Implicit Memory Function Research Articles

Impaired intrinsic functional connectivity among medial temporal lobe and sub-regions related to memory deficits in intracranial dural arteriovenous fistula.

The functional changes concerning memory deficits in dural arteriovenous fistula (dAVF) brain are inadequately understood. This study aimed to understand the functional connectivity alterations of brain regions widely affirmed for explicit and implicit memory functions in dAVF patients (DP) and look into the frequency effects of the altered functional networks. Resting-state functional magnetic resonance imaging (rsfMRI) analysis was done in the memory-associated regions of 30 DP and 30 healthy controls (HC). Frequency decomposition was used to determine potential frequency-dependent functional connectivity changes. They underwent neuropsychological tests and were correlated with changes in memory networks compared with HC. The results showed weaker functional connectivity among the medial temporal lobe and sub-regions in DP suggestive of dysfunction of explicit and implicit memory functions, which corroborated with the positive correlation between memory scores and hippocampal-parahippocampal connectivity of DP, along with a significant group difference of lower memory and cognitive performance in DP assessed by neuropsychological tests. A frequency-dependent study of the altered rsFC revealed lower functional connectivity strength and impaired neural coupling manifested at some sub-band frequencies indicative of disturbed cortical rhythm in DP. This pilot study gives insights into significant intrinsic functional connectivity changes in the memory regions of the dAVF brain. The results may have clinical implications in the choice of interventional management of dAVF and can impact clinical decision making for realizable prevention of progressive memory impairment and irreversible brain damage in such patients.

Experimental Investigation Examining the Effects of Acute Exercise on Implicit Memory Function

Emerging work suggests that acute exercise can enhance explicit memory function. Minimal research, however, has examined whether acute exercise is associated with implicit memory, which was the purpose of this study. Three separate experimental studies were computed (N = 120; Mean age = 21). In Experiment 1, participants were randomly assigned to either a moderate-intensity bout of acute exercise (15-minute) or engaged in a seated control task (15-minute), followed by the completion of a word-fragmentation implicit memory task. Experiment 2 replicated Experiment 1, but instead employed a higher-intensity exercise protocol. For Experiment 3, participants were randomly assigned to either a moderate-intensity bout of acute exercise (15-minute) or engaged in a seated control task (15-minute), followed by the completion of a real world, 3-dimensional implicit memory task. For Experiment 1, the exercise and control groups, respectively, had an implicit memory score of 7.0 (0.5) and 7.5 (0.6) (t(38) = 0.67, p = .51). For Experiment 2, the exercise and control groups, respectively, had an implicit memory score of 6.9 (1.9) and 7.8 (2.4) (t(38) = 1.27, p = .21). These findings suggest that exercise, and the intensity of exercise, does not alter implicit memory from a word fragmentation task. For Experiment 3, the exercise and control groups, respectively, had a discrimination implicit memory index score of 0.48 (0.18) and 0.29 (0.32) (t(38) = 2.16, p = .03). In conclusion, acute exercise does not influence a commonly used laboratory-based assessment of implicit memory but may enhance real world-related implicit memory function.

A league championship algorithm equipped with network structure and backward Q-learning for extracting stock trading rules

Buying and selling stocks are the main activities of investment in the financial markets. During several years, financial experts have designed various indices and indicators to develop intelligent methods to decide whether buy or sell or no trade a specific stock. In this paper, the league championship algorithm (LCA) is adapted and equipped with a network structure (which provides the implicit memory function, compact structure and gives the ability of reusing nodes) for stock trading rule extraction process. The proposed algorithm is able to extract and save various stock trading rules for various kinds of stock market conditions. Each individual in LCA makes role as a sport team which could learn from weaknesses and strengths of others to enhance its performance. The network structure used for each individual is equipped with reinforcement learning to strength the searching power and extracting better rules. The backward Q-learning (BQL), which is a combination of Sarsa learning and Q-learning algorithms, is also hybridized with LCA to increase the learning speed to extract better rules. A trend clustering technique is also developed to determine uptrends and downtrends and to remedy delay in the trend direction reflection along with moving average of stock prices. Finally, to use the trading rules extracted and saved in the rule pools, a new approach through average matching degree is introduced which could create trinary signals of buying, selling, and no trade. The effectiveness of the proposed approach is tested on a set of 20 companies, which have high liquidity and large market capitalization, chosen from various fields of Tehran Stock Exchange. The used historical price data was adjusted through factors of dividends and capital increase. Computational tests and simulations indicate that our hybrid method clearly offers higher profits and lower losses than those of genetic network programming method with rule accumulation (GNP-RA) on all stocks and those of the Buy&Hold strategy on 17 out of 20 instances (85%), especially when the price uptrends are sharp.

