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At least 649 records · Page 36Linked to original sources

PubMed2004

Learning multiple visuomotor transformations: adaptation and context-dependent recall.

Recent motor control theories suggest that the brain uses internal models to plan and control accurate movements. An internal model is thought to represent how the biomechanics of the arm interacting with the outside world would respond to a motor command; therefore it can be seen as a predictive model of the reafference that helps the system plan ahead. Moreover, adaptation studies show that humans can learn multiple internal models. It is not clear, however, whether and how contextual cues are used to switch among competing internal models, which are required to compensate for altered environments. To investigate this question, we asked healthy participants to perform center-out pointing movements under normal and distorted visual feedback (0 degrees , 30 degrees counterclockwise, and 60 degrees clockwise rotation of hand-screen cursor relationships) conditions. The results suggest that humans can learn multiple environments simultaneously and can use contextual cues to facilitate adaptation and to recall the appropriate internal model of the visuomotor transformation.

Adaptation, Physiological↗

PubMedDate not supplied

Boosting: an ensemble learning tool for compound classification and QSAR modeling.

A classification and regression tool, J. H. Friedman's Stochastic Gradient Boosting (SGB), is applied to predicting a compound's quantitative or categorical biological activity based on a quantitative description of the compound's molecular structure. Stochastic Gradient Boosting is a procedure for building a sequence of models, for instance regression trees (as in this paper), whose outputs are combined to form a predicted quantity, either an estimate of the biological activity, or a class label to which a molecule belongs. In particular, the SGB procedure builds a model in a stage-wise manner by fitting each tree to the gradient of a loss function: e.g., squared error for regression and binomial log-likelihood for classification. The values of the gradient are computed for each sample in the training set, but only a random sample of these gradients is used at each stage. (Friedman showed that the well-known boosting algorithm, AdaBoost of Freund and Schapire, could be considered as a particular case of SGB.) The SGB method is used to analyze 10 cheminformatics data sets, most of which are publicly available. The results show that SGB's performance is comparable to that of Random Forest, another ensemble learning method, and are generally competitive with or superior to those of other QSAR methods. The use of SGB's variable importance with partial dependence plots for model interpretation is also illustrated.

ATP Binding Cassette Transporter, Subfamily B, Mem↗

PubMed1988

Social learning theory and the Health Belief Model.

The Health Belief Model, social learning theory (recently relabelled social cognitive theory), self-efficacy, and locus of control have all been applied with varying success to problems of explaining, predicting, and influencing behavior. Yet, there is conceptual confusion among researchers and practitioners about the interrelationships of these theories and variables. This article attempts to show how these explanatory factors may be related, and in so doing, posits a revised explanatory model which incorporates self-efficacy into the Health Belief Model. Specifically, self-efficacy is proposed as a separate independent variable along with the traditional health belief variables of perceived susceptibility, severity, benefits, and barriers. Incentive to behave (health motivation) is also a component of the model. Locus of control is not included explicitly because it is believed to be incorporated within other elements of the model. It is predicted that the new formulation will more fully account for health-related behavior than did earlier formulations, and will suggest more effective behavioral interventions than have hitherto been available to health educators.

Attitude to Health↗

PubMed1993

Prediction of primate splice junction gene sequences with a cooperative knowledge acquisition system.

We propose a cooperative conceptual modelling environment in which two agents interact: the machine and the human expert. The former is able to extract knowledge from data using a symbolic-numeric machine learning system, and the latter is able to control the learning process by accepting and validating the machine results, or by criticizing those results or the explanation that the system produces on them. The improvement of the conceptual modelling relies on the cooperation between the two agents. Results obtained with our method on prediction of primate splice junctions sites in genetic sequences are far better than those reported in the literature with other symbolic machine learning systems, and are as better as those obtained with some artificial neural networks methods reported at present. But in opposite to neural networks which lack of argumentation, our system provides the user a plausible explanation of its prediction.

Algorithms↗

PubMed1993

Is the cerebellum a smith predictor?

The motor system may use internal predictive models of the motor apparatus to achieve better control than would be possible by negative feedback. Several theories have proposed that the cerebellum may form these predictive representations. In this article, we review these theories and try to unify them by reference to an engineering control model known as a Smith Predictor. We suggest that the cerebellum forms two types of internal model. One model is a forward predictive model of the motor apparatus (e.g., limb and muscle), providing a rapid prediction of the sensory consequences of each movement. The second model is of the time delays in the control loop (due to receptor and effector delays, axonal conductances, and cognitive processing delays). This model delays a copy of the rapid prediction so that it can be compared in temporal register with actual sensory feedback from the movement. The result of this comparison is used both to correct for errors in performance and as a training signal to learn the first model. We discuss evidence that the cerebellum could form both of these models and suggest that the cerebellum may hold at least two separate Smith Predictors. One, in the lateral cerebellum, would predict the movement outcome in visual, egocentric, or peripersonal coordinates. Another, in the intermediate cerebellum, would predict the consequences in motor coordinates. Generalization of the Smith Predictor theory is discussed in light of cerebellar involvement in nonmotor control systems, including autonomic functions and cognition.

