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Diversity of sharp wave-ripples in the CA1 of the macaque hippocampus and their brain wide signatures

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

45th Annual Meeting of the Society for Neuroscience (Neuroscience 2015), October 2015 (poster)

link (url) [BibTex]

link (url) [BibTex]


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Causal Inference for Empirical Time Series Based on the Postulate of Independence of Cause and Mechanism

Besserve, M.

53rd Annual Allerton Conference on Communication, Control, and Computing, September 2015 (talk)

[BibTex]

[BibTex]


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Kernel methods in medical imaging

Charpiat, G., Hofmann, M., Schölkopf, B.

In Handbook of Biomedical Imaging, pages: 63-81, 4, (Editors: Paragios, N., Duncan, J. and Ayache, N.), Springer, Berlin, Germany, June 2015 (inbook)

Web link (url) [BibTex]

Web link (url) [BibTex]


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Retrospective rigid motion correction of undersampled MRI data

Loktyushin, A., Babayeva, M., Gallichan, D., Krueger, G., Scheffler, K., Kober, T.

23rd Annual Meeting and Exhibition of the International Society for Magnetic Resonance in Medicine, ISMRM, June 2015 (poster)

[BibTex]

[BibTex]


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Improving Quantitative Susceptibility and R2* Mapping by Applying Retrospective Motion Correction

Feng, X., Loktyushin, A., Deistung, A., Reichenbach, J. R.

23rd Annual Meeting and Exhibition of the International Society for Magnetic Resonance in Medicine, ISMRM, June 2015 (poster)

[BibTex]

[BibTex]


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Independence of cause and mechanism in brain networks

Besserve, M.

DALI workshop on Networks: Processes and Causality, April 2015 (talk)

[BibTex]

[BibTex]


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Increasing the sensitivity of Kepler to Earth-like exoplanets

Foreman-Mackey, D., Hogg, D., Schölkopf, B., Wang, D.

Workshop: 225th American Astronomical Society Meeting 2015 , pages: 105.01D, 2015 (poster)

Web link (url) [BibTex]

Web link (url) [BibTex]


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Information-Theoretic Implications of Classical and Quantum Causal Structures

Chaves, R., Majenz, C., Luft, L., Maciel, T., Janzing, D., Schölkopf, B., Gross, D.

18th Conference on Quantum Information Processing (QIP), 2015 (talk)

Web link (url) [BibTex]

Web link (url) [BibTex]


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Calibrating the pixel-level Kepler imaging data with a causal data-driven model

Wang, D., Foreman-Mackey, D., Hogg, D., Schölkopf, B.

Workshop: 225th American Astronomical Society Meeting 2015 , pages: 258.08, 2015 (poster)

Web link (url) [BibTex]

Web link (url) [BibTex]


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Assessment of brain tissue damage in the Sub-Acute Stroke Region by Multiparametric Imaging using [89-Zr]-Desferal-EPO-PET/MRI

Castaneda, S. G., Katiyar, P., Russo, F., Disselhorst, J. A., Calaminus, C., Poli, S., Maurer, A., Ziemann, U., Pichler, B. J.

World Molecular Imaging Conference, 2015 (talk)

[BibTex]

[BibTex]


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Statistical and Machine Learning Methods for Neuroimaging: Examples, Challenges, and Extensions to Diffusion Imaging Data

O’Donnell, L. J., Schultz, T.

In Visualization and Processing of Higher Order Descriptors for Multi-Valued Data, pages: 299-319, (Editors: Hotz, I. and Schultz, T.), Springer, 2015 (inbook)

[BibTex]

[BibTex]


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Early time point in vivo PET/MR is a promising biomarker for determining efficacy of a novel Db(\alphaEGFR)-scTRAIL fusion protein therapy in a colon cancer model

Divine, M. R., Harant, M., Katiyar, P., Disselhorst, J. A., Bukala, D., Aidone, S., Siegemund, M., Pfizenmaier, K., Kontermann, R., Pichler, B. J.

World Molecular Imaging Conference, 2015 (talk)

[BibTex]

[BibTex]


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Assessment of tumor heterogeneity using unsupervised graph based clustering of multi-modality imaging data

Katiyar, P., Divine, M. R., Pichler, B. J., Disselhorst, J. A.

European Molecular Imaging Meeting, 2015 (poster)

[BibTex]

[BibTex]


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Disparity estimation from a generative light field model

Köhler, R., Schölkopf, B., Hirsch, M.

IEEE International Conference on Computer Vision (ICCV 2015), Workshop on Inverse Rendering, 2015, Note: This work has been presented as a poster and is not included in the workshop proceedings. (poster)

[BibTex]

[BibTex]


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Justifying Information-Geometric Causal Inference

Janzing, D., Steudel, B., Shajarisales, N., Schölkopf, B.

