brain network dynamics, definitely ameliorating the short-term potential of the brain to process and integrate information, a dynamic consistently orchestrated by modulations of the functional connections among specific brain regions. (shrink)

Although visual long-term memory (VLTM) and visual short-term memory (VSTM) can be distinguished from each other (and from visual sensory storage [SS]), they are embodied within the same modality-specific brain regions, but in very different ways: VLTM as patterns of connectivity and VSTM as patterns of activity. Perception and VSTM do not “activate” VLTM. They use VLTM to create novel patterns of activity relevant to novel circumstances.

The parking of cars is a globally recognized problem, especially at locations where there is a high demand for empty parking spaces. Drivers tend to cruise additional distances while searching for empty parking spaces during peak hours leading to problems, such as pollution, congestion, and driver frustration. Providing short-term predictions of parking availability would facilitate the driver in making informed decisions and planning their arrival to be able to choose parking locations with higher availability. Therefore, the aim of (...) this study is to provide short-term predictions of available parking spaces with a low volume of data. The open parking lot provides parking spaces free of charge and one such parking lot, located beside a shopping center, was selected for this study. Parking availability data for 21 days were collected where 19 days were used for training, while multiple periods of the remaining 2 days were used to test and evaluate the prediction methods. The test dataset consists of data from a weekday and a weekend. Based on the reviewed literature, three prediction methods suitable for short-term prediction were selected, namely, long short-term memory, seasonal autoregressive integrated moving average with exogenous variables, and the Ensemble-based method. The LSTM method is a deep learning-based method, while SARIMAX is a regression-based method, and the Ensemble method is based on decision trees and random forest to provide predictions. The performance of the three prediction methods with a low volume of data and the use of visitor trends data as an exogenous variable was evaluated. Based on the test prediction results, the LSTM and Ensemble-based methods provided better short-term predictions at multiple times on a weekday, while the Ensemble-based method provided better predictions over the weekend. However, the use of visitor trend data did not facilitate improving the predictions of SARIMAX and the Ensemble-based method, while it improved the LSTM prediction for the weekend. (shrink)

The binary distinction De Neys questions has been put forward many times since the beginnings of psychology, in slightly different forms and under different names. It has proved enormously useful and has received detailed empirical support and careful modeling. At heart the distinction is that between knowledge in long-term memory and control processes in short-term memory.

Consistent with Ruchkin and colleagues' proceduralist account, recent research on grouping and verbal-spatial binding in immediate memory shows continuity across short- and long-term retention, and activation of classes of information extending beyond those typically allowed in modular models. However, Ruchkin et al.'s account lacks well-specified mechanisms for the retention of serial order, binding, and the control of activation through attention.

As an explicit organizing metaphor, memory aid, and conceptual framework, the prefrontal cortex may be viewed as a five-member ‘Executive Committee,’ as the prefrontal-control extensions of five sub-and-posterior-cortical systems: the ‘Perceiver’ is the frontal extension of the ventral perceptual stream which represents the world and self in object coordinates; the ‘Verbalizer’ is the frontal extension of the language stream which represents the world and self in language coordinates; the ‘Motivator’ is the frontal cortical extension of a subcortical extended-amygdala stream which (...) represents the world and self in motivational/emotional coordinates; the ‘Attender’ is the frontal cortical extension of a subcortical extended-hippocampal stream which represents the world and self in spatiotemporal coordinates and directs attention to internal and external events; and the ‘Coordinator’ is the frontal extension of the dorsal perceptual stream which represents the world and self in body- and eye-coordinates and controls willed action and working memory. This tutorial review examines the interacting roles of these five systems in perception, working memory, attention, long - term memory, motor control, and thinking. (shrink)

The identity of working-memory and long-term memory representations follows from many lines of evidence. However, the data provided by Ruchkin et al. are hardly compelling, as they make unproved assumptions about hypothetical generators. We cite studies from our lab in which congruent slow-wave topographies were found for short-term and long-term memory tasks, strongly suggesting that both activate identical cell assemblies.

This chapter presents two models of priming. The primary task under consideration is the identification of words presented visually at threshold. The first model, REMI, is a model for long-term priming in implicit memory. It explains repetition priming effects by assuming that during study of a word some contextual information is added to the corresponding lexical trace. This contextual information stored during the study task will tend to match the contextual information present during the test task, leading subjects (...) to prefer studied words over non-studied words. The second model, ROUSE, is a model of short-term priming. ROUSE stands for Responding Optimally with Unknown Sources of Evidence, and it is able to explain an intricate pattern of results. (shrink)

Neural models have proposed how short-term memory (STM) storage in working memory and long-term memory (LTM) storage and recall are linked and interact, but are realized by different mechanisms that obey different laws. The authors' data can be understood in the light of these models, which suggest that the authors may have gone too far in obscuring the differences between these processes.

