SEARCH · PubMed Health

Results for “Predictive Learning Models”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 883 records · Page 49Linked to original sources

PubMed2004

Strategy execution in cognitive skill learning: an item-level test of candidate models.

This article investigates the transition to memory-based performance that commonly occurs with practice on tasks that initially require use of a multistep algorithm. In an alphabet arithmetic task, item response times exhibited pronounced step-function decreases after moderate practice that were uniquely predicted by T. C. Rickard's (1997) component power laws model. The results challenge parallel strategy execution models as developed to date and they demonstrate that the shift to retrieval is an item-specific, as opposed to task-general, learning phenomenon. The results also call into question the entire class of smooth speed-up functions as global empirical learning laws. It is shown that overlaying of averaged item fits on averaged data can provide a sensitive test for model sufficiency. Strategy probes agreed with strategy inferences that were based on step-function speed-up patterns, supporting the validity of the probing technique.

Cognition↗

PubMed2001

Effects of Training Goals and Goal Orientation Traits on Multidimensional Training Outcomes and Performance Adaptability.

This research examined the effects of mastery vs. performance training goals and learning and performance goal orientation traits on multidimensional outcomes of training. Training outcomes included declarative knowledge, knowledge structure coherence, training performance, and self-efficacy. We also examined the unique impact of the training outcomes on performance adaptability by predicting generalization to a more difficult and complex version of the task. The experiment involved 60 trainees learning a complex computer simulation over 2 days. The research model posited independent effects for training goals relative to goal orientation traits and independent contributions of training outcomes to the performance adaptability of trainees. The findings were consistent with the proposed model. In particular, self-efficacy and knowledge structure coherence made unique contributions to the prediction of performance adaptability after controlling for prior training performance and declarative knowledge. Implications and extensions are discussed. Copyright 2001 Academic Press.

Journal Article↗

PubMed2004

The "genetics" of driving behavior: parents' driving style predicts their children's driving style.

It can be hypothesized that children inherit their parents' driving habits both through genetic disposition and model learning. A few studies have shown indeed that parents' and their children's traffic convictions and accidents correlate which, however, may be due to life style and other exposure factors. This study aimed at investigating the relationships between parents' and their children's self-reported driving behavior. The subjects were 174 parent-child pairs who independently completed a questionnaire. Driving behavior-driving style-was evaluated by means of Manchester driver behavior questionnaire (DBQ), while data about driving exposure, life style, accidents, and traffic tickets were also collected. A series of regression models indicated that parents' self-reported driving behavior explains their children's respective self-reported behavior, even when exposure and demographic and life-style factors are controlled.

Adolescent↗

PubMed2026

UFold-X: an enhanced Dual & Dynamic U-Mamba model for long-range RNA secondary structure prediction.

RNA secondary structure is essential for understanding the functions of non-coding RNAs, ribosomal RNAs, and viral genomes. However, accurate prediction of long RNA structures remains challenging due to complex long-range interactions and the limited availability of long-RNA training data. We present UFold-X, a dual-branch deep learning framework that combines a convolutional encoder for local structure modeling with a Mamba-based Visual State Space Module for capturing long-range dependencies. A dynamic gating mechanism adaptively integrates the two branches according to sequence length. UFold-X was evaluated on multiple benchmark datasets containing RNAs up to 5000 nucleotides. To rigorously assess generalization, we introduced a cross-clan benchmark for long RNAs. Under this stringent setting, UFold-X achieved performance comparable to state-of-the-art classical approaches while achieving the best performance among deep learning-based methods. Additional cross-family and within-family evaluations further demonstrated robust transferability and competitive predictive performance. UFold-X also maintained excellent computational efficiency, requiring only 0.08 s per sequence on average. To assess biological consistency, we developed a SHAPE-based reactivity prediction variant (UFold-X-R) and an integrated metric, the Hybrid Reactivity-Pairing Score (HRPS). UFold-X-R showed strong agreement with experimental icSHAPE data and achieved the highest HRPS among all evaluated methods. A user-friendly web server is available at https://ufold-x.ai4bread.com.

Nucleic Acid Conformation↗

PubMed1994

Medical students' motivation for internal medicine.

