Long-term Memory Component Research Articles

Optimal reconfiguration of distribution systems considering reliability: Introducing long-term memory component AEO algorithm

This article introduces a modified version of the Artificial Ecosystem Optimization (AEO) algorithm, called Long-term Memory Component AEO (LMAEO), for optimizing the reconfiguration of radial distribution networks. The LMAEO algorithm incorporates a long-term memory component, enabling individuals in the population to make decisions based on past experiences. This integration of long-term memory allows the algorithm to explore a wider range of potential solutions during the optimization process, potentially leading to improved performance and better exploration of the solution space. To verify the effectiveness and superiority of the LMAEO technique, it is compared with the conventional AEO algorithm and other well-known algorithms using seven benchmark functions. The proposed LMAEO algorithm successfully addresses the reconfiguration of distribution systems considering reliability for the modified 12-bus, 33-bus and 69-bus IEEE test systems. Leveraging the strengths of AEO and the long-term memory component, the LMAEO algorithm achieves efficient solutions for this problem. To assess the performance of the proposed LMAEO, a comparison is made with the original AEO algorithm. The results demonstrate that the LMAEO technique surpasses the AEO optimizer in terms of optimal reconfiguration of distribution systems jointly considering reliability, system losses and voltage deviations.

Optimizing energy management of hybrid wind generation-battery energy storage units with long-term memory artificial hummingbird algorithm under daily load-source uncertainties in electrical networks

Due to the unpredictable nature of renewable energy sources, it is necessary to have a storage system to store energy during off-peak hours and supply it during peak hours. However, if the storage units in a network are not properly connected, the benefits of the storage system cannot be realized. To improve the performance of radial distribution networks, this research proposes an optimal locating and sizing problem of battery energy storage (BES) and a renewable source of wind turbine distributed generation (WTDG). To solve this allocation problem, a multi-objective function (MOF) based on techno-economic parameters of total active power losses (TAPL), total voltage deviation (TVD), and investment cost of integrated devices is optimized using a modified version of the Artificial Hummingbird Algorithm (AHA) that incorporates a long-term memory component. With this enhancement, the algorithm can make decisions based on multiple past experiences, allowing it to consider a wider range of promising locations during the optimization process. This integration of long-term memory reduces the risk of premature convergence or stagnation and allows the algorithm to gain a broader perspective, potentially leading to improved performance and exploration of the solution space. The results demonstrate the effectiveness of the Long-term Memory Artificial Hummingbird Algorithm (LMAHA) in optimizing the MOF and enhancing the performance of the electrical distribution networks of IEEE 33 and 69 bus.

ATIN: Attention-embedded time-aware imputation networks for production data anomaly detection

Effective identification of anomalous data from production time series in the oilfield affects future analysis and forecasting. Such time series is often characterized by irregular time intervals due to uneven manual sampling, and missing values caused by incomplete measurements. Therefore, the identification task becomes more challenging. In this paper, an Attention-Embedded Time-Aware Imputation Network (ATIN) with two sub-networks is proposed for this task. First, Time-Aware Imputation LSTM (TI-LSTM) is designed for modeling irregular time intervals and incomplete measurements. It decays the long-term memory component as the producing well conditions may be varied during the water cut stage. Second, Attention-Embedding LSTM (ATEM) is designed to improve the effectiveness of anomaly detection. It focuses on the correlation between the last and historical measurements in a given sequence. Comparison experiments with several state-of-the-art methods, including mTAN, GRU-D, T-LSTM, ATTAIN, and BRITS are conducted. Results show that the proposed ATIN performs better in accuracy, F1-score, and area under curve (AUC).