In Measures of Complexity: Festschrift for Alexey Chervonenkis, pages: 253-265, 18, (Editors: Vovk, V., Papadopoulos, H. and Gammerman, A.), Springer, 2015 (inbook)

DOI [BibTex]

DOI [BibTex]


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The search for single exoplanet transits in the Kepler light curves

Foreman-Mackey, D., Hogg, D. W., Schölkopf, B.

IAU General Assembly, 22, pages: 2258352, 2015 (talk)

link (url) [BibTex]

link (url) [BibTex]

2013


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Studying large-scale brain networks: electrical stimulation and neural-event-triggered fMRI

Logothetis, N., Eschenko, O., Murayama, Y., Augath, M., Steudel, T., Evrard, H., Besserve, M., Oeltermann, A.

Twenty-Second Annual Computational Neuroscience Meeting (CNS*2013), July 2013, journal = {BMC Neuroscience}, year = {2013}, month = {7}, volume = {14}, number = {Supplement 1}, pages = {A1}, (talk)

Web [BibTex]

2013

Web [BibTex]


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A Review of Performance Variations in SMR-Based Brain–Computer Interfaces (BCIs)

Grosse-Wentrup, M., Schölkopf, B.

In Brain-Computer Interface Research, pages: 39-51, 4, SpringerBriefs in Electrical and Computer Engineering, (Editors: Guger, C., Allison, B. Z. and Edlinger, G.), Springer, 2013 (inbook)

PDF DOI [BibTex]

PDF DOI [BibTex]


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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)

[BibTex]

[BibTex]


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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]

PDF [BibTex]


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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]

PDF [BibTex]


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Semi-supervised learning in causal and anticausal settings

Schölkopf, B., Janzing, D., Peters, J., Sgouritsa, E., Zhang, K., Mooij, J.

In Empirical Inference, pages: 129-141, 13, Festschrift in Honor of Vladimir Vapnik, (Editors: Schölkopf, B., Luo, Z. and Vovk, V.), Springer, 2013 (inbook)

DOI [BibTex]

DOI [BibTex]


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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]

DOI [BibTex]


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Tractable large-scale optimization in machine learning

Sra, S.

In Tractability: Practical Approaches to Hard Problems, pages: 202-230, 7, (Editors: Bordeaux, L., Hamadi , Y., Kohli, P. and Mateescu, R. ), Cambridge University Press , 2013 (inbook)

[BibTex]

[BibTex]


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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]

PDF [BibTex]


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The Randomized Dependence Coefficient

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

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

PDF [BibTex]

PDF [BibTex]


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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]

Web [BibTex]


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Domain Generalization via Invariant Feature Representation

Muandet, K.

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

PDF [BibTex]

PDF [BibTex]


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On the Relations and Differences between Popper Dimension, Exclusion Dimension and VC-Dimension

Seldin, Y., Schölkopf, B.

In Empirical Inference - Festschrift in Honor of Vladimir N. Vapnik, pages: 53-57, 6, (Editors: Schölkopf, B., Luo, Z. and Vovk, V.), Springer, 2013 (inbook)

[BibTex]

[BibTex]


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Discrete vs. Continuous: Two Sides of Machine Learning

Zhou, D.

October 2004 (talk)

Abstract
We consider the problem of transductive inference. In many real-world problems, unlabeled data is far easier to obtain than labeled data. Hence transductive inference is very significant in many practical problems. According to Vapnik's point of view, one should predict the function value only on the given points directly rather than a function defined on the whole space, the latter being a more complicated problem. Inspired by this idea, we develop discrete calculus on finite discrete spaces, and then build discrete regularization. A family of transductive algorithms is naturally derived from this regularization framework. We validate the algorithms on both synthetic and real-world data from text/web categorization to bioinformatics problems. A significant by-product of this work is a powerful way of ranking data based on examples including images, documents, proteins and many other kinds of data. This talk is mainly based on the followiing contribution: (1) D. Zhou and B. Sch{\"o}lkopf: Transductive Inference with Graphs, MPI Technical report, August, 2004; (2) D. Zhou, B. Sch{\"o}lkopf and T. Hofmann. Semi-supervised Learning on Directed Graphs. NIPS 2004; (3) D. Zhou, O. Bousquet, T.N. Lal, J. Weston and B. Sch{\"o}lkopf. Learning with Local and Global Consistency. NIPS 2003.

PDF [BibTex]


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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]

Web [BibTex]


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Learning Motor Primitives with Reinforcement Learning

Peters, J., Schaal, S.

