Bayesian approaches to brain function - Revision history

Headbomb: /* Neural coding */ mark free doi

2025-05-31T07:32:14Z

Neural coding: mark free doi

← Previous revision		Revision as of 07:32, 31 May 2025
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	==Neural coding==		==Neural coding==
	Many theoretical studies ask how the nervous system could implement Bayesian algorithms. Examples are the work of Pouget, Zemel, Deneve, Latham, Hinton and Dayan. George and [[Jeff Hawkins\|Hawkins]] published a paper that establishes a model of cortical information processing called [[hierarchical temporal memory]] that is based on Bayesian network of [[Markov chain]]s. They further map this mathematical model to the existing knowledge about the architecture of cortex and show how neurons could recognize patterns by hierarchical Bayesian inference.<ref>George D, Hawkins J, 2009 Towards a Mathematical Theory of Cortical Micro-circuits" ''PLoS Comput Biol'' 5(10) e1000532. {{doi\|10.1371/journal.pcbi.1000532}}</ref>		Many theoretical studies ask how the nervous system could implement Bayesian algorithms. Examples are the work of Pouget, Zemel, Deneve, Latham, Hinton and Dayan. George and [[Jeff Hawkins\|Hawkins]] published a paper that establishes a model of cortical information processing called [[hierarchical temporal memory]] that is based on Bayesian network of [[Markov chain]]s. They further map this mathematical model to the existing knowledge about the architecture of cortex and show how neurons could recognize patterns by hierarchical Bayesian inference.<ref>George D, Hawkins J, 2009 Towards a Mathematical Theory of Cortical Micro-circuits" ''PLoS Comput Biol'' 5(10) e1000532. {{doi\|10.1371/journal.pcbi.1000532\|doi-access=free}}</ref>

	==Electrophysiology==		==Electrophysiology==

Citation bot: Removed URL that duplicated identifier. | Use this bot. Report bugs. | Suggested by Dominic3203 | Category:Computational neuroscience | #UCB_Category 113/122

2024-12-29T16:31:49Z

Removed URL that duplicated identifier. | Use this bot. Report bugs. | Suggested by Dominic3203 | Category:Computational neuroscience | #UCB_Category 113/122

← Previous revision		Revision as of 16:31, 29 December 2024
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	==Psychophysics==		==Psychophysics==
	{{See also\|Bayesian inference in motor learning}}		{{See also\|Bayesian inference in motor learning}}
	A wide range of studies interpret the results of psychophysical experiments in light of Bayesian perceptual models. Many aspects of human perceptual and motor behavior can be modeled with Bayesian statistics. This approach, with its emphasis on behavioral outcomes as the ultimate expressions of neural information processing, is also known for modeling sensory and motor decisions using Bayesian decision theory. Examples are the work of [[Michael S. Landy\|Landy]],<ref>{{Cite journal \|last1=Tassinari \|first1=Hadley \|last2=Hudson \|first2=Todd E. \|last3=Landy \|first3=Michael S. \|date=2006 \|title=Combining Priors and Noisy Visual Cues in a Rapid Pointing Task \|url=https://www.jneurosci.org/content/26/40/10154 \|journal=Journal of Neuroscience \|language=en \|volume=26 \|issue=40 \|pages=10154–10163 \|doi=10.1523/JNEUROSCI.2779-06.2006 \|issn=0270-6474 \|pmc=6674625 \|pmid=17021171}}</ref><ref>{{Cite journal \|last1=Hudson \|first1=Todd E. \|last2=Maloney \|first2=Laurence T. \|last3=Landy \|first3=Michael S. \|date=2008 \|title=Optimal Compensation for Temporal Uncertainty in Movement Planning \|journal=PLOS Computational Biology \|language=en \|volume=4 \|issue=7 \|pages=e1000130 \|doi=10.1371/journal.pcbi.1000130\|doi-access=free \|pmid=18654619 \|pmc=2442880 \|bibcode=2008PLSCB...4E0130H }}</ref> Jacobs,<ref>{{cite journal ~~\| url=http://www.sciencedirect.com/science/article/pii/S0042698999000887~~ \| doi=10.1016/S0042-6989(99)00088-7 \| title=Optimal integration of texture and motion cues to depth \| journal=Vision Research \| date=October 1999 \| volume=39 \| issue=21 \| pages=3621–3629 \| last1=Jacobs \| first1=Robert A. \| pmid=10746132 \| doi-access=free }}</ref><ref>Battaglia PW, Jacobs RA & Aslin RN (2003). [http://www.opticsinfobase.org/abstract.cfm?URI=josaa-20-7-1391 Bayesian integration of visual and auditory signals for spatial localization]. Journal of the Optical Society of America, 20(7), 1391–7.</ref> Jordan, Knill,<ref>Knill DC (2005). [http://journalofvision.org//5/2/2/ Reaching for visual cues to depth: The brain combines depth cues differently for motor control and perception]. Journal of Vision, 5(2), 103:15.</ref><ref>Knill DC (2007). [http://journalofvision.org//7/8/13/ Learning Bayesian priors for depth perception] {{Webarchive\|url=https://web.archive.org/web/20081121060849/http://www.journalofvision.org/7/8/13/ \|date=2008-11-21 }}. Journal of Vision, 7(8), 1–20.</ref> Kording and Wolpert,<ref>{{cite journal \| url=http://www.nature.com/nature/journal/v427/n6971/full/nature02169.html \| doi=10.1038/nature02169 \| title=Bayesian integration in sensorimotor learning \| date=2004 \| last1=Körding \| first1=Konrad P. \| last2=Wolpert \| first2=Daniel M. \| journal=Nature \| volume=427 \| issue=6971 \| pages=244–247 \| pmid=14724638 \| bibcode=2004Natur.427..244K }}</ref><ref>Koerding KP, Ku S & Wolpert DM (2004). [http://jn.physiology.org/cgi/content/abstract/92/5/3161 Bayesian integration in force estimation]" ''Journal of Neurophysiology'' 92, 3161–5.