Online continual learning with declarative memory

Deep neural networks are enjoying unprecedented attention and success in recent years. However, catastrophic forgetting undermines the performance of deep models when the training data are arrived sequentially in an online multi-task learning fashion. To address this issue, we propose a novel method named continual learning with declarative memory (CLDM) in this paper. Specifically, our idea is inspired by the structure of human memory. Declarative memory is a major component of long-term memory which helps human beings memorize past experiences and facts. In this paper, we propose to formulate declarative memory as task memory and instance memory in neural networks to overcome catastrophic forgetting. Intuitively, the instance memory recalls the input–output relations (fact) in previous tasks, which is implemented by jointly rehearsing previous samples and learning current tasks as replaying-based methods act. In addition, the task memory aims to capture long-term task correlation information across task sequences to regularize the learning of the current task, thus preserving task-specific weight realizations (experience) in high task-specific layers. In this work, we implement a concrete instantiation of the proposed task memory by leveraging a recurrent unit. Extensive experiments on seven continual learning benchmarks verify that our proposed method is able to outperform previous approaches with tremendous improvements by retaining the information of both samples and tasks.

Long-term memory, synaptic plasticity and dopamine in rodent medial prefrontal cortex: Role in executive functions.

Mnemonic functions, supporting rodent behavior in complex tasks, include both long-term and (short-term) working memory components. While working memory is thought to rely on persistent activity states in an active neural network, long-term memory and synaptic plasticity contribute to the formation of the underlying synaptic structure, determining the range of possible states. Whereas, the implication of working memory in executive functions, mediated by the prefrontal cortex (PFC) in primates and rodents, has been extensively studied, the contribution of long-term memory component to these tasks received little attention. This review summarizes available experimental data and theoretical work concerning cellular mechanisms of synaptic plasticity in the medial region of rodent PFC and the link between plasticity, memory and behavior in PFC-dependent tasks. A special attention is devoted to unique properties of dopaminergic modulation of prefrontal synaptic plasticity and its contribution to executive functions.

Developing a Serial Search Task to Evaluate Procedural Memory and Investigating the Relationship Between Procedural Memory and Grammar in Farsi-speaking Children: A Preliminary Study

Objective: Procedural memory is a key component of long-term memory that can be assessed in different modalities, such as verbal and motor. Learning through procedural memory occurs in three stages: initial learning, consolidation, and retention. Several tasks evaluate procedural memory, but none have concurrently studied procedural learning in various modalities and learning stages. Besides, according to the declarative-procedural model, there may be an association between the performance of individuals in procedural learning and their state of the grammar of a language. Therefore, this study aimed to design a Serial Search Task (SST) for the simultaneous evaluation of procedural memory in motor and verbal modalities during three stages of learning and determine its reliability. Moreover, the relationship between procedural memory and grammar skills was examined. Materials & Methods: This study is methodological research in the first stage and descriptive analytics in the second stage. First, based on the literature review and the opinions of three speech-language pathologists, some words with appropriate psycholinguistic features were chosen from the Persian picture naming set. Then, corresponding pictures were determined. The task was designed using Java programming language. The face validity of the task was evaluated, then after revisions, the reliability was determined. Face validity was assessed in 10 normal children, and the test-retest reliability was evaluated in 15 normal children aged 7-9 years; all of them were chosen using the convenience sampling method. In the second stage, to assess the relationship between grammatical skills and the SST, 20 normal children aged 7-9 years were selected by a mixed sampling method. Every child had exposed to a verbal modality in three stages: initial learning, consolidation (24 hours later), and retention (one week later). Then the same stages were performed in the motor modality. Each child's language skills were determined using the test of language development-primary (TOLD-P3), and the relationship between the two skills was determined. Statistical methods included the Pearson and Spearman correlation coefficients and repeated measures analysis of variance. Results: The correlation values between two performances in the initial learning stage for the reaction time variable in verbal (r=0.84, P<0.001) and motor (r=0.80, P˂0.001) modalities and the variable of response accuracy in verbal (r=0.81, P=0.011) and motor (r=0.77, P=0.026) modalities were obtained. In the consolidation and retention stages, the correlation values for the reaction time variable in verbal (r=0.737, P=0.002) and motor (r=0.743, P=0.001) modalities and the variable of response accuracy in verbal (r=0.624, P=0.013) and motor (r=0.916, P<0.001) modalities were obtained. The relationship between grammar and procedural learning in the verbal modality was significant in the consolidation stage (P=0.045, CI:0.016-0.797, r=0.491). Conclusion: SST is a reliable task to assess different stages of procedural memory in both verbal and motor modalities. Therefore, this task can be used to evaluate procedural memory in normal children aged 7-9 years. The current study's findings confirmed the predictions made by the declarative-procedural model during the consolidation stage in the verbal modality.

