Description
We investigate the mechanisms of the perception of body movements, and their relationship with motor execution and social signals.
We investigate the mechanisms of the perception of body movements, and their relationship with motor execution and social signals. Our work combines psychophysical experiments and the development of physiologically-inspired neural models in close collaboration with electrophysiologists inside and outside of Tübingen. In addition, exploiting advanced methods from computer animation and Virtual Reality (VR), we investigate the perception of body movements (facial and body expressions) in social communication, and its deficits in psychiatric disorders, such as schizophrenia or autism spectrum disorders. A particular new focus is the study of intentional signals that are conveyed by bodily and facial expressions. For this purpose, we developed highly controlled stimulus sets, exploiting high-end methods from computer graphics. In addition, we develop physiologically-inspired neural models for neural circuits involved in the processing of bodies, actions, and the extraction of intent and social information from visual stimuli.
Researchers
Current Projects
RELEVANCE: How body relevance drives brain organization
Social species, and especially primates, rely heavily on conspecifics for survival. Considerable time is spent watching each other’s behavior for preparing adaptive social responses. The project RELEVANCE aims to understand how the brain evolved special structures to process highly relevant social stimuli, such as bodies and to reveal how social vision sustains adaptive behavior.
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Modelling and Investigation of Facial Expression Perception
Dynamic faces are essential for the communication of humans and non-human primates. However, the exact neural circuits of their processing remain unclear. Based on previous models for cortical neural processes involved for social recognition (of static faces and dynamic bodies), we propose a norm-based mechanism, relying on neurons that represent dierences between the actual facial shape and the neutral facial pose.
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Neural mechanisms underlying the visual analysis of intent
Primates are very efficient in the recognition of intentions from various types of stimuli, involving faces and bodies, but also abstract moving stimuli, such as moving geometrical figures as illustrated in the seminal experiments by Heider and Simmel (1944). How such stimuli are exactly processed and what the underlying neural and computational mechanisms are remains largely unknown.
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Neural model for shading pathway in biological motion stimuli
Biological motion perception is influenced by shading cues. We study the influence of such cues and develop neural models how the shading cues are integrated with other features in action perception.
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Neural field model for multi-stability in action perception
The perception of body movements integrates information over time. The underlying neural system is nonlinear and is charactrized by a dynamics that supports multi-stable perception. We have investigated multisstable body motion perception and have developed physiologically-inspired neural models that account for the observed psychophysical results.
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Dynamical Stability and Synchronization in Character Animation
An important domain of the application of dynamical systems in computer animation is the simulation of autonomous and collective behavior of many characters, e.g. in crowd animation.
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Neural representations of sensory predictions for perception and action
Attribution of percepts to consequences of own actions depends on the consistency between internally predicted and actual visual signals. However, is the attribution of agency rather a binary decision ('I did, or did not cause the visual consequences of the action'), or is this process based on a more gradual attribution of the degree of agency? Both alternatives result in different behaviors of causal inference models, which we try to distinguish by model comparison.
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Neuralphysiologically-inspired models of visual action perception and the perception of causality
The recognition of goal-directed actions is a challenging problem in vision research and requires the recognition not only of the movement of amd effector(e.g. the hand) but also the processing its relationship to goal objects, such as a grasped piece of food. In close collaborations with electrophysiologists, we develop models for the neural circuits in cortex that underly this visual function. These models also account for several properties of 'mirror neurons', and for the processing of stimuli (like the one shown in the icon) that suggest causal interactions between objects. In addition, we studied psychophysically the interaction between action observation and exertion using VR methods.
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Neurodynamic model for multi-stability in action perception
Action perception is related to interesting dynamical phenomena, such as multi-stability and adaptation. The stimulus shown in this demo is bistable and can be seen as walking obliquely coming out or going into the image plane. Such multistability and associated spontaneous perceptual switches result form the dynamics of the neural representation of perceived actions. We investigate this dynamics pasychophysically and model it using neural network models.
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Processing of emotional body expressions in health and disease
Body movements are an important source of information about the emotion of others. The perception of emotional body expressions is impaired in different psychiatric diseases. We have developed methods to generate emotional body motion srimuli with highly-controlled properties, and we exploitz them to study emotion perception in neurologiocal and psychiatric patients.
