nils-herrmann/clean_scirepeval huggingface dataset:
Developmental Constraints Aid the Acquisition of Binocular Disparity Sensitivities

This article considers the hypothesis that systems learning aspects of visual perception may benefit from the use of suitably designed developmental progressions during training. We report the results of simulations in which four models were trained to detect binocular disparities in pairs of visual images. Three of the models were developmental models in the sense that the nature of their visual input changed during the course of training. These models received a relatively impoverished visual input early in training, and the quality of this input improved as training progressed. One model used a coarse-scale-to-multiscale developmental progression, another used a fine-scale-to-multiscale progression, and the third used a random progression. The final model was nondevelopmental in the sense that the nature of its input remained the same throughout the training period. The simulation results show that the two developmental models whose progressions were organized by spatial frequency content consistently outperformed the nondevelopmental and random developmental models. We speculate that the superior performance of these two models is due to two important features of their developmental progressions: (1) these models were exposed to visual inputs at a single scale early in training, and (2) the spatial scale of their inputs progressed in an orderly fashion from one scale to a neighboring scale during training. Simulation results consistent with these speculations are presented. We conclude that suitably designed developmental sequences can be useful to systems learning to detect binocular disparities. The idea that visual development can aid visual learning is a viable hypothesis in need of study.
Visual Development and the Acquisition of Motion Velocity Sensitivities

We consider the hypothesis that systems learning aspects of visual perception may benefit from the use of suitably designed developmental progressions during training. Four models were trained to estimate motion velocities in sequences of visual images. Three of the models were developmental models in the sense that the nature of their visual input changed during the course of training. These models received a relatively impoverished visual input early in training, and the quality of this input improved as training progressed. One model used a coarse-to-multiscale developmental progression (it received coarse-scale motion features early in training and finer-scale features were added to its input as training progressed), another model used a fine-to-multiscale progression, and the third model used a random progression. The final model was nondevelopmental in the sense that the nature of its input remained the same throughout the training period. The simulation results show that the coarse-to-multiscale model performed best. Hypotheses are offered to account for this model's superior performance, and simulation results evaluating these hypotheses are reported. We conclude that suitably designed developmental sequences can be useful to systems learning to estimate motion velocities. The idea that visual development can aid visual learning is a viable hypothesis in need of further study.
A Developmental Approach Aids Motor Learning

Bernstein (1967) suggested that people attempting to learn to perform a difficult motor task try to ameliorate the degrees-of-freedom problem through the use of a developmental progression. Early in training, people maintain a subset of their control parameters (e.g., joint positions) at constant settings and attempt to learn to perform the task by varying the values of the remaining parameters. With practice, people refine and improve this early-learned control strategy by also varying those parameters that were initially held constant. We evaluated Bernstein's proposed developmental progression using six neural network systems and found that a network whose training included developmental progressions of both its trajectory and its feedback gains outperformed all other systems. These progressions, however, yielded performance benefits only on motor tasks that were relatively difficult to learn. We conclude that development can indeed aid motor learning.
Comparing perceptual learning tasks: a review.

We compared perceptual learning in 16 psychophysical studies, ranging from low-level spatial frequency and orientation discrimination tasks to high-level object and face-recognition tasks. All studies examined learning over at least four sessions and were carried out foveally or using free fixation. Comparison of learning effects across this wide range of tasks demonstrates that the amount of learning varies widely between different tasks. A variety of factors seems to affect learning, including the number of perceptual dimensions relevant to the task, external noise, familiarity, and task complexity.
Comparing perceptual learning across tasks: A review

We compared perceptual learning in 16 psychophysical studies, ranging from low-level spatial frequency and orientation discrimination tasks to high-level object and face-recognition tasks. All studies examined learning over at least four sessions and were carried out foveally or using free fixation. Comparison of learning effects across this wide range of tasks demonstrates that the amount of learning varies widely between different tasks. A variety of factors seems to affect learning, including the number of perceptual dimensions relevant to the task, external noise, familiarity, and task complexity.
Fast Temporal Dynamics of Visual Cue Integration

