Stephen Handel
- Published in print:
- 2006
- Published Online:
- September 2007
- ISBN:
- 9780195169645
- eISBN:
- 9780199786732
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780195169645.003.0003
- Subject:
- Psychology, Cognitive Psychology
If the goal of sensory systems is to maximize information transmission, there should be a match between the functioning of the sensory systems and the statistical properties of the objects in the ...
More
If the goal of sensory systems is to maximize information transmission, there should be a match between the functioning of the sensory systems and the statistical properties of the objects in the environment. Analyses of the distribution of acoustical and visual energies indicate that they follow a power law, 1/f, so that there is a constant relationship between frequency and amplitude, namely equal power in all octave regions. To encode this distribution, the auditory and visual systems use cells that resemble Gabor functions that decorrelate local sensory energy to detect the redundancies such as continuous boundaries that signify objects. There is sparse coding so that only a small number of cells fire for any input and those cells minimize the uncertainty problem by trading frequency resolution with orientation or time resolution. The perceptual outcomes are combined with Bayesian prior probabilities to identify the most likely object.Less
If the goal of sensory systems is to maximize information transmission, there should be a match between the functioning of the sensory systems and the statistical properties of the objects in the environment. Analyses of the distribution of acoustical and visual energies indicate that they follow a power law, 1/f, so that there is a constant relationship between frequency and amplitude, namely equal power in all octave regions. To encode this distribution, the auditory and visual systems use cells that resemble Gabor functions that decorrelate local sensory energy to detect the redundancies such as continuous boundaries that signify objects. There is sparse coding so that only a small number of cells fire for any input and those cells minimize the uncertainty problem by trading frequency resolution with orientation or time resolution. The perceptual outcomes are combined with Bayesian prior probabilities to identify the most likely object.
Michele Maggiore
- Published in print:
- 2007
- Published Online:
- January 2008
- ISBN:
- 9780198570745
- eISBN:
- 9780191717666
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780198570745.003.0007
- Subject:
- Physics, Particle Physics / Astrophysics / Cosmology
This chapter deals with experimental aspects of gravitational waves. It defines spectral strain sensitivity, describes the detector's noise and the pattern functions that encode its angular ...
More
This chapter deals with experimental aspects of gravitational waves. It defines spectral strain sensitivity, describes the detector's noise and the pattern functions that encode its angular sensitivity, and discusses various data analysis techniques for GWs. It also introduces the theory of matched filtering. A proper interpretation of the results obtained with matched filtering relies on notions of probability and statistics. These are discussed together with an introduction to the frequentist and the Bayesian frameworks. The reconstruction of the source parameters is discussed, and the general theory is then applied to different classes of signals, namely, bursts, periodic sources, coalescing binaries, and stochastic background.Less
This chapter deals with experimental aspects of gravitational waves. It defines spectral strain sensitivity, describes the detector's noise and the pattern functions that encode its angular sensitivity, and discusses various data analysis techniques for GWs. It also introduces the theory of matched filtering. A proper interpretation of the results obtained with matched filtering relies on notions of probability and statistics. These are discussed together with an introduction to the frequentist and the Bayesian frameworks. The reconstruction of the source parameters is discussed, and the general theory is then applied to different classes of signals, namely, bursts, periodic sources, coalescing binaries, and stochastic background.
Peter Achinstein
- Published in print:
- 2001
- Published Online:
- November 2003
- ISBN:
- 9780195143898
- eISBN:
- 9780199833023
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/0195143892.003.0005
- Subject:
- Philosophy, Philosophy of Science
A new concept of probability ‐ objective epistemic probability ‐ is introduced and defended. It is epistemic because it is a measure of the degree of reasonableness of believing something; it is ...
More
A new concept of probability ‐ objective epistemic probability ‐ is introduced and defended. It is epistemic because it is a measure of the degree of reasonableness of believing something; it is objective because it is independent of the beliefs of any person or group. The view is contrasted with several others, including the subjective Bayesian theory of probability, which is epistemic but not objective; with the propensity theory, which is objective but not epistemic; and with Carnap's view, which, like the view defended, is both epistemic and objective but, unlike it, is relativized to a potential epistemic situation.Less
A new concept of probability ‐ objective epistemic probability ‐ is introduced and defended. It is epistemic because it is a measure of the degree of reasonableness of believing something; it is objective because it is independent of the beliefs of any person or group. The view is contrasted with several others, including the subjective Bayesian theory of probability, which is epistemic but not objective; with the propensity theory, which is objective but not epistemic; and with Carnap's view, which, like the view defended, is both epistemic and objective but, unlike it, is relativized to a potential epistemic situation.
Yoaav Isaacs
John Hawthorne (ed.)
- Published in print:
- 2018
- Published Online:
- March 2018
- ISBN:
- 9780198798705
- eISBN:
- 9780191848469
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/oso/9780198798705.003.0008
- Subject:
- Philosophy, Philosophy of Religion
This chapter argues that the fine-tuning argument for the existence of God is a straightforwardly legitimate argument. The fine-tuning argument takes certain features of fundamental physics to ...