</ref> and Goldreich.<ref>{{cite journal\|last=Goldreich\|first=D\|title=A Bayesian perceptual model replicates the cutaneous rabbit and other tactile spatiotemporal illusions.\|journal=PLOS ONE\|date=Mar 28, 2007\|volume=2\|issue=3\|pages=e333\|pmid=17389923\|doi=10.1371/journal.pone.0000333\|pmc=1828626\|bibcode=2007PLoSO...2..333G\|doi-access=free}}</ref><ref>{{cite journal\|last=Goldreich\|first=Daniel\|author2=Tong, Jonathan\|title=Prediction, Postdiction, and Perceptual Length Contraction: A Bayesian Low-Speed Prior Captures the Cutaneous Rabbit and Related Illusions\|journal=Frontiers in Psychology\|date=10 May 2013\|volume=4\|issue=221\|pages=221\|doi=10.3389/fpsyg.2013.00221\|pmid=23675360\|pmc=3650428\|doi-access=free}}</ref><ref>{{cite journal\|last=Goldreich\|first=D\|author2=Peterson, MA\|s2cid=4931501\|title=A Bayesian observer replicates convexity context effects in figure-ground perception.\|journal=Seeing and Perceiving\|year=2012\|volume=25\|issue=3–4\|pages=365–95\|pmid=22564398\|doi=10.1163/187847612X634445}}</ref>		A wide range of studies interpret the results of psychophysical experiments in light of Bayesian perceptual models. Many aspects of human perceptual and motor behavior can be modeled with Bayesian statistics. This approach, with its emphasis on behavioral outcomes as the ultimate expressions of neural information processing, is also known for modeling sensory and motor decisions using Bayesian decision theory. Examples are the work of [[Michael S. Landy\|Landy]],<ref>{{Cite journal \|last1=Tassinari \|first1=Hadley \|last2=Hudson \|first2=Todd E. \|last3=Landy \|first3=Michael S. \|date=2006 \|title=Combining Priors and Noisy Visual Cues in a Rapid Pointing Task \|url=https://www.jneurosci.org/content/26/40/10154 \|journal=Journal of Neuroscience \|language=en \|volume=26 \|issue=40 \|pages=10154–10163 \|doi=10.1523/JNEUROSCI.2779-06.2006 \|issn=0270-6474 \|pmc=6674625 \|pmid=17021171}}</ref><ref>{{Cite journal \|last1=Hudson \|first1=Todd E. \|last2=Maloney \|first2=Laurence T. \|last3=Landy \|first3=Michael S. \|date=2008 \|title=Optimal Compensation for Temporal Uncertainty in Movement Planning \|journal=PLOS Computational Biology \|language=en \|volume=4 \|issue=7 \|pages=e1000130 \|doi=10.1371/journal.pcbi.1000130\|doi-access=free \|pmid=18654619 \|pmc=2442880 \|bibcode=2008PLSCB...4E0130H }}</ref> Jacobs,<ref>{{cite journal \| doi=10.1016/S0042-6989(99)00088-7 \| title=Optimal integration of texture and motion cues to depth \| journal=Vision Research \| date=October 1999 \| volume=39 \| issue=21 \| pages=3621–3629 \| last1=Jacobs \| first1=Robert A. \| pmid=10746132 \| doi-access=free }}</ref><ref>Battaglia PW, Jacobs RA & Aslin RN (2003). [http://www.opticsinfobase.org/abstract.cfm?URI=josaa-20-7-1391 Bayesian integration of visual and auditory signals for spatial localization]. Journal of the Optical Society of America, 20(7), 1391–7.</ref> Jordan, Knill,<ref>Knill DC (2005). [http://journalofvision.org//5/2/2/ Reaching for visual cues to depth: The brain combines depth cues differently for motor control and perception]. Journal of Vision, 5(2), 103:15.</ref><ref>Knill DC (2007). [http://journalofvision.org//7/8/13/ Learning Bayesian priors for depth perception] {{Webarchive\|url=https://web.archive.org/web/20081121060849/http://www.journalofvision.org/7/8/13/ \|date=2008-11-21 }}. Journal of Vision, 7(8), 1–20.</ref> Kording and Wolpert,<ref>{{cite journal \| url=http://www.nature.com/nature/journal/v427/n6971/full/nature02169.html \| doi=10.1038/nature02169 \| title=Bayesian integration in sensorimotor learning \| date=2004 \| last1=Körding \| first1=Konrad P. \| last2=Wolpert \| first2=Daniel M. \| journal=Nature \| volume=427 \| issue=6971 \| pages=244–247 \| pmid=14724638 \| bibcode=2004Natur.427..244K }}</ref><ref>Koerding KP, Ku S & Wolpert DM (2004). [http://jn.physiology.org/cgi/content/abstract/92/5/3161 Bayesian integration in force estimation]" ''Journal of Neurophysiology'' 92, 3161–5.</ref> and Goldreich.<ref>{{cite journal\|last=Goldreich\|first=D\|title=A Bayesian perceptual model replicates the cutaneous rabbit and other tactile spatiotemporal illusions.\|journal=PLOS ONE\|date=Mar 28, 2007\|volume=2\|issue=3\|pages=e333\|pmid=17389923\|doi=10.1371/journal.pone.0000333\|pmc=1828626\|bibcode=2007PLoSO...2..333G\|doi-access=free}}</ref><ref>{{cite journal\|last=Goldreich\|first=Daniel\|author2=Tong, Jonathan\|title=Prediction, Postdiction, and Perceptual Length Contraction: A Bayesian Low-Speed Prior Captures the Cutaneous Rabbit and Related Illusions\|journal=Frontiers in Psychology\|date=10 May 2013\|volume=4\|issue=221\|pages=221\|doi=10.3389/fpsyg.2013.00221\|pmid=23675360\|pmc=3650428\|doi-access=free}}</ref><ref>{{cite journal\|last=Goldreich\|first=D\|author2=Peterson, MA\|s2cid=4931501\|title=A Bayesian observer replicates convexity context effects in figure-ground perception.\|journal=Seeing and Perceiving\|year=2012\|volume=25\|issue=3–4\|pages=365–95\|pmid=22564398\|doi=10.1163/187847612X634445}}</ref>

