Publications | Max Planck Institute for Intelligent Systems

38 results (View BibTeX file of all listed publications)

2013

Coupling between spiking activity and beta band spatio-temporal patterns in the macaque PFC

Safavi, S., Panagiotaropoulos, T., Kapoor, V., Logothetis, N., Besserve, M.

43rd Annual Meeting of the Society for Neuroscience (Neuroscience), 2013 (poster)

2013

ei Safavi, S., Panagiotaropoulos, T., Kapoor, V., Logothetis, N., Besserve, M. Coupling between spiking activity and beta band spatio-temporal patterns in the macaque PFC 43rd Annual Meeting of the Society for Neuroscience (Neuroscience), 2013 (poster)

[BibTex]

Gaussian Process Vine Copulas for Multivariate Dependence

Lopez-Paz, D., Hernandez-Lobato, J., Ghahramani, Z.

International Conference on Machine Learning (ICML), 2013 (poster)

PDF [BibTex]

ei Lopez-Paz, D., Hernandez-Lobato, J., Ghahramani, Z. Gaussian Process Vine Copulas for Multivariate Dependence International Conference on Machine Learning (ICML), 2013 (poster)

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Domain Generalization via Invariant Feature Representation

Muandet, K., Balduzzi, D., Schölkopf, B.

30th International Conference on Machine Learning (ICML2013), 2013 (poster)

PDF [BibTex]

ei Muandet, K., Balduzzi, D., Schölkopf, B. Domain Generalization via Invariant Feature Representation 30th International Conference on Machine Learning (ICML2013), 2013 (poster)

PDF [BibTex]

Analyzing locking of spikes to spatio-temporal patterns in the macaque prefrontal cortex

Safavi, S., Panagiotaropoulos, T., Kapoor, V., Logothetis, N., Besserve, M.

Bernstein Conference, 2013 (poster)

DOI [BibTex]

ei Safavi, S., Panagiotaropoulos, T., Kapoor, V., Logothetis, N., Besserve, M. Analyzing locking of spikes to spatio-temporal patterns in the macaque prefrontal cortex Bernstein Conference, 2013 (poster)

DOI [BibTex]

One-class Support Measure Machines for Group Anomaly Detection

Muandet, K., Schölkopf, B.

29th Conference on Uncertainty in Artificial Intelligence (UAI), 2013 (poster)

PDF [BibTex]

ei Muandet, K., Schölkopf, B. One-class Support Measure Machines for Group Anomaly Detection 29th Conference on Uncertainty in Artificial Intelligence (UAI), 2013 (poster)

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The Randomized Dependence Coefficient

Lopez-Paz, D., Hennig, P., Schölkopf, B.

Neural Information Processing Systems (NIPS), 2013 (poster)

ei pn

PDF [BibTex]

ei pn Lopez-Paz, D., Hennig, P., Schölkopf, B. The Randomized Dependence Coefficient Neural Information Processing Systems (NIPS), 2013 (poster)

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Characterization of different types of sharp-wave ripple signatures in the CA1 of the macaque hippocampus

Ramirez-Villegas, J., Logothetis, N., Besserve, M.

4th German Neurophysiology PhD Meeting Networks, 2013 (poster)

Web [BibTex]

ei Ramirez-Villegas, J., Logothetis, N., Besserve, M. Characterization of different types of sharp-wave ripple signatures in the CA1 of the macaque hippocampus 4th German Neurophysiology PhD Meeting Networks, 2013 (poster)

Web [BibTex]

Proceedings of the 10th European Workshop on Reinforcement Learning, Volume 24

Deisenroth, M., Szepesvári, C., Peters, J.

pages: 173, JMLR, European Workshop On Reinforcement Learning, EWRL, 2013 (proceedings)

Web [BibTex]

ei Deisenroth, M., Szepesvári, C., Peters, J. Proceedings of the 10th European Workshop on Reinforcement Learning, Volume 24 pages: 173, JMLR, European Workshop On Reinforcement Learning, EWRL, 2013 (proceedings)

Web [BibTex]

2005

Kernel methods for dependence testing in LFP-MUA

Gretton, A., Belitski, A., Murayama, Y., Schölkopf, B., Logothetis, N.

35(689.17), 35th Annual Meeting of the Society for Neuroscience (Neuroscience), November 2005 (poster)

Abstract

A fundamental problem in neuroscience is determining whether or not particular neural signals are dependent. The correlation is the most straightforward basis for such tests, but considerable work also focuses on the mutual information (MI), which is capable of revealing dependence of higher orders that the correlation cannot detect. That said, there are other measures of dependence that share with the MI an ability to detect dependence of any order, but which can be easier to compute in practice. We focus in particular on tests based on the functional covariance, which derive from work originally accomplished in 1959 by Renyi. Conceptually, our dependence tests work by computing the covariance between (infinite dimensional) vectors of nonlinear mappings of the observations being tested, and then determining whether this covariance is zero - we call this measure the constrained covariance (COCO). When these vectors are members of universal reproducing kernel Hilbert spaces, we can prove this covariance to be zero only when the variables being tested are independent. The greatest advantage of these tests, compared with the mutual information, is their simplicity – when comparing two signals, we need only take the largest eigenvalue (or the trace) of a product of two matrices of nonlinearities, where these matrices are generally much smaller than the number of observations (and are very simple to construct). We compare the mutual information, the COCO, and the correlation in the context of finding changes in dependence between the LFP and MUA signals in the primary visual cortex of the anaesthetized macaque, during the presentation of dynamic natural stimuli. We demonstrate that the MI and COCO reveal dependence which is not detected by the correlation alone (which we prove by artificially removing all correlation between the signals, and then testing their dependence with COCO and the MI); and that COCO and the MI give results consistent with each other on our data.

