Sven Eberhardt

Peripheral pooling is tuned to the localization task

The human visual system exhibits substantially different properties between foveal and peripheral vision. Peripheral vision is special in that it has to compress data onto fewer units by reduced visual acuity and larger receptive fields, yielding greatly reduced performance on many tasks such as object recognition. However, here we show that the pooling operations implemented by peripheral vision provide exactly the invariance properties required by a self-localization task. We test the effect of different pooling sizes, as well as acuity reduction, on localization, object recognition, and scene categorization tasks. We find that peripheral pooling, but not reduced acuity, affects localization performance positively, whereas it is detrimental to object recognition performance.

Dynamics of Dual Prism Adaptation: Relating Novel Experimental Results to a Minimalistic Neural Model

In everyday life, humans interact with a dynamic environment often requiring rapid adaptation of visual perception and motor control. In particular, new visuo–motor mappings must be learned while old skills have to be kept, such that after adaptation, subjects may be able to quickly change between two different modes of generating movements (‘dual–adaptation’). A fundamental question is how the adaptation schedule determines the acquisition speed of new skills. Given a fixed number of movements in two different environments, will dual–adaptation be faster if switches (‘phase changes’) between the environments occur more frequently? We investigated the dynamics of dual–adaptation under different training schedules in a virtual pointing experiment. Surprisingly, we found that acquisition speed of dual visuo–motor mappings in a pointing task is largely independent of the number of phase changes. Next, we studied the neuronal mechanisms underlying this result and other key phenomena of dual–adaptation by relating model simulations to experimental data. We propose a simple and yet biologically plausible neural model consisting of a spatial mapping from an input layer to a pointing angle which is subjected to a global gain modulation. Adaptation is performed by reinforcement learning on the model parameters. Despite its simplicity, the model provides a unifying account for a broad range of experimental data: It quantitatively reproduced the learning rates in dual–adaptation experiments for both direct effect, i.e. adaptation to prisms, and aftereffect, i.e. behavior after removal of prisms, and their independence on the number of phase changes. Several other phenomena, e.g. initial pointing errors that are far smaller than the induced optical shift, were also captured. Moreover, the underlying mechanisms, a local adaptation of a spatial mapping and a global adaptation of a gain factor, explained asymmetric spatial transfer and generalization of prism adaptation, as observed in other experiments.

Self-localization on texture statistics

The ability to localize ourselves in the outdoor world based on visual input even in absence of prior positional information is an important skill of our daily lives that comes naturally to us. However, the underlying mechanisms of this ability are poorly understood. Here, we show how simple texture statistics can be sufficient to provide a strong prior for the self-localization tasks. We find that statistics of common outdoor features such as tree density, foliage type or road structure provide a stronger cue for self-localization than the matching and recognition of less common landmarks such as lamp posts. We encourage the use of such common feature vectors as priors for self-localization systems and hypothesize that humans may use similar priors to assess the location from an unknown image.