Exercise and Implicit Memory: A Brief Systematic Review.

Emerging work demonstrates that acute exercise may enhance explicit memory function. No published systematic reviews have evaluated the totality of research examining the effects of exercise on implicit memory function, which was the purpose of this systematic review. Databases (PubMed, PsychInfo, and Sports Discus) were searched to evaluate animal and human experimental studies evaluating the effects of exercise on implicit memory function. Among the 10 evaluated studies that met inclusionary criteria for this systematic review, 7 were conducted in animal models (rats or mice), with 3 utilizing human samples. Among the seven animal model studies, all employed a fear conditioning paradigm to evaluate implicit memory performance. All six animal studies employing a chronic exercise paradigm demonstrated evidence for associations between chronic exercise and enhanced fear conditioning. The one animal study evaluating a high-intensity acute bout of exercise showed that high-intensity acute exercise worsened implicit memory retrieval. Among the three human studies, one demonstrated a beneficial effect of acute exercise on implicit memory function. There is consistent evidence in animal models that chronic exercise enhances implicit memory, as assessed by fear conditioning. However, there have been too few human studies investigating this topic to render any meaningful conclusions regarding the relationship between exercise and cognitive-based implicit memory among humans.

A nonmusician with severe Alzheimer’s dementia learns a new song

ABSTRACTThe hallmark symptom of Alzheimer’s Dementia (AD) is impaired memory, but memory for familiar music can be preserved. We explored whether a non-musician with severe AD could learn a new song. A 91 year old woman (NC) with severe AD was taught an unfamiliar song. We assessed her delayed song recall (24 hours and 2 weeks), music cognition, two word recall (presented within a familiar song lyric, a famous proverb, or as a word stem completion task), and lyrics and proverb completion. NC’s music cognition (pitch and rhythm perception, recognition of familiar music, completion of lyrics) was relatively preserved. She recalled 0/2 words presented in song lyrics or proverbs, but 2/2 word stems, suggesting intact implicit memory function. She could sing along to the newly learnt song on immediate and delayed recall (24 hours and 2 weeks later), and with intermittent prompting could sing it alone. This is the first detailed study of preserved ability to learn a new song in a non-musician with severe AD, and contributes to observations of relatively preserved musical abilities in people with dementia.

Common and distinct neural effects of risperidone and olanzapine during procedural learning in schizophrenia: a randomised longitudinal fMRI study.

RationaleMost cognitive domains show only minimal improvement following typical or atypical antipsychotic treatments in schizophrenia, and some may even worsen. One domain that may worsen is procedural learning, an implicit memory function relying mainly on the integrity of the fronto-striatal system.ObjectivesWe investigated whether switching to atypical antipsychotics would improve procedural learning and task-related neural activation in patients on typical antipsychotics. Furthermore, we explored the differential effects of the atypical antipsychotics risperidone and olanzapine.MethodsThirty schizophrenia patients underwent functional magnetic resonance imaging during a 5-min procedural (sequence) learning task on two occasions: at baseline and 7–8 weeks later. Of 30 patients, 10 remained on typical antipsychotics, and 20 were switched randomly in equal numbers to receive either olanzapine (10–20 mg) or risperidone (4–8 mg) for 7–8 weeks.ResultsAt baseline, patients (all on typical antipsychotics) showed no procedural learning. At follow-up, patients who remained on typical antipsychotics continued to show a lack of procedural learning, whereas those switched to atypical antipsychotics displayed significant procedural learning (p = 0.001) and increased activation in the superior-middle frontal gyrus, anterior cingulate and striatum (cluster-corrected p < 0.05). These neural effects were present as a linear increase over five successive 30-s blocks of sequenced trials. A switch to either risperidone or olanzapine resulted in comparable performance but with both overlapping and distinct task-related activations.ConclusionsAtypical antipsychotics restore procedural learning deficits and associated neural activity in schizophrenia. Furthermore, different atypical antipsychotics produce idiosyncratic task-related neural activations, and this specificity may contribute to their differential long-term clinical profiles.

