Integration of force and position cues for shape perception through active touch

Drewing, Knut; Ernst, Marc O.

doi:10.1016/j.brainres.2005.12.026

Cited by 73 publications

(58 citation statements)

References 27 publications

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“…These pieces are not necessarily veridical. If one regards perception as a process in which relevant sensory signals are combined into a single perceptual estimate (e.g., [1]), illusory perception boils down to the blending of (a) biased sensory signal(s) into this estimate (thus causing its non-veridicality). Therefore, to understand an illusion, one has to uncover the source(s) of this biased sensory input.…”

Section: Introductionmentioning

confidence: 99%

Muscular Torque Can Explain Biases in Haptic Length Perception: A Model Study on the Radial-Tangential Illusion

Debats

Kingma

Beek

et al. 2010

Haptics: Generating and Perceiving Tangible Sensations

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Abstract. In haptic length perception biases occur that have previously been shown to depend on stimulus orientation and stimulus length. We propose that these biases arise from the muscular torque needed to counteract the gravitational forces acting on the arm. In a model study, we founded this hypothesis by showing that differences in muscular torque can indeed explain the pattern of biases obtained in several experimental studies.

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Section: Introductionmentioning

confidence: 99%

Muscular Torque Can Explain Biases in Haptic Length Perception: A Model Study on the Radial-Tangential Illusion

Debats

Kingma

Beek

et al. 2010

Haptics: Generating and Perceiving Tangible Sensations

View full text Add to dashboard Cite

show abstract

“…Drewing and colleagues (Drewing & Ernst, 2006;Drewing & Kaim, 2009;Kaim & Drewing, 2010) proposed that a maximum likelihood estimation combination of cues explains haptic perception of shape properties. Conceivably, some similar combination of influences affected IFD judgments under the conditions tested in the present experiments.…”

Section: Discussionmentioning

confidence: 99%

“…Drewing and colleagues (Drewing & Ernst, 2006;Drewing & Kaim, 2009;Drewing, Wiecki, & Ernst, 2008;Kaim & Drewing, 2010) have documented the utility of hand-arm position information along a curved path as a basis for haptic discrimination of (typically, Gaussian) curves. A number of other studies examining haptic curvature perception (e.g., Kappers, Koenderink, & Lichtenegger, 1994) have implicated finger-hand-arm position information as a basis for discrimination of curvature differences.…”

Section: Sensitivity To Hand-arm Positionmentioning

confidence: 99%

“…Drewing and colleagues (Drewing & Ernst, 2006;Drewing & Kaim, 2009;Drewing et al, 2008;Kaim & Drewing, 2010) pointed out that some haptic spatial perceptual effects (i.e., surface curvature judgments) apparently depend on an amalgam of information from changing finger position and force change, both available as the finger moves across a curved surface. Robles-de-la-Torre and Hayward (2001) demonstrated that when force and geometry (essentially, finger position) conflict, force relationships take priority in categorizing curved versus flat surfaces.…”

Section: Force-based Explanationsmentioning

confidence: 99%

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Haptic distal spatial perception mediated by strings: Size at a distance and egocentric localization based on ellipse geometry

Cabe

2012

Atten Percept Psychophys

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Participants explored ellipse perimeters defined by fixed-length strings, held taut by their moving finger, with ends attached to two fixed hooks (foci). Participants haptically judged ellipse interfocal distance (IFD; in effect, exocentric separation, or size at a distance) or location of ellipse major axes (i.e., egocentric localization relative to the moving finger). In Experiment 1A, perceivers made reliable and accurate multialternative forced choice IFD judgments. Experiment 1B showed similar reliability for direct estimations but showed greater scaling error. In Experiment 2, perceivers reliably localized ellipse major axes. Both experiments derived from a priori geometrical analysis, consistent with the Gibsonian perceptual research program and previous string-mediated haptic distal spatial studies. Results are discussed with respect to haptic perception as a telemodality and to dynamic touch.

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“…Hillis et al (2004) proposed a model for combining texture and disparity cues for perceiving the slant of a plane. One last example is given by Drewing and Ernst (2006), who modeled the perception of curvature from haptic feedback, using both position and force information.…”

Section: Fusionmentioning

confidence: 99%

Common Bayesian Models for Common Cognitive Issues

Colas

Diard²,

Bessìère³

2010

Acta Biotheor

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How can an incomplete and uncertain model of the environment be used to perceive, infer, decide and act efficiently? This is the challenge that both living and artificial cognitive systems have to face. Symbolic logic is, by its nature, unable to deal with this question. The subjectivist approach to probability is an extension to logic that is designed specifically to face this challenge. In this paper, we review a number of frequently encountered cognitive issues and cast them into a common Bayesian formalism. The concepts we review are ambiguities, fusion, multimodality, conflicts, modularity, hierarchies and loops. First, each of these concepts is introduced briefly using some examples from the neuroscience, psychophysics or robotics literature. Then, the concept is formalized using a template Bayesian model. The assumptions and common features of these models, as well as their major differences, are outlined and discussed.

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Integration of force and position cues for shape perception through active touch

Cited by 73 publications

References 27 publications

Muscular Torque Can Explain Biases in Haptic Length Perception: A Model Study on the Radial-Tangential Illusion

Muscular Torque Can Explain Biases in Haptic Length Perception: A Model Study on the Radial-Tangential Illusion

Haptic distal spatial perception mediated by strings: Size at a distance and egocentric localization based on ellipse geometry

Common Bayesian Models for Common Cognitive Issues

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