	==Neural coding==		==Neural coding==

DreamRimmer Alt: per User talk:Doug Weller#Lamptonian

2024-12-27T10:37:55Z

per User talk:Doug Weller#Lamptonian

← Previous revision		Revision as of 10:37, 27 December 2024
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	[[Category:Bayesian statistics\|Brain]]		[[Category:Bayesian statistics\|Brain]]
	[[Category:Probabilistic models]]		[[Category:Probabilistic models]]
	[[Category:Theoretical accounts of general intelligence]]

Lamptonian at 13:26, 22 December 2024

2024-12-22T13:26:02Z

← Previous revision		Revision as of 13:26, 22 December 2024
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	[[Category:Bayesian statistics\|Brain]]		[[Category:Bayesian statistics\|Brain]]
	[[Category:Probabilistic models]]		[[Category:Probabilistic models]]
			[[Category:Theoretical accounts of general intelligence]]

OAbot: Open access bot: doi updated in citation with #oabot.

2024-09-16T03:06:23Z

Open access bot: doi updated in citation with #oabot.

← Previous revision		Revision as of 03:06, 16 September 2024
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	==Psychophysics==		==Psychophysics==
	{{See also\|Bayesian inference in motor learning}}		{{See also\|Bayesian inference in motor learning}}
	A wide range of studies interpret the results of psychophysical experiments in light of Bayesian perceptual models. Many aspects of human perceptual and motor behavior can be modeled with Bayesian statistics. This approach, with its emphasis on behavioral outcomes as the ultimate expressions of neural information processing, is also known for modeling sensory and motor decisions using Bayesian decision theory. Examples are the work of [[Michael S. Landy\|Landy]],<ref>{{Cite journal \|last1=Tassinari \|first1=Hadley \|last2=Hudson \|first2=Todd E. \|last3=Landy \|first3=Michael S. \|date=2006 \|title=Combining Priors and Noisy Visual Cues in a Rapid Pointing Task \|url=https://www.jneurosci.org/content/26/40/10154 \|journal=Journal of Neuroscience \|language=en \|volume=26 \|issue=40 \|pages=10154–10163 \|doi=10.1523/JNEUROSCI.2779-06.2006 \|issn=0270-6474 \|pmc=6674625 \|pmid=17021171}}</ref><ref>{{Cite journal \|last1=Hudson \|first1=Todd E. \|last2=Maloney \|first2=Laurence T. \|last3=Landy \|first3=Michael S. \|date=2008 \|title=Optimal Compensation for Temporal Uncertainty in Movement Planning \|journal=PLOS Computational Biology \|language=en \|volume=4 \|issue=7 \|pages=e1000130 \|doi=10.1371/journal.pcbi.1000130\|doi-access=free \|pmid=18654619 \|pmc=2442880 \|bibcode=2008PLSCB...4E0130H }}</ref> Jacobs,<ref>{{cite journal \| url=http://www.sciencedirect.com/science/article/pii/S0042698999000887 \| doi=10.1016/S0042-6989(99)00088-7 \| title=Optimal integration of texture and motion cues to depth \| journal=Vision Research \| date=October 1999 \| volume=39 \| issue=21 \| pages=3621–3629 \| last1=Jacobs \| first1=Robert A. \| pmid=10746132 }}</ref><ref>Battaglia PW, Jacobs RA & Aslin RN (2003). [http://www.opticsinfobase.org/abstract.cfm?URI=josaa-20-7-1391 Bayesian integration of visual and auditory signals for spatial localization]. Journal of the Optical Society of America, 20(7), 1391–7.</ref> Jordan, Knill,<ref>Knill DC (2005). [http://journalofvision.org//5/2/2/ Reaching for visual cues to depth: The brain combines depth cues differently for motor control and perception]. Journal of Vision, 5(2), 103:15.</ref><ref>Knill DC (2007). [http://journalofvision.org//7/8/13/ Learning Bayesian priors for depth perception] {{Webarchive\|url=https://web.archive.org/web/20081121060849/http://www.journalofvision.org/7/8/13/ \|date=2008-11-21 }}. Journal of Vision, 7(8), 1–20.</ref> Kording and Wolpert,<ref>{{cite journal \| url=http://www.nature.com/nature/journal/v427/n6971/full/nature02169.html \| doi=10.1038/nature02169 \| title=Bayesian integration in sensorimotor learning \| date=2004 \| last1=Körding \| first1=Konrad P. \| last2=Wolpert \| first2=Daniel M. \| journal=Nature \| volume=427 \| issue=6971 \| pages=244–247 \| pmid=14724638 \| bibcode=2004Natur.427..244K }}</ref><ref>Koerding KP, Ku S & Wolpert DM (2004). [http://jn.physiology.org/cgi/content/abstract/92/5/3161 Bayesian integration in force estimation]" ''Journal of Neurophysiology'' 92, 3161–5.</ref> and Goldreich.<ref>{{cite journal\|last=Goldreich\|first=D\|title=A Bayesian perceptual model replicates the cutaneous rabbit and other tactile spatiotemporal illusions.