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PubMed2002

The effect of stimulus probability on pupillary response as an indicator of cognitive processing in human learning and categorization.

Stimuli presented with a low probability of occurrence elicit larger pupillary dilations than those presented with a high frequency. Utilizing this stimulus probability effect, we conducted two Go/NoGo reaction time experiments to analyze category learning and compound coding in the context of associative learning. Experiment 1 showed no stimulus probability effect when participants used an abstract rule to classify stimuli with different probabilities into the same category. When no rules could be applied, however, the stimulus probability effect was observed. Hence, this effect can be utilized to identify the application of rules in category learning. Experiment 2 demonstrated that the stimulus probability effect can also be used in determining whether compounds are processed in terms of elemental stimuli or as new entities. The results of Experiment 2 supported elemental rather than configural processing. Obviously, the probability effect can be used as a tool for the evaluation of categorization and compound processing.

Adult↗
PubMed2006

Lectures are such an effective teaching method because they exploit evolved human psychology to improve learning.

Lectures are probably the best teaching method for many students in many circumstances; especially for communicating conceptual knowledge, and where there is a significant knowledge gap between lecturer and audience. However, the lack of a convincing rationale has been a factor in under-estimating the importance of lectures, and there are many who advocate their replacement with written communications or electronic media. I suggest that lectures are so effective because they exploit the spontaneous human aptitude for learning from spoken (rather than written) information. Literacy is a recent cultural artefact, and for most of their evolutionary history humans communicated by direct speech. By contrast with speech, all communication technologies--whether reading a book or a computer monitor--are artificial and unnatural. Furthermore, learning is easier during formal, quiet, real-time social events. The structure of a lecture artificially manipulates human psychology to increase vigilance, focus attention, and generate authority for the lecturer--all of which make communications more memorable for the student. Instead of trying to phase-out lectures, we should strive to make them better by understanding that lectures are essentially formal, spoken, social events.

Humans↗
PubMed2024

Expert opinion elicitation for assisting deep learning based Lyme disease classifier with patient data.

BACKGROUND: Diagnosing erythema migrans (EM) skin lesion, the most common early symptom of Lyme disease, using deep learning techniques can be effective to prevent long-term complications. Existing works on deep learning based EM recognition only utilizes lesion image due to the lack of a dataset of Lyme disease related images with associated patient data. Doctors rely on patient information about the background of the skin lesion to confirm their diagnosis. To assist deep learning model with a probability score calculated from patient data, this study elicited opinions from fifteen expert doctors. To the best of our knowledge, this is the first expert elicitation work to calculate Lyme disease probability from patient data. METHODS: For the elicitation process, a questionnaire with questions and possible answers related to EM was prepared. Doctors provided relative weights to different answers to the questions. We converted doctors' evaluations to probability scores using Gaussian mixture based density estimation. We exploited formal concept analysis and decision tree for elicited model validation and explanation. We also proposed an algorithm for combining independent probability estimates from multiple modalities, such as merging the EM probability score from a deep learning image classifier with the elicited score from patient data. RESULTS: We successfully elicited opinions from fifteen expert doctors to create a model for obtaining EM probability scores from patient data. CONCLUSIONS: The elicited probability score and the proposed algorithm can be utilized to make image based deep learning Lyme disease pre-scanners robust. The proposed elicitation and validation process is easy for doctors to follow and can help address related medical diagnosis problems where it is challenging to collect patient data.

Humans↗
PubMed2004

Utilization of hierarchical, stochastic relationship modeling for Hangul character recognition.

In structural character recognition, a character is usually viewed as a set of strokes and the spatial relationships between them. Therefore, strokes and their relationships should be properly modeled for effective character representation. For this purpose, we propose a modeling scheme by which strokes as well as relationships are stochastically represented by utilizing the hierarchical characteristics of target characters. A character is defined by a multivariate random variable over the components and its probability distribution is learned from a training data set. To overcome difficulties of the learning due to the high order of the probability distribution (a problem of curse of dimensionality), the probability distribution is factorized and approximated by a set of lower-order probability distributions by applying the idea of relationship decomposition recursively to components and subcomponents. Based on the proposed method, a handwritten Hangul (Korean) character recognition system is developed. Recognition experiments conducted on a public database show the effectiveness of the proposed relationship modeling. The recognition accuracy increased by 5.5 percent in comparison to the most successful system ever reported.

Algorithms↗
PubMed2005

The value of basic science in clinical diagnosis: creating coherence among signs and symptoms.

BACKGROUND: We investigated whether learning basic science mechanisms may have mnemonic value in helping students remember signs and symptoms, in comparison with learning the relation between symptoms and diagnoses directly. PURPOSE: To compare 2 approaches to learning diagnosis: learning how features of various conditions relate to underlying pathophysiological mechanisms and learning the conditional probabilities of features and diseases. METHODS: Undergraduate students (n = 36) were taught 4 disorders (upper motor neuron lesion, lower motor neuron lesion, neuromuscular junction disease and muscular disease), either using basic science explanations or (symptom x disease) probabilities. They were tested with diagnostic cases immediately after learning and 1 week later. RESULTS: On the immediate test, there was no difference in the results. One week later, the accuracy of the mechanism group remained at 0.52, but the performance of the probability group had dropped to 0.43. CONCLUSIONS: Knowledge of basic science may have value in clinical diagnosis by helping students recall or reconstruct the relationships between features and diagnoses.

Clinical Competence↗
PubMed2006

What do we learn from binding features? Evidence for multilevel feature integration.

