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PubMed2002

The neural basis of human error processing: reinforcement learning, dopamine, and the error-related negativity.

The authors present a unified account of 2 neural systems concerned with the development and expression of adaptive behaviors: a mesencephalic dopamine system for reinforcement learning and a "generic" error-processing system associated with the anterior cingulate cortex. The existence of the error-processing system has been inferred from the error-related negativity (ERN), a component of the event-related brain potential elicited when human participants commit errors in reaction-time tasks. The authors propose that the ERN is generated when a negative reinforcement learning signal is conveyed to the anterior cingulate cortex via the mesencephalic dopamine system and that this signal is used by the anterior cingulate cortex to modify performance on the task at hand. They provide support for this proposal using both computational modeling and psychophysiological experimentation.

Analysis of Variance↗
PubMed1994

Deviation in cerebral excitability: possible clinical implications.

Schizophrenia, a chemical signaling disorder in the brain, is also a deteriorating neurological disorder. The deficit in cerebral excitability, and associated reduced synaptic density, imply a risk of cortical breakdown of circuitry accompanied by an insufficient fill-in mechanism, and persistent silent spots, but no total loss of function, only dysfunction. This is subjectively experienced as deficiencies of cognition, perception and sensorimotor phenomena depending upon localization and connections of the disconnected circuitry. Considering the adversity inherent in this neural network, both the fast Hebbian pre-post form of learning and the slow pre-modulatory coincidence form of learning are probably impaired. The use of Feed Back Loops which usually govern our behaviour might also be impaired. In addition, we have to consider the daily problem of insufficient drive and motivation. Manic depressive psychosis, a chemical signaling disorder in the brain, is a true functional psychosis. The raised excitatory drive and raised synaptic density imply raised risk of uncoupling of circadian rhythms via the direct glutamatergic input to the suprachiasmatic nucleus of hypothalamus (SCN). This episodic brain stem dysfunction illustrates how a deficit in inhibition renders the brain unstable. The requirements of the fast Hebbian form of learning should easily be met, and neither should the slow forms of learning present a problem in networks characterized by excessive density.(ABSTRACT TRUNCATED AT 250 WORDS)

Cerebral Cortex↗
PubMed2004

New training strategies for constructive neural networks with application to regression problems.

Regression problem is an important application area for neural networks (NNs). Among a large number of existing NN architectures, the feedforward NN (FNN) paradigm is one of the most widely used structures. Although one-hidden-layer feedforward neural networks (OHL-FNNs) have simple structures, they possess interesting representational and learning capabilities. In this paper, we are interested particularly in incremental constructive training of OHL-FNNs. In the proposed incremental constructive training schemes for an OHL-FNN, input-side training and output-side training may be separated in order to reduce the training time. A new technique is proposed to scale the error signal during the constructive learning process to improve the input-side training efficiency and to obtain better generalization performance. Two pruning methods for removing the input-side redundant connections have also been applied. Numerical simulations demonstrate the potential and advantages of the proposed strategies when compared to other existing techniques in the literature.

Algorithms↗
PubMed1992

A movement pattern generator model using artificial neural networks.

Artificial neural networks (ANN's) allow a new approach to biological modeling. The main applications of ANN's have been geared towards the modeling of the association and learning mechanisms of the brain; only a few researchers have explored them for motor control. The fact that ANN's are based on biological systems indicates their potential application for a biological act such as locomotion. Towards this goal, we have developed a "movement pattern generator," using an ANN for generating periodic movement trajectories. This model is based on the concept of "central pattern generators." Jordan's sequential network, which is capable of learning sequences of patterns, was modified and used to generate several bipedal trajectories (or gaits), coded in task space, at different frequencies. The network model successfully learned all of the trajectories presented to it. The model has many attractive properties such as limit cycle behavior, generalization of trajectories and frequencies, phase maintenance, and fault tolerance. The movement pattern generator model is potentially applicable for improved understanding of animal locomotion and for use in legged robots and rehabilitation medicine.

Animals↗
PubMed1986

Young children's understanding of random phenomena.