Coupling Feasibility Pump and Large Neighborhood Search to solve the Steiner team orienteering problem

The Steiner Team Orienteering Problem (STOP) is defined on a digraph in which arcs are associated with traverse times, and whose vertices are labeled as either mandatory or profitable, being the latter provided with rewards (profits). Given a homogeneous fleet of vehicles M, the goal is to find up to m=|M| disjoint routes (from an origin vertex to a destination one) that maximize the total sum of rewards collected while satisfying a given limit on the route’s duration. Naturally, all mandatory vertices must be visited. In this work, we show that solely finding a feasible solution for STOP is NP-hard and propose a Large Neighborhood Search (LNS) heuristic for the problem. The algorithm is provided with initial solutions obtained by means of the matheuristic framework known as Feasibility Pump (FP). In our implementation, FP uses as backbone a commodity-based formulation reinforced by three classes of valid inequalities. To our knowledge, two of them are also introduced in this work. The LNS heuristic itself combines classical local searches from the literature of routing problems with a long-term memory component based on Path Relinking. We use the primal bounds provided by a state-of-the-art cutting-plane algorithm from the literature to evaluate the quality of the solutions obtained by the heuristic. Computational experiments show the efficiency and effectiveness of the proposed heuristic in solving a benchmark of 387 instances. Overall, the heuristic solutions imply an average percentage gap of only 0.54% when compared to the bounds of the cutting-plane baseline. In particular, the heuristic reaches the best previously known bounds on 382 of the 387 instances. Additionally, in 21 of these cases, our heuristic is even able to improve over the best known bounds.

Modeling the Mental Lexicon as Part of Long-Term and Working Memory and Simulating Lexical Access in a Naming Task Including Semantic and Phonological Cues.

BackgroundTo produce and understand words, humans access the mental lexicon. From a functional perspective, the long-term memory component of the mental lexicon is comprised of three levels: the concept level, the lemma level, and the phonological level. At each level, different kinds of word information are stored. Semantic as well as phonological cues can help to facilitate word access during a naming task, especially when neural dysfunctions are present. The processing corresponding to word access occurs in specific parts of working memory. Neural models for simulating speech processing help to uncover the complex relationships that exist between neural dysfunctions and corresponding behavioral patterns.MethodsThe Neural Engineering Framework (NEF) and the Semantic Pointer Architecture (SPA) are used to develop a quantitative neural model of the mental lexicon and its access during speech processing. By simulating a picture-naming task (WWT 6-10), the influence of cues is investigated by introducing neural dysfunctions within the neural model at different levels of the mental lexicon.ResultsFirst, the neural model is able to simulate the test behavior for normal children that exhibit no lexical dysfunction. Second, the model shows worse results in test performance as larger degrees of dysfunction are introduced. Third, if the severity of dysfunction is not too high, phonological and semantic cues are observed to lead to an increase in the number of correctly named words. Phonological cues are observed to be more effective than semantic cues.ConclusionOur simulation results are in line with human experimental data. Specifically, phonological cues seem not only to activate phonologically similar items within the phonological level. Moreover, phonological cues support higher-level processing during access of the mental lexicon. Thus, the neural model introduced in this paper offers a promising approach to modeling the mental lexicon, and to incorporating the mental lexicon into a complex model of language processing.

When does conscious memory become dependent on the hippocampus? The role of memory load and the differential relevance of left hippocampal integrity for short- and long-term aspects of verbal memory performance.

