During sustained presentation of an ambiguous stimulus, an individual’s perceptual experience will generally switch between the different possible alternatives rather than stay fixed on one interpretation. In a recent study (Einhäuser et al. 2008) we demonstrated that switches in a variety of such rivalry stimuli (Necker cube, plaid motion segregation, structure from motion and auditory stream segregation) are robustly accompanied by increases in pupil diameter. Since pupil dilation under constant illumination reflects the activation of the locus coeruleus (LC) – the brainstem nucleus responsible for synthesis and release of noradrenaline (NA) throughout the cortex – our data suggest the involvement of the LC-NA system in rivalry. This is, the LC-NA complex may play exactly the same role in perception as it is understood to be playing in behavioral selection. Consequently, we hypothesize that resolving perceptual ambiguity may be understood as a form of decision-making. In current research, we are investigating the relation between such perceptual and behavioral decision-making processes.

Main Collaborators: Olivia Carter (Harvard); Christof Koch (Caltech)

In order to cope with the wealth of information contained in a natural scene, human observers typically allocate their processing resources by shifting attention to subsets of the visual stimulus. To understand the neural processes underlying this attention allocation, our lab follows three complementary paradigms. First, we measure eye movements, while observers view modified natural scenes and perform different tasks (e.g., Einhäuser & König, 2003; Einhäuser, Rutishauser & Koch, 2008). Second, we engage observers in rapid recognition tasks to probe the limits of their recognition performance under different attentional conditions (e.g., Einhäuser, Koch & Makeig, 2007) Finally, we try and transfer mathematical models of attention to predict rapid scene recognition (e.g., Einhäuser, Mundhenk et al., 2007). In our current research, we are aiming at modeling object recognition and spatial attention in a common framework, to foster our understanding of the underlying neuronal circuitry and to improve state-of-the-art computer vision algorithms.

Main Collaborators: Laurent Itti (USC, Bayesian models of attention); Scott Makeig (UCSD, EEG in rapid scene recognition); Merielle Spain/Pietro Perona (Caltech, object recognition); Ueli Rutishauser/Christof Koch (Caltech, effects of task), Peter König (University of Osnabrück, scene statistics)

Can a small number of general coding principles explain many properties of the visual system? In a number of studies we address this issue and link the principle of temporal coherence to complex cell properties in Primary visual cortex (Kayser et al. 2001; Einhäuser et al. 2002; Körding et al 2004), the extraction of complemetary features (Einhäuser et al. 2003), the learning of texture representations and the generation of representations that are well-suited for invariant object classification (Einhäuser, Hipp, et al., 2005). We extended the same principle to somatosensory representations. (Hipp, Einhäuser, et al., 2005). In current research we try to link these results to hierarchical models of object recognition and attention.

C-Code for temporal coherence simulations is available, just send an email