5,102 research outputs found
A predictive processing theory of sensorimotor contingencies: explaining the puzzle of perceptual presence and its absence in synesthesia
Normal perception involves experiencing objects within perceptual scenes as real, as existing in the world. This property of “perceptual presence” has motivated “sensorimotor theories” which understand perception to involve the mastery of sensorimotor contingencies. However, the mechanistic basis of sensorimotor contingencies and their mastery has remained unclear. Sensorimotor theory also struggles to explain instances of perception, such as synesthesia, that appear to lack perceptual presence and for which relevant sensorimotor contingencies are difficult to identify. On alternative “predictive processing” theories, perceptual content emerges from probabilistic inference on the external causes of sensory signals, however, this view has addressed neither the problem of perceptual presence nor synesthesia. Here, I describe a theory of predictive perception of sensorimotor contingencies which (1) accounts for perceptual presence in normal perception, as well as its absence in synesthesia, and (2) operationalizes the notion of sensorimotor contingencies and their mastery. The core idea is that generative models underlying perception incorporate explicitly counterfactual elements related to how sensory inputs would change on the basis of a broad repertoire of possible actions, even if those actions are not performed. These “counterfactually-rich” generative models encode sensorimotor contingencies related to repertoires of sensorimotor dependencies, with counterfactual richness determining the degree of perceptual presence associated with a stimulus. While the generative models underlying normal perception are typically counterfactually rich (reflecting a large repertoire of possible sensorimotor dependencies), those underlying synesthetic concurrents are hypothesized to be counterfactually poor. In addition to accounting for the phenomenology of synesthesia, the theory naturally accommodates phenomenological differences between a range of experiential states including dreaming, hallucination, and the like. It may also lead to a new view of the (in)determinacy of normal perception
Scalable Group Level Probabilistic Sparse Factor Analysis
Many data-driven approaches exist to extract neural representations of
functional magnetic resonance imaging (fMRI) data, but most of them lack a
proper probabilistic formulation. We propose a group level scalable
probabilistic sparse factor analysis (psFA) allowing spatially sparse maps,
component pruning using automatic relevance determination (ARD) and subject
specific heteroscedastic spatial noise modeling. For task-based and resting
state fMRI, we show that the sparsity constraint gives rise to components
similar to those obtained by group independent component analysis. The noise
modeling shows that noise is reduced in areas typically associated with
activation by the experimental design. The psFA model identifies sparse
components and the probabilistic setting provides a natural way to handle
parameter uncertainties. The variational Bayesian framework easily extends to
more complex noise models than the presently considered.Comment: 10 pages plus 5 pages appendix, Submitted to ICASSP 1
Quick and energy-efficient Bayesian computing of binocular disparity using stochastic digital signals
Reconstruction of the tridimensional geometry of a visual scene using the
binocular disparity information is an important issue in computer vision and
mobile robotics, which can be formulated as a Bayesian inference problem.
However, computation of the full disparity distribution with an advanced
Bayesian model is usually an intractable problem, and proves computationally
challenging even with a simple model. In this paper, we show how probabilistic
hardware using distributed memory and alternate representation of data as
stochastic bitstreams can solve that problem with high performance and energy
efficiency. We put forward a way to express discrete probability distributions
using stochastic data representations and perform Bayesian fusion using those
representations, and show how that approach can be applied to diparity
computation. We evaluate the system using a simulated stochastic implementation
and discuss possible hardware implementations of such architectures and their
potential for sensorimotor processing and robotics.Comment: Preprint of article submitted for publication in International
Journal of Approximate Reasoning and accepted pending minor revision
Visual motion processing and human tracking behavior
The accurate visual tracking of a moving object is a human fundamental skill
that allows to reduce the relative slip and instability of the object's image
on the retina, thus granting a stable, high-quality vision. In order to
optimize tracking performance across time, a quick estimate of the object's
global motion properties needs to be fed to the oculomotor system and
dynamically updated. Concurrently, performance can be greatly improved in terms
of latency and accuracy by taking into account predictive cues, especially
under variable conditions of visibility and in presence of ambiguous retinal
information. Here, we review several recent studies focusing on the integration
of retinal and extra-retinal information for the control of human smooth
pursuit.By dynamically probing the tracking performance with well established
paradigms in the visual perception and oculomotor literature we provide the
basis to test theoretical hypotheses within the framework of dynamic
probabilistic inference. We will in particular present the applications of
these results in light of state-of-the-art computer vision algorithms
Chance, long tails, and inference: a non-Gaussian, Bayesian theory of vocal learning in songbirds
Traditional theories of sensorimotor learning posit that animals use sensory
error signals to find the optimal motor command in the face of Gaussian sensory
and motor noise. However, most such theories cannot explain common behavioral
observations, for example that smaller sensory errors are more readily
corrected than larger errors and that large abrupt (but not gradually
introduced) errors lead to weak learning. Here we propose a new theory of
sensorimotor learning that explains these observations. The theory posits that
the animal learns an entire probability distribution of motor commands rather
than trying to arrive at a single optimal command, and that learning arises via
Bayesian inference when new sensory information becomes available. We test this
theory using data from a songbird, the Bengalese finch, that is adapting the
pitch (fundamental frequency) of its song following perturbations of auditory
feedback using miniature headphones. We observe the distribution of the sung
pitches to have long, non-Gaussian tails, which, within our theory, explains
the observed dynamics of learning. Further, the theory makes surprising
predictions about the dynamics of the shape of the pitch distribution, which we
confirm experimentally
On Neuromechanical Approaches for the Study of Biological Grasp and Manipulation
Biological and robotic grasp and manipulation are undeniably similar at the
level of mechanical task performance. However, their underlying fundamental
biological vs. engineering mechanisms are, by definition, dramatically
different and can even be antithetical. Even our approach to each is
diametrically opposite: inductive science for the study of biological systems
vs. engineering synthesis for the design and construction of robotic systems.
The past 20 years have seen several conceptual advances in both fields and the
quest to unify them. Chief among them is the reluctant recognition that their
underlying fundamental mechanisms may actually share limited common ground,
while exhibiting many fundamental differences. This recognition is particularly
liberating because it allows us to resolve and move beyond multiple paradoxes
and contradictions that arose from the initial reasonable assumption of a large
common ground. Here, we begin by introducing the perspective of neuromechanics,
which emphasizes that real-world behavior emerges from the intimate
interactions among the physical structure of the system, the mechanical
requirements of a task, the feasible neural control actions to produce it, and
the ability of the neuromuscular system to adapt through interactions with the
environment. This allows us to articulate a succinct overview of a few salient
conceptual paradoxes and contradictions regarding under-determined vs.
over-determined mechanics, under- vs. over-actuated control, prescribed vs.
emergent function, learning vs. implementation vs. adaptation, prescriptive vs.
descriptive synergies, and optimal vs. habitual performance. We conclude by
presenting open questions and suggesting directions for future research. We
hope this frank assessment of the state-of-the-art will encourage and guide
these communities to continue to interact and make progress in these important
areas
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