Supraspan list learning tests are sensitive measures used to assess temporal lobe dysfunction. Most frequently employed is the Rey Auditory Verbal Learning and Memory Test (RAVLT). The test's structure is determined by a short- and long-term memory component. During the first of five learning trials, the short-term memory component is the highest and steadily decreases over the following trials, while the long-term memory component concurrently increases and reaches its maximum at the delayed recall after a retention interval of 30min. The study aimed to test the hypothesis that the functional relevance of left hippocampal integrityfor conscious memory rises along with the increasing degree of the long-term memory component. Moreover, we investigated whether classical measures of short-term and working memory are also dependent on the hippocampus. The analysis was based on 37 adult patients who had undergone surgery for left mesial temporal lobe epilepsy. Neuronal cell densities of the resected left hippocampus were correlated with the presurgical memory performance across trials of the VLMT (the German RAVLT) and with digit span and working memory capacity (WMS-R). Whereas digit span and working memory capacity were not related to hippocampal cell counts, there was a significant correlation between left hippocampal integrity and VLMT memory performance, already regarding the first supraspan learning trial. Correlations steadily increased during the learning course. The highest correlation was seen regarding the delayed free recall. The results indicate an increasing correspondence between the integrity of the left hippocampus and verbal memory with an increasing long-term memory component. Immediate recall of verbal material became already dependent on left hippocampal integrity when the verbal memory load exceeded the memory span (supraspan list learning), while classical span measures that assess verbal short-term and working memory were not affected by left hippocampal pathology.

Analysis of the Noise Immunity of Emission Prediction in the Dynamics of the Heartbeat Using Information about Short-Term and Long-Term Dependencies

A comparative analysis of the effectiveness of prediction using PRT and RIA approaches, using, respectively, exclusively the linear component of long-term memory and, along with the linear, non-linear component, is given. The noise immunity of prediction is considered in both approaches in the presence of ad-ditive noise with a normal or uniform distribution.

Sensory Memory Is Allocated Exclusively to the Current Event-Segment.

The Atkinson-Shiffrin modal model forms the foundation of our understanding of human memory. It consists of three stores (Sensory Memory (SM), also called iconic memory, Short-Term Memory (STM), and Long-Term Memory (LTM)), each tuned to a different time-scale. Since its inception, the STM and LTM components of the modal model have undergone significant modifications, while SM has remained largely unchanged, representing a large capacity system funneling information into STM. In the laboratory, visual memory is usually tested by presenting a brief static stimulus and, after a delay, asking observers to report some aspect of the stimulus. However, under ecological viewing conditions, our visual system receives a continuous stream of inputs, which is segmented into distinct spatio-temporal segments, called events. Events are further segmented into event-segments. Here we show that SM is not an unspecific general funnel to STM but is allocated exclusively to the current event-segment. We used a Multiple-Object Tracking (MOT) paradigm in which observers were presented with disks moving in different directions, along bi-linear trajectories, i.e., linear trajectories, with a single deviation in direction at the mid-point of each trajectory. The synchronized deviation of all of the trajectories produced an event stimulus consisting of two event-segments. Observers reported the pre-deviation or the post-deviation directions of the trajectories. By analyzing observers' responses in partial- and full-report conditions, we investigated the involvement of SM for the two event-segments. The hallmarks of SM hold only for the current event segment. As the large capacity SM stores only items involved in the current event-segment, the need for event-tagging in SM is eliminated, speeding up processing in active vision. By characterizing how memory systems are interfaced with ecological events, this new model extends the Atkinson-Shiffrin model by specifying how events are stored in the first stage of multi-store memory systems.

Hippocampal-cortical contributions to strategic exploration during perceptual discrimination.

The hippocampus is crucial for long-term memory; its involvement in short-term or immediate expressions of memory is more controversial. Rodent hippocampus has been implicated in an expression of memory that occurs on-line during exploration termed "vicarious trial-and-error" (VTE) behavior. VTE occurs when rodents iteratively explore options during perceptual discrimination or at choice points. It is strategic in that it accelerates learning and improves later memory. VTE has been associated with activity of rodent hippocampal neurons, and lesions of hippocampus disrupt VTE and associated learning and memory advantages. Analogous findings of VTE in humans would support the role of hippocampus in active use of short-term memory to guide strategic behavior. We therefore measured VTE using eye-movement tracking during perceptual discrimination and identified relevant neural correlates with functional magnetic resonance imaging. A difficult perceptual-discrimination task was used that required visual information to be maintained during a several second trial, but with no long-term memory component. VTE accelerated discrimination. Neural correlates of VTE included robust activity of hippocampus and activity of a network of medial prefrontal and lateral parietal regions involved in memory-guided behavior. This VTE-related activity was distinct from activity associated with simply viewing visual stimuli and making eye movements during the discrimination task, which occurred in regions frequently associated with visual processing and eye-movement control. Subjects were mostly unaware of performing VTE, thus further distancing VTE from explicit long-term memory processing. These findings bridge the rodent and human literatures on neural substrates of memory-guided behavior, and provide further support for the role of hippocampus and a hippocampal-centered network of cortical regions in the immediate use of memory in on-line processing and the guidance of behavior.