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Production and perception of interactive emotional body expressions
A substantial amount of research has addressed the expression and perception of emotions with human faces. Body movements likely also contribute to our expression of emotions. However, this topic has received much less research interest so far. We use techniques from machine learning to synthesize highly-controlled emotional body movements and use them to study the perception and the neural mechanisms of the perception of emotion from bodily emotion expression.
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Understanding the semantic structure of the neural code with Formal Concept Analysis
Mammalian brains consist of billions of neurons, each capable of independent electrical activity. From an information-theoretic perspective, the patterns of activation of these neurons can be understood as the codewords comprising the neural code. The neural code describes which pattern of activity corresponds to what information item. We are interested in the structure of the neural code.
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Lappe, A., Bognár, A., Nejad, G. G., Raman, R., Mukovskiy, A., Martini, L. M. et al (2024). Predictive Features in Deep Neural Network Models of Macaque Body Patch Selectivity. Journal of Vision September 2024 . Vision Science Society.
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Predictive Features in Deep Neural Network Models of Macaque Body Patch Selectivity
Abstract:
Previous work has shown that neurons from body patches in macaque superior temporal sulcus (STS) respond selectively to images of bodies. However, the visual features leading to this body selectivity remain unclear. METHODS: We conducted experiments using 720 stimuli presenting a monkey avatar in various poses and viewpoints. Spiking activity was recorded from mid-STS (MSB) and anterior-STS (ASB) body patches, previously identified using fMRI. To identify visual features driving the neural responses, we used a model with a deep network as frontend and a linear readout model that was fitted to predict the neuron activities. Computing the gradients of the outputs backwards along the neural network, we identified the image regions that were most influential for the model neuron output. Since previous work suggests that neurons from this area also respond to some extent to images of objects, we used a similar approach to visualize object parts eliciting responses from the model neurons. Based on an object dataset, we identified the shapes that activate each model unit maximally. Computing and combining the pixel-wise gradients of model activations from object and body processing, we were able to identify common visual features driving neural activity in the model. RESULTS: Linear models fit the data well, with mean noise-corrected correlations with neural data of 0.8 in ASB and 0.94 in MSB. Gradient analysis on the body stimuli did not reveal clear preferences of certain body parts and were difficult to interpret visually. However, the joint gradients between objects and bodies traced visually similar features in both images. CONCLUSION: Deep neural networks model STS data well, even though for all tested models, explained variance was substantially lower in the more anterior region. Further work will test if the features that the deep network relies on are also used by body patch neurons.
Authors:
Lappe, Alexander; Bognár, Anna Nejad, Ghazaleh Ghamkhari Raman, Rajani Mukovskiy, Albert; Martini, Lucas M.; Vogels, Rufin Giese, Martin A.
Type of Publication:
In Collection
JRESEARCH_BOOK_TITLE:
Journal of Vision September 2024
Publisher:
Vision Science Society
Month:
September
Smekal, V., Solanas, T. S., Lappe, A., Giese, M. A. & de Gelder, B (2024). Data-driven Features of Human Body Movements and their Neural Correlate . ESCAN2024.
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Data-driven Features of Human Body Movements and their Neural Correlate
Authors:
Smekal, Vojtěch Solanas, Tamás Szűcs Marta Poyo Lappe, Alexander; Giese, Martin A.; de Gelder, Beatrice
Type of Publication:
In Collection
Martini, L. M., Bognár, A., Vogels, R. & Giese, M. A (2024). Macaques show an uncanny valley in body perception. Journal of Vision September 2024 . Vision Science Society.