\|journal=PLOS ONE\|date=Mar 28, 2007\|volume=2\|issue=3\|pages=e333\|pmid=17389923\|doi=10.1371/journal.pone.0000333\|pmc=1828626\|bibcode=2007PLoSO...2..333G\|doi-access=free}}</ref><ref>{{cite journal\|last=Goldreich\|first=Daniel\|author2=Tong, Jonathan\|title=Prediction, Postdiction, and Perceptual Length Contraction: A Bayesian Low-Speed Prior Captures the Cutaneous Rabbit and Related Illusions\|journal=Frontiers in Psychology\|date=10 May 2013\|volume=4\|issue=221\|pages=221\|doi=10.3389/fpsyg.2013.00221\|pmid=23675360\|pmc=3650428\|doi-access=free}}</ref><ref>{{cite journal\|last=Goldreich\|first=D\|author2=Peterson, MA\|s2cid=4931501\|title=A Bayesian observer replicates convexity context effects in figure-ground perception.\|journal=Seeing and Perceiving\|year=2012\|volume=25\|issue=3–4\|pages=365–95\|pmid=22564398\|doi=10.1163/187847612X634445}}</ref>		A wide range of studies interpret the results of psychophysical experiments in light of Bayesian perceptual models. Many aspects of human perceptual and motor behavior can be modeled with Bayesian statistics. This approach, with its emphasis on behavioral outcomes as the ultimate expressions of neural information processing, is also known for modeling sensory and motor decisions using Bayesian decision theory. Examples are the work of [[Michael S. Landy\|Landy]],<ref>{{Cite journal \|last1=Tassinari \|first1=Hadley \|last2=Hudson \|first2=Todd E. \|last3=Landy \|first3=Michael S. \|date=2006 \|title=Combining Priors and Noisy Visual Cues in a Rapid Pointing Task \|url=https://www.jneurosci.org/content/26/40/10154 \|journal=Journal of Neuroscience \|language=en \|volume=26 \|issue=40 \|pages=10154–10163 \|doi=10.1523/JNEUROSCI.2779-06.2006 \|issn=0270-6474 \|pmc=6674625 \|pmid=17021171}}</ref><ref>{{Cite journal \|last1=Hudson \|first1=Todd E. \|last2=Maloney \|first2=Laurence T. \|last3=Landy \|first3=Michael S. \|date=2008 \|title=Optimal Compensation for Temporal Uncertainty in Movement Planning \|journal=PLOS Computational Biology \|language=en \|volume=4 \|issue=7 \|pages=e1000130 \|doi=10.1371/journal.pcbi.1000130\|doi-access=free \|pmid=18654619 \|pmc=2442880 \|bibcode=2008PLSCB...4E0130H }}</ref> Jacobs,<ref>{{cite journal \| url=http://www.sciencedirect.com/science/article/pii/S0042698999000887 \| doi=10.1016/S0042-6989(99)00088-7 \| title=Optimal integration of texture and motion cues to depth \| journal=Vision Research \| date=October 1999 \| volume=39 \| issue=21 \| pages=3621–3629 \| last1=Jacobs \| first1=Robert A. \| pmid=10746132 \| doi-access=free }}</ref><ref>Battaglia PW, Jacobs RA & Aslin RN (2003). [http://www.opticsinfobase.org/abstract.cfm?URI=josaa-20-7-1391 Bayesian integration of visual and auditory signals for spatial localization]. Journal of the Optical Society of America, 20(7), 1391–7.</ref> Jordan, Knill,<ref>Knill DC (2005). [http://journalofvision.org//5/2/2/ Reaching for visual cues to depth: The brain combines depth cues differently for motor control and perception]. Journal of Vision, 5(2), 103:15.</ref><ref>Knill DC (2007). [http://journalofvision.org//7/8/13/ Learning Bayesian priors for depth perception] {{Webarchive\|url=https://web.archive.org/web/20081121060849/http://www.journalofvision.org/7/8/13/ \|date=2008-11-21 }}. Journal of Vision, 7(8), 1–20.</ref> Kording and Wolpert,<ref>{{cite journal \| url=http://www.nature.com/nature/journal/v427/n6971/full/nature02169.html \| doi=10.1038/nature02169 \| title=Bayesian integration in sensorimotor learning \| date=2004 \| last1=Körding \| first1=Konrad P. \| last2=Wolpert \| first2=Daniel M. \| journal=Nature \| volume=427 \| issue=6971 \| pages=244–247 \| pmid=14724638 \| bibcode=2004Natur.427..244K }}</ref><ref>Koerding KP, Ku S & Wolpert DM (2004). [http://jn.physiology.org/cgi/content/abstract/92/5/3161 Bayesian integration in force estimation]" ''Journal of Neurophysiology'' 92, 3161–5.</ref> and Goldreich.<ref>{{cite journal\|last=Goldreich\|first=D\|title=A Bayesian perceptual model replicates the cutaneous rabbit and other tactile spatiotemporal illusions.\|journal=PLOS ONE\|date=Mar 28, 2007\|volume=2\|issue=3\|pages=e333\|pmid=17389923\|doi=10.1371/journal.pone.0000333\|pmc=1828626\|bibcode=2007PLoSO...2..333G\|doi-access=free}}</ref><ref>{{cite journal\|last=Goldreich\|first=Daniel\|author2=Tong, Jonathan\|title=Prediction, Postdiction, and Perceptual Length Contraction: A Bayesian Low-Speed Prior Captures the Cutaneous Rabbit and Related Illusions\|journal=Frontiers in Psychology\|date=10 May 2013\|volume=4\|issue=221\|pages=221\|doi=10.3389/fpsyg.2013.00221\|pmid=23675360\|pmc=3650428\|doi-access=free}}</ref><ref>{{cite journal\|last=Goldreich\|first=D\|author2=Peterson, MA\|s2cid=4931501\|title=A Bayesian observer replicates convexity context effects in figure-ground perception.\|journal=Seeing and Perceiving\|year=2012\|volume=25\|issue=3–4\|pages=365–95\|pmid=22564398\|doi=10.1163/187847612X634445}}</ref>