Journal Article↗

PubMed2006

A computational approach toward label-free protein quantification using predicted peptide detectability.

We propose here a new concept of peptide detectability which could be an important factor in explaining the relationship between a protein's quantity and the peptides identified from it in a high-throughput proteomics experiment. We define peptide detectability as the probability of observing a peptide in a standard sample analyzed by a standard proteomics routine and argue that it is an intrinsic property of the peptide sequence and neighboring regions in the parent protein. To test this hypothesis we first used publicly available data and data from our own synthetic samples in which quantities of model proteins were controlled. We then applied machine learning approaches to demonstrate that peptide detectability can be predicted from its sequence and the neighboring regions in the parent protein with satisfactory accuracy. The utility of this approach for protein quantification is demonstrated by peptides with higher detectability generally being identified at lower concentrations over those with lower detectability in the synthetic protein mixtures. These results establish a direct link between protein concentration and peptide detectability. We show that for each protein there exists a level of peptide detectability above which peptides are detected and below which peptides are not detected in an experiment. We call this level the minimum acceptable detectability for identified peptides (MDIP) which can be calibrated to predict protein concentration. Triplicate analysis of a biological sample showed that these MDIP values are consistent among the three data sets.

Algorithms↗

PubMed2004

Reduction of single-neuron firing uncertainty by cortical ensembles during motor skill learning.

Motor skill learning is usually characterized by shortening of response time and performance of faster, more stereotypical movements. However, little is known about the changes in neural activity that underlie these behavioral changes. Here we used chronically implanted electrode arrays to record neuronal activity in the rat primary motor cortex (MI) as animals learned to execute movements in two directions. Strong modulation of MI single-neuron activity was observed while movement duration of the animal decreased. Despite many learning-induced changes, the precision with which single neurons fire did not improve with learning. Hence, prediction of movement direction from single neurons was bounded. In contrast, prediction of movement direction using neuronal ensembles improved significantly with learning, suggesting that, with practice, neuronal ensembles learn to overcome the uncertainty introduced by single-neuron stochastic activity.

Acoustic Stimulation↗

PubMed1998

Extending principles learned in model systems to clinical trials design.

Clinical results with irinotecan (CPT-11 [Camptosar]) and other camptothecin derivatives in various cancers, although encouraging, have fallen short of the expectations predicted by preclinical models. One proposed explanation for this is that preclinical xenograft models do not predict for the sensitivity of human cancer. In this article, we describe the results of several studies suggesting that this explanation is incorrect. Instead, our results indicate that the discrepancy between clinical response rates and findings in preclinical models may be due to a failure to incorporate the principles learned from preclinical studies into the design of clinical trials. Our analysis suggests that if differences in host tolerance are taken into account, the xenograft models are quite accurate predictors of clinical response. Moreover, application of the principles derived from preclinical models to the design of clinical trials may significantly enhance clinical response rates. Thus, the camptothecin analogs provide a paradigm for better integrated, pharmacokinetically driven, preclinical and clinical development of new drugs.

Animals↗

PubMed2025

Harnessing deep learning for proteome-scale detection of amyloid signaling motifs.

MOTIVATION: Amyloid signaling sequences adopt the cross-β fold that is capable of self-replication in the templating process. Propagation of the amyloid fold from the receptor to the effector protein is used for signal transduction in the immune response pathways in animals, fungi, and bacteria. So far, a dozen of families of amyloid signaling motifs (ASMs) have been classified. Unfortunately, due to the wide variety of ASMs it is difficult to identify them in large protein databases available, which limits the possibility of conducting experimental studies. To date, various deep learning (DL) models have been applied across a range of protein-related tasks, including domain family classification and the prediction of protein structure and protein-protein interactions. RESULTS: In this study, we develop tailor-made bidirectional LSTM and BERT-based architectures to model ASM, and compare their performance against a state-of-the-art machine learning grammatical model. Our research is focused on developing a discriminative model of generalized ASMs, capable of detecting ASMs in large datasets. The DL-based models are trained on a diverse set of motif families and a global negative set, and used to identify ASMs from remotely related families. We analyze how both models represent the data and demonstrate that the DL-based approaches effectively detect ASMs, including novel motifs, even at the genome scale. AVAILABILITY AND IMPLEMENTATION: The models are provided as a Python package, asmscan-bilstm, and a Docker image at https://github.com/chrispysz/asmscan-proteinbert-run. The source code can be accessed at https://github.com/jakub-galazka/asmscan-bilstm and https://github.com/chrispysz/asmscan-proteinbert. Data and results are at https://github.com/wdyrka-pwr/ASMscan.

