On September 20-21, 2016, we will host a workshop on
Learning at the interface of vision and oculomotor control
It will be a satellite event of the Bernstein Conference.

Program:
An overview of the program is available here.

Abstract:
Motor control and sensorimotor integration have long been of outstanding interest for their fundamental theoretical and empirical contributions to neuroscience. Elegant behavioral, computational, and physiological studies have revealed that skilled sensorimotor function builds on experience acquired over timescales from milliseconds to a lifetime. In spite of being one of the most thriving fields of research, interactions with another core neuroscience area—the impact of self-movement on perception—remain largely unaddressed to date. In particular, research on the acquisition and maintenance of perceptual continuity across rapid movements of the eyes (saccades) has eluded comparable degree of detail and still lacks detailed mechanistic formulation. This workshop aims to bridge these differences, advancing new perspectives on how insights from sensorimotor learning may help understand transsaccadic visual processes.

To this end, we bring together behavioral, theoretical, and biological insights from eight innovative researchers, each well known for their inspiring contributions to sensorimotor control or transsaccadic visual learning. Our aim is to facilitate and inspire the transfer of knowledge between these largely disparate fields of study. Structured discussions will put particular emphasis on possible conceptual and computational links between the latest psychophysical and neurophysiological findings. We expect two major outcomes of this workshop. First, its dual focus will promote interaction between the fields of sensorimotor learning and the perceptual consequences manifested in transsaccadic recalibration, integration, and learning. Second, bringing together leading experts among physiologists, computational modelers, and psychophysicists will help establish constraints on current theories and models, explore their validity in novel domains, stimulate formulation of new hypothesis, and identify missing experiments that may falsify existing accounts.

Venue:
Humboldt-Universität zu Berlin
Seminargebäude am Hegelplatz
Dorotheenstr. 24
10117 Berlin, Germany
Link to Google maps.

Registration:
Online registration will be open until September 12^th here. On site registration is possible on the day of the workshop.

Please note that the room has a limited number of seats (about 50) — if more participants register for the workshop, participation will be possible on first come, first serve basis.

Tutorial:  Motor learning: An overview of methods and models
The workshop will also feature an added tutorial given by David Herzfeld from John Hopkins University in the morning of Tuesday 20^th September (9:30 am) at the same venue. All registered workshop participants are welcome to participate in this tutorial, but the available number of seats will be limited.

Abstract for the tutorial:
The study of motor learning seeks to answer a fundamental question: how does the nervous system learn from a motor error? The experience of even a single movement error results in adaptation, suggesting that the motor system is constantly engaged in the process of learning. In this tutorial we begin with behavior – how do healthy subjects learn to compensate for a constant perturbation? Using studies from saccade adaptation, force-field learning, and visuomotor rotation, we will then assess a number of behavioral assays including error-sensitivity, generalization, savings, meta-learning, and implicit/explicit processes. Emphasis will be placed on mathematical models of these phenomena, including multi-rate models, the Kalman filter, a memory of errors, and models of generalization. When possible, we will describe studies suggesting the specific brain regions involved in motor learning, focusing on cerebellar contributions to adaptation.

David generously allowed us to upload the slides of his tutorial here.