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Macaques show an uncanny valley in body perception
Abstract:
Previous work has shown that neurons from body patches in macaque superior temporal sulcus (STS) respond selectively to images of bodies. However, the visual features leading to this body selectivity remain unclear. METHODS: We conducted experiments using 720 stimuli presenting a monkey avatar in various poses and viewpoints. Spiking activity was recorded from mid-STS (MSB) and anterior-STS (ASB) body patches, previously identified using fMRI. To identify visual features driving the neural responses, we used a model with a deep network as frontend and a linear readout model that was fitted to predict the neuron activities. Computing the gradients of the outputs backwards along the neural network, we identified the image regions that were most influential for the model neuron output. Since previous work suggests that neurons from this area also respond to some extent to images of objects, we used a similar approach to visualize object parts eliciting responses from the model neurons. Based on an object dataset, we identified the shapes that activate each model unit maximally. Computing and combining the pixel-wise gradients of model activations from object and body processing, we were able to identify common visual features driving neural activity in the model. RESULTS: Linear models fit the data well, with mean noise-corrected correlations with neural data of 0.8 in ASB and 0.94 in MSB. Gradient analysis on the body stimuli did not reveal clear preferences of certain body parts and were difficult to interpret visually. However, the joint gradients between objects and bodies traced visually similar features in both images. CONCLUSION: Deep neural networks model STS data well, even though for all tested models, explained variance was substantially lower in the more anterior region. Further work will test if the features that the deep network relies on are also used by body patch neurons.
Authors:
Martini, Lucas M.; Bognár, Anna Vogels, Rufin Giese, Martin A.
Type of Publication:
In Collection
Abassi, E., Bognár, A., de Gelder, B., Giese, M. A., Isik, L., Lappe, A. et al. (2024). Neural Encoding of Bodies for Primate Social Perception. Journal of Neuroscience, 44(40).
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Neural Encoding of Bodies for Primate Social Perception
Abstract:
Primates, as social beings, have evolved complex brain mechanisms to navigate intricate social environments. This review explores the neural bases of body perception in both human and nonhuman primates, emphasizing the processing of social signals conveyed by body postures, movements, and interactions. Early studies identified selective neural responses to body stimuli in macaques, particularly within and ventral to the superior temporal sulcus (STS). These regions, known as body patches, represent visual features that are present in bodies but do not appear to be semantic body detectors. They provide information about posture and viewpoint of the body. Recent research using dynamic stimuli has expanded the understanding of the body-selective network, highlighting its complexity and the interplay between static and dynamic processing. In humans, body-selective areas such as the extrastriate body area (EBA) and fusiform body area (FBA) have been implicated in the perception of bodies and their interactions. Moreover, studies on social interactions reveal that regions in the human STS are also tuned to the perception of dyadic interactions, suggesting a specialized social lateral pathway. Computational work developed models of body recognition and social interaction, providing insights into the underlying neural mechanisms. Despite advances, significant gaps remain in understanding the neural mechanisms of body perception and social interaction. Overall, this review underscores the importance of integrating findings across species to comprehensively understand the neural foundations of body perception and the interaction between computational modeling and neural recording.
Authors:
Abassi, Etienne Bognár, Anna de Gelder, Bea Giese, Martin A.; Isik, Leyla Lappe, Alexander; Mukovskiy, Albert; Solanas, Marta Poyo Taubert, Jessica Vogels, Rufin
Type of Publication:
Article
Journal:
Journal of Neuroscience
Volume:
44
Number:
40
Year:
2024
Full text: Online version
Kumar, P., Taubert, N., Raman, R., Bognár, A., Nejad, G. G., Vogels, R. et al (2023). Neurodynamical Model of the Visual Recognition of Dynamic Bodily Actions from Silhouettes. In Iliadis, Lazaros, Papaleonidas, Antonios, Angelov, Plamen et al (editors), Artificial Neural Networks and Machine Learning -- ICANN 2023 , 533-544. Cham : Springer Nature Switzerland.
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Neurodynamical Model of the Visual Recognition of Dynamic Bodily Actions from Silhouettes
Abstract:
For social species, including primates, the recognition of dynamic body actions is crucial for survival. However, the detailed neural circuitry underlying this process is currently not well understood. In monkeys, body-selective patches in the visual temporal cortex may contribute to this processing. We propose a physiologically-inspired neural model of the visual recognition of body movements, which combines an existing image-computable model (`ShapeComp') that produces high-dimensional shape vectors of object silhouettes, with a neurodynamical model that encodes dynamic image sequences exploiting sequence-selective neural fields. The model successfully classifies videos of body silhouettes performing different actions. At the population level, the model reproduces characteristics of macaque single-unit responses from the rostral dorsal bank of the Superior Temporal Sulcus (Anterior Medial Upper Body (AMUB) patch). In the presence of time gaps in the stimulus videos, the predictions made by the model match the data from real neurons. The underlying neurodynamics can be analyzed by exploiting the framework of neural field dynamics.