More
This chapter argues that the fine-tuning argument for the existence of God is a straightforwardly legitimate argument. The fine-tuning argument takes certain features of fundamental physics to confirm the existence of God because these features of fundamental physics are more likely given the existence of God than they are given the non-existence of God. And any such argument is straightforwardly legitimate, as such arguments follow a canonically legitimate form of empirical argumentation. The chapter explores various objections to the fine-tuning argument: that it requires an ill-defined notion of small changes in the laws of physics, that it over-generalizes, that it requires implausible presuppositions about divine intentions, and that it is debunked by anthropic reasoning. In each case it finds either that the putatively objectionable feature of the fine-tuning argument is inessential to it or that the putatively objectionable feature of the fine-tuning argument is not actually objectionable.Less
This chapter argues that the fine-tuning argument for the existence of God is a straightforwardly legitimate argument. The fine-tuning argument takes certain features of fundamental physics to confirm the existence of God because these features of fundamental physics are more likely given the existence of God than they are given the non-existence of God. And any such argument is straightforwardly legitimate, as such arguments follow a canonically legitimate form of empirical argumentation. The chapter explores various objections to the fine-tuning argument: that it requires an ill-defined notion of small changes in the laws of physics, that it over-generalizes, that it requires implausible presuppositions about divine intentions, and that it is debunked by anthropic reasoning. In each case it finds either that the putatively objectionable feature of the fine-tuning argument is inessential to it or that the putatively objectionable feature of the fine-tuning argument is not actually objectionable.
David C. Knill
- Published in print:
- 2006
- Published Online:
- August 2013
- ISBN:
- 9780262042383
- eISBN:
- 9780262294188
- Item type:
- chapter
- Publisher:
- The MIT Press
- DOI:
- 10.7551/mitpress/9780262042383.003.0009
- Subject:
- Neuroscience, Disorders of the Nervous System
This chapter discusses how the Bayesian probability theory can be used as a framework in integrating multiple sensory cues, introduces elements of Bayesian theories of perception, and describes some ...
More
This chapter discusses how the Bayesian probability theory can be used as a framework in integrating multiple sensory cues, introduces elements of Bayesian theories of perception, and describes some psychophysical tests of Bayesian cue integration.Less
This chapter discusses how the Bayesian probability theory can be used as a framework in integrating multiple sensory cues, introduces elements of Bayesian theories of perception, and describes some psychophysical tests of Bayesian cue integration.
Robert H. Swendsen
- Published in print:
- 2019
- Published Online:
- February 2020
- ISBN:
- 9780198853237
- eISBN:
- 9780191887703
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/oso/9780198853237.003.0005
- Subject:
- Physics, Condensed Matter Physics / Materials, Theoretical, Computational, and Statistical Physics
The theory of probability developed in Chapter 3 for discrete random variables is extended to probability distributions, in order to treat the continuous momentum variables. The Dirac delta function ...
More
The theory of probability developed in Chapter 3 for discrete random variables is extended to probability distributions, in order to treat the continuous momentum variables. The Dirac delta function is introduced as a convenient tool to transform continuous random variables, in analogy with the use of the Kronecker delta for discrete random variables. The properties of the Dirac delta function that are needed in statistical mechanics are presented and explained. The addition of two continuous random numbers is given as a simple example. An application of Bayesian probability is given to illustrate its significance. However, the components of the momenta of the particles in an ideal gas are continuous variables.Less
The theory of probability developed in Chapter 3 for discrete random variables is extended to probability distributions, in order to treat the continuous momentum variables. The Dirac delta function is introduced as a convenient tool to transform continuous random variables, in analogy with the use of the Kronecker delta for discrete random variables. The properties of the Dirac delta function that are needed in statistical mechanics are presented and explained. The addition of two continuous random numbers is given as a simple example. An application of Bayesian probability is given to illustrate its significance. However, the components of the momenta of the particles in an ideal gas are continuous variables.
Jan Lauwereyns
- Published in print:
- 2010
- Published Online:
- August 2013
- ISBN:
- 9780262123105
- eISBN:
- 9780262277990
- Item type:
- book
- Publisher:
- The MIT Press
- DOI:
- 10.7551/mitpress/9780262123105.001.0001
- Subject:
- Psychology, Neuropsychology
This book examines the neural underpinnings of decision-making using “bias” as its core concept, rather than the more common but noncommittal terms “selection” and “attention.” It offers an ...
More
This book examines the neural underpinnings of decision-making using “bias” as its core concept, rather than the more common but noncommittal terms “selection” and “attention.” It offers an integrative, interdisciplinary account of the structure and function of bias, which it defines as a basic brain mechanism that attaches different weights to different information sources, prioritizing some cognitive representations at the expense of others. The author introduces the concepts of bias and sensitivity based on notions from Bayesian probability, which he translates into easily recognizable neural signatures, introduced by concrete examples from the experimental literature. He examines, among other topics, positive and negative motivations for giving priority to different sensory inputs, and looks for the neural underpinnings of racism, sexism, and other forms of “familiarity bias.” The author—a poet and essayist as well as a scientist—connects findings and ideas in neuroscience to analogous concepts in such diverse fields as post-Lacanian psychoanalysis, literary theory, philosophy of mind, evolutionary psychology, and experimental economics.Less
This book examines the neural underpinnings of decision-making using “bias” as its core concept, rather than the more common but noncommittal terms “selection” and “attention.” It offers an integrative, interdisciplinary account of the structure and function of bias, which it defines as a basic brain mechanism that attaches different weights to different information sources, prioritizing some cognitive representations at the expense of others. The author introduces the concepts of bias and sensitivity based on notions from Bayesian probability, which he translates into easily recognizable neural signatures, introduced by concrete examples from the experimental literature. He examines, among other topics, positive and negative motivations for giving priority to different sensory inputs, and looks for the neural underpinnings of racism, sexism, and other forms of “familiarity bias.” The author—a poet and essayist as well as a scientist—connects findings and ideas in neuroscience to analogous concepts in such diverse fields as post-Lacanian psychoanalysis, literary theory, philosophy of mind, evolutionary psychology, and experimental economics.