OBJECTIVE: To verify that motivational concepts tested in other educational settings are relevant to understanding medical students' choice of a career in internal medicine. More specifically, to compare the effects of "facilitating students' interest" versus "controlling students' learning" as educational models during the internal medicine clerkship. DESIGN: An observational retrospective study of 89 fourth-year medical students. Structural equation modeling compared the two models statistically. MAIN OUTCOME MEASURE: Student choice of internal medicine residency. RESULTS: Instructors who supported students' autonomy engendered in students greater feelings of competence and interest in internal medicine than did controlling instructors. Perceived competence further enhanced students' interest in internal medicine. In turn, interest predicted students' choosing an internal medicine residency. Overall, the facilitating students' interest model better explained students' choice of internal medicine than did the controlling students' learning model. CONCLUSIONS: The results verify that the nature of the learning climate during the internal medicine clerkship is an important predictor of students' subsequent pursuit of internal medicine training. Instructors who teach in an autonomy-supportive manner enhance students' perceived competence and interest in internal medicine, which increases the likelihood they will select an internal medicine residency.

Career Choice↗

PubMedDate not supplied

Predicting the learning and consultation time in a computerized primary healthcare clinic.

Managers would like to know how long it takes healthcare service providers to achieve the same throughput of patients per day that they were used to with a pen-and-paper system. This study has been undertaken to derive a model for predicting the time it takes a service provider from a previously disadvantaged community to enter a patient's record in terms of his or her experience and the number of data units that have to be captured. A model was also derived to predict the average consultation time in terms of the number of data units that are captured by an experienced service provider. It can be inferred that healthcare service providers should be allowed at least 6 months of computerized system experience before any decisions about the success of the technology introduction can be made.

Ambulatory Care Information Systems↗

PubMed2002

Limited attention and cue order consistency affect predictive learning: a test of similarity measures.

The authors empirically tested the similarity metrics underlying 2 predictive-learning theories: J. K. Kruschke's (1992) attention learning covering map and J. M. Pearce's (1987, 1994) configural models. In Experiment 1, participants concurrently learned 3 types of discriminations: simple (A- vs. B+), common cue (XC- vs. XD+), and compound (YE- vs. ZF+). Accuracy was ordered: simple > compound > common cue. Neither model anticipated this ordering. In Experiment 2, cue order in 2-element configurations was either inconsistent (e.g., YE and EY) as in Experiment 1 or consistent (e.g., EY throughout). Although accuracy differences were smaller under consistent ordering, the relative difficulty of the tasks was the same as in Experiment 1. In Experiment 3, common cue and compound discriminations were tested in different participants to determine whether the ordering of difficulty in Experiments 1 and 2 was caused by differential generalization mediated by the number of elements; the ordering was the same as in Experiments 1 and 2. These results suggest the need for differential attention to event presence and absence and to mechanisms that incorporate limited attentional capacity.

Analysis of Variance↗

PubMed2002

Learning and predicting time series by neural networks.

Artificial neural networks which are trained on a time series are supposed to achieve two abilities: first, to predict the series many time steps ahead and second, to learn the rule which has produced the series. It is shown that prediction and learning are not necessarily related to each other. Chaotic sequences can be learned but not predicted while quasiperiodic sequences can be well predicted but not learned.

Algorithms↗

PubMed1999

Quantitative examinations of internal representations for arm trajectory planning: minimum commanded torque change model.

Quantitative examinations of internal representations for arm trajectory planning: minimum commanded torque change model. A number of invariant features of multijoint planar reaching movements have been observed in measured hand trajectories. These features include roughly straight hand paths and bell-shaped speed profiles where the trajectory curvatures between transverse and radial movements have been found to be different. For quantitative and statistical investigations, we obtained a large amount of trajectory data within a wide range of the workspace in the horizontal and sagittal planes (400 trajectories for each subject). A pair of movements within the horizontal and sagittal planes was set to be equivalent in the elbow and shoulder flexion/extension. The trajectory curvatures of the corresponding pair in these planes were almost the same. Moreover, these curvatures can be accurately reproduced with a linear regression from the summation of rotations in the elbow and shoulder joints. This means that trajectory curvatures systematically depend on the movement location and direction represented in the intrinsic body coordinates. We then examined the following four candidates as planning spaces and the four corresponding computational models for trajectory planning. The candidates were as follows: the minimum hand jerk model in an extrinsic-kinematic space, the minimum angle jerk model in an intrinsic-kinematic space, the minimum torque change model in an intrinsic-dynamic-mechanical space, and the minimum commanded torque change model in an intrinsic-dynamic-neural space. The minimum commanded torque change model, which is proposed here as a computable version of the minimum motor command change model, reproduced actual trajectories best for curvature, position, velocity, acceleration, and torque. The model's prediction that the longer the duration of the movement the larger the trajectory curvature was also confirmed. Movements passing through via-points in the horizontal plane were also measured, and they converged to those predicted by the minimum commanded torque change model with training. Our results indicated that the brain may plan, and learn to plan, the optimal trajectory in the intrinsic coordinates considering arm and muscle dynamics and using representations for motor commands controlling muscle tensions.