Authors:
Kumar, Prerana; Taubert, Nick; Raman, Rajani Bognár, Anna Nejad, Ghazaleh Ghamkhari Vogels, Rufin Giese, Martin A.
Type of Publication:
In Collection
Kumar, P., Taubert, N., Raman, R., Vogels, R., de Gelder, B. & Giese, M. A (2022). Neural model for the representation of static and dynamic bodies in cortical body patches . VSS 2022.
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Neural model for the representation of static and dynamic bodies in cortical body patches
Authors:
Kumar, Prerana; Taubert, Nick; Raman, Rajani Vogels, Rufin de Gelder, Beatrice Giese, Martin A.
Type of Publication:
In Collection
Mukovskiy, A., Hovaidi-Ardestani, M., Salatiello, A., Stettler, M., Vogels, R. & Giese, M. A (2022). Neurophysiologically-inspired computational model of the visual recognition of social behavior and intent . FENS Forum, Paris.
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Neurophysiologically-inspired computational model of the visual recognition of social behavior and intent
Abstract:
AIMS: Humans recognize social interactions and intentions from videos of moving abstract stimuli, including simple geometric figures (Heider {&} Simmel, 1944). The neural machinery supporting such social interaction perception is completely unclear. Here, we present a physiologically plausible neural model of social interaction recognition that identifies social interactions in videos of simple geometric figures and fully articulating animal avatars, moving in naturalistic environments. METHODS: We generated the trajectories for both geometric and animal avatars using an algorithm based on a dynamical model of human navigation (Hovaidi-Ardestani, et al., 2018, Warren, 2006). Our neural recognition model combines a Deep Neural Network, realizing a shape-recognition pathway (VGG16), with a top-level neural network that integrates RBFs, motion energy detectors, and dynamic neural fields. The model implements robust tracking of interacting agents based on interaction-specific visual features (relative position, speed, acceleration, and orientation). RESULTS: A simple neural classifier, trained to predict social interaction categories from the features extracted by our neural recognition model, makes predictions that resemble those observed in previous psychophysical experiments on social interaction recognition from abstract (Salatiello, et al. 2021) and naturalistic videos. CONCLUSION: The model demonstrates that recognition of social interactions can be achieved by simple physiologically plausible neural mechanisms and makes testable predictions about single-cell and population activity patterns in relevant brain areas. Acknowledgments: ERC 2019-SyG-RELEVANCE-856495, HFSP RGP0036/2016, BMBF FKZ 01GQ1704, SSTeP-KiZ BMG: ZMWI1-2520DAT700, and NVIDIA Corporation.
Authors:
Mukovskiy, Albert; Hovaidi-Ardestani, Mohammad Salatiello, Alessandro; Stettler, Michael; Vogels, Rufin Giese, Martin A.
Type of Publication:
In Collection
Giese, M. A., BOGNÁR, A. & Vogels, R (2022). Physiologically-inspired neural model for anorthoscopic perception .
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Physiologically-inspired neural model for anorthoscopic perception
Authors:
Giese, Martin A.; BOGNÁR, A. Vogels, Rufin
Type of Publication:
In Collection
Kumar, P., Taubert, N., Raman, R., Vogels, R., de Gelder, B. & Giese, M. A (2021). Physiologically-inspired neural model for the visual recognition of dynamic bodies . Neuroscience 2021.
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Physiologically-inspired neural model for the visual recognition of dynamic bodies
Authors:
Kumar, Prerana; Taubert, Nick; Raman, Rajani Vogels, Rufin de Gelder, Beatrice Giese, Martin A.
Type of Publication:
In Collection
Kumar, P., Taubert, N., Stettler, M., Vogels, R., de Gelder, B. & Giese, M. A (2021). Neurodynamical model for the visual recognition of dynamic bodies . ECVP 2021.
