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PubMed2000

Dynamics of learning with restricted training sets

We study the dynamics of supervised learning in layered neural networks, in the regime where the size p of the training set is proportional to the number N of inputs. Here the local fields are no longer described by Gaussian probability distributions and the learning dynamics is of a spin-glass nature, with the composition of the training set playing the role of quenched disorder. We show how dynamical replica theory can be used to predict the evolution of macroscopic observables, including the two relevant performance measures (training error and generalization error), incorporating the old formalism developed for complete training sets in the limit alpha=p/N-->infinity as a special case. For simplicity, we restrict ourselves in this paper to single-layer networks and realizable tasks. In the case of (on-line and batch) Hebbian learning, where a direct exact solution is possible, we show that our theory provides exact results at any time in many different verifiable cases. For non-Hebbian learning rules, such as PERCEPTRON and ADATRON, we find very good agreement between the predictions of our theory and numerical simulations. Finally, we derive three approximation schemes aimed at eliminating the need to solve a functional saddle-point equation at each time step, and we assess their performance. The simplest of these schemes leads to a fully explicit and relatively simple nonlinear diffusion equation for the joint field distribution, which already describes the learning dynamics surprisingly well over a wide range of parameters.

Journal Article↗
PubMed2005

Contiguity and the outcome density bias in action-outcome contingency judgements.

In cause-outcome contingency judgement tasks, judgements often reflect the actual contingency but are also influenced by the overall probability of the outcome, P(O). Action-outcome instrumental learning tasks can foster a pattern in which judgements of positive contingencies become less positive as P(O) increases. Variable contiguity between the action and the outcome may produce this bias. Experiment 1 recorded judgements of positive contingencies that were largely uninfluenced by P(O) using an immediate contiguity procedure. Experiment 2 directly compared variable versus constant contiguity. The predicted interaction between contiguity and P(O) was observed for positive contingencies. These results stress the sensitivity of the causal learning mechanism to temporal contiguity.

Association Learning↗
PubMed1987

Visual symbol and manual sign learning by children with phonologic programming deficit syndrome.

A study was done of eight children with phonologic programming deficit syndrome (PPDS) and of eight normally developing children, to evaluate learning and recall of Blissymbols and of Signed English manual signs. The results support the view that children with this syndrome not only have motor speech deficits, but also deficits in symbolic processing. Decisions about intervention for nonverbal children should take account of their probable deficits in symbol learning.

Articulation Disorders↗
PubMed2004

Early administration of nicotinamide prevents learning and memory impairment in mice induced by 1-methyl-4-phenyl-1, 2, 3, 6-tetrahydropyridine.

BACKGROUND AND PURPOSE: NAD has been reported to improve the dementia of the Alzheimer type or sensory register, short- and long-term memory loss in the aged. Although nicotinamide has been confirmed to decrease infarct volumes and neurological deficit findings in several animal stroke models, it is not clear whether its neuroprotective effects can prevent memory damage sequelae. METHODS: We have addressed this topic by designing two behavioral paradigms. A memory impairment and cognitive change model was used in mice following 1-methyl-4-phenyl-l, 2, 3, 6-tetrahydropyridine (MPTP) exposure. Step-down and step-through tests were performed to examine the effects of nicotinamide on learning and memory impairment. RESULTS: It was found that the early administration of nicotinamide (2 h after the injection of MPTP) could decrease error numbers, lessen stimulation time and prolong residence duration on the safety platform in the step-down test. Delayed administration of nicotinamide resulted in decreased effects. Similar results were found in the step-through test. Nicotinamide administrated 12 h after the induction of a memory-impairment model still exerted its effects on memory dysfunction. CONCLUSIONS: The injection of MPTP can cause a loss of brain functions including learning and memory. Learning and memory dysfunction probably occurs secondary to damage to arterioles and dopaminergic neurons by MPTP. By inhibiting oxidative stress, increasing NAD synthesis and ATP production and inhibiting poly (ADP-ribose) polymerase, nicotinamide is known to rescue the still viable, but injured, cells. This rescue process may partially restore learning and memory.