2 experiments on the development of the understanding of random phenomena are reported. Of interest was whether children understand the characteristic uncertainty in the physical nature of random phenomena as well as the unpredictability of outcomes. Children were asked, for both a random and a determined phenomenon, whether they knew what its next outcome would be and why. In Experiment 1, 4-, 5-, and 7-year-olds correctly differentiated their responses to the question of outcome predictability; the 2 older groups also mentioned appropriate characteristics of the random mechanism in explaining why they did not know what its outcome would be. Although 3-year-olds did not differentiate the random and determined phenomena, neither did they treat both phenomena as predictable. This latter result is inconsistent with Piaget and Inhelder's characterization of an early stage of development. Experiment 2 was designed to control for the possibility that children in Experiment 1 learned how to respond on the basis of pretest experience with the 2 different phenomena. 5- and 7-year-olds performed at a comparable level to the same-aged children in Experiment 1. Results suggest an earlier understanding of random phenomena than previously has been reported and support results in the literature indicating an early understanding of causality.

Child↗
PubMed1997

Effects of habenular lesions upon two-way active avoidance conditioning in rats.

To evaluate if habenular nuclei lesions improve, impair, or have no effects on two-way active avoidance acquisition and/or retention, rats in a Lesion group were subjected to bilateral electrolytical lesions of this complex, while control rats were sham-operated (Sham group). Once recovered from the stereotaxic procedures, rats were submitted to 5 training sessions (10 trials each, one session per day) of two-way active avoidance conditioning. Ten days after the last training session, another session was administered in order to test the long-term retention of the task. Results indicated that habenular lesions did not affect the overall performance of the rats during either the acquisition sessions or the retention session of two-way active avoidance. We suggest that habenular lesions can affect the acquisition of several learning tasks, probably through their role in modulating stress responses and/or arousal states. The nature of these effects (whether facilitative, detrimental, or neutral) might depend on the interaction between several factors such as the kind of task, the specific conditioning procedures (which may generate different stress levels), and the specific area destroyed by the lesion.

Animals↗
PubMed2002

A computational account of altered error processing in older age: dopamine and the error-related negativity.

When participants commit errors or receive feedback signaling that they have made an error, a negative brain potential is elicited. According to Holroyd and Coles's (in press) neurocomputational model of error processing, this error-related negativity (ERN) is elicited when the brain first detects that the consequences of an action are worse than expected. To study age-related changes in error processing, we obtained performance and ERN measures of younger and high-functioning older adults. Experiment 1 demonstrated reduced ERN amplitudes in older adults in the context of otherwise intact brain potentials. This result could not be attributed to uncertainty about the required response in older adults. Experiment 2 revealed impaired performance and reduced response- and feedback-related ERNs of older adults in a probabilistic learning task. These age changes could be simulated by manipulation of a single parameter of the neurocomputational model, this manipulation corresponding to weakened phasic activity of the mesencephalic dopamine system.

Adolescent↗
PubMed1998

Influences of prior knowledge on selective weighting of category members.

Three experiments addressed how prior theories affect categorization, comparing the influence of theory-congruent versus theory-incongruent category members. Subjects observed descriptions of persons, some congruent with prior knowledge and some incongruent, then made transfer judgments. In Experiment 1, subjects were given a relatively long time to study each description, whereas in Experiment 2 study time was manipulated between subjects. In Experiment 3, learning was self-paced by each subject. It was found that, with enough study time, prior knowledge had 2 distinct influences. First, prior knowledge provided an initial representation, subsequently revised in light of new observations. Second, incongruent observations had more impact than congruent observations on categorization. In comparison, when study time was more limited, revision proceeded in a Bayesian manner, in that congruent and incongruent observations had equal impacts.

Adult↗
PubMed1994

[Discrete dynamic systems: the effect of perceptual structuring on composition and transfer of knowledge about operating sequences].