AAAI Fall Symposium on Real-Life Reinforcement Learning 2004, 2004, pages: 1, October 2004 (poster)

Web [BibTex]

Web [BibTex]


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Pattern detection methods and systems and face detection methods and systems

Blake, A., Romdhani, S., Schölkopf, B., Torr, P. H. S.

United States Patent, No 6804391, October 2004 (patent)

[BibTex]

[BibTex]


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Grundlagen von Support Vector Maschinen und Anwendungen in der Bildverarbeitung

Eichhorn, J.

September 2004 (talk)

Abstract
Invited talk at the workshop "Numerical, Statistical and Discrete Methods in Image Processing" at the TU M{\"u}nchen (in GERMAN)

PDF [BibTex]


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The benefit of liquid Helium cooling for Cryo-Electron Tomography: A quantitative comparative study

Schweikert, G., Luecken, U., Pfeifer, G., Baumeister, W., Plitzko, J.

The thirteenth European Microscopy Congress, August 2004 (talk)

[BibTex]

[BibTex]


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Analysis of differential gene expression in healthy and osteoarthritic cartilage and isolated chondrocytes by microarray analysis

Aigner, T., Saas, J., Zien, A., Zimmer, R., Gebhard, P., Knorr, T.

In Volume 1: Cellular and Molecular Tools, pages: 109-128, (Editors: Sabatini, M., P. Pastoureau and F. De Ceuninck), Humana Press, July 2004 (inbook)

Abstract
The regulation of chondrocytes in osteoarthritic cartilage and the expression of specific gene products by these cells during early-onset and late-stage osteoarthritis are not well characterized. With the introduction of cDNA array technology, the measurement of thousands of different genes in one small tissue sample can be carried out. Interpretation of gene expression analyses in articular cartilage is aided by the fact that this tissue contains only one cell type in both normal and diseased conditions. However, care has to be taken not to over- and misinterpret results, and some major challenges must be overcome in order to utilize the potential of this technology properly in the field of osteoarthritis.

Web [BibTex]

Web [BibTex]


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Riemannian Geometry on Graphs and its Application to Ranking and Classification

Zhou, D.

June 2004 (talk)

Abstract
We consider the problem of transductive inference. In many real-world problems, unlabeled data is far easier to obtain than labeled data. Hence transductive inference is very significant in many practical problems. According to Vapnik's point of view, one should predict the function value only on the given points directly rather than a function defined on the whole space, the latter being a more complicated problem. Inspired by this idea, we develop discrete calculus on finite discrete spaces, and then build discrete regularization. A family of transductive algorithms is naturally derived from this regularization framework. We validate the algorithms on both synthetic and real-world data from text/web categorization to bioinformatics problems. A significant by-product of this work is a powerful way of ranking data based on examples including images, documents, proteins and many other kinds of data.

PDF [BibTex]


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Distributed Command Execution

Stark, S., Berlin, M.

In BSD Hacks: 100 industrial-strength tips & tools, pages: 152-152, (Editors: Lavigne, Dru), O’Reilly, Beijing, May 2004 (inbook)

Abstract
Often you want to execute a command not only on one computer, but on several at once. For example, you might want to report the current statistics on a group of managed servers or update all of your web servers at once.

[BibTex]

[BibTex]


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Learning Motor Primitives with Reinforcement Learning

Peters, J., Schaal, S.

11th Joint Symposium on Neural Computation (JSNC 2004), 11, pages: 1, May 2004 (poster)

Abstract
One of the major challenges in action generation for robotics and in the understanding of human motor control is to learn the "building blocks of move- ment generation," or more precisely, motor primitives. Recently, Ijspeert et al. [1, 2] suggested a novel framework how to use nonlinear dynamical systems as motor primitives. While a lot of progress has been made in teaching these mo- tor primitives using supervised or imitation learning, the self-improvement by interaction of the system with the environment remains a challenging problem. In this poster, we evaluate different reinforcement learning approaches can be used in order to improve the performance of motor primitives. For pursuing this goal, we highlight the difficulties with current reinforcement learning methods, and line out how these lead to a novel algorithm which is based on natural policy gradients [3]. We compare this algorithm to previous reinforcement learning algorithms in the context of dynamic motor primitive learning, and show that it outperforms these by at least an order of magnitude. We demonstrate the efficiency of the resulting reinforcement learning method for creating complex behaviors for automous robotics. The studied behaviors will include both discrete, finite tasks such as baseball swings, as well as complex rhythmic patterns as they occur in biped locomotion.

Web [BibTex]

Web [BibTex]


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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]

Web [BibTex]


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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]

Web [BibTex]


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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]

Web [BibTex]


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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.

PDF Web [BibTex]

PDF Web [BibTex]


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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.

PDF Web [BibTex]

PDF Web [BibTex]