1-Methyl-4-phenyl-1,2,3,6-tetrahydropyridine↗
PubMed2006

The effect of gonadal hormones and gender on anxiety and emotional learning.

Disorders of anxiety and fear dysregulation are highly prevalent. These disorders affect women approximately 2 times more than they affect men, occur predominately during a woman's reproductive years, and are especially prevalent at times of hormonal flux. This implies that gender differences and sex steroids play a key role in the regulation of anxiety and fear. However, the underlying mechanism by which these factors regulate emotional states in either sex is still largely unknown. This review discusses animal studies describing sex-differences in and gonadal steroid effects on affect and emotional learning. The effects of gonadal hormones on the modulation of anxiety, with particular emphasis on progesterone's ability to reduce the responsiveness of female rats to corticotropin releasing factor and the sex-specific effect of testosterone in the reduction of anxiety in male rats, is discussed. In addition, gonadal hormone and gender modulation of emotional learning is considered and preliminary data are presented showing that estrogen (E2) disrupts fear learning in female rats, probably through the antagonistic effect of ERalpha and ERbeta activation.

Animals↗
PubMed2003

An unsupervised automatic method for sorting neuronal spike waveforms in awake and freely moving animals.

The present study introduces an approach to automatic classification of extracellularly recorded action potentials of neurons. The classification of spike waveform is considered a pattern recognition problem of special segments of signal that correspond to the appearance of spikes. The spikes generated by one neuron should be recognized as members of the same class. The spike waveforms are described by the nonlinear oscillating model as an ordinary differential equation with perturbation, thus characterizing the signal distortions in both amplitude and phase. It is shown that the use of local variables reduces the problem of spike recognition to the separation of a mixture of normal distributions in the transformed feature space. We have developed an unsupervised iteration-learning algorithm that estimates the number of classes and their centers according to the distance between spike trajectories in phase space. This algorithm scans the learning set to evaluate spike trajectories with maximal probability density in their neighborhood. Following the learning, the procedure of minimal distance is used to perform spike recognition. Estimation of trajectories in phase space requires calculation of the first- and second-order derivatives, and integral operators with piecewise polynomial kernels were used. This provided the computational efficiency of the developed approach for real-time application as required by recordings in behaving animals and in human neurosurgical operations. The new method of spike sorting was tested on simulated and real data and performed better than other approaches currently used in neurophysiology.

Action Potentials↗
PubMed2006

Theory-based Bayesian models of inductive learning and reasoning.

Inductive inference allows humans to make powerful generalizations from sparse data when learning about word meanings, unobserved properties, causal relationships, and many other aspects of the world. Traditional accounts of induction emphasize either the power of statistical learning, or the importance of strong constraints from structured domain knowledge, intuitive theories or schemas. We argue that both components are necessary to explain the nature, use and acquisition of human knowledge, and we introduce a theory-based Bayesian framework for modeling inductive learning and reasoning as statistical inferences over structured knowledge representations.

Association Learning↗
PubMed1980

The use of probability trees in genetic counselling.

Calculation of genetic risks of persons who may be carriers of X-linked recessive conditions or autosomal dominant diseases with incomplete or delayed penetrance often requires the use of Bayes' theorem. Available methods of computing such risks are often too difficult or too time-consuming for clinicians to use routinely. Probability trees provide a rapid and simple graphical means of estimating genetic risks even in complex clinical situations. The use of probability trees is easily learned because the logic upon which they are based is inherent in their structure.

Adult↗
PubMedDate not supplied

[Learning approaches used by undergraduate interns in the development of a medical specialty].