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Neurodynamical model for the visual recognition of dynamic bodies
Authors:
Kumar, Prerana; Taubert, Nick; Stettler, Michael; Vogels, Rufin de Gelder, Beatrice Giese, Martin A.
Type of Publication:
In Collection
Kumar, P., Taubert, N., Stettler, M., Vogels, R., de Gelder, B. & Giese, M. A (2021). Neurodynamical model for the visual recognition of dynamic bodies . CNS 2021.
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Neurodynamical model for the visual recognition of dynamic bodies
Authors:
Kumar, Prerana; Taubert, Nick; Stettler, Michael; Vogels, Rufin de Gelder, Beatrice Giese, Martin A.
Type of Publication:
In Collection
Giese, M. A., Mukovskiy, A., Hovaidi-Ardestani, M., Salatiello, A. & Stettler, M (2021). Neurophysiologically-inspired model for social interactions recognition from abstract and naturalistic stimuli. VSS 2021, May 21-26 .
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Neurophysiologically-inspired model for social interactions recognition from abstract and naturalistic stimuli
Authors:
Giese, Martin A.; Mukovskiy, Albert; Hovaidi-Ardestani, Mohammad Salatiello, Alessandro; Stettler, Michael
Type of Publication:
In Collection
Mukovskiy, A., Ardestani, M. H., Salatiello, A., Stettler, M. & Giese, M. A (2021). Physiologically-inspired neural model for social interactions recognition from abstract and naturalistic stimuli. Göttingen Meeting of the German Neuroscience Society 2021, Germany .
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Physiologically-inspired neural model for social interactions recognition from abstract and naturalistic stimuli
Authors:
Mukovskiy, Albert; Ardestani, Mohammad Hovaidi Salatiello, Alessandro; Stettler, Michael; Giese, Martin A.
Type of Publication:
In Collection
Pomper, J. K., Spadacenta, S., Bunjes, F., Arnstein, D., Giese, M. A. & Thier, P. (2020). Representation of the observer's predicted outcome value in mirror and nonmirror neurons of macaque F5 ventral premotor cortex. J Neurophysiol, 124(3), 941-961.
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Representation of the observer's predicted outcome value in mirror and nonmirror neurons of macaque F5 ventral premotor cortex
Authors:
Pomper, Joern K Spadacenta, Silvia Bunjes, Friedemann Arnstein, Daniel Giese, Martin A.; Thier, Peter
Type of Publication:
Article
Full text: Online version
Taubert, N., St-Amand, J., Kumar, P., Gizzi, L. & Giese, M. A. (2020). Reactive Hand Movements from Arm Kinematics and EMG Signals Based on Hierarchical Gaussian Process Dynamical Models. Artificial Neural Networks and Machine Learning – ICANN 2020 29th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 15–18, 2020, Proceedings, Part I. Springer, Berlin(127-140).
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Reactive Hand Movements from Arm Kinematics and EMG Signals Based on Hierarchical Gaussian Process Dynamical Models
Type of Publication:
Article
Full text: Online version
Stettler, M., Taubert, N., Azizpour, T., Siebert, R., Spadacenta, S., Dicke, P. et al. (2020). Physiologically-inspired Neural Circuits for the Recognition of Dynamic Faces. Artificial Neural Networks and Machine Learning – ICANN 2020 29th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 15–18, 2020, Proceedings, Part I. Springer, Berlin(168-179).
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Physiologically-inspired Neural Circuits for the Recognition of Dynamic Faces
Authors:
Stettler, Michael; Taubert, Nick; Azizpour, Tahereh Siebert, Ramona Spadacenta, Silvia Dicke, Peter Thier, Hans Peter Giese, Martin A.
Type of Publication:
Article
Full text: Online version
Ardestani, M. H., Mukovskiy, A., Stettler, M., Saini, N. & Giese, M. A (2020). Physiologically-inspired neural model for the visual recognition of social interactions from abstract and natural stimuli. VSS 2020, 19-24 Jun .
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Physiologically-inspired neural model for the visual recognition of social interactions from abstract and natural stimuli
Authors:
Ardestani, Mohammad Hovaidi Mukovskiy, Albert; Stettler, Michael; Saini, Nitin Giese, Martin A.
