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At least 937 records · Page 52Linked to original sources
PubMed2002

Latent learning, shortcuts and detours: a computational model.

Voicu and Schmajuk (Rob. Auto. Syst. 35 (2001a) 23) described a model of spatial navigation and exploration that includes an action system capable of guiding, with the help of a cognitive system, the search for specific goals as determined by a motivation system. Whereas in the original model the cognitive map stores information about the connectivity between places in the environment, in the present version the cognitive map also stores information about the paths traversed by the agent. Computer simulations show that the network correctly describes experimental results including latent learning in a maze, detours in a maze, and shortcuts in an open field. In addition, the model generates novel predictions about detours and shortcuts in an open field.

Journal Article↗
PubMed2004

Integrating four theories of adolescent smoking.

The ability of the Theory of Planned Behavior, Social Learning Theory, Social Attachment Theory, and Problem Behavior Theory to predict smoking at Grade 10 was tested against an integrated model incorporating predictors from all the theories. The integrated model also tested whether constructs from each theory contribute distinct variance to the prediction of smoking. Predictors measured at Grade 7 (in 1985) were used to model smoking 3 years later (in 1988) among 4186 youth, using logistic regression. Constructs emphasized by each theory were important, independent predictors of later smoking. The integrated model was superior to all of the theory-based models. A few predictors varied for current vs. frequent smoking outcomes. Results emphasize the need for a multifaceted approach to understanding and preventing adolescent smoking.

Adolescent↗
PubMed2002

Biological information: making it accessible and integrated (and trying to make sense of it).

The availability of the genome sequences of human and mouse, human sequence variation data and other large genetic data sets will lead to a revolution in understanding of the human machine and the treatment of its diseases. The success of the international genome sequencing consortiums shows what can be achieved by well coordinated large scale public domain projects and the benefits of data access to all. It is already clear that the availability of this sequence is having a huge impact on research worldwide. Complete genome sequences provide a framework to pull all biological data together such that each piece has the potential to say something about biology as a whole. Biology is too complex for any organisation to have a monopoly of ideas or data, so the collection, analysis and access to this data can be contributed to by research institutes around the world. However, although it is possible for all this data to be accessible to all through the internet, the more organisations provide data or analysis separately, the harder it becomes for anyone to collect and integrate the results. To address these problems of intergration of data, open standards for biological data exchange, such as the 'Distributed Annotation System' (DAS) are being developed and bioinformatics (Dowell et al., 2001) as a whole is now being strongly driven by the open source software (OSS) model for collaborative software development (Hubbard and Birney, 1999). The leading provider of human genome annotation, the Ensembl project (http://www.ensembl.org), is entirely an OSS project and has been widely adopted by academic and commerical organisations alike (Hubbard et al., 2002). Accurate automatic annotation of features such as genes in vertebrate genomes currently relies on supporting evidence in the form of homologies to mRNAs, ESTs or protein. However, it appears that sufficient high quality experimentally curated annotation now exists to be used as a substrate for machine learning algorithms to create effective models of biological signal sequences (Down and Hubbard, 2002). Is there hope for ab initio prediction methods after all?

Chromosome Mapping↗
PubMed2005

A memory learning framework for effective image retrieval.

Most current content-based image retrieval systems are still incapable of providing users with their desired results. The major difficulty lies in the gap between low-level image features and high-level image semantics. To address the problem, this study reports a framework for effective image retrieval by employing a novel idea of memory learning. It forms a knowledge memory model to store the semantic information by simply accumulating user-provided interactions. A learning strategy is then applied to predict the semantic relationships among images according to the memorized knowledge. Image queries are finally performed based on a seamless combination of low-level features and learned semantics. One important advantage of our framework is its ability to efficiently annotate images and also propagate the keyword annotation from the labeled images to unlabeled images. The presented algorithm has been integrated into a practical image retrieval system. Experiments on a collection of 10,000 general-purpose images demonstrate the effectiveness of the proposed framework.

Algorithms↗
PubMed2006

Motor adaptation to single force pulses: sensitive to direction but insensitive to within-movement pulse placement and magnitude.

Although previous experiments have identified that errors in movement induce adaptation, the precise manner in which errors determine subsequent control is poorly understood. Here we used transient pulses of force, distributed pseudo-randomly throughout a movement set, to study how the timing of feedback within a movement influenced subsequent predictive control. Human subjects generated a robust adaptive response in postpulse movements that opposed the pulse direction. Regardless of the location or magnitude of the pulse, all pulses yielded similar changes in predictive control. All current supervised and unsupervised theories of motor learning presume that adaptation is proportional to error. Current neural models that broadly encode movement velocity and adapt proportionally to motor error can mimic human insensitivity to pulse location, but cannot mimic human insensitivity to pulse magnitude. We conclude that single trial adaptation to force pulses reveals a categorical strategy that humans adopt to counter the direction, rather than the magnitude, of movement error.

Adaptation, Physiological↗
PubMed1984

Attributional style and the generality of learned helplessness.