Deep Learning↗

PubMed2003

A critical evaluation of the force control hypothesis in motor control.

The ability to formulate explicit mathematical models of motor systems has played a central role in recent progress in motor control research. As a result of these modeling efforts and in particular the incorporation of concepts drawn from control systems theory, ideas about motor control have changed substantially. There is growing emphasis on motor learning and particularly on predictive or anticipatory aspects of control that are related to the neural representation of dynamics. Two ideas have become increasingly prominent in mathematical modeling of motor function--forward internal models and inverse dynamics. The notion of forward internal models which has drawn from work in adaptive control arises from the recognition that the nervous system takes account of dynamics in motion planning. Inverse dynamics, a complementary way of adjusting control signals to deal with dynamics, has proved a simple means to establish the joint torques necessary to produce desired movements. In this paper, we review the force control formulation in which inverse dynamics and forward internal models play a central role. We present evidence in its favor and describe its limitations. We note that inverse dynamics and forward models are potential solutions to general problems in motor control--how the nervous system establishes a mapping between desired movements and associated control signals, and how control signals are adjusted in the context of motor learning, dynamics and loads. However, we find little empirical evidence that specifically supports the inverse dynamics or forward internal model proposals per se. We further conclude that the central idea of the force control hypothesis--that control levels operate through the central specification of forces--is flawed. This is specifically evident in the context of attempts to incorporate physiologically realistic muscle and reflex mechanisms into the force control model. In particular, the formulation offers no means to shift between postures without triggering resistance due to postural stabilizing mechanisms.

Animals↗

PubMed2000

Blocking and backward blocking involve learned inattention.

Four experiments examine blocking of associative learning by human participants in a disease diagnosis procedure. The results indicate that after a cue is blocked, subsequent learning about the cue is attenuated. This attenuated learning after blocking is obtained for both standard blocking and for backward blocking. Attenuated learning after blocking cannot be accounted for by theories such as the Rescorla-Wagner model that rely on lack of learning about a redundant cue, nor can it be accounted for by extensions of the Rescorla-Wagner model designed to address backward blocking that encode absent cues with negative values. The results are predicted by the hypothesis that people learn not to attend to the blocked cue.

Adolescent↗

PubMed1997

Neural correlates of motor memory consolidation.

Computational studies suggest that acquisition of a motor skill involves learning an internal model of the dynamics of the task, which enables the brain to predict and compensate for mechanical behavior. During the hours that follow completion of practice, representation of the internal model gradually changes, becoming less fragile with respect to behavioral interference. Here, functional imaging of the brain demonstrates that within 6 hours after completion of practice, while performance remains unchanged, the brain engages new regions to perform the task; there is a shift from prefrontal regions of the cortex to the premotor, posterior parietal, and cerebellar cortex structures. This shift is specific to recall of an established motor skill and suggests that with the passage of time, there is a change in the neural representation of the internal model and that this change may underlie its increased functional stability.

Adult↗

PubMed1987

Short- and long-term prediction of self-reported cigarette smoking in a cohort of late adolescents: report of an 8-year follow-up of public school students.

The purpose of this study was to assess accuracy in predicting adolescent smoking status using attitude, knowledge, behavioral, and sociodemographic variables. A cohort of 4,641 schoolchildren was tested in 1975 (grades 4-6), in 1978 (grades 6-8), and, finally, in 1983 (grades 10-12). From 1978 to 1983, eight variables were used to account for 25% of the variance in smoking status. Over the 8-year period, six variables accounted for 13% of the variance. In both models, prior experience with cigarettes, peer and parental smoking, sex, and student intention were used. Tests of the Ajzen-Fishbein model and social learning theory indicate that social factors were more important than attitudinal ones in predicting future smoking, but these relationships were relatively weak. However, for both long- and short-term prediction, previous behavior proved to be the best predictor. Implications for the design of prevention programs are discussed.

Adolescent↗

PubMed2003

The efficiency of multimedia learning into old age.