	==Neural coding==		==Neural coding==

Josve05a: /* Psychophysics */ | Add: bibcode, pmc, pmid, doi-access, authors 1-1. Removed URL that duplicated identifier. Removed parameters. Some additions/deletions were parameter name changes. | Use this tool. Report bugs. | #UCB_Gadget

2024-09-08T18:48:56Z

Psychophysics: | Add: bibcode, pmc, pmid, doi-access, authors 1-1. Removed URL that duplicated identifier. Removed parameters. Some additions/deletions were parameter name changes. | Use this tool. Report bugs. | #UCB_Gadget

← Previous revision		Revision as of 18:48, 8 September 2024
Line 12:		Line 12:
	==Psychophysics==		==Psychophysics==
	{{See also\|Bayesian inference in motor learning}}		{{See also\|Bayesian inference in motor learning}}
	A wide range of studies interpret the results of psychophysical experiments in light of Bayesian perceptual models. Many aspects of human perceptual and motor behavior can be modeled with Bayesian statistics. This approach, with its emphasis on behavioral outcomes as the ultimate expressions of neural information processing, is also known for modeling sensory and motor decisions using Bayesian decision theory. Examples are the work of [[Michael S. Landy\|Landy]],<ref>{{Cite journal \|~~last~~=Tassinari \|~~first~~=Hadley \|last2=Hudson \|first2=Todd E. \|last3=Landy \|first3=Michael S. \|date=2006 \|title=Combining Priors and Noisy Visual Cues in a Rapid Pointing Task \|url=https://www.jneurosci.org/content/26/40/10154 \|journal=Journal of Neuroscience \|language=en \|volume=26 \|issue=40 \|pages=10154–10163 \|doi=10.1523/JNEUROSCI.2779-06.2006 \|issn=0270-6474 \|pmc=6674625 \|pmid=17021171}}</ref><ref>Hudson ~~TE,~~ Maloney LT & Landy MS. (2008~~). [http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1000130~~ Optimal ~~compensation~~ for ~~temporal~~ ~~uncertainty~~ in ~~movement~~ ~~planning].~~ ~~PLoS~~ Computational Biology, 4(7).</ref> Jacobs,<ref>{{cite journal \| url=http://www.sciencedirect.com/science/article/pii/S0042698999000887 \| doi=10.1016/S0042-6989(99)00088-7 \| title=Optimal integration of texture and motion cues to depth \| journal=Vision Research \| date=October 1999 \| volume=39 \| issue=21 \| pages=3621–3629 \| last1=Jacobs \| first1=Robert A. \| pmid=10746132 }}</ref><ref>Battaglia PW, Jacobs RA & Aslin RN (2003). [http://www.opticsinfobase.org/abstract.cfm?URI=josaa-20-7-1391 Bayesian integration of visual and auditory signals for spatial localization]. Journal of the Optical Society of America, 20(7), 1391–7.</ref> Jordan, Knill,<ref>Knill DC (2005). [http://journalofvision.org//5/2/2/ Reaching for visual cues to depth: The brain combines depth cues differently for motor control and perception]. Journal of Vision, 5(2), 103:15.</ref><ref>Knill DC (2007). [http://journalofvision.org//7/8/13/ Learning Bayesian priors for depth perception] {{Webarchive\|url=https://web.archive.org/web/20081121060849/http://www.journalofvision.org/7/8/13/ \|date=2008-11-21 }}. Journal of Vision, 7(8), 1–20.</ref> Kording and Wolpert,<ref>{{cite journal \| url=http://www.nature.com/nature/journal/v427/n6971/full/nature02169.html \| doi=10.1038/nature02169 \| title=Bayesian integration in sensorimotor learning \| date=2004 \| last1=Körding \| first1=Konrad P. \| last2=Wolpert \| first2=Daniel M. \| journal=Nature \| volume=427 \| issue=6971 \| pages=244–247 \| pmid=14724638 \| bibcode=2004Natur.427..244K }}</ref><ref>Koerding KP, Ku S & Wolpert DM (2004). [http://jn.physiology.org/cgi/content/abstract/92/5/3161 Bayesian integration in force estimation]" ''Journal of Neurophysiology'' 92, 3161–5.</ref> and Goldreich.<ref>{{cite journal\|last=Goldreich\|first=D\|title=A Bayesian perceptual model replicates the cutaneous rabbit and other tactile spatiotemporal illusions.\|journal=PLOS ONE\|date=Mar 28, 2007\|volume=2\|issue=3\|pages=e333\|pmid=17389923\|doi=10.1371/journal.pone.0000333\|pmc=1828626\|bibcode=2007PLoSO...2..333G\|doi-access=free}}</ref><ref>{{cite journal\|last=Goldreich\|first=Daniel\|author2=Tong, Jonathan\|title=Prediction, Postdiction, and Perceptual Length Contraction: A Bayesian Low-Speed Prior Captures the Cutaneous Rabbit and Related Illusions\|journal=Frontiers in Psychology\|date=10 May 2013\|volume=4\|issue=221\|pages=221\|doi=10.3389/fpsyg.2013.00221\|pmid=23675360\|pmc=3650428\|doi-access=free}}</ref><ref>{{cite journal\|last=Goldreich\|first=D\|author2=Peterson, MA\|s2cid=4931501\|title=A Bayesian observer replicates convexity context effects in figure-ground perception.\|journal=Seeing and Perceiving\|year=2012\|volume=25\|issue=3–4\|pages=365–95\|pmid=22564398\|doi=10.1163/187847612X634445}}</ref>		A wide range of studies interpret the results of psychophysical experiments in light of Bayesian perceptual models. Many aspects of human perceptual and motor behavior can be modeled with Bayesian statistics. This approach, with its emphasis on behavioral outcomes as the ultimate expressions of neural information processing, is also known for modeling sensory and motor decisions using Bayesian decision theory. Examples are the work of [[Michael S. Landy\|Landy]],<ref>{{Cite journal \|last1=Tassinari \|first1=Hadley \|last2=Hudson \|first2=Todd E. \|last3=Landy \|first3=Michael S. \|date=2006 \|title=Combining Priors and Noisy Visual Cues in a Rapid Pointing Task \|url=https://www.jneurosci.org/content/26/40/10154 \|journal=Journal of Neuroscience \|language=en \|volume=26 \|issue=40 \|pages=10154–10163 \|doi=10.1523/JNEUROSCI.2779-06.2006 \|issn=0270-6474 \|pmc=6674625 \|pmid=17021171}}</ref><ref>{{Cite journal \|last1=Hudson \|first1=Todd E. \|last2=Maloney \|first2=Laurence T. \|last3=Landy \|first3=Michael S. \|date=2008 \|title=Optimal Compensation for Temporal Uncertainty in Movement Planning \|journal=PLOS Computational Biology \|language=en \|volume=4 \|issue=7 \|pages=e1000130 \|doi=10.1371/journal.pcbi.1000130\|doi-access=free \|pmid=18654619 \|pmc=2442880 \|bibcode=2008PLSCB...4E0130H }}</ref> Jacobs,<ref>{{cite journal \| url=http://www.sciencedirect.com/science/article/pii/S0042698999000887 \| doi=10.1016/S0042-6989(99)00088-7 \| title=Optimal integration of texture and motion cues to depth \| journal=Vision Research \| date=October 1999 \| volume=39 \| issue=21 \| pages=3621–3629 \| last1=Jacobs \| first1=Robert A. \| pmid=10746132 }}</ref><ref>Battaglia PW, Jacobs RA & Aslin RN (2003). [http://www.opticsinfobase.org/abstract.cfm?URI=josaa-20-7-1391 Bayesian integration of visual and auditory signals for spatial localization]. Journal of the Optical Society of America, 20(7), 1391–7.</ref> Jordan, Knill,<ref>Knill DC (2005). [http://journalofvision.org//5/2/2/ Reaching for visual cues to depth: The brain combines depth cues differently for motor control and perception]. Journal of Vision, 5(2), 103:15.</ref><ref>Knill DC (2007). [http://journalofvision.org//7/8/13/ Learning Bayesian priors for depth perception] {{Webarchive\|url=https://web.archive.org/web/20081121060849/http://www.journalofvision.org/7/8/13/ \|date=2008-11-21 }}. Journal of Vision, 7(8), 1–20.</ref> Kording and Wolpert,<ref>{{cite journal \| url=http://www.nature.com/nature/journal/v427/n6971/full/nature02169.html \| doi=10.1038/nature02169 \| title=Bayesian integration in sensorimotor learning \| date=2004 \| last1=Körding \| first1=Konrad P. \| last2=Wolpert \| first2=Daniel M. \| journal=Nature \| volume=427 \| issue=6971 \| pages=244–247 \| pmid=14724638 \| bibcode=2004Natur.427..244K }}</ref><ref>Koerding KP, Ku S & Wolpert DM (2004). [http://jn.physiology.org/cgi/content/abstract/92/5/3161 Bayesian integration in force estimation]" ''Journal of Neurophysiology'' 92, 3161–5.</ref> and Goldreich.<ref>{{cite journal\|last=Goldreich\|first=D\|title=A Bayesian perceptual model replicates the cutaneous rabbit and other tactile spatiotemporal illusions.\|journal=PLOS ONE\|date=Mar 28, 2007\|volume=2\|issue=3\|pages=e333\|pmid=17389923\|doi=10.1371/journal.pone.0000333\|pmc=1828626\|bibcode=2007PLoSO...2..333G\|doi-access=free}}</ref><ref>{{cite journal\|last=Goldreich\|first=Daniel\|author2=Tong, Jonathan\|title=Prediction, Postdiction, and Perceptual Length Contraction: A Bayesian Low-Speed Prior Captures the Cutaneous Rabbit and Related Illusions\|journal=Frontiers in Psychology\|date=10 May 2013\|volume=4\|issue=221\|pages=221\|doi=10.3389/fpsyg.2013.00221\|pmid=23675360\|pmc=3650428\|doi-access=free}}</ref><ref>{{cite journal\|last=Goldreich\|first=D\|author2=Peterson, MA\|s2cid=4931501\|title=A Bayesian observer replicates convexity context effects in figure-ground perception.\|journal=Seeing and Perceiving\|year=2012\|volume=25\|issue=3–4\|pages=365–95\|pmid=22564398\|doi=10.1163/187847612X634445}}</ref>