INTRODUCTION: Two learning approaches are examined: a superficial and a profound one. The purpose of the first one is to pass the subjects with the minimum effort; in the second one, there is a genuine interest in grasping knowledge and being acknowledged for one's achievements. The objective of this research was to identify the learning approach of undergraduate interns, according to sex, age, specialty and grade. MATERIAL AND METHODS: An analysis of conglomerates was used as a statistical tool. The sample was formed by 179 undergraduate interns of 19 medical and surgical specialties offered at a concentration hospital. The form R-SPQ-2F, proposed by Biggs, was used as an instrument. The reliability and validity of the instrument was estimated by means of Cronbach's alpha and factorial analysis of learning. RESULTS: 60.3% of the students had a deep approach to learning; women and students older than 28 years old in greater proportion, as well as those studying internal medicine and those in the third year. CONCLUSIONS: Undergraduate interns are students with a high level of participation in their own learning process. This is probably due to the demands of the profession, the filters of selection and the nature of the subjects.

Adult↗
PubMed1995

Nonlinear gated experts for time series: discovering regimes and avoiding overfitting.

In the analysis and prediction of real-world systems, two of the key problems are nonstationarity (often in the form of switching between regimes) and overfitting (particularly serious for noisy processes). This article addresses these problems using gated experts, consisting of a (nonlinear) gating network, and several (also nonlinear) competing experts. Each expert learns to predict the conditional mean, and each expert adapts its width to match the noise level in its regime. The gating network learns to predict the probability of each expert, given the input. This article focuses on the case where the gating network bases its decision on information from the inputs. This can be contrasted to hidden Markov models where the decision is based on the previous state(s) (i.e. on the output of the gating network at the previous time step), as well as to averaging over several predictors. In contrast, gated experts soft-partition the input space, only learning to model their region. This article discusses the underlying statistical assumptions, derives the weight update rules, and compares the performance of gated experts to standard methods on three time series: (1) a computer-generated series, obtained by randomly switching between two nonlinear processes; (2) a time series from the Santa Fe Time Series Competition (the light intensity of a laser in chaotic state); and (3) the daily electricity demand of France, a real-world multivariate problem with structure on several time scales. The main results are: (1) the gating network correctly discovers the different regimes of the process; (2) the widths associated with each expert are important for the segmentation task (and they can be used to characterize the sub-processes); and (3) there is less overfitting compared to single networks (homogeneous multilayer perceptrons), since the experts learn to match their variances to the (local) noise levels. This can be viewed as matching the local complexity of the model to the local complexity of the data.

Computers↗
PubMed1996

[Slip technique, process dissociation model and multinomial modeling: new tools for experimental detection of "Freudian slips"].

The study reported here was conducted as a test of the so-called "weak Freudian hypothesis", which claims that unconscious thoughts are relevant for the generation of speech errors. Spoonerisms were induced experimentally using the so-called SLIP technique. Motley and Baars (1976) demonstrated an increase in speech error rates when spoonerisms were primed semantically. The extensively discussed problems of "unawareness" of briefly presented stimuli were circumvented by using a modified version of Jacoby's process dissociation technique which allows a model-based estimation of conscious and unconscious processes within a task. The two reported experiments combined a wordstem completion task for estimating probabilities of perceptual processes and a SLIP task under identical perceptual conditions. A joint multinomial model was constructed for data analysis. The SLIP technique was successfully applied using German stimuli, adequate experimental variations raised the error rate from 7% in experiment 1 to 19% in experiment 2. Neither the replication of Motley and Baars' results nor unconscious priming of speech errors were statistically confirmed. Despite this negative result, the descriptive pattern of parameter estimates is psychologically meaningful: primes that remained unconscious resulted in a higher speech error probability than primes that were perceived consciously. Conscious perception might trigger control processes that act in opposition of speech errors. Statistical problems of the particular multinomial model and possible solutions in future research are discussed.

Adult↗
PubMed2004

Adaptive stochastic resonance in noisy neurons based on mutual information.