Sensory Memory is Allocated Exclusively to the Current Event Segment

The Atkinson-Shiffrin modal model forms the foundation of our understanding of human memory. It consists of three stores (Sensory Memory (SM), also called iconic memory, Short-Term Memory (STM), and Long-Term Memory (LTM)), each tuned to a different time-scale. Since its inception, the STM and LTM components of the modal model have undergone significant modifications, while, SM has remained largely unchanged, representing a large capacity system funneling information into STM. Moreover, how SM can work in ecological viewing conditions remains an unsolved problem. In the laboratory, visual memory is usually tested by presenting a brief static stimulus and, after a delay, asking observers to report some aspect of the stimulus. However, under ecological viewing conditions, our visual system receives a continuous stream of inputs, which is segmented into distinct spatio-temporal segments, called events. Events are further segmented into event-segments. We show, in a set of 5 experiments, that SM is not an unspecific general funnel to STM but is allocated exclusively to the current event-segment. We presented observers with an event stimulus consisting of multiple dots (n=1 to 4) moving along bilinear trajectories with a single deviation at the mid-point of each trajectory. The synchronized deviations in the trajectories yielded two event-segments, corresponding to pre-deviation and post-deviation event-segments. Observers were cued to report the directions of pre- or post-deviation trajectories. By analyzing observers’ responses in partial- and full-report conditions, we investigated the involvement of SM for the two event-segments. The hallmarks of SM hold only for the current event segment. As the large capacity SM stores only items involved in the current event-segment, the need for event-tagging in SM is eliminated, speeding up processing in active vision. By characterizing how memory systems are interfaced with ecological events, this new model extends the Atkinson-Shiffrin model from laboratory settings to ecological conditions. Meeting abstract presented at VSS 2015

Artificial intelligence meeting neuropsychology. Semantic memory in normal and pathological aging

Artificial intelligence (IA) is the subject of much research, but also many fantasies. It aims to reproduce human intelligence in its learning capacity, knowledge storage and computation. In 2014, the Defense Advanced Research Projects Agency (DARPA) started the restoring active memory (RAM) program that attempt to develop implantable technology to bridge gaps in the injured brain and restore normal memory function to people with memory loss caused by injury or disease. In another IA's field, computational ontologies (a formal and shared conceptualization) try to model knowledge in order to represent a structured and unambiguous meaning of the concepts of a target domain. The aim of these structures is to ensure a consensual understanding of their meaning and a univariant use (the same concept is used by all to categorize the same individuals). The first representations of knowledge in the AI's domain are largely based on model tests of semantic memory. This one, as a component of long-term memory is the memory of words, ideas, concepts. It is the only declarative memory system that resists so remarkably to the effects of age. In contrast, non-specific cognitive changes may decrease the performance of elderly in various events and instead report difficulties of access to semantic representations that affect the semantics stock itself. Some dementias, like semantic dementia and Alzheimer's disease, are linked to alteration of semantic memory. We propose in this paper, using the computational ontologies model, a formal and relatively thin modeling, in the service of neuropsychology: 1) for the practitioner with decision support systems, 2) for the patient as cognitive prosthesis outsourced, and 3) for the researcher to study semantic memory.

Genome-wide Functional Analysis of CREB/Long-Term Memory-Dependent Transcription Reveals Distinct Basal and Memory Gene Expression Programs

Induced CREB activity is a hallmark of long-term memory, but the full repertoire of CREB transcriptional targets required specifically for memory is not known in any system. To obtain a more complete picture of the mechanisms involved in memory, we combined memory training with genome-wide transcriptional analysis of C. elegans CREB mutants. This approach identified 757 significant CREB/memory-induced targets and confirmed the involvement of known memory genes from other organisms, but also suggested new mechanisms and novel components that may be conserved through mammals. CREB mediates distinct basal and memory transcriptional programs at least partially through spatial restriction of CREB activity: basal targets are regulated primarily in nonneuronal tissues, while memory targets are enriched for neuronal expression, emanating from CREB activity in AIM neurons. This suite of novel memory-associated genes will provide a platform for the discovery of orthologous mammalian long-term memory components.