Repetitive Transcranial Magnetic Stimulation of the Left Dorsolateral Prefrontal Cortex Improves Probabilistic Category Learning

Traditionally, it has been thought that probabilistic category learning, one of the implicit memory functions, is dependent on the basal ganglia. However, there is growing evidence indicating the involvement of the dorsolateral prefrontal cortex (DLPFC) in probabilistic category learning. In order to identify the causal role of DLPFC in probabilistic category learning, we investigated whether repetitive transcranial magnetic stimulation (rTMS) over the left DLPFC influences the learning ability in healthy subjects using the weather prediction task (WPT). Application of 10Hz rTMS over the left DLPFC induced significant improvement in the total hit rate during the total trials of the WPT, compared with sham stimulation. Specifically, the improvement was significant in the early and late learning blocks of the WPT, but not in the intermediate block of learning. These results indicate that the left DLPFC may play an important role in probabilistic category learning, possibly by influencing not only on embodied information application in late stage of the learning but also on memory encoding for working memory demands in early stage of the learning via frontostriatal and frontohippocampal circuits, respectively. They also may lend support to the possibility that rTMS can improve implicit learning ability in patients with basal ganglia disorders.

Does a BIS-guided Maintenance of Anesthetic Depth Prevent Implicit Memory?

Recent studies investigating the relationship between depth of anesthesia and implicit memory have conflicting results. Limitations of these studies include lack of standardization in surgical procedures and failure to control depth of anesthesia prospectively. We assessed implicit memory function at two different (BIS-guided and pro-spectively controlled) anesthetic depths during surgical stimulus. A list of words was played via headphones to 37 patients during orthopedic surgery under general anesthesia. The Bispectral (BIS) Index was monitored and patients were randomized to remain in a deeper (BIS 40-45) or lighter (BIS 55-60) plane of surgical anesthesia during word presentation. Postoperatively, implicit memory performance was tested using a simple auditory word-stem completion test for presented as well as non-presented words. Absence of explicit memory was evaluated by asking four standard questions regarding intraoperative awareness. All patients received sevoflu-rane and fentanyl for general anesthesia. There was no evidence of implicit memory in either study group. Hit rates for presented and non-presented words were 0.23 ± 0.14 and 0.25 ± 0.09, respectively. No explicit memory was reported by any patient. Depth of anesthesia did not affect implicit memory formation in anesthetized pa-tients undergoing surgery. General anesthesia, even at a higher BIS range, appears to abolish implicit memory as assessed by a simple word-stem completion test.

Generating stock trading rules using genetic network programming with flag nodes and adjustment of importance indexes

AbstractGenetic network programming (GNP) is an evolutionary algorithm which represents its solutions by means of graph structures. Since GNP can create quite compact programs and has an implicit memory function, it works especially well in dynamic environments. In addition, a study of the creation of trading rules on stock markets using GNP with Importance Index (GNP‐IMX) has been made. The IMX is one of the criteria for decision making. However, the values of the IMXs must be determined by experience and knowledge. Therefore, in this paper, IMXs are adjusted appropriately during stock trading in order to predict the rise and fall of the stocks. In addition, newly defined flag nodes are introduced into GNP, which makes it possible to appropriately judge the current situation of the stock prices, and also contributes to the use of many kinds of nodes in the GNP program. In a simulation, programs were evolved using the stock prices of 20 companies. Then the generalization ability of the method was tested and compared with GNP without flag nodes, GNP without IMX adjustment, and Buy &amp; Hold. © 2010 Wiley Periodicals, Inc. Electron Comm Jpn, 94(1): 25–33, 2011; Published online in Wiley Online Library (wileyonlinelibrary.com). DOI 10.1002/ecj.10269