Web [BibTex]

2005

ei Gretton, A., Belitski, A., Murayama, Y., Schölkopf, B., Logothetis, N. Kernel methods for dependence testing in LFP-MUA 35(689.17), 35th Annual Meeting of the Society for Neuroscience (Neuroscience), November 2005 (poster)

Web [BibTex]

Rapid animal detection in natural scenes: Critical features are local

Wichmann, F., Rosas, P., Gegenfurtner, K.

Journal of Vision, 5(8):376, Fifth Annual Meeting of the Vision Sciences Society (VSS), September 2005 (poster)

Abstract

Thorpe et al (Nature 381, 1996) first showed how rapidly human observers are able to classify natural images as to whether they contain an animal or not. Whilst the basic result has been replicated using different response paradigms (yes-no versus forced-choice), modalities (eye movements versus button presses) as well as while measuring neurophysiological correlates (ERPs), it is still unclear which image features support this rapid categorisation. Recently Torralba and Oliva (Network: Computation in Neural Systems, 14, 2003) suggested that simple global image statistics can be used to predict seemingly complex decisions about the absence and/or presence of objects in natural scences. They show that the information contained in a small number (N=16) of spectral principal components (SPC)—principal component analysis (PCA) applied to the normalised power spectra of the images—is sufficient to achieve approximately 80% correct animal detection in natural scenes. Our goal was to test whether human observers make use of the power spectrum when rapidly classifying natural scenes. We measured our subjects' ability to detect animals in natural scenes as a function of presentation time (13 to 167 msec); images were immediately followed by a noise mask. In one condition we used the original images, in the other images whose power spectra were equalised (each power spectrum was set to the mean power spectrum over our ensemble of 1476 images). Thresholds for 75% correct animal detection were in the region of 20–30 msec for all observers, independent of the power spectrum of the images: this result makes it very unlikely that human observers make use of the global power spectrum. Taken together with the results of Gegenfurtner, Braun & Wichmann (Journal of Vision [abstract], 2003), showing the robustness of animal detection to global phase noise, we conclude that humans use local features, like edges and contours, in rapid animal detection.

Web DOI [BibTex]

ei Wichmann, F., Rosas, P., Gegenfurtner, K. Rapid animal detection in natural scenes: Critical features are local Journal of Vision, 5(8):376, Fifth Annual Meeting of the Vision Sciences Society (VSS), September 2005 (poster)

Web DOI [BibTex]

Learning an Interest Operator from Eye Movements

Kienzle, W., Franz, M., Wichmann, F., Schölkopf, B.

International Workshop on Bioinspired Information Processing (BIP 2005), 2005, pages: 1, September 2005 (poster)

PDF Web [BibTex]

ei Kienzle, W., Franz, M., Wichmann, F., Schölkopf, B. Learning an Interest Operator from Eye Movements International Workshop on Bioinspired Information Processing (BIP 2005), 2005, pages: 1, September 2005 (poster)

PDF Web [BibTex]

Classification of natural scenes using global image statistics

Drewes, J., Wichmann, F., Gegenfurtner, K.

Journal of Vision, 5(8):602, Fifth Annual Meeting of the Vision Sciences Society (VSS), September 2005 (poster)

Abstract

The algorithmic classification of complex, natural scenes is generally considered a difficult task due to the large amount of information conveyed by natural images. Work by Simon Thorpe and colleagues showed that humans are capable of detecting animals within novel natural scenes with remarkable speed and accuracy. This suggests that the relevant information for classification can be extracted at comparatively limited computational cost. One hypothesis is that global image statistics such as the amplitude spectrum could underly fast image classification (Johnson & Olshausen, Journal of Vision, 2003; Torralba & Oliva, Network: Comput. Neural Syst., 2003). We used linear discriminant analysis to classify a set of 11.000 images into animal and non-animal images. After applying a DFT to the image, we put the Fourier spectrum into bins (8 orientations with 6 frequency bands each). Using all bins, classification performance on the Fourier spectrum reached 70%. However, performance was similar (67%) when only the high spatial frequency information was used and decreased steadily at lower spatial frequencies, reaching a minimum (50%) for the low spatial frequency information. Similar results were obtained when all bins were used on spatially filtered images. A detailed analysis of the classification weights showed that a relatively high level of performance (67%) could also be obtained when only 2 bins were used, namely the vertical and horizontal orientation at the highest spatial frequency band. Our results show that in the absence of sophisticated machine learning techniques, animal detection in natural scenes is limited to rather modest levels of performance, far below those of human observers. If limiting oneself to global image statistics such as the DFT then mostly information at the highest spatial frequencies is useful for the task. This is analogous to the results obtained with human observers on filtered images (Kirchner et al, VSS 2004).

Web DOI [BibTex]

ei Drewes, J., Wichmann, F., Gegenfurtner, K. Classification of natural scenes using global image statistics Journal of Vision, 5(8):602, Fifth Annual Meeting of the Vision Sciences Society (VSS), September 2005 (poster)

Web DOI [BibTex]

Comparative evaluation of Independent Components Analysis algorithms for isolating target-relevant information in brain-signal classification

Hill, N., Schröder, M., Lal, T., Schölkopf, B.