Noise can improve how memoryless neurons process signals and maximize their throughput information. Such favorable use of noise is the so-called "stochastic resonance" or SR effect at the level of threshold neurons and continuous neurons. This paper presents theoretical and simulation evidence that 1) lone noisy threshold and continuous neurons exhibit the SR effect in terms of the mutual information between random input and output sequences, 2) a new statistically robust learning law can find this entropy-optimal noise level, and 3) the adaptive SR effect is robust against highly impulsive noise with infinite variance. Histograms estimate the relevant probability density functions at each learning iteration. A theorem shows that almost all noise probability density functions produce some SR effect in threshold neurons even if the noise is impulsive and has infinite variance. The optimal noise level in threshold neurons also behaves nonlinearly as the input signal amplitude increases. Simulations further show that the SR effect persists for several sigmoidal neurons and for Gaussian radial-basis-function neurons.

Algorithms↗
PubMed2004

Causal learning across domains.

Five studies investigated (a) children's ability to use the dependent and independent probabilities of events to make causal inferences and (b) the interaction between such inferences and domain-specific knowledge. In Experiment 1, preschoolers used patterns of dependence and independence to make accurate causal inferences in the domains of biology and psychology. Experiment 2 replicated the results in the domain of biology with a more complex pattern of conditional dependencies. In Experiment 3, children used evidence about patterns of dependence and independence to craft novel interventions across domains. In Experiments 4 and 5, children's sensitivity to patterns of dependence was pitted against their domain-specific knowledge. Children used conditional probabilities to make accurate causal inferences even when asked to violate domain boundaries.

Association Learning↗
PubMed1980

Failure of rats to escape from a potentially lethal microwave field.

Ocularly pigmented rats, all mature females of the Long-Evans strain, were repeatedly presented an opportunity to escape from an intense 918-MHz field (whole-body dose rate = 60 mW/g) to a field of lower intensity (40, 30, 20, or 2 mW/g) by performing a simple locomotor response. Other rats could escape 800-microamperemeter faradic shock to the feet and tail by performing the same response in the same milieu, a multimode cavity. None of 20 irradiated rats learned to associate entry into a visually well-demarcated area of the cavity with immediate reduction of dose rate, in spite of field-induced elevations of body temperature to levels that exceeded 41 degrees C and would have been lethal but for a limit on durations of irradiation. In contrast, all of ten rats motivated by faradic shock rapidly learned to escape. The failure of escape learning by irradiated animals probably arose from deficiencies of motivation and, especially, sensory feedback. Whole-body hyperthermia induced by a multipath field may lack the painful or directional sensory properties that optimally promote the motive to escape. Moreover, a decline of body temperature after an escape-response-contingent reduction of field strength will be relatively slow because of the large thermal time constants of mammalian tissues. Without timely sensory feedback, which is an essential element of negative reinforcement, stimulus-response associability would be imparied, which could retard or preclude learning of an escape response.

Animals↗
PubMed2001

Overconfidence effects in category learning: a comparison of connectionist and exemplar memory models.

Exemplar and connectionist models were compared on their ability to predict overconfidence effects in category learning data. In the standard task, participants learned to classify hypothetical patients with particular symptom patterns into disease categories and reported confidence judgments in the form of probabilities. The connectionist model asserts that classifications and confidence are based on the strength of learned associations between symptoms and diseases. The exemplar retrieval model (ERM) proposes that people learn by storing examples and that their judgments are often based on the first example they happen to retrieve. Experiments 1 and 2 established that overconfidence increases when the classification step of the process is bypassed. Experiments 2 and 3 showed that a direct instruction to retrieve many exemplars reduces overconfidence. Only the ERM predicted the major qualitative phenomena exhibited in these experiments.

Adult↗
PubMed2006

Value transfer across odor stimuli using probability of reinforcement in the rat.

In a series of experiments value transfer across odor stimuli using probability of reinforcement was demonstrated in rats. Rats were trained with the following pairs of simple simultaneous discriminations A(100) B(0) and C(50) D(0) where the letter represents a particular scent mixed with sand presented in a cup and the number in parentheses represents the probability of reinforcement given that the rats dug to the bottom of the cup. In test, the rats were confronted with a choice between the B and D stimuli and (in experiments 2 and 3) the rats preferred to dig in the B stimulus providing evidence for value transfer using procedures similar to those that have been used in the pigeon literature.

Animals↗