Adult↗

PubMed2005

Modeling individual differences in cognition.

Many evaluations of cognitive models rely on data that have been averaged or aggregated across all experimental subjects, and so fail to consider the possibility of important individual differences between subjects. Other evaluations are done at the single-subject level, and so fail to benefit from the reduction of noise that data averaging or aggregation potentially provides. To overcome these weaknesses, we have developed a general approach to modeling individual differences using families of cognitive models in which different groups of subjects are identified as having different psychological behavior. Separate models with separate parameterizations are applied to each group of subjects, and Bayesian model selection is used to determine the appropriate number of groups. We evaluate this individual differences approach in a simulation study and show that it is superior in terms of the key modeling goals of prediction and understanding. We also provide two practical demonstrations of the approach, one using the ALCOVE model of category learning with data from four previously analyzed category learning experiments, the other using multidimensional scaling representational models with previously analyzed similarity data for colors. In both demonstrations, meaningful individual differences are found and the psychological models are able to account for this variation through interpretable differences in parameterization. The results highlight the potential of extending cognitive models to consider individual differences.

Cognition↗

PubMed1983

[Spatial orientation capability in the elderly].

A labyrinth-learning experiment was conducted with 83 elderly subjects (median 53 years), which was validated against the architectural "reality" of the clinic in which the test was conducted. The orientation capability of psychologically normal subjects was compared with the performance of patients with a depressive syndrome and/or with an organic mental syndrome. Factor Analysis revealed the independent influence of component functions, the most important being spatial imaging, memory, goal-seeking orientation, labyrinth learning and right-left coordination. Orientation in reality is not accurately predicted by performance on the labyrinth test model. Contrary to our expectation, the presence of OMS alone did not decrease performance; however the presence of a depressive syndrome, both alone as well as in conjunction with OMS did. Diagnostic and therapeutic implications are discussed.

Aged↗

PubMed1992

Effects of similarity and repetition on memory: registration without learning?

We investigated judgments of the frequency of test items (Y) that were highly similar to studied items (X) to test a prediction made by several memory models: that the judged frequency of Y should be proportional to the judged frequency of X. Whether stimuli were pictures or words, judged frequency of Y was bimodally distributed with 1 mode at zero, suggesting that frequency judgments involve a 2-stage process in which a zero judgment is made if there is a mismatch between retrieved information and the test item. Nonzero judgements, taken by themselves, were consistent with the prediction of proportionality. In 2 experiments, the percentage of zero judgments made to Y increased with repetition of X, but in 2 others the percentage did not change beyond frequency = 1. The percentage of "new" judgments in recognition memory followed this same pattern. Because the judged frequency of X increased even as X-Y discrimination showed no improvement, we characterize the result as "registration without learning."

Adult↗

PubMed2005

Grammatical inference in bioinformatics.

Bioinformatics is an active research area aimed at developing intelligent systems for analyses of molecular biology. Many methods based on formal language theory, statistical theory, and learning theory have been developed for modeling and analyzing biological sequences such as DNA, RNA, and proteins. Especially, grammatical inference methods are expected to find some grammatical structures hidden in biological sequences. In this article, we give an overview of a series of our grammatical approaches to biological sequence analyses and related researches and focus on learning stochastic grammars from biological sequences and predicting their functions based on learned stochastic grammars.

Algorithms↗

PubMed2002

Fuzzy logic model of Langmuir probe discharge data.