BACKGROUND: On the basis of a multimodal model of working memory, cognitive load theory predicts that a multimedia-based instructional format leads to a better acquisition of complex subject matter than a purely visual instructional format. AIMS: This study investigated the extent to which age and instructional format had an impact on training efficiency among both young and old adults. It was hypothesised that studying worked examples that are presented as a narrated animation (multimedia condition) is a more efficient means of complex skill training than studying visually presented worked examples (unimodal condition) and solving conventional problems. Furthermore, it was hypothesised that multimedia-based worked examples are especially helpful for elderly learners, who have to deal with a general decline of working-memory resources, because they address both mode-specific working-memory stores. SAMPLE: The sample consisted of 60 young (mean age = 15.98 years) and 60 old adults (mean age = 64.48 years). METHODS: Participants of both age groups were trained in either a conventional, a unimodal, or a multimedia condition. Subsequently, they had to solve a series of test problems. Dependent variables were perceived cognitive load during the training, performance on the test, and efficiency in terms of the ratio between these two variables. RESULTS: Results showed that for both age groups multimedia-based worked examples were more efficient than the other training formats in that less cognitive load led to at least an equal performance level. CONCLUSION: Although no difference in the beneficial effect of multimedia learning was found between the age groups, multimedia-based instructions seem promising for the elderly.

Adolescent↗

PubMed1976

Effect of modeling on sexual imagery.

Social learning theory was used to examine the effects of a model's sexual imagery on the observer's sexual imagery. In the guise of a creative writing experiment, male and female college students were asked to listen to a tape recording of a same- or opposite-sex model relating a story in response to a sample TAT card. The story described a man and a woman in a physical sex encounter (high sex), a romantic date (medium sex), or a casual study date (low sex). The sample TAT picture and model's story were omitted in the control groups. All subjects wrote stories in response to two other TAT cards. These stories were scored for sexual imagery by a male and a female judge who were blind to experimental conditions and who used a standard sexual imagery scoring manual. The following prediction were based on social learning theory: There would be greater sexual imagery in the stories of subjects who heard the high sex model than in the stories of those who heard the medium or low sex model or no model. Past research implied the prediction that the modeling effects would be greater for males than for females in the high sex model condition and greater for females than for males in the medium sex model condition. The results were analyzed using two factorial analyses of variance. There was greater sexual imagery by subjects who heard the high sex model than by those who heard the low sex model or model. The sexual imagery by subjects who heard the medium sex model was intermediate between that by those who heard the high sex model and that by those who heard the low sex model. The modeling effect was greater in males. The results also confirmed the prediction that sexual imagery would be greater for males in the high sex model condition but did not confirm the prediction that sexual imagery would be greater for females in the medium sex model condition.

Female↗

PubMed2003

Abstract and associatively based representations in human sequence learning.

We give an analysis of performance in an artificial neural network for which the claim had been made that it could learn abstract representations. Our argument is that this network is associative in nature, and cannot develop abstract representations. The network thus converges to a solution that is solely based on the statistical regularities of the training set. Inspired by human experiments that have shown that humans can engage in both associative (statistical) and abstract learning, we present a new, hybrid computational model that combines associative and more abstract, cognitive processes. To cross-validate the model we attempted to predict human behaviour in further experiments. One of these experiments reveals some evidence for the use of abstract representations, whereas the others provide evidence for associatively based performance. The predictions of the hybrid model stand in line with our empirical data.

Computer Simulation↗

PubMed1997

A neural substrate of prediction and reward.

The capacity to predict future events permits a creature to detect, model, and manipulate the causal structure of its interactions with its environment. Behavioral experiments suggest that learning is driven by changes in the expectations about future salient events such as rewards and punishments. Physiological work has recently complemented these studies by identifying dopaminergic neurons in the primate whose fluctuating output apparently signals changes or errors in the predictions of future salient and rewarding events. Taken together, these findings can be understood through quantitative theories of adaptive optimizing control.

Algorithms↗

PubMed2005

Behavioral and neural predictors of upcoming decisions.

Although it is widely known that brain regions such as the prefrontal cortex, the amygdala, and the ventral striatum play large roles in decision making, their precise contributions remain unclear. Here, we used functional magnetic resonance imaging and principles of reinforcement learning theory to investigate the relationship between current reinforcements and future decisions. In the experiment, subjects chose between high-risk (i.e., low probability of a large monetary reward) and low-risk (high probability of a small reward) decisions. For each subject, we estimated value functions that represented the degree to which reinforcements affected the value of decision options on the subsequent trial. Individual differences in value functions predicted not only trial-to-trial behavioral strategies, such as choosing high-risk decisions following high-risk rewards, but also the relationship between activity in prefrontal and subcortical regions during one trial and the decision made in the subsequent trial. These findings provide a novel link between behavior and neural activity by demonstrating that value functions are manifested both in adjustments in behavioral strategies and in the neural activity that accompanies those adjustments.

Adult↗