Allocating attention determines what we remember later. Attentional demands vary in a task-switching paradigm, with greater demands for switch than for repeat trials. This also results in lower subsequent memory performance for switch compared to repeat trials. The main goal of the present study was to investigate the consequences of task switching after a long study-test interval and to examine the contributions of the two memory components, recollection and familiarity. In the study phase, the participants performed a task-switching procedure (...) in which they had to switch between two classifications tasks with pictures. After a short vs. a long study-test interval of a week, the participants performed a surprise memory test for the pictures and gave remember/know judgements. The results showed that recognition memory declined after 1 week and this was mainly due to a decrease in “remember” responses. The results also showed that the task-switching effect on memory was enduring. Whereas the results of the immediate test were mixed, the results of the delayed tests showed that the task-switching effect was based on recollection, expressed in more “remember” responses for repeat than for switch trials. As recollection is more sensitive to attention manipulations than familiarity, the results align with the notion that attentional requirements at study determine what we remember, in particular after a long study-test interval. (shrink)

People unfamiliar with Chinese characters show poorer visual working memory (VWM) performance for Chinese characters than do literates in Chinese. In a series of experiments, we investigated the reasons for this expertise advantage. Experiments 1 and 2 showed that the advantage of Chinese literates does not transfer to novel material. Experts had similar resolution as novices for material outside of their field of expertise, and the memory of novices and experts did not differ when detecting a big change, e.g., when (...) a character’s color was changed. Memorizing appears to function as a rather abstract representation of word forms because memory for characters’ fonts was poor independently of expertise (Experiment 3), though still visual. Distractors that were highly similar conceptually did not increase memory errors, but visually similar distractors impaired memory (Experiment 4). We hypothesized that literates in Chinese represent characters in VWM as tokens of visual word forms made available by long-term memory. In Experiment 5, we provided novices with visual word form knowledge. Participants subsequently performed a change detection task with trained and novel characters in a functional magnetic resonance experiment. We analyzed set size- and training-dependent effects in the intraparietal sulcus (IPS) and the visual word form area. VWM for trained characters was better than for novel characters. Neural activity increased with set size and at a slower rate for trained than for novel characters. All conditions approached the same maximum, but novel characters reached the maximum at a smaller set size than trained characters. The time course of the bold response depended on set size and knowledge status. Starting from the same initial maximum, neural activity at small set sizes returned to baseline more quickly for trained characters than for novel characters. Additionally, high performers showed generally more neural activity in the IPS than low performers. We conclude that experts’ better performance in working memory (WM) is caused by the availability of visual long-term representations (word form types) that allow a sparse representation of the perceived stimuli and make even small changes big because they cause a type change that is easily detected. (shrink)

Statistical learning (SL) involving sensitivity to distributional regularities in the environment has been suggested to be an important factor in many aspects of cognition, including language. However, the degree to which statistically-learned information is retained over time is not well understood. To establish whether or not learners are able to preserve such regularities over time, we examined performance on an artificial second language learning task both immediately after training and also at a follow-up session 2 weeks later. Participants were exposed (...) to an artificial language (Brocanto2), half of them receiving simplified training items in which only 20% of sequences contained complex structures, whereas the other half were exposed to a training set in which 80% of the items were composed of complex sequences. Overall, participants showed signs of learning at the first session and retention at the second, but the degree of learning was affected by the nature of the training they received. Participants exposed to the simplified input outperformed those in the more complex training condition. A GLMM was used to model the relationship between stimulus properties and participants’ endorsement strategies across both sessions. The results indicate that participants in the complex training condition relied more on an item’s chunk strength than those in the simple training condition. Taken together, this set of findings shows that statistically learned regularities are retained over the course of 2 weeks. The results also demonstrate that training on input featuring simple items leads to improved learning and retention of grammatical regularities. (shrink)

The syntagmatic paradigmatic model is a distributed, memory‐based account of verbal processing. Built on a Bayesian interpretation of string edit theory, it characterizes the control of verbal cognition as the retrieval of sets of syntagmatic and paradigmatic constraints from sequential and relational long‐term memory and the resolution of these constraints in working memory. Lexical information is extracted directly from text using a version of the expectation maximization algorithm. In this article, the model is described and then illustrated on (...) a number of phenomena, including sentence processing, semantic categorization and rating, short‐term serial recall, and analogical and logical inference. Subsequently, the model is used to answer questions about a corpus of tennis news articles taken from the Internet. The model's success demonstrates that it is possible to extract propositional information from naturally occurring text without employing a grammar, defining a set of heuristics, or specifying a priori a set of semantic roles. (shrink)

Dartnall claims that visual short-term memory stores nonconceptual content , in the form of compressed images. In this paper I argue against the claim that NCC can be stored in VSTM. I offer four reasons why NCC cannot be stored in visual memory and why only conceptual information can: NCC lasts for a very short time and does not reach either visual short-term memory or visual long-term memory; the content of visual states is (...) stored in memory only if and when object-centered attention modulates visual processing and this modulation signifies the onset of the conceptualization of that content; only categorical high-level information that characterizes conceptual content and not metric and precise iconic information that characterizes NCC can be stored in visual memory for long periods; and if NCC were stored in visual memory then this would allow recognitional judgments pertaining to NCC—one could recognize the precise shade of a color that one had seen before. However NCC does not allow such recognitional judgments. (shrink)

Future work is needed to establish that pure short-term memory is a coherent individual difference attribute that is separable from traditional compound short-term memory measures. Psychometric support for latent pure short-term memory capacity will provide an important starting point for future fine-grained analyses of the intrinsic factors that influence individual differences in math skills.

In debates in philosophy and cognitive science concerning short-term memory mechanisms and perceptual experience, most discussion has focused on the working memory and the various forms of sensory memory such as iconic memory. In this paper, I present a summary of some evidence for a proposed further form of memory termed conceptual short-term memory. I go on to outline some of the ways in which this additional distinctive sort of short-term memory might be of (...) relevance to ongoing philosophical debates, specifically in relation to questions about high-level perceptual phenomenology, the relationship between consciousness and reportability, and the boundary between cognition and perception. I conclude that conceptual short-term memory offers a promising new direction of research and philosophical investigation. (shrink)