Temporal dynamics of recovery from extinction shortly after extinction acquisition

Evidence suggests that extinction is new learning. Memory acquisition involves both short-term memory (STM) and long-term memory (LTM) components; however, few studies have examined early phases of extinction retention. Retention of auditory fear extinction was examined at various time points. Shortly (1-4 h) after extinction acquisition spontaneous recovery was high compared to that after longer delays (8-24 h). Recall of a consolidated extinction trace was also impaired if it was preceded 1 h by extinction of a novel CS; propranolol did not attenuate this effect. These results suggest poor extinction retention reflects a retrieval impairment caused by the aversive experience of extinction training.

Common Representations between Working Memory and Long-term Memory: Evidence from Event-Related Potentials

Human memory can be characterized as an elaborate network of stored representations. Researchers propose that both working memory (WM) and long-term memory (LTM) utilize same representations, and the representations they activate are in diverse patterns. So the similarity and coherence between WM and LTM are emphasized in the activation-based model, and the priming effect of LTM on WM and vice versa should be both found. By simultaneously recording event-related potentials (ERPs), the present study aimed to investigate two long-term semantic priming effects to illustrate the common representations involved in WM and LTM. Fifteen college students (mean age=20.33±1.91; 7 male) participated in the experiment. The participants were all right-hand, had normal or corrected-to-normal vision and had no neurological or psychological disorders. Each participant signed a consent form prior to experiment and was paid after experiment. We combined a WM component (short-term verbal encoding) and a LTM component (category comparison) into one task, so that WM and LTM processing could be concurrently investigated. LTM priming on WM was manipulated by asking participants to learn before short-term verbal encoding, and the priming effect of WM on related LTM should be found in category comparison. Results revealed two long-term semantic priming effects in behavior data and ERPs. The first effect was found in short-term encoding phase, which was reflected as the priming of LTM on WM. Compared with new targets in WM, the reaction time of studied targets was shorter. They elicitedearlier N2 and P3, and they also decreased N2 amplitude. These may index the so-called neural priming (or repetition suppression) of scalp potentials. However, there was no difference between new and studied distractors in WM. Furthermore, the neural priming of WM on LTM also exhibited in category comparison. Specific contents, which were formerly concerned or ignored in short-term encoding phase, led relevant representations of LTM to diverse activation patterns. N400 elicited by target category (items in which were the same category as the targets in short-term encoding phase)was more positive than that of unprimed category, which also index the neural priming of scalp potentials. But this neural priming was modulated by repetation, that is, when target category was relevant to studied targets rather than new ones, the N400 would be more negative. Results indicate that common representations are used both in WM and in LTM, supporting the activation-based model. Furthermore, these results point to the key role of attention as a modulator in the linkage between WM and LTM.