The integration of information from different sensors, cues, or modalities lies at the very heart of perception. We are studying adaptive phenomena in visual cue integration. To this end, we have designed a visual tracking task, where subjects track a target object among distractors and try to identify the target after an occlusion. Objects are defined by three different attributes (color, shape, size) which change randomly within a single trial. When the attributes differ in their reliability (two change frequently, one is stable), our results show that subjects dynamically adapt their processing. The results are consistent with the hypothesis that subjects rapidly re-weight the information provided by the different cues by emphasizing the information from the stable cue. This effect seems to be automatic, ie not requiring subjects' awareness of the differential reliabilities of the cues. The hypothesized re-weighting seems to take place in about 1 s. Our results suggest that cue integration can exhibit adaptive phenomena on a very fast time scale. We propose a probabilistic model with temporal dynamics that accounts for the observed effect.
Factorial Hidden Markov Models and the Generalized Backfitting Algorithm

Previous researchers developed new learning architectures for sequential data by extending conventional hidden Markov models through the use of distributed state representations. Although exact inference and parameter estimation in these architectures is computationally intractable, Ghahramani and Jordan (1997) showed that approximate inference and parameter estimation in one such architecture, factorial hidden Markov models (FHMMs), is feasible in certain circumstances. However, the learning algorithm proposed by these investigators, based on variational techniques, is difficult to understand and implement and is limited to the study of real-valued data sets. This chapter proposes an alternative method for approximate inference and parameter estimation in FHMMs based on the perspective that FHMMs are a generalization of a well-known class of statistical models known as generalized additive models (GAMs; Hastie & Tibshirani, 1990). Using existing statistical techniques for GAMs as a guide, we have developed the generalized backfitting algorithm. This algorithm computes customized error signals for each hidden Markov chain of an FHMM and then trains each chain one at a time using conventional techniques from the hidden Markov models literature. Relative to previous perspectives on FHMMs, we believe that the viewpoint taken here has a number of advantages. First, it places FHMMs on firm statistical foundations by relating them to a class of models that are well studied in the statistics community, yet it generalizes this class of models in an interesting way. Second, it leads to an understanding of how FHMMs can be applied to many different types of time-series data, including Bernoulli and multinomial data, not just data that are real valued. Finally, it leads to an effective learning procedure for FHMMs that is easier to understand and easier to implement than existing learning procedures. Simulation results suggest that FHMMs trained with the generalized backfitting algorithm are a practical and powerful tool for analyzing sequential data.
Interactions between development and learning during the acquisition of binocular disparity sensitivities

Investigates the use of developmental progressions in the acquisition of binocular disparity sensitivity, In an earlier paper (2001) we presented results of simulations comparing a non-developmental neural network model and two developmental neural network models trained to detect binocular disparities and concluded that the developmental models consistently performed better. Here we report the results of simulations on additional neural network models, compare them to the three original models and perform additional analysis. We conclude that the benefits of development are not solely due to limiting the size of the input in early stages, and that in order for development to be helpful to the task of binocular disparity detection, the developmental progressions should be designed to take advantage of known simplifications.
Motor timing learned without motor training

Improvements due to perceptual training are often specific to the trained task and do not generalize to similar perceptual tasks. Surprisingly, given this history of highly constrained, context-specific perceptual learning, we found that training on a perceptual task showed significant transfer to a motor task. This result provides evidence for a common neural architecture underlying analysis of sensory input and control of motor output, and suggests a potential role for perception in motor development and rehabilitation.
Modeling the Combination of Motion, Stereo, and Vergence Angle Cues to Visual Depth

Three models of visual cue combination were simulated: a weak fusion model, a modified weak model, and a strong model. Their relative strengths and weaknesses are evaluated on the basis of their performances on the tasks of judging the depth and shape of an ellipse. The models differ in the amount of interaction that they permit among the cues of stereo, motion, and vergence angle. Results suggest that the constrained nonlinear interaction of the modified weak model allows better performance than either the linear interaction of the weak model or the unconstrained nonlinear interaction of the strong model. Further examination of the modified weak model revealed that its weighting of motion and stereo cues was dependent on the task, the viewing distance, and, to a lesser degree, the noise model. Although the dependencies were sensible from a computational viewpoint, they were sometimes inconsistent with psychophysical experimental data. In a second set of experiments, the modified weak model was given contradictory motion and stereo information. One cue was informative in the sense that it indicated an ellipse, while the other cue indicated a flat surface. The modified weak model rapidly reweighted its use of stereo and motion cues as a function of each cue's informativeness. Overall, the simulation results suggest that relative to the weak and strong models, the modified weak fusion model is a good candidate model of the combination of motion, stereo, and vergence angle cues, although the results also highlight areas in which this model needs modification or further elaboration.
Nature, nurture, and the development of functional specializations: A computational approach