This paper reports two experiments in which we explored the impact of perceptual grouping of elements on the organization and use of knowledge about how to operate a device. Experiment 1 explored the effects of different perceptual display regions on the creation of chunks when sequences of inputs had to be reproduced. The effects of regions were not homogeneous, but rather their influence depended on interactions between different modalities and learning conditions. Experiment 2 investigated the influence of grouping-induced composition of knowledge elements on the transfer of sequential knowledge. Two different learning criteria were used in the acquisition phase to manipulate the degree of composition of knowledge elements. In the transfer phase, subjects could transfer (1) the whole sequence of one region, (2) two partial sequences of adjacent regions, or (3) single components. It was found that regional invariance and immediate succession of components were both important for transfer performance. These results suggests that the temporal order of regions is important for the organization and use of sequential knowledge, and not the grouping of elements by itself.

Adult↗
PubMedDate not supplied

Self-organizing maps with recursive neighborhood adaptation.

Self-organizing maps (SOMs) are widely used in several fields of application, from neurobiology to multivariate data analysis. In that context, this paper presents variants of the classic SOM algorithm. With respect to the traditional SOM, the modifications regard the core of the algorithm, (the learning rule), but do not alter the two main tasks it performs, i.e. vector quantization combined with topology preservation. After an intuitive justification based on geometrical considerations, three new rules are defined in addition to the original one. They develop interesting properties such as recursive neighborhood adaptation and non-radial neighborhood adaptation. In order to assess the relative performances and speeds of convergence, the four rules are used to train several maps and the results are compared according to several error measures (quantization error and topology preservation criterions).

Algorithms↗
PubMed2004

Prefrontal cortex and decision making in a mixed-strategy game.

In a multi-agent environment, where the outcomes of one's actions change dynamically because they are related to the behavior of other beings, it becomes difficult to make an optimal decision about how to act. Although game theory provides normative solutions for decision making in groups, how such decision-making strategies are altered by experience is poorly understood. These adaptive processes might resemble reinforcement learning algorithms, which provide a general framework for finding optimal strategies in a dynamic environment. Here we investigated the role of prefrontal cortex (PFC) in dynamic decision making in monkeys. As in reinforcement learning, the animal's choice during a competitive game was biased by its choice and reward history, as well as by the strategies of its opponent. Furthermore, neurons in the dorsolateral prefrontal cortex (DLPFC) encoded the animal's past decisions and payoffs, as well as the conjunction between the two, providing signals necessary to update the estimates of expected reward. Thus, PFC might have a key role in optimizing decision-making strategies.

Animals↗
PubMed1989

On the status of nonconscious processes in human cognition: comment on Reber.

Two issues are discussed. The first one pertains to the generality of the nonconscious learning processes and their somewhat paradoxical status in cognitive psychology. We argue that the ability of the human cognitive system to nonconsciously acquire complex knowledge structures is one of its elementary and indispensable properties. Moreover, the existence of this ability constitutes one of the necessary metatheoretical assumptions of contemporary cognitive psychology. Nevertheless, the contemporary cognitive psychology literature often implies that it is only one of many controversial and unusual phenomena. The second issue pertains to the distinction between the so-called primitive unconscious and the sophisticated unconscious as proposed by Reber (1989).

Concept Formation↗
PubMed2004

Multi-layered greedy network-growing algorithm: extension of greedy network-growing algorithm to multi-layered networks.

In this paper, we extend our greedy network-growing algorithm to multi-layered networks. With multi-layered networks, we can solve many complex problems that single-layered networks fail to solve. In addition, the network-growing algorithm is used in conjunction with teacher-directed learning that produces appropriate outputs without computing errors between targets and outputs. Thus, the present algorithm is a very efficient network-growing algorithm. The new algorithm was applied to three problems: the famous vertical-horizontal lines detection problem, a medical data problem and a road classification problem. In all these cases, experimental results confirmed that the method could solve problems that single-layered networks failed to. In addition, information maximization makes it possible to extract salient features in input patterns.

Algorithms↗
PubMedDate not supplied

Applications of the self-organising map to reinforcement learning.