According to the logic of the attribution reformulation of learned helplessness, the interaction of two factors influences whether helplessness experienced in one situation will transfer to a new situation. The model predicts that people who exhibit a style of attributing negative outcomes to global factors will show helplessness deficits in new situations that are either similar or dissimilar to the original situation in which they were helpless. In contrast, people who exhibit a style of attributing negative outcomes to only specific factors will show helplessness deficits in situations that are similar, but not dissimilar, to the original situation in which they were helpless. To test these predictions, we conducted two studies in which undergraduates with either a global or specific attributional style for negative outcomes were given one of three pretreatments in the typical helplessness triadic design: controllable bursts of noise, uncontrollable bursts of noise, or no noise. In Experiment 1, students were tested for helplessness deficits in a test situation similar to the pretreatment setting, whereas in Experiment 2, they were tested in a test situation dissimilar to the pretreatment setting. The findings were consistent with predictions of the reformulated helplessness theory.

Adult↗
PubMed1998

Internal models for motor control.

The process of moving the hand to a target in space involves a series of sensorimotor transformations that translate visual and other sensory information about the location of the target object and the limbs into a set of motor commands that will bring the hand to the desired position. Recent work at various laboratories has provided strong support for the hypothesis that the CNS learns and maintains internal models of sensorimotor transformations. An internal model is a neural system that mimics the behaviour of the sensorimotor system and objects in the external environment. Internal models enable the CNS to predict the consequences of motor commands and to determine the motor commands required to perform specific tasks. In this chapter, we first summarize recent computational, behavioural and neurophysiological studies that address the theoretical necessity of internal models, the locations of internal models, and the neural mechanism for acquiring internal models through learning. Then, we propose a new computational model of multiple internal models.

Animals↗
PubMed1991

Geschwind's theory of cerebral lateralization: developing a formal, causal model.

Geschwind and Galaburda (1987) have proposed a complex and influential model of cerebral lateralization that is based on the argument that increased fetal testosterone levels modify neural development, immune development, and neural crest development. The theory can explain many aspects of cerebral lateralization and its relation to learning disorders, giftedness, and immune deficits. This article clarifies the structure of the theory by presenting it as a causal-path model. The internal coherence of the model is then evaluated by assessing the central concept of anomalous dominance, the role of timing in the articulation of the model, and the invocation of nonlinear processes. Finally, the article considers the problems implicit in testing a "grand" theoretical model and derives some principles for assessing the testability of various predictions, given the practical constraints of sample size and the problems of measurement error.

Autoimmune Diseases↗
PubMed1994

Predictive control of nonlinear systems based on identification by backpropagation networks.

Using the property of universal approximation of multilayer perceptron neural network, a class of discrete nonlinear dynamical systems are modeled by a perceptron with two hidden layers. A backpropagation algorithm is then used to train the model to identify the nonlinear systems to a desired level of accuracy. Based on the identified model, a one-step-ahead predictive control scheme is proposed in which the future control inputs are obtained through some nonlinear optimization process. Making use of the online learning properties of neural networks, the predictive control scheme is further developed into an adaptive one which is robust to the incompleteness of identification. Simulation results show that this neural control scheme works well even for some very complicated nonlinear systems.

Mathematics↗
PubMed1996

A framework for mesencephalic dopamine systems based on predictive Hebbian learning.

We develop a theoretical framework that shows how mesencephalic dopamine systems could distribute to their targets a signal that represents information about future expectations. In particular, we show how activity in the cerebral cortex can make predictions about future receipt of reward and how fluctuations in the activity levels of neurons in diffuse dopamine systems above and below baseline levels would represent errors in these predictions that are delivered to cortical and subcortical targets. We present a model for how such errors could be constructed in a real brain that is consistent with physiological results for a subset of dopaminergic neurons located in the ventral tegmental area and surrounding dopaminergic neurons. The theory also makes testable predictions about human choice behavior on a simple decision-making task. Furthermore, we show that, through a simple influence on synaptic plasticity, fluctuations in dopamine release can act to change the predictions in an appropriate manner.

Animals↗
PubMed2026

Survival prediction for clear cell renal cell carcinoma based on deep multimodal synergistic survival network.