Oral cortisol impairs implicit sequence learning

Glucocorticoids have been shown to affect declarative memory, an explicit form of memory for facts and events operated by medial temporal lobe structures. Recent neuroimaging data suggest that the medial temporal lobe (including the hippocampus) is also active in implicit sequence learning. The aim of the present study was to investigate whether implicit sequence learning may also be affected by glucocorticoid administration. Oral cortisol (30 mg) was given to 29 healthy subjects whereas 31 control subjects received placebo. One hour after treatment all volunteers performed five consecutive blocks of a five-choice serial reaction time task by responding to colored lights by pressing buttons of the same color. The subjects responded without knowing to a quasi-randomized stimulus sequence, including higher-order sequential regularities (a combination of two colors that predicted the following target color). The reaction speed of every button-press (100 per block) was determined and difference scores were calculated as a proof of learning. Both groups showed significant implicit sequence learning throughout the experiment. However, we found an impaired learning performance of the cortisol group compared with the placebo group. Further analysis revealed that a delayed learning in the cortisol group occurred at the very beginning of the task. This study is the first human investigation indicating impaired implicit memory function after exogenous administration of the stress hormone cortisol. This effect may depend on hippocampus engagement in implicit sequence learning, but the involvement of other brain structures is also discussed.

Implicit Memory Phenomena under Anesthesia Are Not Spurious

Emory University School of Medicine, Atlanta, Georgia. ckersse@emory.eduI read with great interest the article by Hadzidiakos et al .1in the August issue of Anesthesiology. These investigators conducted a study of memory function under anesthesia using the process dissociation procedure (PDP), a method that my colleagues and I have used in the same context in the past.2–4In contrast to our studies, Hadzidiakos et al. report no evidence of memory function in terms of word stem completion test performance, a discrepancy for which the authors provide plausible explanations such as the depth of anesthesia and midazolam premedication. However, notwithstanding their null finding, one of the PDP models—the original—produced parameters suggesting the presence of controlled (explicit) and automatic (implicit) memory processes. By extending the model to include guessing parameters, the authors go on to show that the original model produces faulty estimates and that other published results using the original model are faulty. That is, Hadzidiakos et al. find no evidence of any memory processes in three of the four inspected studies when the extended measurement model is applied. They conclude that in these studies there was no contribution (i.e. , evidence) of memory at all and that past findings are spurious.I take issue with this conclusion for several reasons. Foremost, a model that generates discrepant parameters depending on its assumptions or underlying structure should not invalidate the behavioral findings it attempts to model. When significant differences are found in patients' postoperative behavioral responses to old material presented under anesthesia versus new material not presented before, this difference is real and evidences memory for old material regardless of how the underlying process is labeled. Dismissing these behavioral observations ignores an overwhelming body of evidence in favor of implicit memory (“priming”) phenomena in the cognitive psychology and neurology literature and surely cannot have been the intent of Hadzidiakos et al . Although their critique of the original PDP model may be warranted in that the modeling heavily depends on (controversial) assumptions and proper test instructions,5the evidence for or against memory function under anesthesia is based on actual response data and not on models. In many of the anesthesia studies cited by Hadzidiakos et al. , and many more, response tendencies demonstrated memory for material presented under anesthesia, and the quest for understanding this phenomenon continues.6Therefore, it would be wrong to imply or believe that memory function under anesthesia is a spurious phenomenon. Second, the authors failed to include studies that used the extended PDP model and found evidence of automatic memory processes.2,7Although one study may not have properly implemented the PDP methodology and produced skewed estimates as a result,5another found robust evidence of implicit memory function under seemingly adequate levels of anesthesia based on patient response data and Bucher's PDP model.2It is not clear why this evidence was disregarded.I commend the authors on undertaking their study and welcome their critical examination of a popular yet tricky methodological approach but regret their simplified argument and failure to distinguish between modeled and actual reality.Emory University School of Medicine, Atlanta, Georgia. ckersse@emory.edu