Brain-Computer Interface Technology, 3, pages: 95, June 2005 (poster)

PDF [BibTex]

ei Hill, N., Schröder, M., Lal, T., Schölkopf, B. Comparative evaluation of Independent Components Analysis algorithms for isolating target-relevant information in brain-signal classification Brain-Computer Interface Technology, 3, pages: 95, June 2005 (poster)

PDF [BibTex]

Classification of natural scenes using global image statistics

Drewes, J., Wichmann, F., Gegenfurtner, K.

47, pages: 88, 47. Tagung Experimentell Arbeitender Psychologen, April 2005 (poster)

[BibTex]

ei Drewes, J., Wichmann, F., Gegenfurtner, K. Classification of natural scenes using global image statistics 47, pages: 88, 47. Tagung Experimentell Arbeitender Psychologen, April 2005 (poster)

[BibTex]

Classification of Natural Scenes using Global Image Statistics

Drewes, J., Wichmann, F., Gegenfurtner, K.

8, pages: 88, 8th T{\"u}bingen Perception Conference (TWK), February 2005 (poster)

Abstract

The algorithmic classification of complex, natural scenes is generally considered a difficult task due to the large amount of information conveyed by natural images. Work by Simon Thorpe and colleagues showed that humans are capable of detecting animals within novel natural scenes with remarkable speed and accuracy. This suggests that the relevant information for classification can be extracted at comparatively limited computational cost. One hypothesis is that global image statistics such as the amplitude spectrum could underly fast image classification (Johnson & Olshausen, Journal of Vision, 2003; Torralba & Oliva, Network: Comput. Neural Syst., 2003). We used linear discriminant analysis to classify a set of 11.000 images into animal and nonanimal images. After applying a DFT to the image, we put the Fourier spectrum of each image into 48 bins (8 orientations with 6 frequency bands). Using all of these bins, classification performance on the Fourier spectrum reached 70%. In an iterative procedure, we then removed the bins whose absence caused the smallest damage to the classification performance (one bin per iteration). Notably, performance stayed at about 70% until less then 6 bins were left. A detailed analysis of the classification weights showed that a comparatively high level of performance (67%) could also be obtained when only 2 bins were used, namely the vertical orientations at the highest spatial frequency band. When using only a single frequency band (8 bins) we found that 67% classification performance could be reached when only the high spatial frequency information was used, which decreased steadily at lower spatial frequencies, reaching a minimum (50%) for the low spatial frequency information. Similar results were obtained when all bins were used on spatially pre-filtered images. Our results show that in the absence of sophisticated machine learning techniques, animal detection in natural scenes is limited to rather modest levels of performance, far below those of human observers. If limiting oneself to global image statistics such as the DFT then mostly information at the highest spatial frequencies is useful for the task. This is analogous to the results obtained with human observers on filtered images (Kirchner et al, VSS 2004).

Web [BibTex]

ei Drewes, J., Wichmann, F., Gegenfurtner, K. Classification of Natural Scenes using Global Image Statistics 8, pages: 88, 8th T{\"u}bingen Perception Conference (TWK), February 2005 (poster)

Web [BibTex]

Efficient Adaptive Sampling of the Psychometric Function by Maximizing Information Gain

Tanner, T., Hill, N., Rasmussen, C., Wichmann, F.

8, pages: 109, (Editors: Bülthoff, H. H., H. A. Mallot, R. Ulrich and F. A. Wichmann), 8th T{\"u}bingen Perception Conference (TWK), February 2005 (poster)

Abstract

A psychometric function can be described by its shape and four parameters: position or threshold, slope or width, false alarm rate or chance level, and miss or lapse rate. Depending on the parameters of interest some points on the psychometric function may be more informative than others. Adaptive methods attempt to place trials on the most informative points based on the data collected in previous trials. We introduce a new adaptive bayesian psychometric method which collects data for any set of parameters with high efficency. It places trials by minimizing the expected entropy [1] of the posterior pdf over a set of possible stimuli. In contrast to most other adaptive methods it is neither limited to threshold measurement nor to forced-choice designs. Nuisance parameters can be included in the estimation and lead to less biased estimates. The method supports block designs which do not harm the performance when a sufficient number of trials are performed. Block designs are useful for control of response bias and short term performance shifts such as adaptation. We present the results of evaluations of the method by computer simulations and experiments with human observers. In the simulations we investigated the role of parametric assumptions, the quality of different point estimates, the effect of dynamic termination criteria and many other settings. [1] Kontsevich, L.L. and Tyler, C.W. (1999): Bayesian adaptive estimation of psychometric slope and threshold. Vis. Res. 39 (16), 2729-2737.

Web [BibTex]

ei Tanner, T., Hill, N., Rasmussen, C., Wichmann, F. Efficient Adaptive Sampling of the Psychometric Function by Maximizing Information Gain 8, pages: 109, (Editors: Bülthoff, H. H., H. A. Mallot, R. Ulrich and F. A. Wichmann), 8th T{\"u}bingen Perception Conference (TWK), February 2005 (poster)

Web [BibTex]

Bayesian Inference for Psychometric Functions

Kuss, M., Jäkel, F., Wichmann, F.