	==Neural coding==		==Neural coding==

Josve05a: /* Psychophysics */

2024-09-08T18:44:56Z

Psychophysics

← Previous revision		Revision as of 18:44, 8 September 2024
Line 12:		Line 12:
	==Psychophysics==		==Psychophysics==
	{{See also\|Bayesian inference in motor learning}}		{{See also\|Bayesian inference in motor learning}}
	A wide range of studies interpret the results of psychophysical experiments in light of Bayesian perceptual models. Many aspects of human perceptual and motor behavior can be modeled with Bayesian statistics. This approach, with its emphasis on behavioral outcomes as the ultimate expressions of neural information processing, is also known for modeling sensory and motor decisions using Bayesian decision theory. Examples are the work of [[Michael S. Landy\|Landy]],<ref>Tassinari H, Hudson TE & Landy MS. (2006). ~~[http~~://www.jneurosci.org~~/cgi~~/content~~/abstract~~/26/40/10154 ~~Combining priors and noisy visual cues in a rapid pointing task]" ''~~Journal of Neuroscience'' 26(40), 10154–10163.</ref><ref>Hudson TE, Maloney LT & Landy MS. (2008). [http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1000130 Optimal compensation for temporal uncertainty in movement planning]. PLoS Computational Biology, 4(7).</ref> Jacobs,<ref>{{cite journal \| url=http://www.sciencedirect.com/science/article/pii/S0042698999000887 \| doi=10.1016/S0042-6989(99)00088-7 \| title=Optimal integration of texture and motion cues to depth \| journal=Vision Research \| date=October 1999 \| volume=39 \| issue=21 \| pages=3621–3629 \| last1=Jacobs \| first1=Robert A. \| pmid=10746132 }}</ref><ref>Battaglia PW, Jacobs RA & Aslin RN (2003). [http://www.opticsinfobase.org/abstract.cfm?URI=josaa-20-7-1391 Bayesian integration of visual and auditory signals for spatial localization]. Journal of the Optical Society of America, 20(7), 1391–7.</ref> Jordan, Knill,<ref>Knill DC (2005). [http://journalofvision.org//5/2/2/ Reaching for visual cues to depth: The brain combines depth cues differently for motor control and perception]. Journal of Vision, 5(2), 103:15.</ref><ref>Knill DC (2007). [http://journalofvision.org//7/8/13/ Learning Bayesian priors for depth perception] {{Webarchive\|url=https://web.archive.org/web/20081121060849/http://www.journalofvision.org/7/8/13/ \|date=2008-11-21 }}. Journal of Vision, 7(8), 1–20.</ref> Kording and Wolpert,<ref>{{cite journal \| url=http://www.nature.com/nature/journal/v427/n6971/full/nature02169.html \| doi=10.1038/nature02169 \| title=Bayesian integration in sensorimotor learning \| date=2004 \| last1=Körding \| first1=Konrad P. \| last2=Wolpert \| first2=Daniel M. \| journal=Nature \| volume=427 \| issue=6971 \| pages=244–247 \| pmid=14724638 \| bibcode=2004Natur.427..244K }}</ref><ref>Koerding KP, Ku S & Wolpert DM (2004). [http://jn.physiology.org/cgi/content/abstract/92/5/3161 Bayesian integration in force estimation]" ''Journal of Neurophysiology'' 92, 3161–5.</ref> and Goldreich.<ref>{{cite journal\|last=Goldreich\|first=D\|title=A Bayesian perceptual model replicates the cutaneous rabbit and other tactile spatiotemporal illusions.\|journal=PLOS ONE\|date=Mar 28, 2007\|volume=2\|issue=3\|pages=e333\|pmid=17389923\|doi=10.1371/journal.pone.0000333\|pmc=1828626\|bibcode=2007PLoSO...2..333G\|doi-access=free}}</ref><ref>{{cite journal\|last=Goldreich\|first=Daniel\|author2=Tong, Jonathan\|title=Prediction, Postdiction, and Perceptual Length Contraction: A Bayesian Low-Speed Prior Captures the Cutaneous Rabbit and Related Illusions\|journal=Frontiers in Psychology\|date=10 May 2013\|volume=4\|issue=221\|pages=221\|doi=10.3389/fpsyg.2013.00221\|pmid=23675360\|pmc=3650428\|doi-access=free}}</ref><ref>{{cite journal\|last=Goldreich\|first=D\|author2=Peterson, MA\|s2cid=4931501\|title=A Bayesian observer replicates convexity context effects in figure-ground perception.\|journal=Seeing and Perceiving\|year=2012\|volume=25\|issue=3–4\|pages=365–95\|pmid=22564398\|doi=10.1163/187847612X634445}}</ref>		A wide range of studies interpret the results of psychophysical experiments in light of Bayesian perceptual models. Many aspects of human perceptual and motor behavior can be modeled with Bayesian statistics. This approach, with its emphasis on behavioral outcomes as the ultimate expressions of neural information processing, is also known for modeling sensory and motor decisions using Bayesian decision theory. Examples are the work of [[Michael S. Landy\|Landy]],<ref>{{Cite journal \|last=Tassinari \|first=Hadley \|last2=Hudson \|first2=Todd E. \|last3=Landy \|first3=Michael S. \|date=2006 \|title=Combining Priors and Noisy Visual Cues in a Rapid Pointing Task \|url=https://www.jneurosci.org/content/26/40/10154 \|journal=Journal of Neuroscience \|language=en \|volume=26 \|issue=40 \|pages=10154–10163 \|doi=10.1523/JNEUROSCI.2779-06.2006 \|issn=0270-6474 \|pmc=6674625 \|pmid=17021171}}</ref><ref>Hudson TE, Maloney LT & Landy MS. (2008). [http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1000130 Optimal compensation for temporal uncertainty in movement planning]. PLoS Computational Biology, 4(7).</ref> Jacobs,<ref>{{cite journal \| url=http://www.sciencedirect.com/science/article/pii/S0042698999000887 \| doi=10.1016/S0042-6989(99)00088-7 \| title=Optimal integration of texture and motion cues to depth \| journal=Vision Research \| date=October 1999 \| volume=39 \| issue=21 \| pages=3621–3629 \| last1=Jacobs \| first1=Robert A. \| pmid=10746132 }}</ref><ref>Battaglia PW, Jacobs RA & Aslin RN (2003). [http://www.opticsinfobase.org/abstract.cfm?URI=josaa-20-7-1391 Bayesian integration of visual and auditory signals for spatial localization]. Journal of the Optical Society of America, 20(7), 1391–7.</ref> Jordan, Knill,<ref>Knill DC (2005). [http://journalofvision.org//5/2/2/ Reaching for visual cues to depth: The brain combines depth cues differently for motor control and perception]. Journal of Vision, 5(2), 103:15.</ref><ref>Knill DC (2007). [http://journalofvision.org//7/8/13/ Learning Bayesian priors for depth perception] {{Webarchive\|url=https://web.archive.org/web/20081121060849/http://www.journalofvision.org/7/8/13/ \|date=2008-11-21 }}. Journal of Vision, 7(8), 1–20.</ref> Kording and Wolpert,<ref>{{cite journal \| url=http://www.nature.com/nature/journal/v427/n6971/full/nature02169.html \| doi=10.1038/nature02169 \| title=Bayesian integration in sensorimotor learning \| date=2004 \| last1=Körding \| first1=Konrad P. \| last2=Wolpert \| first2=Daniel M. \| journal=Nature \| volume=427 \| issue=6971 \| pages=244–247 \| pmid=14724638 \| bibcode=2004Natur.427..244K }}</ref><ref>Koerding KP, Ku S & Wolpert DM (2004). [http://jn.physiology.org/cgi/content/abstract/92/5/3161 Bayesian integration in force estimation]" ''Journal of Neurophysiology'' 92, 3161–5.</ref> and Goldreich.<ref>{{cite journal\|last=Goldreich\|first=D\|title=A Bayesian perceptual model replicates the cutaneous rabbit and other tactile spatiotemporal illusions.\|journal=PLOS ONE\|date=Mar 28, 2007\|volume=2\|issue=3\|pages=e333\|pmid=17389923\|doi=10.1371/journal.pone.0000333\|pmc=1828626\|bibcode=2007PLoSO...2..333G\|doi-access=free}}</ref><ref>{{cite journal\|last=Goldreich\|first=Daniel\|author2=Tong, Jonathan\|title=Prediction, Postdiction, and Perceptual Length Contraction: A Bayesian Low-Speed Prior Captures the Cutaneous Rabbit and Related Illusions\|journal=Frontiers in Psychology\|date=10 May 2013\|volume=4\|issue=221\|pages=221\|doi=10.3389/fpsyg.2013.00221\|pmid=23675360\|pmc=3650428\|doi-access=free}}</ref><ref>{{cite journal\|last=Goldreich\|first=D\|author2=Peterson, MA\|s2cid=4931501\|title=A Bayesian observer replicates convexity context effects in figure-ground perception.\|journal=Seeing and Perceiving\|year=2012\|volume=25\|issue=3–4\|pages=365–95\|pmid=22564398\|doi=10.1163/187847612X634445}}</ref>

	==Neural coding==		==Neural coding==

Josve05a: /* Psychophysics */ | Altered template type. Add: bibcode, pmid, pages, issue, volume, journal, date, title, doi, authors 1-2. Changed bare reference to CS1/2. | Use this tool. Report bugs. | #UCB_Gadget

2024-09-08T18:42:36Z

Psychophysics: | Altered template type. Add: bibcode, pmid, pages, issue, volume, journal, date, title, doi, authors 1-2. Changed bare reference to CS1/2. | Use this tool. Report bugs. | #UCB_Gadget