Traffic Flow Prediction with Genetic Network Programming (GNP)

In this paper, a method for traffic flow prediction has been proposed to obtain prediction rules from the past traffic data using Genetic Network Programming (GNP). GNP is an evolutionary approach which can evolve itself and find the optimal solutions. It has been clarified that GNP works well especially in dynamic environments since GNP is consisted of directed graph structures, creates quite compact programs and has an implicit memory function. In this paper, GNP is applied to create a traffic flow prediction model. And we proposed the spatial adjacency model for the prediction and two kinds of models forN-step prediction. Additionally, the adaptive penalty functions are adopted for the fitness function in order to alleviate the infeasible solutions containing loops in the training process. Furthermore, the sharing function is also used to avoid the premature convergence.

Genetic Network Programming with Rule Accumulation and its Application to Tile-World Problem

Genetic Network Programming (GNP) is an evolutionary algorithm derived from GA and GP. Directed graph structure, reusability of nodes, and implicit memory function enable GNP to deal with complex problems in dynamic environments efficiently and effectively, as many paper demonstrated. This paper proposed a new method to optimize GNP by extracting and using rules. The basic idea of GNP with Rule Accumulation (GNP with RA) is to extract rules with higher fitness values from the elite individuals and store them in the pool every generation. A rule is defined as a sequence of successive judgment results and a processing node, which represent the good experiences of the past behavior. As a result, the rule pool serves as an experience set of GNP obtained in the evolutionary process. By extracting the rules during the evolutionary period and then matching them with the situations of the environment, we could, for example, guide agents' behavior properly and get better performance of the agents. In this paper, we apply GNP with RA to the problem of determining agents' behaviors in the Tile-world environment in order to evaluate its effectiveness. The simulation results demonstrate that GNP with RA could have better performances than the conventional GNP both in the average fitness value and its stability.

Memory for Music in Alzheimer’s Disease: Unforgettable?

The notion that memory for music can be preserved in patients with Alzheimer's Disease (AD) has been raised by a number of case studies. In this paper, we review the current research examining musical memory in patients with AD. In keeping with models of memory described in the non-musical domain, we propose that various forms of musical memory exist, and may be differentially impaired in AD, reflecting the pattern of neuropathological changes associated with the condition. Our synthesis of this literature reveals a dissociation between explicit and implicit musical memory functions. Implicit, specifically procedural musical memory, or the ability to play a musical instrument, can be spared in musicians with AD. In contrast, explicit musical memory, or the recognition of familiar or unfamiliar melodies, is typically impaired. Thus, the notion that music is unforgettable in AD is not wholly supported. Rather, it appears that the ability to play a musical instrument may be unforgettable in some musicians with AD.

Trading Rules on Stock Markets Using Genetic Network Programming with Sarsa Learning

In this paper, the Genetic Network Programming (GNP) for creating trading rules on stocks is described. GNP is an evolutionary computation, which represents its solutions using graph structures and has some useful features inherently. It has been clarified that GNP works well especially in dynamic environments since GNP can create quite compact programs and has an implicit memory function. In this paper, GNP is applied to creating a stock trading model. There are three important points: The first important point is to combine GNP with Sarsa Learning which is one of the reinforcement learning algorithms. Evolution-based methods evolve their programs after task execution because they must calculate fitness values, while reinforcement learning can change programs during task execution, therefore the programs can be created efficiently. The second important point is that GNP uses candlestick chart and selects appropriate technical indices to judge the timing of the buying and selling stocks. The third important point is that sub-nodes are used in each node to determine appropriate actions (buying/selling) and to select appropriate stock price information depending on the situation. In the simulations, the trading model is trained using the stock prices of 16 brands in 2001, 2002 and 2003. Then the generalization ability is tested using the stock prices in 2004. From the simulation results, it is clarified that the trading rules of the proposed method obtain much higher profits than Buy&amp;Hold method and its effectiveness has been confirmed.