Type of Publication:
In Collection
Siebert, R., Taubert, N., Spadacenta, S., Dicke, P. W., Giese, M. A. & Thier, P. (2020). A naturalistic dynamic monkey head avatar elicits species-typical reactions and overcomes the uncanny valley. ENEURO.0524-19.2020.
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A naturalistic dynamic monkey head avatar elicits species-typical reactions and overcomes the uncanny valley
Authors:
Siebert, Ramona Taubert, Nick; Spadacenta, Silvia Dicke, Peter W. Giese, Martin A.; Thier, Peter
Type of Publication:
Article
Full text: Online version
Stettler, M., Taubert, N., Sting, L., Siebert, R., Spadacenta, S., Dicke, P. et al (2019). Cross-species differences in the perception of dynamic facial expressions. Talk at ECVP Conference 2019, Perception 48(2S),63 .
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Cross-species differences in the perception of dynamic facial expressions
Authors:
Stettler, Michael; Taubert, Nick; Sting, Louisa Siebert, Ramona Spadacenta, Silvia Dicke, Peter Thier, Hans-Peter Giese, Martin A.
Type of Publication:
In Collection
Ardestani, M. H., Saini, N. & Giese, M. A (2019). Neural model for the visual recognition of agency and social interaction. ECVP Conference 2019, Perception 48(2S),104 .
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Neural model for the visual recognition of agency and social interaction
Authors:
Ardestani, Mohammad Hovaidi Saini, N. Giese, Martin A.
Type of Publication:
In Collection
Ardestani, M. H., Saini, N. & Giese, M. A (2019). Neural model for the visual recognition of social interactions. BMC Neuroscience 2019, 20(Suppl 1):P92 .
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Neural model for the visual recognition of social interactions
Authors:
Ardestani, Mohammad Hovaidi Saini, N. Giese, Martin A.
Type of Publication:
In Collection
Junker, M., Endres, D., Sun, Z. P., Dicke, P. W., Giese, M. A. & Thier, P. (2018). Learning from the past: A reverberation of past errors in the cerebellar climbing fiber signal. PLOS Biology, 16(8), e2004344.
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Learning from the past: A reverberation of past errors in the cerebellar climbing fiber signal
Abstract:
The cerebellum allows us to rapidly adjust motor behavior to the needs of the situation. It is commonly assumed that cerebellum-based motor learning is guided by the difference between the desired and the actual behavior, i.e., by error information. Not only immediate but also future behavior will benefit from an error because it induces lasting changes of parallel fiber synapses on Purkinje cells (PCs), whose output mediates the behavioral adjustments. Olivary climbing fibers, likewise connecting with PCs, are thought to transport information on instant errors needed for the synaptic modification yet not to contribute to error memory. Here, we report work on monkeys tested in a saccadic learning paradigm that challenges this concept. We demonstrate not only a clear complex spikes (CS) signature of the error at the time of its occurrence but also a reverberation of this signature much later, before a new manifestation of the behavior, suitable to improve it.
Authors:
Junker, M Endres, Dominik Sun, Zong Peng Dicke, Peter W. Giese, Martin A.; Thier, Peter
Type of Publication:
Article
Full text: Online version
Fedorov, L., Chang, D., Giese, M. A., B\"ulthoff, H. & de la Rosa, S. (2018). Adaptation aftereffects reveal representations for encoding of contingent social actions. PNAS, 115(29), 7515-7520.
