Unbiased decoding of biologically motivated visual feature descriptors

Visual feature descriptors are essential elements in most computer and robot vision systems. They typically lead to an abstraction of the input data, images, or video, for further processing, such as clustering and machine learning. In clustering applications, the cluster center represents the prototypical descriptor of the cluster and estimates the corresponding signal value, such as color value or dominating flow orientation, by decoding the prototypical descriptor. Machine learning applications determine the relevance of respective descriptors and a visualization of the corresponding decoded information is very useful for the analysis of the learning algorithm. Thus decoding of feature descriptors is a relevant problem, frequently addressed in recent work. Also, the human brain represents sensorimotor information at a suitable abstraction level through varying activation of neuron populations. In previous work, computational models have been derived that agree with findings of neurophysiological experiments on the represen-tation of visual information by decoding the underlying signals. However, the represented variables have a bias toward centers or boundaries of the tuning curves. Despite the fact that feature descriptors in computer vision are motivated from neuroscience, the respec-tive decoding methods have been derived largely independent. From first principles, we derive unbiased decoding schemes for biologically motivated feature descriptors with a minimum amount of redundancy and suitable invariance properties. These descriptors establish a non-parametric density estimation of the underlying stochastic process with a particular algebraic structure. Based on the resulting algebraic constraints, we show formally how the decoding problem is formulated as an unbiased maximum likelihood estimator and we derive a recurrent inverse diffusion scheme to infer the dominating mode of the distribution. These methods are evaluated in experiments, where stationary points and bias from noisy image data are compared to existing methods.EMC2VIDICUASVPSELLIITCADIC

Unbiased decoding of biologically motivated visual feature descriptors

Visual feature descriptors are essential elements in most computer and robot vision systems. They typically lead to an abstraction of the input data, images, or video, for further processing, such as clustering and machine learning. In clustering applications, the cluster center represents the prototypical descriptor of the cluster and estimates the corresponding signal value, such as color value or dominating flow orientation, by decoding the prototypical descriptor. Machine learning applications determine the relevance of respective descriptors and a visualization of the corresponding decoded information is very useful for the analysis of the learning algorithm. Thus decoding of feature descriptors is a relevant problem, frequently addressed in recent work. Also, the human brain represents sensorimotor information at a suitable abstraction level through varying activation of neuron populations. In previous work, computational models have been derived that agree with findings of neurophysiological experiments on the represen-tation of visual information by decoding the underlying signals. However, the represented variables have a bias toward centers or boundaries of the tuning curves. Despite the fact that feature descriptors in computer vision are motivated from neuroscience, the respec-tive decoding methods have been derived largely independent. From first principles, we derive unbiased decoding schemes for biologically motivated feature descriptors with a minimum amount of redundancy and suitable invariance properties. These descriptors establish a non-parametric density estimation of the underlying stochastic process with a particular algebraic structure. Based on the resulting algebraic constraints, we show formally how the decoding problem is formulated as an unbiased maximum likelihood estimator and we derive a recurrent inverse diffusion scheme to infer the dominating mode of the distribution. These methods are evaluated in experiments, where stationary points and bias from noisy image data are compared to existing methods.EMC2VIDICUASVPSELLIITCADIC

Psychophysiological responsivity to script-driven imagery : an exploratory study of the effects of eye movements on public speaking flashforwards

A principle characteristic of public speaking anxiety relates to intrusive mental images of potential future disasters. Previous research has found that the self-reported emotionality of such “flashforwards” can be reduced by a cognitively demanding, dual-task (e.g., making eye movements) performed whilst holding the mental image in-mind. The outcome measure in these earlier studies was participants’ self-reported emotional intensity of the mental image. The current study (N = 34) explored whether an objective measure of emotionality would yield similar results in students with public speaking anxiety. A script-driven imagery procedure was used to measure psychophysiological responsivity to an audio script depicting a feared (public speaking) scenario before and after an eye movement intervention. Relative to the control condition (imagery only), those who made eye movements whilst holding a mental image of this scenario in-mind demonstrated a significant decrease in heart rate, which acted as a measure of emotionality. These findings add to a previous body of research demonstrating the beneficial qualities of dual-tasks and their potential for treatment of both past and future-oriented anxieties. Keywords: flashforwards, eye movements, experiment, heart rate, anxiety, dual-task Citation: Kearns M and Engelhard IM (2015) Psychophysiological responsivity to script-driven imagery: an exploratory study of the effects of eye movements on public speaking flashforwards. Front. Psychiatry 6:115. doi: 10.3389/fpsyt.2015.00115 Received: 31 October 2014; Accepted: 31 July 2015; Published: 14 August 2015 Edited by: Julie Krans, University of Leuven, Belgium Reviewed by: David G. Pearson, University of Aberdeen, UK Franck Salomé, University of Nantes, France Copyright: © 2015 Kearns and Engelhard. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. *Correspondence: Iris M. Engelhard, Division of Clinical and Health Psychology, Utrecht University, PO Box 80140, Utrecht 3584 CS, Netherlands, [email protected] †Present address: Michelle Kearns, Department of Psychology, University of Limerick, Ireland Write a comment... Add People also looked at Understanding help-seeking amongst university students: the role of group identity, stigma, and exposure to suicide and help-seeking Michelle Kearns, Orla T. Muldoon, Rachel M. Msetfi and Paul W. G. Surgenor Family identification: a beneficial process for young adults who grow up in homes affected by parental intimate partner violence Catherine M. Naughton, Aisling T. O’Donnell and Orla T. Muldoon A Retrospective Review of CyberKnife Stereotactic Body Radiotherapy for Adrenal Tumors (Primary and Metastatic): Winthrop University Hospital Experience Amishi Desai, Hema Rai, Jonathan Haas, Matthew Witten, Seth Blacksburg and Jeffrey G. Schneider Barriers to Utilization of Antenatal Care Services in Eastern Nepal Krishna Kumar Deo, Yuba Raj Paudel, Resham Bahadur Khatri, Ravi Kumar Bhaskar, Rajan Paudel, Suresh Mehata and Rajendra Raj Wagle Commentary: “Consistent Superiority of Selective Serotonin Reuptake Inhibitors Over Placebo in Reducing Depressed Mood in Patients with Major Depression” Eiko I. Fried, Lynn Boschloo, Claudia D. van Borkulo, Robert A. Schoevers, Jan-Willem Romeijn, Marieke Wichers, Peter de Jonge, Randolph M. Nesse, Francis Tuerlinckx and Denny Borsboom The Case for Making Health Care Advocacy a Discipline of Medicine; The Paradigm of a Vascular Patient Elias J. Arbid and Ibrahim G. Eid Unbiased Decoding of Biologically Motivated Visual Feature Descriptors Michael Felsberg, Kristoffer Öfjäll and Reiner Lenz Considerations for the Optimization of Induced White Matter Injury Preclinical Models Abdullah Shafique Ahmad, Irawan Satriotomo, Jawad Fazal, Stephen E. Nadeau and Sylvain Doré Differentiating Burnout from Depression: Personality Matters! Martin Christoph Melchers, Thomas Plieger, Rolf Meermann and Martin Reuter Non-Neuronal Acetylcholine: The Missing Link Between Sepsis, Cancer, and Delirium? Adonis Sfera, Michael Cummings and Carolina Osorio Editorial: Cognition Across the Psychiatric Disorder Spectrum: From Mental Health to Clinical Diagnosis Caroline Gurvich and Susan L. Rossel