Objective.To propose a deep multimodal synergistic survival analysis framework (Deep Multimodal Synergistic Survival Network, DMSSN) to achieve accurate prognostic analysis for clear cell renal cell carcinoma (ccRCC).Methods.This study (DMSSN) utilized matched multimodal data from the Cancer Genome Atlas-KIRC database, including CT imaging data, whole slide images, copy number variation (CNV) features, and clinical data. Deep Canonical Correlation Analysis was employed to map heterogeneous modalities into a shared latent space. Contrastive learning was introduced to enhance semantic consistency across multimodal features, and a gating network was utilized for the adaptive fusion of multimodal information to achieve precise survival risk prediction for patients.Results.Experimental results demonstrated that DMSSN achieved a Concordance Index (C-index) of 0.8153 ± 0.0994, with a Log-rank testp-value of 1.6553×10-11. DMSSN exhibited significant performance advantages over traditional statistical methods like Log-rank-Cox (0.7055 ± 0.0670) and machine learning methods such as Random Survival Forest (RSF) (0.6836 ± 0.1048). Furthermore, in comparison with similar deep learning approaches, DMSSN outperformed late fusion strategies (0.7493 ± 0.1211) and discrete-time survival models such as DeepHit (0.7655 ± 0.1041) and Nnet-surv (0.7694 ± 0.0635). Notably, DMSSN still achieved the best predictive performance when compared to the classic deep survival model DeepSurv (0.7919 ± 0.0978) and advanced state-of-the-art multimodal fusion frameworks like Context-Aware Transformer (0.7735 ± 0.0818) and Multimodal Co-Attention Transformer (0.8102 ± 0.0972). Ablation studies showed that removing any single modality led to a decline in performance, with the largest numerical decrease occurring after removing CT imaging features (C-index decreased to 0.7327), validating the complementarity of multimodal data and the pivotal role of radiomic features in prognostic assessment. Module ablation experiments further confirmed the effectiveness of the core components.Conclusion:By effectively integrating imaging, pathology, genomic, and clinical features, the DMSSN framework demonstrates superior performance and robustness in the survival prediction of ccRCC.

Carcinoma, Renal Cell↗
PubMed1994

Models of the effects of prior knowledge on category learning.

When people learn about a new category, they are influenced by prior knowledge of other categories. In 5 experiments, subjects made categorization judgments after observing descriptions of persons from a location referred to as City W. In these experiments, prior knowledge as well as observations within City W were manipulated. The integration, weighting, and distortion models of categorization explain prior knowledge effects in different ways. The integration model, which assumes that categorization is influenced by prior examples from other categories, predicted the results of the experiments. It was found that the effect of prior knowledge was independent of the observed proportion of category membership in City W, that the prior knowledge effect was diminished with more observations, and that learning about City W affected subjects' judgments about the general population. The weighting and distortion models could not account for all of the results.

Humans↗
PubMed1991

Predicting chemically induced duodenal ulcer and adrenal necrosis with classification trees.

Binary tree-structured statistical classification algorithms and properties of 56 model alkyl nucleophiles were brought to bear on two problems of experimental pharmacology and toxicology. Each rat of a learning sample of 745 was administered one compound and autopsied to determine the presence of duodenal ulcer or adrenal hemorrhagic necrosis. The cited statistical classification schemes were then applied to these outcomes and 67 features of the compounds to ascertain those characteristics that are associated with biologic activity. For predicting duodenal ulceration, dipole moment, melting point, and solubility in octanol are particularly important, while for predicting adrenal necrosis, important features include the number of sulfhydryl groups and double bonds. These methods may constitute inexpensive but powerful ways to screen untested compounds for possible organ-specific toxicity. Mechanisms for the etiology and pathogenesis of the duodenal and adrenal lesions are suggested, as are additional avenues for drug design.

Adrenal Gland Diseases↗
PubMed2006

Weighted quality estimates in machine learning.

MOTIVATION: Machine learning methods such as neural networks, support vector machines, and other classification and regression methods rely on iterative optimization of the model quality in the space of the parameters of the method. Model quality measures (accuracies, correlations, etc.) are frequently overly optimistic because the training sets are dominated by particular families and subfamilies. To overcome the bias, the dataset is usually reduced by filtering out closely related objects. However, such filtering uses fixed similarity thresholds and ignores a part of the training information. RESULTS: We suggested a novel approach to calculate prediction model quality based on assigning to each data point inverse density weights derived from the postulated distance metric. We demonstrated that our new weighted measures estimate the model generalization better and are consistent with the machine learning theory. The Vapnik-Chervonenkis theorem was reformulated and applied to derive the space-uniform error estimates. Two examples were used to illustrate the advantages of the inverse density weighting. First, we demonstrated on a set with a built-in bias that the unweighted cross-validation procedure leads to an overly optimistic quality estimate, while the density-weighted quality estimates are more realistic. Second, an analytical equation for weighted quality estimates was used to derive an SVM model for signal peptide prediction using a full set of known signal peptides, instead of the usual filtered subset.

Algorithms↗
PubMed2005

A new mathematical model for assessment of memorization dynamics.

A new memory model is proposed based on regression analysis and exponential- shaped learning curves. The efficacy of the model is tested with several types of experiments including food aversion in snails, maze learning in rats and memory tests for adults and children. The model is also tested on drug abusers and alcoholics. The results of goodness of fit tests indicate that our model can accurately be used to predict the memory dynamics of diverse experiments and populations. The model can also be used to predict both group and individual performance. The application of the model to detect memory impairment is discussed, as are limitations.

Adult↗
PubMed2004

Asymmetry of generalization decrement in causal learning.

Two experiments required volunteers to learn which of various "planes" caused high levels of pollution. Novel test items were then rated as causes of pollution. Items created by adding novel features were rated at the same level as that of the original training items but items created by removing features received reduced ratings. This asymmetry of generalization decrement was not predicted by a well-known configural model of stimulus representation (Pearce, 1987, 1994) but was predicted by a recently proposed model of stimulus representation, the replaced-elements model (Brandon, Vogel, & Wagner, 2000).

Adult↗