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Daniel Wolpert

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5 papers

1 author row

NeurIPS Conference 2016 Conference Paper

Efficient state-space modularization for planning: theory, behavioral and neural signatures

Daniel McNamee
Daniel Wolpert
Mate Lengyel

Even in state-spaces of modest size, planning is plagued by the “curse of dimensionality”. This problem is particularly acute in human and animal cognition given the limited capacity of working memory, and the time pressures under which planning often occurs in the natural environment. Hierarchically organized modular representations have long been suggested to underlie the capacity of biological systems to efficiently and flexibly plan in complex environments. However, the principles underlying efficient modularization remain obscure, making it difficult to identify its behavioral and neural signatures. Here, we develop a normative theory of efficient state-space representations which partitions an environment into distinct modules by minimizing the average (information theoretic) description length of planning within the environment, thereby optimally trading off the complexity of planning across and within modules. We show that such optimal representations provide a unifying account for a diverse range of hitherto unrelated phenomena at multiple levels of behavior and neural representation.

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NeurIPS Conference 2003 Conference Paper

Probabilistic Inference in Human Sensorimotor Processing

Konrad Körding
Daniel Wolpert

When we learn a new motor skill, we have to contend with both the vari- ability inherent in our sensors and the task. The sensory uncertainty can be reduced by using information about the distribution of previously ex- perienced tasks. Here we impose a distribution on a novel sensorimotor task and manipulate the variability of the sensory feedback. We show that subjects internally represent both the distribution of the task as well as their sensory uncertainty. Moreover, they combine these two sources of information in a way that is qualitatively predicted by optimal Bayesian processing. We further analyze if the subjects can represent multimodal distributions such as mixtures of Gaussians. The results show that the CNS employs probabilistic models during sensorimotor learning even when the priors are multimodal.

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NeurIPS Conference 1998 Conference Paper

Multiple Paired Forward-Inverse Models for Human Motor Learning and Control

Masahiko Haruno
Daniel Wolpert
Mitsuo Kawato

Humans demonstrate a remarkable ability to generate accurate and appropriate motor behavior under many different and oftpn uncprtain environmental conditions. This paper describes a new modular ap(cid: 173) proach to human motor learning and control, baspd on multiple pairs of inverse (controller) and forward (prpdictor) models. This architecture simultaneously learns the multiple inverse models necessary for control as well as how to select the inverse models appropriate for a given em'i(cid: 173) ronm0nt. Simulations of object manipulation demonstrates the ability to learn mUltiple objects, appropriate generalization to novel objects and the inappropriate activation of motor programs based on visual cues, followed by on-line correction, seen in the "size-weight illusion".

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NeurIPS Conference 1994 Conference Paper

Computational Structure of coordinate transformations: A generalization study

Zoubin Ghahramani
Daniel Wolpert
Michael Jordan

One of the fundamental properties that both neural networks and the central nervous system share is the ability to learn and gener(cid: 173) alize from examples. While this property has been studied exten(cid: 173) sively in the neural network literature it has not been thoroughly explored in human perceptual and motor learning. We have chosen a coordinate transformation system-the visuomotor map which transforms visual coordinates into motor coordinates-to study the generalization effects of learning new input-output pairs. Using a paradigm of computer controlled altered visual feedback, we have studied the generalization of the visuomotor map subsequent to both local and context-dependent remappings. A local remapping of one or two input-output pairs induced a significant global, yet decaying, change in the visuomotor map, suggesting a representa(cid: 173) tion for the map composed of units with large functional receptive fields. Our study of context-dependent remappings indicated that a single point in visual space can be mapped to two different fin(cid: 173) ger locations depending on a context variable-the starting point of the movement. Furthermore, as the context is varied there is a gradual shift between the two remappings, consistent with two visuomotor modules being learned and gated smoothly with the context.

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NeurIPS Conference 1994 Conference Paper

Forward dynamic models in human motor control: Psychophysical evidence

Daniel Wolpert
Zoubin Ghahramani
Michael Jordan

Based on computational principles, with as yet no direct experi(cid: 173) mental validation, it has been proposed that the central nervous system (CNS) uses an internal model to simulate the dynamic be(cid: 173) havior of the motor system in planning, control and learning (Sut(cid: 173) ton and Barto, 1981; Ito, 1984; Kawato et aI. , 1987; Jordan and Rumelhart, 1992; Miall et aI. , 1993). We present experimental re(cid: 173) sults and simulations based on a novel approach that investigates the temporal propagation of errors in the sensorimotor integration process. Our results provide direct support for the existence of an internal model.

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