8, pages: 106, (Editors: Bülthoff, H. H., H. A. Mallot, R. Ulrich and F. A. Wichmann), 8th T{\"u}bingen Perception Conference (TWK), February 2005 (poster)

Abstract

In psychophysical studies of perception the psychometric function is used to model the relation between the physical stimulus intensity and the observer's ability to detect or discriminate between stimuli of different intensities. We propose the use of Bayesian inference to extract the information contained in experimental data to learn about the parameters of psychometric functions. Since Bayesian inference cannot be performed analytically we use a Markov chain Monte Carlo method to generate samples from the posterior distribution over parameters. These samples can be used to estimate Bayesian confidence intervals and other characteristics of the posterior distribution. We compare our approach with traditional methods based on maximum-likelihood parameter estimation combined with parametric bootstrap techniques for confidence interval estimation. Experiments indicate that Bayesian inference methods are superior to bootstrap-based methods and are thus the method of choice for estimating the psychometric function and its confidence-intervals.

Web [BibTex]

ei Kuss, M., Jäkel, F., Wichmann, F. Bayesian Inference for Psychometric Functions 8, pages: 106, (Editors: Bülthoff, H. H., H. A. Mallot, R. Ulrich and F. A. Wichmann), 8th T{\"u}bingen Perception Conference (TWK), February 2005 (poster)

Web [BibTex]

Global image statistics of natural scenes

Drewes, J., Wichmann, F., Gegenfurtner, K.

Bioinspired Information Processing, 08, pages: 1, 2005 (poster)

[BibTex]

ei Drewes, J., Wichmann, F., Gegenfurtner, K. Global image statistics of natural scenes Bioinspired Information Processing, 08, pages: 1, 2005 (poster)

[BibTex]

Kernel-Methods, Similarity, and Exemplar Theories of Categorization

Jäkel, F., Wichmann, F.

ASIC, 4, 2005 (poster)

Abstract

Kernel-methods are popular tools in machine learning and statistics that can be implemented in a simple feed-forward neural network. They have strong connections to several psychological theories. For example, Shepard‘s universal law of generalization can be given a kernel interpretation. This leads to an inner product and a metric on the psychological space that is different from the usual Minkowski norm. The metric has psychologically interesting properties: It is bounded from above and does not have additive segments. As categorization models often rely on Shepard‘s law as a model for psychological similarity some of them can be recast as kernel-methods. In particular, ALCOVE is shown to be closely related to kernel logistic regression. The relationship to the Generalized Context Model is also discussed. It is argued that functional analysis which is routinely used in machine learning provides valuable insights also for psychology.

Web [BibTex]

ei Jäkel, F., Wichmann, F. Kernel-Methods, Similarity, and Exemplar Theories of Categorization ASIC, 4, 2005 (poster)

Web [BibTex]

Rapid animal detection in natural scenes: critical features are local

Wichmann, F., Rosas, P., Gegenfurtner, K.

Experimentelle Psychologie. Beitr{\"a}ge zur 47. Tagung experimentell arbeitender Psychologen, 47, pages: 225, 2005 (poster)

[BibTex]

ei Wichmann, F., Rosas, P., Gegenfurtner, K. Rapid animal detection in natural scenes: critical features are local Experimentelle Psychologie. Beitr{\"a}ge zur 47. Tagung experimentell arbeitender Psychologen, 47, pages: 225, 2005 (poster)

[BibTex]

The human brain as large margin classifier

Graf, A., Wichmann, F., Bülthoff, H., Schölkopf, B.

Proceedings of the Computational & Systems Neuroscience Meeting (COSYNE), 2, pages: 1, 2005 (poster)

[BibTex]

ei Graf, A., Wichmann, F., Bülthoff, H., Schölkopf, B. The human brain as large margin classifier Proceedings of the Computational & Systems Neuroscience Meeting (COSYNE), 2, pages: 1, 2005 (poster)

[BibTex]

2004

S-cones contribute to flicker brightness in human vision

Wehrhahn, C., Hill, NJ., Dillenburger, B.

34(174.12), 34th Annual Meeting of the Society for Neuroscience (Neuroscience), October 2004 (poster)

Abstract

In the retina of primates three cone types sensitive to short, middle and long wavelengths of light convert photons into electrical signals. Many investigators have presented evidence that, in color normal observers, the signals of cones sensitive to short wavelengths of light (S-cones) do not contribute to the perception of brightness of a colored surface when this is alternated with an achromatic reference (flicker brightness). Other studies indicate that humans do use S-cone signals when performing this task. Common to all these studies is the small number of observers, whose performance data are reported. Considerable variability in the occurrence of cone types across observers has been found, but, to our knowledge, no cone counts exist from larger populations of humans. We reinvestigated how much the S-cones contribute to flicker brightness. 76 color normal observers were tested in a simple psychophysical procedure neutral to the cone type occurence (Teufel & Wehrhahn (2000), JOSA A 17: 994 - 1006). The data show that, in the majority of our observers, S-cones provide input with a negative sign - relative to L- and M-cone contribution - in the task in question. There is indeed considerable between-subject variability such that for 20 out of 76 observers the magnitude of this input does not differ significantly from 0. Finally, we argue that the sign of S-cone contribution to flicker brightness perception by an observer cannot be used to infer the relative sign their contributions to the neuronal signals carrying the information leading to the perception of flicker brightness. We conclude that studies which use only a small number of observers may easily fail to find significant evidence for the small but significant population tendency for the S-cones to contribute to flicker brightness. Our results confirm all earlier results and reconcile their contradictory interpretations.

Web [BibTex]

2004

ei Wehrhahn, C., Hill, NJ., Dillenburger, B. S-cones contribute to flicker brightness in human vision 34(174.12), 34th Annual Meeting of the Society for Neuroscience (Neuroscience), October 2004 (poster)

Web [BibTex]

Advanced Lectures on Machine Learning

Bousquet, O., von Luxburg, U., Rätsch, G.