The roles assigned to nature and nurture in the acquisition of functional specializations have been modified in recent years due to increasing evidence that experience-dependent processes are more influential in determining a brain region’s functional properties than was previously supposed. Consequently, one may study the developmental principles that play a role in the acquisition of functional specializations. This article studies the hypothesis that a combination of structure-function correspondences plus the use of competition between modules leads to functional specializations. This principle has been instantiated in a family of neural network architectures referred to as “mixtures-ofexperts” architectures. These architectures are sensitive to structure-function relationships in the sense that they often learn to allocate to each task a network whose structure is well matched to that task. The viewpoint advocated here represents a middle ground between nativist and constructivist views of modularity.
Bias/Variance Analyses of Mixtures-of-Experts Architectures

This article investigates the bias and variance of mixtures-of-experts (ME) architectures. The variance of an ME architecture can be expressed as the sum of two terms: the first term is related to the variances of the expert networks that comprise the architecture and the second term is related to the expert networks' covariances. One goal of this article is to study and quantify a number of properties of ME architectures via the metrics of bias and variance. A second goal is to clarify the relationships between this class of systems and other systems that have recently been proposed. It is shown that in contrast to systems that produce unbiased experts whose estimation errors are uncorrelated, ME architectures produce biased experts whose estimates are negatively correlated.
Bayesian inference for hierarchical mixtures-of-experts with applications to regression and classification

This paper studies the problems of inference and prediction in a class of models known as hierarchical mixtures-of-experts (HME). The statistical model underlying an HME is a mixture model in which both the mixture coefficients and the mixture components are generalized linear models. Bayesian inference regarding an HME's parameters is presented in the contexts of regression and classification using Markov chain Monte Carlo methods. A benefit of this Bayesian approach is the ability to obtain a sample from the posterior distribution of any functional of the parameters of the given model. In this way, more information is obtained than provided by a point estimate. The methods are illustrated on a nonlinear regression problem and on a breast cancer classification problem. The results indicate that the HME showed good prediction performance, and also gave the additional benefit of providing for the opportunity to assess the degree of certainty of the model in its predictions.
On Computational Evidence for Different Types of Spatial Relations Encoding: Reply to Cook et al. (1995)

Computational models in psychology play an increasingly important role in characterizing theoretical distinctions, understanding empirical results, and formulating new predictions. However, the proper use of models is subject to debate and interpretation, as Cook, Fruh, and Landis (1995) have demonstrated in a critique of neural network simulations reported by Kosslyn, Chabris, Marsolek, and Koenig (1992). These simulation results supported a distinction between two types of spatial relations encoding. Cook et al. argue that Kosslyn et al.'s models did not process "spatial" representations and that input-output correlations rather than properties of spatial relations encoding processes explain the performance of the models. This article provides conceptual and analytic rebuttals of those criticisms.
On computational evidence for different types of spatial relations encoding: reply to Cook et al. (1995).

Computational models in psychology play an increasingly important role in characterizing theoretical distinctions, understanding empirical results, and formulating new predictions. However, the proper use of models is subject to debate and interpretation, as Cook, Früh, and Landis (1995) have demonstrated in a critique of neural network simulations reported by Kosslyn, Chabris, Marsolek, and Koenig (1992). These simulation results supported a distinction between two types of spatial relations encoding. Cook et al. argue that Kosslyn et al.'s models did not process "spatial" representations and that input-output correlations rather than properties of spatial relations encoding processes explain the performance of the models. This article provides conceptual and analytic rebuttals of those criticisms.
Methods For Combining Experts' Probability Assessments

This article reviews statistical techniques for combining multiple probability distributions. The framework is that of a decision maker who consults several experts regarding some events. The experts express their opinions in the form of probability distributions. The decision maker must aggregate the experts' distributions into a single distribution that can be used for decision making. Two classes of aggregation methods are reviewed. When using a supra Bayesian procedure, the decision maker treats the expert opinions as data that may be combined with its own prior distribution via Bayes' rule. When using a linear opinion pool, the decision maker forms a linear combination of the expert opinions. The major feature that makes the aggregation of expert opinions difficult is the high correlation or dependence that typically occurs among these opinions. A theme of this paper is the need for training procedures that result in experts with relatively independent opinions or for aggregation methods that implicitly or explicitly model the dependence among the experts. Analyses are presented that show that m dependent experts are worth the same as k independent experts where k m. In some cases, an exact value for k can be given; in other cases, lower and upper bounds can be placed on k.
Targeting c-Myc-activated genes with a correlation method: detection of global changes in large gene expression network dynamics.