Four experiments were conducted to investigate the relationship between the binding of visual features (as measured by their aftereffects on subsequent binding) and the learning of feature-conjunction probabilities. Both binding and learning effects were obtained, but they did not interact. Interestingly, (shape-color) binding effects disappeared with increasing practice, presumably because of the fact that only 1 of the features involved was relevant to the task. However, this instability was only observed for arbitrary, not highly overlearned combinations of simple geometric features and not for real objects (colored pictures of a banana and strawberry), where binding effects were strong and resistant to practice. These findings suggest that learning has no direct impact on the strength or resistance of bindings or on speed with which features are bound; however, learning does affect the amount of attention particular feature dimensions attract, which again can influence which features are considered in binding.

Analysis of Variance↗
PubMed2006

Multiple learning modes in the development of performance on a rule-based category-learning task.

Behavioral and neuropsychological data suggest that multiple systems are involved in category-learning. In this paper, the existence and the development of multiple modes of learning of a rule-based category structure was examined, and features of different learning processes were identified. Data were obtained in a cross-sectional study by Raijmakers et al. [Raijmakers, M. E. J., Dolan, C. V., & Molenaar, P. C. M. (2001). Finite mixture distribution models of simple discrimination learning. Memory and Cognition, 29, 659-677], in which subjects aged 4-20 years carried out a rule-based category-learning task. Learning models were employed to investigate the development of the learning processes in the sample. The results support the hypothesis of two distinct learning modes, rather than a single general mode of learning with a continuum of appearances. One mode represents sudden rational learning by means of hypothesis testing. In the second, slow learning mode, learning also occurs suddenly as opposed to incrementally. The probability of rational learning increases with age, and seems to be related to dimension preference in the younger age groups. However, the finding of distinct learning modes does not necessarily imply that distinct learning systems are involved. Implications for the interpretation and clinical use of tasks with a category-learning component, such as the Wisconsin Card Sorting Test (WCST [Heaton, R. K., Chelune, G. J., Talley, J. L., Kay, G. G., & Curtis, G. (Eds.). (1993). Wisconsin card sorting test manual: Revised and expanded. Odessa, FL: Psychological Assessment Resources]), are discussed.

Adolescent↗
PubMed1998

Probability density methods for smooth function approximation and learning in populations of tuned spiking neurons.

This article proposes a new method for interpreting computations performed by populations of spiking neurons. Neural firing is modeled as a rate-modulated random process for which the behavior of a neuron in response to external input can be completely described by its tuning function. I show that under certain conditions, cells with any desired tuning functions can be approximated using only spike coincidence detectors and linear operations on the spike output of existing cells. I show examples of adaptive algorithms based on only spike data that cause the underlying cell-tuning curves to converge according to standard supervised and unsupervised learning algorithms. Unsupervised learning based on principal components analysis leads to independent cell spike trains. These results suggest a duality relationship between the random discrete behavior of spiking cells and the deterministic smooth behavior of their tuning functions. Classical neural network approximation methods and learning algorithms based on continuous variables can thus be implemented within networks of spiking neurons without the need to make numerical estimates of the intermediate cell firing rates.

Action Potentials↗
PubMed2004

Dynamic analysis of learning in behavioral experiments.

Understanding how an animal's ability to learn relates to neural activity or is altered by lesions, different attentional states, pharmacological interventions, or genetic manipulations are central questions in neuroscience. Although learning is a dynamic process, current analyses do not use dynamic estimation methods, require many trials across many animals to establish the occurrence of learning, and provide no consensus as how best to identify when learning has occurred. We develop a state-space model paradigm to characterize learning as the probability of a correct response as a function of trial number (learning curve). We compute the learning curve and its confidence intervals using a state-space smoothing algorithm and define the learning trial as the first trial on which there is reasonable certainty (>0.95) that a subject performs better than chance for the balance of the experiment. For a range of simulated learning experiments, the smoothing algorithm estimated learning curves with smaller mean integrated squared error and identified the learning trials with greater reliability than commonly used methods. The smoothing algorithm tracked easily the rapid learning of a monkey during a single session of an association learning experiment and identified learning 2 to 4 d earlier than accepted criteria for a rat in a 47 d procedural learning experiment. Our state-space paradigm estimates learning curves for single animals, gives a precise definition of learning, and suggests a coherent statistical framework for the design and analysis of learning experiments that could reduce the number of animals and trials per animal that these studies require.

Algorithms↗
PubMed1983

Probable role of reverse transcription in learning: correlation between hippocampal RNA-dependent DNA synthesis and learning ability in rats.

The activities of RNA-dependent DNA polymerase and DNA-dependent DNA polymerase were measured in hippocampus of fast and slow learning Wistar rats. The RNA-dependent DNA polymerase activity in the hippocampus of fast learning rats exceeds two-fold that in the slow learning ones, while the rates of the DNA-dependent DNA polymerase activities are similar. A significant increase in RNA-dependent DNA polymerase only was found in the hippocampus of rats 20 min after training for the conditioned food response before the trace consolidation registered 40 min after the training session. The data obtained are consistent with the suggestion that reverse transcription plays an important role in memory consolidation.

Animals↗
PubMed1997

Estimating Learning Curves of Concept Learning.

In this paper, we describe an approximation method which enables us to study the average generalization performance of learning directly via hypothesis testing inequalities. This unites the learning and the hypothesis testing in a common viewpoint. In particular, we investigate learning curves of a so-called ill-disposed learning algorithm, which can provide useful implications regarding the problem of overfitting from a practical and yet scientific viewpoint. The learning problem is stated in the probably approximately correct (PAC) learning model, but only the average generalization performance is addressed. The resulting bounds are directly related to the number of system weights. The advantages of the theory are that it alleviates the practical pessimism frequently claimed for the results of the VC theory, and provides general insights. We illustrate the results with some numerical simulations. Copyright 1997 Elsevier Science Ltd.

Journal Article↗