Production and Identification Repetition Priming in AmnesticMild Cognitive Impairment

ABSTRACT The experiment reported here examined implicit memory function, as measured through repetition priming, in amnestic mild cognitive impairment (MCI) to examine whether impairments exist in this aspect of memory function. Young adults, healthy older controls, Alzheimer's disease patients, and MCI participants were asked to perform two types of implicit memory tests (word stem completion and threshold identification repetition priming tasks), as well as a recognition test for studied items. As expected, young adults performed better than the other participants on the recognition test and the word stem completion task; there was equivalent priming across groups on the word identification task. While both the older control and MCI participants showed lower levels of priming on the word stem completion task relative to the young adults, the magnitude of priming was equivalent for these two groups, and reliably greater than that of the dementia participants. These results suggest that not all aspects of memory function are impaired in MCI relative to healthy aging.

フラグノードおよび重要度指標の調整を用いた遺伝的ネットワークプログラミングによる株式売買ルールの生成

Genetic network programming (GNP) is an evolutionary algorithm which represents its solutions by means of graph structures. Since GNP can create quite compact programs and has an implicit memory function, it works especially well in dynamic environments. In addition, a study of the creation of trading rules on stock markets using GNP with Importance Index (GNP-IMX) has been made. The IMX is one of the criteria for decision making. However, the values of the IMXs must be determined by experience and knowledge. Therefore, in this paper, IMXs are adjusted appropriately during stock trading in order to predict the rise and fall of the stocks. In addition, newly defined flag nodes are introduced into GNP, which makes it possible to appropriately judge the current situation of the stock prices, and also contributes to the use of many kinds of nodes in the GNP program. In a simulation, programs were evolved using the stock prices of 20 companies. Then the generalization ability of the method was tested and compared with GNP without flag nodes, GNP without IMX adjustment, and Buy & Hold. © 2010 Wiley Periodicals, Inc. Electron Comm Jpn, 94(1): 25–33, 2011; Published online in Wiley Online Library (wileyonlinelibrary.com). DOI 10.1002/ecj.10269

Buying and Selling Stocks of Multi Brands Using Genetic Network Programming with Control Nodes

A new evolutionary method named “Genetic Network Programming with control nodes, GNPcn” has been applied to determine the timing of buying or selling stocks. GNPcn represents its solutions as directed graph structures which has some useful features inherently. For example, GNPcn has an implicit memory function which memorizes the past action sequences of agents and GNPcn can re-use nodes repeatedly in the network flow, so very compact graph structures can be made. GNPcn can determine the strategy of buying and selling stocks of multi issues. The effectiveness of the proposed method is confirmed by simulations.

A Graph-Based Evolutionary Algorithm: Genetic Network Programming (GNP) and Its Extension Using Reinforcement Learning

This paper proposes a graph-based evolutionary algorithm called Genetic Network Programming (GNP). Our goal is to develop GNP, which can deal with dynamic environments efficiently and effectively, based on the distinguished expression ability of the graph (network) structure. The characteristics of GNP are as follows. 1) GNP programs are composed of a number of nodes which execute simple judgment/processing, and these nodes are connected by directed links to each other. 2) The graph structure enables GNP to re-use nodes, thus the structure can be very compact. 3) The node transition of GNP is executed according to its node connections without any terminal nodes, thus the past history of the node transition affects the current node to be used and this characteristic works as an implicit memory function. These structural characteristics are useful for dealing with dynamic environments. Furthermore, we propose an extended algorithm, "GNP with Reinforcement Learning (GNPRL)" which combines evolution and reinforcement learning in order to create effective graph structures and obtain better results in dynamic environments. In this paper, we applied GNP to the problem of determining agents' behavior to evaluate its effectiveness. Tileworld was used as the simulation environment. The results show some advantages for GNP over conventional methods.

Using the Process Dissociation Procedure: The Meaning and Value of Comparable Base Rates

Using the Process Dissociation Procedure: The Meaning and Value of Comparable Base Rates