ML Summer Schools 2003, LNAI 3176, pages: 240, Springer, Berlin, Germany, ML Summer Schools, September 2004 (proceedings)

Abstract

Machine Learning has become a key enabling technology for many engineering applications, investigating scientific questions and theoretical problems alike. To stimulate discussions and to disseminate new results, a summer school series was started in February 2002, the documentation of which is published as LNAI 2600. This book presents revised lectures of two subsequent summer schools held in 2003 in Canberra, Australia, and in T{\"u}bingen, Germany. The tutorial lectures included are devoted to statistical learning theory, unsupervised learning, Bayesian inference, and applications in pattern recognition; they provide in-depth overviews of exciting new developments and contain a large number of references. Graduate students, lecturers, researchers and professionals alike will find this book a useful resource in learning and teaching machine learning.

Web [BibTex]

ei Bousquet, O., von Luxburg, U., Rätsch, G. Advanced Lectures on Machine Learning ML Summer Schools 2003, LNAI 3176, pages: 240, Springer, Berlin, Germany, ML Summer Schools, September 2004 (proceedings)

Web [BibTex]

Pattern Recognition: 26th DAGM Symposium, LNCS, Vol. 3175

Rasmussen, C., Bülthoff, H., Giese, M., Schölkopf, B.

Proceedings of the 26th Pattern Recognition Symposium (DAGM‘04), pages: 581, Springer, Berlin, Germany, 26th Pattern Recognition Symposium, August 2004 (proceedings)

Web DOI [BibTex]

ei Rasmussen, C., Bülthoff, H., Giese, M., Schölkopf, B. Pattern Recognition: 26th DAGM Symposium, LNCS, Vol. 3175 Proceedings of the 26th Pattern Recognition Symposium (DAGM‘04), pages: 581, Springer, Berlin, Germany, 26th Pattern Recognition Symposium, August 2004 (proceedings)

Web DOI [BibTex]

Advances in Neural Information Processing Systems 16: Proceedings of the 2003 Conference

Thrun, S., Saul, L., Schölkopf, B.

Proceedings of the Seventeenth Annual Conference on Neural Information Processing Systems (NIPS 2003), pages: 1621, MIT Press, Cambridge, MA, USA, 17th Annual Conference on Neural Information Processing Systems (NIPS), June 2004 (proceedings)

Abstract

The annual Neural Information Processing (NIPS) conference is the flagship meeting on neural computation. It draws a diverse group of attendeesphysicists, neuroscientists, mathematicians, statisticians, and computer scientists. The presentations are interdisciplinary, with contributions in algorithms, learning theory, cognitive science, neuroscience, brain imaging, vision, speech and signal processing, reinforcement learning and control, emerging technologies, and applications. Only thirty percent of the papers submitted are accepted for presentation at NIPS, so the quality is exceptionally high. This volume contains all the papers presented at the 2003 conference.

Web [BibTex]

ei Thrun, S., Saul, L., Schölkopf, B. Advances in Neural Information Processing Systems 16: Proceedings of the 2003 Conference Proceedings of the Seventeenth Annual Conference on Neural Information Processing Systems (NIPS 2003), pages: 1621, MIT Press, Cambridge, MA, USA, 17th Annual Conference on Neural Information Processing Systems (NIPS), June 2004 (proceedings)

Web [BibTex]

Human Classification Behaviour Revisited by Machine Learning

Graf, A., Wichmann, F., Bülthoff, H., Schölkopf, B.

7, pages: 134, (Editors: Bülthoff, H.H., H.A. Mallot, R. Ulrich and F.A. Wichmann), 7th T{\"u}bingen Perception Conference (TWK), Febuary 2004 (poster)

Abstract

We attempt to understand visual classication in humans using both psychophysical and machine learning techniques. Frontal views of human faces were used for a gender classication task. Human subjects classied the faces and their gender judgment, reaction time (RT) and condence rating (CR) were recorded for each face. RTs are longer for incorrect answers than for correct ones, high CRs are correlated with low classication errors and RTs decrease as the CRs increase. This results suggest that patterns difcult to classify need more computation by the brain than patterns easy to classify. Hyperplane learning algorithms such as Support Vector Machines (SVM), Relevance Vector Machines (RVM), Prototype learners (Prot) and K-means learners (Kmean) were used on the same classication task using the Principal Components of the texture and oweld representation of the faces. The classication performance of the learning algorithms was estimated using the face database with the true gender of the faces as labels, and also with the gender estimated by the subjects. Kmean yield a classication performance close to humans while SVM and RVM are much better. This surprising behaviour may be due to the fact that humans are trained on real faces during their lifetime while they were here tested on articial ones, while the algorithms were trained and tested on the same set of stimuli. We then correlated the human responses to the distance of the stimuli to the separating hyperplane (SH) of the learning algorithms. On the whole stimuli far from the SH are classied more accurately, faster and with higher condence than those near to the SH if we pool data across all our subjects and stimuli. We also nd three noteworthy results. First, SVMs and RVMs can learn to classify faces using the subjects' labels but perform much better when using the true labels. Second, correlating the average response of humans (classication error, RT or CR) with the distance to the SH on a face-by-face basis using Spearman's rank correlation coefcients shows that RVMs recreate human performance most closely in every respect. Third, the mean-of-class prototype, its popularity in neuroscience notwithstanding, is the least human-like classier in all cases examined.

