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[Stochastic simulation of the instrumental reflex in probability learning].

A method of computer imitation modelling is worked out of the process of instrumental reflex elaboration allowing to prognosticate the rate of learning at various combinations of probabilistic medium parameters and individual properties of the learning subject. By means of imitating modelling it is easy to find out the localization of the optima and pessima zones in the space of parameters influencing the learning. For building the model the empiric data are not required--they are used only for checking the obtained results. Therefore the conformity obtained with the literature data and the results of own studies allows to suggest that firstly, the worked out model possesses a good forecasting power and secondly, it can be used for studying such conditions of learning for which the appropriate experimental material is not yet collected.

Animals↗
PubMed1980

Visual probability learning of institutionalized profoundly retarded adult males.

Eighteen profoundly retarded men were given repeated presentations of a two-choice visual discrimination using a modified Wisconsin General Test Apparatus and two probabilistic reinforcement schedules counterbalanced for order. Individuals received 240, 70:30 trials and up to as many 100:0 trials; when schedules were shifted, majority/minority (A1/A2) designations were reversed. The 100:0 results indicated preshift mean terminal rates that approached the reinforcement level and postshift rates that were inhibited by prior 70:30 training. The 70:30 results indicate learning only for those given a particular stimulus as their a1: overmatching prior to the shift that was reduced by previous 100:0 training to the matching level. Learning failures were explaiined in terms of attention to a preferred stimulus and the need for more trials, and areas for further inquiry were suggested.

Adult↗
PubMed2004

An fMRI study of reward-related probability learning.

The human striatum has been implicated in processing reward-related information. More recently, activity in the striatum, particularly the caudate nucleus, has been observed when a contingency between behavior and reward exists, suggesting a role for the caudate in reinforcement-based learning. Using a gambling paradigm, in which affective feedback (reward and punishment) followed simple, random guesses on a trial by trial basis, we sought to investigate the role of the caudate nucleus as reward-related learning progressed. Participants were instructed to make a guess regarding the value of a presented card (if the value of the card was higher or lower than 5). They were told that five different cues would be presented prior to making a guess, and that each cue indicated the probability that the card would be high or low. The goal was to learn the contingencies and maximize the reward attained. Accuracy, as measured by participant's choices, improved throughout the experiment for cues that strongly predicted reward, while no change was observed for unpredictable cues. Event-related fMRI revealed that activity in the caudate nucleus was more robust during the early phases of learning, irrespective of contingencies, suggesting involvement of this region during the initial stages of trial and error learning. Further, the reward feedback signal in the caudate nucleus for well-learned cues decreased as learning progressed, suggesting an evolving adaptation of reward feedback expectancy as a behavior-outcome contingency becomes more predictable.

Adult↗
PubMed2001

Probability density function learning by unsupervised neurons.

In a recent work, we introduced the concept of pseudo-polynomial adaptive activation function neuron (FAN) and presented an unsupervised information-theoretic learning theory for such structure. The learning model is based on entropy optimization and provides a way of learning probability distributions from incomplete data. The aim of the present paper is to illustrate some theoretical features of the FAN neuron, to extend its learning theory to asymmetrical density function approximation, and to provide an analytical and numerical comparison with other known density function estimation methods, with special emphasis to the universal approximation ability. The paper also provides a survey of PDF learning from incomplete data, as well as results of several experiments performed on real-world problems and signals.

Algorithms↗
PubMed2003

Learning new words II: Phonotactic probability in verb learning.

Phonotactic probability, a measure of the likelihood of occurrence of a sound sequence, appears to facilitate noun learning (H. L. Storkel, 2001). Nouns and verbs, however, tend to differ in rate of acquisition, indicating that word-learning mechanisms may differ across grammatical class. The purpose of the current study was to examine the effect of phonotactic probability on verb learning. Thirty-four typically developing preschool children participated in a multitrial word-learning task involving nonwords varying in phonotactic probability paired with unfamiliar actions. Multiple measures of word learning were obtained at increasing numbers of exposures. Correct responses were analyzed to examine rate of word learning. Results paralleled those of the previous noun-learning study, with common sound sequences being learned more rapidly than rare sound sequences. The results are interpreted in relation to the effect of distributional regularities on acquisition and the reported discrepancy between noun and verb learning in English.

Analysis of Variance↗
PubMed2001

Reparametrization-covariant theory for on-line learning of probability distributions.

We discuss the on-line learning of probability distributions in a reparametrization covariant formulation. Reparametrization covariance plays an essential role not only to respect an intrinsic property of "information" but also for pattern recognition problems. We can obtain an optimal on-line learning algorithm with reparametrization invariance, where the conformal gauge connects a covariant formulation with a noncovariant one in a natural way.

Journal Article↗