← Previous revision		Revision as of 18:42, 8 September 2024
Line 12:		Line 12:
	==Psychophysics==		==Psychophysics==
	{{See also\|Bayesian inference in motor learning}}		{{See also\|Bayesian inference in motor learning}}
	A wide range of studies interpret the results of psychophysical experiments in light of Bayesian perceptual models. Many aspects of human perceptual and motor behavior can be modeled with Bayesian statistics. This approach, with its emphasis on behavioral outcomes as the ultimate expressions of neural information processing, is also known for modeling sensory and motor decisions using Bayesian decision theory. Examples are the work of [[Michael S. Landy\|Landy]],<ref>Tassinari H, Hudson TE & Landy MS. (2006). [http://www.jneurosci.org/cgi/content/abstract/26/40/10154 Combining priors and noisy visual cues in a rapid pointing task]" ''Journal of Neuroscience'' 26(40), 10154–10163.</ref><ref>Hudson TE, Maloney LT & Landy MS. (2008). [http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1000130 Optimal compensation for temporal uncertainty in movement planning]. PLoS Computational Biology, 4(7).</ref> Jacobs,<ref>{{cite journal \| url=http://www.sciencedirect.com/science/article/pii/S0042698999000887 \| doi=10.1016/S0042-6989(99)00088-7 \| title=Optimal integration of texture and motion cues to depth \| journal=Vision Research \| date=October 1999 \| volume=39 \| issue=21 \| pages=3621–3629 \| last1=Jacobs \| first1=Robert A. \| pmid=10746132 }}</ref><ref>Battaglia PW, Jacobs RA & Aslin RN (2003). [http://www.opticsinfobase.org/abstract.cfm?URI=josaa-20-7-1391 Bayesian integration of visual and auditory signals for spatial localization]. Journal of the Optical Society of America, 20(7), 1391–7.</ref> Jordan, Knill,<ref>Knill DC (2005). [http://journalofvision.org//5/2/2/ Reaching for visual cues to depth: The brain combines depth cues differently for motor control and perception]. Journal of Vision, 5(2), 103:15.</ref><ref>Knill DC (2007). [http://journalofvision.org//7/8/13/ Learning Bayesian priors for depth perception] {{Webarchive\|url=https://web.archive.org/web/20081121060849/http://www.journalofvision.org/7/8/13/ \|date=2008-11-21 }}. Journal of Vision, 7(8), 1–20.</ref> Kording and Wolpert,<ref>~~Koerding~~ KP & ~~Wolpert DM (2004). [~~http://www.nature.com/nature/journal/v427/n6971/full/nature02169.html Bayesian integration in sensorimotor learning]. ''Nature~~'',~~ 427, ~~244–7~~.</ref><ref>Koerding KP, Ku S & Wolpert DM (2004). [http://jn.physiology.org/cgi/content/abstract/92/5/3161 Bayesian integration in force estimation]" ''Journal of Neurophysiology'' 92, 3161–5.</ref> and Goldreich.<ref>{{cite journal\|last=Goldreich\|first=D\|title=A Bayesian perceptual model replicates the cutaneous rabbit and other tactile spatiotemporal illusions.\|journal=PLOS ONE\|date=Mar 28, 2007\|volume=2\|issue=3\|pages=e333\|pmid=17389923\|doi=10.1371/journal.pone.0000333\|pmc=1828626\|bibcode=2007PLoSO...2..333G\|doi-access=free}}</ref><ref>{{cite journal\|last=Goldreich\|first=Daniel\|author2=Tong, Jonathan\|title=Prediction, Postdiction, and Perceptual Length Contraction: A Bayesian Low-Speed Prior Captures the Cutaneous Rabbit and Related Illusions\|journal=Frontiers in Psychology\|date=10 May 2013\|volume=4\|issue=221\|pages=221\|doi=10.3389/fpsyg.2013.00221\|pmid=23675360\|pmc=3650428\|doi-access=free}}</ref><ref>{{cite journal\|last=Goldreich\|first=D\|author2=Peterson, MA\|s2cid=4931501\|title=A Bayesian observer replicates convexity context effects in figure-ground perception.\|journal=Seeing and Perceiving\|year=2012\|volume=25\|issue=3–4\|pages=365–95\|pmid=22564398\|doi=10.1163/187847612X634445}}</ref>		A wide range of studies interpret the results of psychophysical experiments in light of Bayesian perceptual models. Many aspects of human perceptual and motor behavior can be modeled with Bayesian statistics. This approach, with its emphasis on behavioral outcomes as the ultimate expressions of neural information processing, is also known for modeling sensory and motor decisions using Bayesian decision theory. Examples are the work of [[Michael S. Landy\|Landy]],<ref>Tassinari H, Hudson TE & Landy MS. (2006). [http://www.jneurosci.org/cgi/content/abstract/26/40/10154 Combining priors and noisy visual cues in a rapid pointing task]" ''Journal of Neuroscience'' 26(40), 10154–10163.</ref><ref>Hudson TE, Maloney LT & Landy MS. (2008). [http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1000130 Optimal compensation for temporal uncertainty in movement planning]. PLoS Computational Biology, 4(7).</ref> Jacobs,<ref>{{cite journal \| url=http://www.sciencedirect.com/science/article/pii/S0042698999000887 \| doi=10.1016/S0042-6989(99)00088-7 \| title=Optimal integration of texture and motion cues to depth \| journal=Vision Research \| date=October 1999 \| volume=39 \| issue=21 \| pages=3621–3629 \| last1=Jacobs \| first1=Robert A. \| pmid=10746132 }}</ref><ref>Battaglia PW, Jacobs RA & Aslin RN (2003). [http://www.opticsinfobase.org/abstract.cfm?URI=josaa-20-7-1391 Bayesian integration of visual and auditory signals for spatial localization]. Journal of the Optical Society of America, 20(7), 1391–7.</ref> Jordan, Knill,<ref>Knill DC (2005). [http://journalofvision.org//5/2/2/ Reaching for visual cues to depth: The brain combines depth cues differently for motor control and perception]. Journal of Vision, 5(2), 103:15.</ref><ref>Knill DC (2007). [http://journalofvision.org//7/8/13/ Learning Bayesian priors for depth perception] {{Webarchive\|url=https://web.archive.org/web/20081121060849/http://www.journalofvision.org/7/8/13/ \|date=2008-11-21 }}. Journal of Vision, 7(8), 1–20.</ref> Kording and Wolpert,<ref>{{cite journal \| url=http://www.nature.com/nature/journal/v427/n6971/full/nature02169.html \| doi=10.1038/nature02169 \| title=Bayesian integration in sensorimotor learning \| date=2004 \| last1=Körding \| first1=Konrad P. \| last2=Wolpert \| first2=Daniel M. \| journal=Nature \| volume=427 \| issue=6971 \| pages=244–247 \| pmid=14724638 \| bibcode=2004Natur.427..244K }}</ref><ref>Koerding KP, Ku S & Wolpert DM (2004). [http://jn.physiology.org/cgi/content/abstract/92/5/3161 Bayesian integration in force estimation]" ''Journal of Neurophysiology'' 92, 3161–5.</ref> and Goldreich.<ref>{{cite journal\|last=Goldreich\|first=D\|title=A Bayesian perceptual model replicates the cutaneous rabbit and other tactile spatiotemporal illusions.\|journal=PLOS ONE\|date=Mar 28, 2007\|volume=2\|issue=3\|pages=e333\|pmid=17389923\|doi=10.1371/journal.pone.0000333\|pmc=1828626\|bibcode=2007PLoSO...2..333G\|doi-access=free}}</ref><ref>{{cite journal\|last=Goldreich\|first=Daniel\|author2=Tong, Jonathan\|title=Prediction, Postdiction, and Perceptual Length Contraction: A Bayesian Low-Speed Prior Captures the Cutaneous Rabbit and Related Illusions\|journal=Frontiers in Psychology\|date=10 May 2013\|volume=4\|issue=221\|pages=221\|doi=10.3389/fpsyg.2013.00221\|pmid=23675360\|pmc=3650428\|doi-access=free}}</ref><ref>{{cite journal\|last=Goldreich\|first=D\|author2=Peterson, MA\|s2cid=4931501\|title=A Bayesian observer replicates convexity context effects in figure-ground perception.\|journal=Seeing and Perceiving\|year=2012\|volume=25\|issue=3–4\|pages=365–95\|pmid=22564398\|doi=10.1163/187847612X634445}}</ref>