Web [BibTex]

ei Graf, A., Wichmann, F., Bülthoff, H., Schölkopf, B. Human Classification Behaviour Revisited by Machine Learning 7, pages: 134, (Editors: Bülthoff, H.H., H.A. Mallot, R. Ulrich and F.A. Wichmann), 7th T{\"u}bingen Perception Conference (TWK), Febuary 2004 (poster)

Web [BibTex]

m-Alternative-Forced-Choice: Improving the Efficiency of the Method of Constant Stimuli

Jäkel, F., Hill, J., Wichmann, F.

7, pages: 118, 7th T{\"u}bingen Perception Conference (TWK), February 2004 (poster)

Abstract

We explored several ways to improve the efficiency of measuring psychometric functions without resorting to adaptive procedures. a) The number m of alternatives in an m-alternative-forced-choice (m-AFC) task improves the efficiency of the method of constant stimuli. b) When alternatives are presented simultaneously on different positions on a screen rather than sequentially time can be saved and memory load for the subject can be reduced. c) A touch-screen can further help to make the experimental procedure more intuitive. We tested these ideas in the measurement of contrast sensitivity and compared them to results obtained by sequential presentation in two-interval-forced-choice (2-IFC). Qualitatively all methods (m-AFC and 2-IFC) recovered the characterictic shape of the contrast sensitivity function in three subjects. The m-AFC paradigm only took about 60% of the time of the 2-IFC task. We tried m=2,4,8 and found 4-AFC to give the best model fits and 2-AFC to have the least bias.

Web [BibTex]

ei Jäkel, F., Hill, J., Wichmann, F. m-Alternative-Forced-Choice: Improving the Efficiency of the Method of Constant Stimuli 7, pages: 118, 7th T{\"u}bingen Perception Conference (TWK), February 2004 (poster)

Web [BibTex]

Efficient Approximations for Support Vector Classifiers

Kienzle, W., Franz, M.

7, pages: 68, 7th T{\"u}bingen Perception Conference (TWK), February 2004 (poster)

Abstract

In face detection, support vector machines (SVM) and neural networks (NN) have been shown to outperform most other classication methods. While both approaches are learning-based, there are distinct advantages and drawbacks to each method: NNs are difcult to design and train but can lead to very small and efcient classiers. In comparison, SVM model selection and training is rather straightforward, and, more importantly, guaranteed to converge to a globally optimal (in the sense of training errors) solution. Unfortunately, SVM classiers tend to have large representations which are inappropriate for time-critical image processing applications. In this work, we examine various existing and new methods for simplifying support vector decision rules. Our goal is to obtain efcient classiers (as with NNs) while keeping the numerical and statistical advantages of SVMs. For a given SVM solution, we compute a cascade of approximations with increasing complexities. Each classier is tuned so that the detection rate is near 100%. At run-time, the rst (simplest) detector is evaluated on the whole image. Then, any subsequent classier is applied only to those positions that have been classied as positive throughout all previous stages. The false positive rate at the end equals that of the last (i.e. most complex) detector. In contrast, since many image positions are discarded by lower-complexity classiers, the average computation time per patch decreases signicantly compared to the time needed for evaluating the highest-complexity classier alone.

Web [BibTex]

ei Kienzle, W., Franz, M. Efficient Approximations for Support Vector Classifiers 7, pages: 68, 7th T{\"u}bingen Perception Conference (TWK), February 2004 (poster)

Web [BibTex]

Selective Attention to Auditory Stimuli: A Brain-Computer Interface Paradigm

Hill, N., Lal, T., Schröder, M., Hinterberger, T., Birbaumer, N., Schölkopf, B.

7, pages: 102, (Editors: Bülthoff, H.H., H.A. Mallot, R. Ulrich and F.A. Wichmann), 7th T{\"u}bingen Perception Conference (TWK), February 2004 (poster)

Abstract

During the last 20 years several paradigms for Brain Computer Interfaces have been proposed see [1] for a recent review. They can be divided into (a) stimulus-driven paradigms, using e.g. event-related potentials or visual evoked potentials from an EEG signal, and (b) patient-driven paradigms such as those that use premotor potentials correlated with imagined action, or slow cortical potentials (e.g. [2]). Our aim is to develop a stimulus-driven paradigm that is applicable in practice to patients. Due to the unreliability of visual perception in locked-in patients in the later stages of disorders such as Amyotrophic Lateral Sclerosis, we concentrate on the auditory modality. Speci- cally, we look for the effects, in the EEG signal, of selective attention to one of two concurrent auditory stimulus streams, exploiting the increased activation to attended stimuli that is seen under some circumstances [3]. We present the results of our preliminary experiments on normal subjects. On each of 400 trials, two repetitive stimuli (sequences of drum-beats or other pulsed stimuli) could be heard simultaneously. The two stimuli were distinguishable from one another by their acoustic properties, by their source location (one from a speaker to the left of the subject, the other from the right), and by their differing periodicities. A visual cue preceded the stimulus by 500 msec, indicating which of the two stimuli to attend to, and the subject was instructed to count the beats in the attended stimulus stream. There were up to 6 beats of each stimulus: with equal probability on each trial, all 6 were played, or the fourth was omitted, or the fth was omitted. The 40-channel EEG signals were analyzed ofine to reconstruct which of the streams was attended on each trial. A linear Support Vector Machine [4] was trained on a random subset of the data and tested on the remainder. Results are compared from two types of pre-processing of the signal: for each stimulus stream, (a) EEG signals at the stream's beat periodicity are emphasized, or (b) EEG signals following beats are contrasted with those following missing beats. Both forms of pre-processing show promising results, i.e. that selective attention to one or the other auditory stream yields signals that are classiable signicantly above chance performance. In particular, the second pre-processing was found to be robust to reduction in the number of features used for classication (cf. [5]), helping us to eliminate noise.