This article is concerned with the representation and generalisation of continuous action spaces in reinforcement learning (RL) problems. A model is proposed based on the self-organising map (SOM) of Kohonen [Self Organisation and Associative Memory, 1987] which allows either the one-to-one, many-to-one or one-to-many structure of the desired state-action mapping to be captured. Although presented here for tasks involving immediate reward, the approach is easily extended to delayed reward. We conclude that the SOM is a useful tool for providing real-time, on-line generalisation in RL problems in which the latent dimensionalities of the state and action spaces are small. Scalability issues are also discussed.

Neural Networks, Computer↗
PubMed2000

Unsupervised learning of binary vectors: a Gaussian scenario.

We study a model of unsupervised learning where the real-valued data vectors are isotropically distributed, except for a single symmetry-breaking binary direction Bin¿-1,+1¿(N), onto which the projections have a Gaussian distribution. We show that a candidate vector J undergoing Gibbs learning in this discrete space, approaches the perfect match J=B exponentially. In addition to the second-order "retarded learning" phase transition for unbiased distributions, we show that first-order transitions can also occur. Extending the known result that the center of mass of the Gibbs ensemble has Bayes-optimal performance, we show that taking the sign of the components of this vector (clipping) leads to the vector with optimal performance in the binary space. These upper bounds are shown generally not to be saturated with the technique of transforming the components of a special continuous vector, except in asymptotic limits and in a special linear case. Simulations are presented which are in excellent agreement with the theoretical results.

Bayes Theorem↗
PubMed2004

Human midbrain sensitivity to cognitive feedback and uncertainty during classification learning.

Mesencephalic dopaminergic system (MDS) neurons may participate in learning by providing a prediction error signal to their targets, which include ventral striatal, orbital, and medial frontal regions, as well as by showing sensitivity to the degree of uncertainty associated with individual stimuli. We investigated the mechanisms of probabilistic classification learning in humans using functional magnetic resonance imaging to examine the effects of feedback and uncertainty. The design was optimized for separating neural responses to stimulus, delay, and negative and positive feedback components. Compared with fixation, stimulus and feedback activated brain regions consistent with the MDS, whereas the delay period did not. Midbrain activity was significantly different for negative versus positive feedback (consistent with coding of the "prediction error") and was reliably correlated with the degree of uncertainty as well as with activity in MDS target regions. Purely cognitive feedback apparently engages the same regions as rewarding stimuli, consistent with a broader characterization of this network.

Adult↗
PubMedDate not supplied

Generalized relevance learning vector quantization.

We propose a new scheme for enlarging generalized learning vector quantization (GLVQ) with weighting factors for the input dimensions. The factors allow an appropriate scaling of the input dimensions according to their relevance. They are adapted automatically during training according to the specific classification task whereby training can be interpreted as stochastic gradient descent on an appropriate error function. This method leads to a more powerful classifier and to an adaptive metric with little extra cost compared to standard GLVQ. Moreover, the size of the weighting factors indicates the relevance of the input dimensions. This proposes a scheme for automatically pruning irrelevant input dimensions. The algorithm is verified on artificial data sets and the iris data from the UCI repository. Afterwards, the method is compared to several well known algorithms which determine the intrinsic data dimension on real world satellite image data.

Algorithms↗
PubMed2003

From covariation to causation: a test of the assumption of causal power.

How humans infer causation from covariation has been the subject of a vigorous debate, most recently between the computational causal power account (P. W. Cheng, 1997) and associative learning theorists (e.g., K. Lober & D. R. Shanks, 2000). Whereas most researchers in the subject area agree that causal power as computed by the power PC theory offers a normative account of the inductive process. Lober and Shanks, among others, have questioned the empirical validity of the theory. This article offers a full report and additional analyses of the original study featured in Lober and Shanks's critique (M. J. Buehner & P. W. Cheng, 1997) and reports tests of Lober and Shanks's and other explanations of the pattern of causal judgments. Deviations from normativity, including the outcome-density bias, were found to be misperceptions of the input or other artifacts of the experimental procedures rather than inherent to the process of causal induction.

Analysis of Variance↗