Plasma models are crucial to gain physical insights into complex discharges as well as to optimizing plasma-driven processes. As an alternative to physical model, a qualitative model was constructed using adaptive fuzzy logic called adaptive network fuzzy inference system (ANFIS). Prediction performance of ANFIS was evaluated on two sets of experimental discharge data. One referred to as hemispherical inductively coupled plasma (HICP) was characterized with a 2(4) full factorial experiment, in which the factors that were varied include source power, pressure, chuck position, and Cl2 flow rate. The other called multipole ICP was characterized by performing a 3(3) full factorial experiment on the factors, including source power, pressure, and Ar flow rate. Trained ANFIS models were tested on eight and 16 experiments not pertaining to previous training data for HICP and MICP, respectively. Plasma attributes modeled include electron density. electron temperature, and plasma potential. The performance of ANFIS was optimized as a function of a type of membership function, number of membership function, and two learning factors. The number of membership functions was different depending on the type of plasma data and employing too large number of membership functions resulted in a drastic degradation in prediction performances. Optimized ANFIS models were compared to statistical regression models and demonstrated improved predictions in all comparisons.

Journal Article↗

PubMed2004

Encoder: a connectionist model of how learning to visually encode fixated text images improves reading fluency.

This article proposes that visual encoding learning improves reading fluency by widening the span over which letters are recognized from a fixated text image so that fewer fixations are needed to cover a text line. Encoder is a connectionist model that learns to convert images like the fixated text images human readers encode into the corresponding letter sequences. The computational theory of classification learning predicts that fixated text-image size makes this learning difficult but that reducing image variability and biasing learning should help. Encoder confirms these predictions. It fails to learn as image size increases but achieves humanlike visual encoding accuracy when image variability is reduced by regularities in fixation positions and letter sequences and when learning is biased to discover mapping functions based on the sequential, componential structure of text. After training, Encoder exhibits many humanlike text familiarity effects.

Cognition↗

PubMedDate not supplied

Support vector machines-based quantitative structure-property relationship for the prediction of heat capacity.

The support vector machine (SVM), as a novel type of learning machine, for the first time, was used to develop a Quantitative Structure-Property Relationship (QSPR) model of the heat capacity of a diverse set of 182 compounds based on the molecular descriptors calculated from the structure alone. Multiple linear regression (MLR) and radial basis function networks (RBFNNs) were also utilized to construct quantitative linear and nonlinear models to compare with the results obtained by SVM. The root-mean-square (rms) errors in heat capacity predictions for the whole data set given by MLR, RBFNNs, and SVM were 4.648, 4.337, and 2.931 heat capacity units, respectively. The prediction results are in good agreement with the experimental value of heat capacity; also, the results reveal the superiority of the SVM over MLR and RBFNNs models.

Journal Article↗

PubMed1998

Knowledge acquisition, accessibility, and use in person perception and stereotyping: simulation with a recurrent connectionist network.

Connectionist models contrast in many ways with the symbolic models that have traditionally been applied within social psychology. In this article the authors apply an autoassociative connectionist model originally developed by J. L. McClelland and D. E. Rumelhart (1986) to reproduce several well-replicated and theoretically important phenomena related to person perception and stereotyping. These phenomena are exemplar-based inference, group-based stereotyping, the simultaneous application of several stereotypes to generate emergent characteristics, and the effects of recency and frequency of prior exposures on accessibility (the probability of a representation's use). Though many of these phenomena are explained by current theories in social psychology, the simulation contributes to parsimony and theoretical integration by showing that a single, very simple mechanism can generate them all. The model also predicts a new phenomenon--rapid recovery of accessibility after it has declined to zero.

Computer Simulation↗

PubMed1994

On the use of machine learning to identify topological rules in the packing of beta-strands.

The machine learning program GOLEM was applied to discover topological rules in the packing of beta-sheets in alpha/beta-domain proteins. Rules (constraints) were determined for four features of beta-sheet packing: (i) whether a beta-strand is at an edge; (ii) whether two consecutive beta-strands pack parallel or anti-parallel; (iii) whether two beta-strands pack adjacently; and (iv) the winding direction of two consecutive beta-strands. Rules were found with high predictive accuracy and coverage. The errors were generally associated with complications in domain folds, especially in one doubly would domains. Investigation of the rules revealed interesting patterns, some of which were known previously, others that are novel. Novel features include (i) the relationship between pairs of sequential strands is in general one of decreasing size; (ii) more sequential pairs of strands wind in the direction out than in; and (iii) it takes a larger alteration in hydrophobicity to change a strand from winding in the direction out than in. These patterns in the data may be the result of folding pathways in the domains. The rules found are of predictive value and could be used in the combinatorial prediction of protein structure, or as a general test of model structures, e.g. those produced by threading. We conclude that machine learning has a useful role in the analysis of protein structures.

Amino Acid Sequence↗