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ei Hill, N., Lal, T., Schröder, M., Hinterberger, T., Birbaumer, N., Schölkopf, B. Selective Attention to Auditory Stimuli: A Brain-Computer Interface Paradigm 7, pages: 102, (Editors: Bülthoff, H.H., H.A. Mallot, R. Ulrich and F.A. Wichmann), 7th T{\"u}bingen Perception Conference (TWK), February 2004 (poster)

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Texture and Haptic Cues in Slant Discrimination: Measuring the Effect of Texture Type

Rosas, P., Wichmann, F., Ernst, M., Wagemans, J.

7, pages: 165, (Editors: Bülthoff, H. H., H. A. Mallot, R. Ulrich, F. A. Wichmann), 7th T{\"u}bingen Perception Conference (TWK), February 2004 (poster)

Abstract

In a number of models of depth cue combination the depth percept is constructed via a weighted average combination of independent depth estimations. The inuence of each cue in such average depends on the reliability of the source of information [1,5]. In particular, Ernst and Banks (2002) formulate such combination as that of the minimum variance unbiased estimator that can be constructed from the available cues. We have observed systematic differences in slant discrimination performance of human observers when different types of textures were used as cue to slant [4]. If the depth percept behaves as described above, our measurements of the slopes of the psychometric functions provide the predicted weights for the texture cue for the ranked texture types. However, the results for slant discrimination obtained when combining these texture types with object motion results are difcult to reconcile with the minimum variance unbiased estimator model [3]. This apparent failure of such model might be explained by the existence of a coupling of texture and motion, violating the assumption of independence of cues. Hillis, Ernst, Banks, and Landy (2002) [2] have shown that while for between-modality combination the human visual system has access to the single-cue information, for withinmodality combination (visual cues) the single-cue information is lost. This suggests a coupling between visual cues and independence between visual and haptic cues. Then, in the present study we combined the different texture types with haptic information in a slant discrimination task, to test whether in the between-modality condition these cues are combined as predicted by an unbiased, minimum variance estimator model. The measured weights for the cues were consistent with a combination rule sensitive to the reliability of the sources of information, but did not match the predictions of a statistically optimal combination.

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ei Rosas, P., Wichmann, F., Ernst, M., Wagemans, J. Texture and Haptic Cues in Slant Discrimination: Measuring the Effect of Texture Type 7, pages: 165, (Editors: Bülthoff, H. H., H. A. Mallot, R. Ulrich, F. A. Wichmann), 7th T{\"u}bingen Perception Conference (TWK), February 2004 (poster)

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Efficient Approximations for Support Vector Classiers

Kienzle, W., Franz, M.

7, pages: 68, 7th T{\"u}bingen Perception Conference (TWK), February 2004 (poster)

Abstract

Web [BibTex]

ei Kienzle, W., Franz, M. Efficient Approximations for Support Vector Classiers 7, pages: 68, 7th T{\"u}bingen Perception Conference (TWK), February 2004 (poster)

Web [BibTex]

EEG Channel Selection for Brain Computer Interface Systems Based on Support Vector Methods

Schröder, M., Lal, T., Bogdan, M., Schölkopf, B.

7, pages: 50, (Editors: Bülthoff, H.H., H.A. Mallot, R. Ulrich and F.A. Wichmann), 7th T{\"u}bingen Perception Conference (TWK), February 2004 (poster)

Abstract

A Brain Computer Interface (BCI) system allows the direct interpretation of brain activity patterns (e.g. EEG signals) by a computer. Typical BCI applications comprise spelling aids or environmental control systems supporting paralyzed patients that have lost motor control completely. The design of an EEG based BCI system requires good answers for the problem of selecting useful features during the performance of a mental task as well as for the problem of classifying these features. For the special case of choosing appropriate EEG channels from several available channels, we propose the application of variants of the Support Vector Machine (SVM) for both problems. Although these algorithms do not rely on prior knowledge they can provide more accurate solutions than standard lter methods [1] for feature selection which usually incorporate prior knowledge about neural activity patterns during the performed mental tasks. For judging the importance of features we introduce a new relevance measure and apply it to EEG channels. Although we base the relevance measure for this purpose on the previously introduced algorithms, it does in general not depend on specic algorithms but can be derived using arbitrary combinations of feature selectors and classifiers.

Web [BibTex]

ei Schröder, M., Lal, T., Bogdan, M., Schölkopf, B. EEG Channel Selection for Brain Computer Interface Systems Based on Support Vector Methods 7, pages: 50, (Editors: Bülthoff, H.H., H.A. Mallot, R. Ulrich and F.A. Wichmann), 7th T{\"u}bingen Perception Conference (TWK), February 2004 (poster)

Web [BibTex]

Learning Depth

Sinz, F., Franz, MO.

pages: 69, (Editors: H.H.Bülthoff, H.A.Mallot, R.Ulrich,F.A.Wichmann), 7th T{\"u}bingen Perception Conference (TWK), February 2004 (poster)