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Adaptation aftereffects reveal representations for encoding of contingent social actions
Abstract:
A hallmark of human social behavior is the effortless ability to relate one’s own actions to that of the interaction partner, e.g., when stretching out one’s arms to catch a tripping child. What are the behavioral properties of the neural substrates that support this indispensable human skill? Here we examined the processes underlying the ability to relate actions to each other, namely the recognition of spatiotemporal contingencies between actions (e.g., a “giving” that is followed by a “taking”). We used a behavioral adaptation paradigm to examine the response properties of perceptual mechanisms at a behavioral level. In contrast to the common view that action-sensitive units are primarily selective for one action (i.e., primary action, e.g., ‘throwing”), we demonstrate that these processes also exhibit sensitivity to a matching contingent action (e.g., “catching”). Control experiments demonstrate that the sensitivity of action recognition processes to contingent actions cannot be explained by lower-level visual features or amodal semantic adaptation. Moreover, we show that action recognition processes are sensitive only to contingent actions, but not to noncontingent actions, demonstrating their selective sensitivity to contingent actions. Our findings show the selective coding mechanism for action contingencies by action-sensitive processes and demonstrate how the representations of individual actions in social interactions can be linked in a unified representation
Authors:
Fedorov, LA Chang, DS Giese, Martin A.; B\"ulthoff, HH de la Rosa, S
Type of Publication:
Article
Full text: Online version
Jastorff, J., Kourtzi, Z. & Giese, M. A. (2006). Learning to discriminate complex movements: Natural vs artificial trajectories. Journal of Vision, 6(8), 791-804.
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Learning to discriminate complex movements: Natural vs artificial trajectories
Authors:
Jastorff, J. Kourtzi, Zoe Giese, Martin A.
Type of Publication:
Article
Full text: Online version
Leopold, D. A., Bondar, I. V. & Giese, M. A. (2006). Norm-based face encoding by single neurons in the monkey inferotemporal cortex. Nature, 442(7102), 572-575.
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Norm-based face encoding by single neurons in the monkey inferotemporal cortex
Authors:
Leopold, David A. Bondar, Igor V. Giese, Martin A.
Type of Publication:
Article
Full text: Online version
Omlor, L. & Giese, M. A. (2006). Unsupervised learning of spatio-temporal primitives of emotional gait. Perception and Interactive Technologies 2006, Lecture Notes in Artificial Intelligence, 4021, 188-192.
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Unsupervised learning of spatio-temporal primitives of emotional gait
Authors:
Omlor, Lars Giese, Martin A.
Type of Publication:
Article
Full text: Online version
Casile, A. & Rucci, M. (2006). A theoretical analysis of the influence of fixational instability on the development of thalamocortical connectivity. Neural Computation, 18(3), 569-590.
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A theoretical analysis of the influence of fixational instability on the development of thalamocortical connectivity
Authors:
Casile, Antonino Rucci, Michele
Type of Publication:
Article
Full text: Online version
Casile, A. & Giese, M. A. (2006). Non-visual motor learning influences the recognition of biological motion. Current Biology, 16(1), 69-74.
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Non-visual motor learning influences the recognition of biological motion
Authors:
Casile, Antonino Giese, Martin A.
Type of Publication:
Article
Full text: Online version
Omlor, L., Roether, C. L. & Giese, M. A. (2006). Optimal integration of movement components for the visual recognition of emotional body expressions. In: H.H. B\"ulthoff, K. Gegenfurtner, H.A. Mallot {{&}} R. Ulrich (eds.) Beiträge zur 9. T\"ubinger Wahrnehmungskonferenz. Kirchentellinsfurt: Knirsch.
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Optimal integration of movement components for the visual recognition of emotional body expressions
Authors:
Omlor, Lars Roether, C. L. Giese, Martin A.
Type of Publication:
Article
Omlor, L., Giese, M. A. & Roether, C. L. (2006). Extraction of spatio-temporal primitives from emotional gait patterns. 5th Forum of European Neurosciences (FENS), Vienna, Austria.
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Extraction of spatio-temporal primitives from emotional gait patterns
Authors:
Omlor, Lars Giese, Martin A.; Roether, C. L.
Type of Publication:
Article
Giese, M. A. & Poggio, T. A. (2003). Neural mechanisms for the recognition of biological movements and action. Nature Reviews Neuroscience, 4, 179-192.
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Neural mechanisms for the recognition of biological movements and action
Authors:
Giese, Martin A.; Poggio, Tomaso A.
Type of Publication:
Article
Full text: Online version
Giese, M. A., BOGNÁR, A. & Vogels, R. Physiologically-inspired neurodynamical model for anorthoscopic perception .
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Physiologically-inspired neurodynamical model for anorthoscopic perception
Authors:
Giese, Martin A.; BOGNÁR, A. Vogels, Rufin
Type of Publication:
In Collection