自传体记忆系统及其神经机制<br>Autobiographical Memory System and Its Neural Correlates

自传体记忆作为一个复杂的，涉及众多因素的长时记忆成分，具有独特的记忆结构；相应地，自传体记忆的神经机制也包括了众多脑区。基于自传体记忆的复杂性，本文总结了先前对于自传体记忆及其神经机制的研究，从记忆系统的角度对自传体记忆包含的认知加工成分及相应的神经机制进行了归纳总结，并就各认知加工成分间的相互联系和相互作用进行了详尽的分析；同时分析了目前自传体记忆神经机制研究存在的一些局限性，包括偏重强调实验条件间差异性而对不同认知加工成分脑机制间的相互联系分析不足，只关注脑区激活的空间定位而忽视时间维度的差异信息等。最后本文提出在将来的研究中需要更多地从系统的观点出发，重视认知加工成分间的相互联系，引入多体素分析和时间序列分析以便能为自传体记忆神经机制研究提供更深入的证据。 Autobiographical memory, with special inherent structure, is a complex component of long-term memory. It has a close relationship with semantic memory and episodic memory and is also connected with many other psychological cognitive processes, such as the emotional process, self-referential process etc. Accordingly, the neural correlates of autobiographical memory consist of many brain areas, including the frontal lobe, temporal lobe, superior parietal lobe and the adjacent occipital area etc. All of the brain areas mentioned above are mainly on the left hemisphere. This article summarizes the previous researches on autobiographical memory and its neural correlates. Given the complexity of autobiographical memory, we divide different cognitive processes of autobiographical memory into 5 operating components from the perspective of memory-system. These operating components include semantic component, episodic component, self-referential component, emotional component and general cognitive processing component (cognitive process related to general memory retrieval), and each component has its underlying neural correlates. What is more important, these operating components interact with each other coherently within the autobiographical memory system and this article discusses these interactions and connections in depth. These interactions analyzed in this article includes the semantic & episodic interaction, the self-referential processing & general cognitive processing interaction, the emotional & episodic interaction, the general cognitive processing & episodic interaction and the general cognitive processing & emotional interaction. By taking the interactions among the operating components into consideration, different ideas and opinions to integrate the experiment findings are offered, and new ways and solutions to settle some heating debates in the field of autobiographical memory are proposed. Furthermore, this article points out that even though the operating components of autobiographical memory system are closely connected with one another, the previous researches didn’t pay enough attention to it. This article analyzes two problems existing in the previous studies. First, most of the previous researches spent much effort in analyzing the different activated brain areas among different experiment tasks, while leaving the common brain activation alone, which might reflect the interaction and connection among the operating components. Second, those studies investigating neural correlates of autobiographical memory using brain imaging techniques tended to overlook the different brain activations at different time points, and these temporal differences may carry important information about the neural mechanism of autobiographical memory. Finally, the article proposes two analytical methods in order to solve the problems mentioned above to some extent. First is using multi-voxel analysis instead of the conventional analysis method to reveal the useful information held by the common activated brain areas, second is employing time series analysis to analyze the changes of brain activation pattern in the temporal dimension. In sum, if researchers are going to investigate the neural mechanism of autobiographical memory thoroughly and offered more convincing evidence, they should treat the various cognitive processes of autobiographical memory as a whole, take the interactions among different components into consideration and pay more attention to the neural correlates underlying these interactions.

Models of verbal working memory capacity: what does it take to make them work?

Theories of working memory (WM) capacity limits will be more useful when we know what aspects of performance are governed by the limits and what aspects are governed by other memory mechanisms. Whereas considerable progress has been made on models of WM capacity limits for visual arrays of separate objects, less progress has been made in understanding verbal materials, especially when words are mentally combined to form multiword units or chunks. Toward a more comprehensive theory of capacity limits, we examined models of forced-choice recognition of words within printed lists, using materials designed to produce multiword chunks in memory (e.g., leather brief case). Several simple models were tested against data from a variety of list lengths and potential chunk sizes, with test conditions that only imperfectly elicited the interword associations. According to the most successful model, participants retained about 3 chunks on average in a capacity-limited region of WM, with some chunks being only subsets of the presented associative information (e.g., leather brief case retained with leather as one chunk and brief case as another). The addition to the model of an activated long-term memory component unlimited in capacity was needed. A fixed-capacity limit appears critical to account for immediate verbal recognition and other forms of WM. We advance a model-based approach that allows capacity to be assessed despite other important processing contributions. Starting with a psychological-process model of WM capacity developed to understand visual arrays, we arrive at a more unified and complete model.

Casimir: an architecture for mental spatial knowledge processing.

Mental spatial knowledge processing often uses spatio-analogical or quasipictorial representation structures such as spatial mental models or mental images. The cognitive architecture Casimir is designed to provide a framework for computationally modeling human spatial knowledge processing relying on these kinds of representation formats. In this article, we present an overview of Casimir and its components. We briefly describe the long-term memory component and the interaction with external diagrammatic representations. Particular emphasis is placed on Casimir's working memory and control mechanisms. Regarding working memory, we describe the conceptual foundations and the processing mechanisms employed in mental spatial reasoning. With respect to control, we explain how it is realized as a distributed, emergent facility within Casimir.