This work studies the dynamics of a gene expression time series network. The network, which is obtained from the correlation of gene expressions, exhibits global dynamic properties that emerge after a cell state perturbation. The main features of this network appear to be more robust when compared with those obtained with a network obtained from a linear Markov model. In particular, the network properties strongly depend on the exact time sequence relationships between genes and are destroyed by random temporal data shuffling. We discuss in detail the problem of finding targets of the c-myc protooncogene, which encodes a transcriptional regulator whose inappropriate expression has been correlated with a wide array of malignancies. The data used for network construction are a time series of gene expression, collected by microarray analysis of a rat fibroblast cell line expressing a conditional Myc-estrogen receptor oncoprotein. We show that the correlation-based model can establish a clear relationship between network structure and the cascade of c-myc-activated genes.
Improved noise tolerance for sonar applications in critical environments

Sonar systems are a key element in harbor security applications. One of the major challenges in their utilization is the presence of high levels of noise and cluster. In this paper, we analyze the distribution of errors in the time-delay estimation for a sonar system using a train of pulses and show that a robust fusion of echoes from multiple pings can greatly improve its resilience to noise. As a result, an increased range of operation can be achieved in applications to extremely noisy environments such as harbors, ports and costal facilities
Time-Frequency Computational Model for Echo-Delay Resolution in Sonar Images of the Big Brown Bat, Eptesicus Fuscus

Biosonar animals have a remarkably accurate and noise tolerant sonar. Some bats use their auditory system to achieve full 3D navigation capabilities and prey discrimination. They reach a resolution in the sub-millimeter range. Likewise, some dolphins utilize their auditory system to achieve a combination of 3D navigation and internal object examination that far exceeds the abilities of our current ultrasound and underwater sonar technology. We have devoted our research efforts during the last year of the project into utilizing the knowledge about biosonar that we have gained in previous years to developing practical sonar techniques for the benefit of homeland security tasks as well as medical applications. Specifically, we demonstrate improved underwater sonar resolution and ultrasound imagery.
Robot motion estimation improves underwater sonar accuracy

Underwater sonar system is currently the best solution to distinguish a hazardous target from the harmless objects or a flock of fish at a far distance in the water, subsequently protect the harbors' security. A good sonar system should have the ability to survey a long range, in the meantime reserve a high accuracy. Noise is a crucial problem affecting the accuracy. One possible solution to increase the signal-to-noise ratio (SNR) is to average return signals over multiple sonar pings. A problem with this approach is the relative motion between the sonar and the target during pinging which strongly reduces the effect of the averaging. In this paper, we present a robust motion estimation using multiple receive sensors that estimates the relative motion between multiple pings and aligns all the pings to a base ping through appropriate motion compensation. We further show that the fusion of multiple pings for time-delay estimation after realignment has the potential of improving both the resilience to noise and the accuracy, consequently increasing the operating range
Automatic extraction of physiological features from vibro-acoustic heart signals: correlation with echo-doppler

The mechanical processes within the cardiovascular system produce low-frequency vibrations and sounds. These vibro-acoustic signals carry valuable physiological information that can be potentially used for cardiac monitoring. In this work, heart sounds, apical pulse, and arterial pulse signals were simultaneously acquired, along with electrocardiogram and echo-Doppler audio signals. Processing algorithms were developed to extract temporal and morphological feature from the signals. Spectral analysis was used to reconstruct the Doppler sonograms and estimate reference values. A good agreement was observed between systolic and diastolic time intervals estimated by both methods. Strong beat-to-beat correlations were shown both in rest and during pharmacological stress test. The results demonstrate the technological and medical feasibility of using automatic analysis of vibro-acoustic heart signals for continuous non-invasive monitoring of cardiac functionality
An integration of online and pseudo-online information for cursive word recognition

In this paper, we present a novel method to extract stroke order independent information from online data. This information, which we term pseudo-online, conveys relevant information on the offline representation of the word. Based on this information, a combination of classification decisions from online and pseudo-online cursive word recognizers is performed to improve the recognition of online cursive words. One of the most valuable aspects of this approach with respect to similar methods that combine online and offline classifiers for word recognition is that the pseudo-online representation is similar to the online signal and, hence, word recognition is based on a single engine. Results demonstrate that the pseudo-online representation is useful as the combination of classifiers perform better than those based solely on pure online information.