Abstract

The depth of a point in space can be estimated by observing its image position from two different viewpoints. The classical approach to stereo vision calculates depth from the two projection equations which together form a stereocamera model. An unavoidable preparatory work for this solution is a calibration procedure, i.e., estimating the external (position and orientation) and internal (focal length, lens distortions etc.) parameters of each camera from a set of points with known spatial position and their corresponding image positions. This is normally done by iteratively linearizing the single camera models and reestimating their parameters according to the error on the known datapoints. The advantage of the classical method is the maximal usage of prior knowledge about the underlying physical processes and the explicit estimation of meaningful model parameters such as focal length or camera position in space. However, the approach neglects the nonlinear nature of the problem such that the results critically depend on the choice of the initial values for the parameters. In this study, we approach the depth estimation problem from a different point of view by applying generic machine learning algorithms to learn the mapping from image coordinates to spatial position. These algorithms do not require any domain knowledge and are able to learn nonlinear functions by mapping the inputs into a higher-dimensional space. Compared to classical calibration, machine learning methods give a direct solution to the depth estimation problem which means that the values of the stereocamera parameters cannot be extracted from the learned mapping. On the poster, we compare the performance of classical camera calibration to that of different machine learning algorithms such as kernel ridge regression, gaussian processes and support vector regression. Our results indicate that generic learning approaches can lead to higher depth accuracies than classical calibration although no domain knowledge is used.

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ei Sinz, F., Franz, MO. Learning Depth pages: 69, (Editors: H.H.Bülthoff, H.A.Mallot, R.Ulrich,F.A.Wichmann), 7th T{\"u}bingen Perception Conference (TWK), February 2004 (poster)

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Neural mechanisms underlying control of a Brain-Computer-Interface (BCI): Simultaneous recording of bold-response and EEG

Hinterberger, T., Wilhelm, B., Veit, R., Weiskopf, N., Lal, TN., Birbaumer, N.

2004 (poster)

Abstract

Brain computer interfaces (BCI) enable humans or animals to communicate or activate external devices without muscle activity using electric brain signals. The BCI Thought Translation Device (TTD) uses learned regulation of slow cortical potentials (SCPs), a skill most people and paralyzed patients can acquire with training periods of several hours up to months. The neurophysiological mechanisms and anatomical sources of SCPs and other event-related brain macro-potentials are well understood, but the neural mechanisms underlying learning of the self-regulation skill for BCI-use are unknown. To uncover the relevant areas of brain activation during regulation of SCPs, the TTD was combined with functional MRI and EEG was recorded inside the MRI scanner in twelve healthy participants who have learned to regulate their SCP with feedback and reinforcement. The results demonstrate activation of specific brain areas during execution of the brain regulation skill: successf! ul control of cortical positivity allowing a person to activate an external device was closely related to an increase of BOLD (blood oxygen level dependent) response in the basal ganglia and frontal premotor deactivation indicating learned regulation of a cortical-striatal loop responsible for local excitation thresholds of cortical assemblies. The data suggest that human users of a BCI learn the regulation of cortical excitation thresholds of large neuronal assemblies as a prerequisite of direct brain communication: the learning of this skill depends critically on an intact and flexible interaction between these cortico-basal ganglia-circuits. Supported by the Deutsche Forschungsgemeinschaft (DFG) and the National Institute of Health (NIH).

[BibTex]

ei Hinterberger, T., Wilhelm, B., Veit, R., Weiskopf, N., Lal, TN., Birbaumer, N. Neural mechanisms underlying control of a Brain-Computer-Interface (BCI): Simultaneous recording of bold-response and EEG 2004 (poster)

[BibTex]

Masking by plaid patterns revisited

Wichmann, F.

Experimentelle Psychologie. Beitr{\"a}ge zur 46. Tagung experimentell arbeitender Psychologen, 46, pages: 285, 2004 (poster)

[BibTex]

ei Wichmann, F. Masking by plaid patterns revisited Experimentelle Psychologie. Beitr{\"a}ge zur 46. Tagung experimentell arbeitender Psychologen, 46, pages: 285, 2004 (poster)

[BibTex]

Early visual processing—data, theory, models

Wichmann, F.

Experimentelle Psychologie. Beitr{\"a}ge zur 46. Tagung experimentell arbeitender Psychologen, 46, pages: 24, 2004 (poster)

[BibTex]

ei Wichmann, F. Early visual processing—data, theory, models Experimentelle Psychologie. Beitr{\"a}ge zur 46. Tagung experimentell arbeitender Psychologen, 46, pages: 24, 2004 (poster)

[BibTex]

Implicit Wiener series for capturing higher-order interactions in images

Franz, M., Schölkopf, B.

Sensory coding and the natural environment, (Editors: Olshausen, B.A. and M. Lewicki), 2004 (poster)

Abstract

The information about the objects in an image is almost exclusively described by the higher-order interactions of its pixels. The Wiener series is one of the standard methods to systematically characterize these interactions. However, the classical estimation method of the Wiener expansion coefficients via cross-correlation suffers from severe problems that prevent its application to high-dimensional and strongly nonlinear signals such as images. We propose an estimation method based on regression in a reproducing kernel Hilbert space that overcomes these problems using polynomial kernels as known from Support Vector Machines and other kernel-based methods. Numerical experiments show performance advantages in terms of convergence, interpretability and system sizes that can be handled. By the time of the conference, we will be able to present first results on the higher-order structure of natural images.

[BibTex]

ei Franz, M., Schölkopf, B. Implicit Wiener series for capturing higher-order interactions in images Sensory coding and the natural environment, (Editors: Olshausen, B.A. and M. Lewicki), 2004 (poster)

[BibTex]