	==Neural coding==		==Neural coding==

Josve05a: /* Psychophysics */ | Altered template type. Add: pmid, pages, issue, volume, date, journal, title, doi, authors 1-1. Changed bare reference to CS1/2. | Use this tool. Report bugs. | #UCB_Gadget

2024-09-08T18:41:44Z

Psychophysics: | Altered template type. Add: pmid, pages, issue, volume, date, journal, title, doi, authors 1-1. Changed bare reference to CS1/2. | Use this tool. Report bugs. | #UCB_Gadget

← Previous revision		Revision as of 18:41, 8 September 2024
Line 12:		Line 12:
	==Psychophysics==		==Psychophysics==
	{{See also\|Bayesian inference in motor learning}}		{{See also\|Bayesian inference in motor learning}}
	A wide range of studies interpret the results of psychophysical experiments in light of Bayesian perceptual models. Many aspects of human perceptual and motor behavior can be modeled with Bayesian statistics. This approach, with its emphasis on behavioral outcomes as the ultimate expressions of neural information processing, is also known for modeling sensory and motor decisions using Bayesian decision theory. Examples are the work of [[Michael S. Landy\|Landy]],<ref>Tassinari H, Hudson TE & Landy MS. (2006). [http://www.jneurosci.org/cgi/content/abstract/26/40/10154 Combining priors and noisy visual cues in a rapid pointing task]" ''Journal of Neuroscience'' 26(40), 10154–10163.</ref><ref>Hudson TE, Maloney LT & Landy MS. (2008). [http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1000130 Optimal compensation for temporal uncertainty in movement planning]. PLoS Computational Biology, 4(7).</ref> Jacobs,<ref>~~Jacobs~~ RA ~~(1999).~~ ~~[https://archive.today/20130201225038/~~http://www.sciencedirect.com/science~~?_ob=ArticleURL&_udi~~=~~B6T0W~~-~~3X3BTP4~~-~~D&_user=10&_rdoc=1&_fmt=&_orig=search&_sort=d&_docanchor=&view~~=~~c&_acct=C000050221&_version=1&_urlVersion=0&_userid=10&md5=839657cf68a414de392abcfaca2e198f~~ Optimal integration of texture and motion cues to depth]" ''Vision Research'' 39(21), ~~3621–9~~.</ref><ref>Battaglia PW, Jacobs RA & Aslin RN (2003). [http://www.opticsinfobase.org/abstract.cfm?URI=josaa-20-7-1391 Bayesian integration of visual and auditory signals for spatial localization]. Journal of the Optical Society of America, 20(7), 1391–7.</ref> Jordan, Knill,<ref>Knill DC (2005). [http://journalofvision.org//5/2/2/ Reaching for visual cues to depth: The brain combines depth cues differently for motor control and perception]. Journal of Vision, 5(2), 103:15.</ref><ref>Knill DC (2007). [http://journalofvision.org//7/8/13/ Learning Bayesian priors for depth perception] {{Webarchive\|url=https://web.archive.org/web/20081121060849/http://www.journalofvision.org/7/8/13/ \|date=2008-11-21 }}. Journal of Vision, 7(8), 1–20.</ref> Kording and Wolpert,<ref>Koerding KP & Wolpert DM (2004). [http://www.nature.com/nature/journal/v427/n6971/full/nature02169.html Bayesian integration in sensorimotor learning]. ''Nature'', 427, 244–7.</ref><ref>Koerding KP, Ku S & Wolpert DM (2004). [http://jn.physiology.org/cgi/content/abstract/92/5/3161 Bayesian integration in force estimation]" ''Journal of Neurophysiology'' 92, 3161–5.</ref> and Goldreich.<ref>{{cite journal\|last=Goldreich\|first=D\|title=A Bayesian perceptual model replicates the cutaneous rabbit and other tactile spatiotemporal illusions.\|journal=PLOS ONE\|date=Mar 28, 2007\|volume=2\|issue=3\|pages=e333\|pmid=17389923\|doi=10.1371/journal.pone.0000333\|pmc=1828626\|bibcode=2007PLoSO...2..333G\|doi-access=free}}</ref><ref>{{cite journal\|last=Goldreich\|first=Daniel\|author2=Tong, Jonathan\|title=Prediction, Postdiction, and Perceptual Length Contraction: A Bayesian Low-Speed Prior Captures the Cutaneous Rabbit and Related Illusions\|journal=Frontiers in Psychology\|date=10 May 2013\|volume=4\|issue=221\|pages=221\|doi=10.3389/fpsyg.2013.00221\|pmid=23675360\|pmc=3650428\|doi-access=free}}</ref><ref>{{cite journal\|last=Goldreich\|first=D\|author2=Peterson, MA\|s2cid=4931501\|title=A Bayesian observer replicates convexity context effects in figure-ground perception.\|journal=Seeing and Perceiving\|year=2012\|volume=25\|issue=3–4\|pages=365–95\|pmid=22564398\|doi=10.1163/187847612X634445}}</ref>		A wide range of studies interpret the results of psychophysical experiments in light of Bayesian perceptual models. Many aspects of human perceptual and motor behavior can be modeled with Bayesian statistics. This approach, with its emphasis on behavioral outcomes as the ultimate expressions of neural information processing, is also known for modeling sensory and motor decisions using Bayesian decision theory. Examples are the work of [[Michael S. Landy\|Landy]],<ref>Tassinari H, Hudson TE & Landy MS. (2006). [http://www.jneurosci.org/cgi/content/abstract/26/40/10154 Combining priors and noisy visual cues in a rapid pointing task]" ''Journal of Neuroscience'' 26(40), 10154–10163.</ref><ref>Hudson TE, Maloney LT & Landy MS. (2008). [http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1000130 Optimal compensation for temporal uncertainty in movement planning]. PLoS Computational Biology, 4(7).</ref> Jacobs,<ref>{{cite journal \| url=http://www.sciencedirect.com/science/article/pii/S0042698999000887 \| doi=10.1016/S0042-6989(99)00088-7 \| title=Optimal integration of texture and motion cues to depth \| journal=Vision Research \| date=October 1999 \| volume=39 \| issue=21 \| pages=3621–3629 \| last1=Jacobs \| first1=Robert A. \| pmid=10746132 }}</ref><ref>Battaglia PW, Jacobs RA & Aslin RN (2003). [http://www.opticsinfobase.org/abstract.cfm?URI=josaa-20-7-1391 Bayesian integration of visual and auditory signals for spatial localization]. Journal of the Optical Society of America, 20(7), 1391–7.</ref> Jordan, Knill,<ref>Knill DC (2005). [http://journalofvision.org//5/2/2/ Reaching for visual cues to depth: The brain combines depth cues differently for motor control and perception]. Journal of Vision, 5(2), 103:15.</ref><ref>Knill DC (2007). [http://journalofvision.org//7/8/13/ Learning Bayesian priors for depth perception] {{Webarchive\|url=https://web.archive.org/web/20081121060849/http://www.journalofvision.org/7/8/13/ \|date=2008-11-21 }}. Journal of Vision, 7(8), 1–20.</ref> Kording and Wolpert,<ref>Koerding KP & Wolpert DM (2004). [http://www.nature.com/nature/journal/v427/n6971/full/nature02169.html Bayesian integration in sensorimotor learning]. ''Nature'', 427, 244–7.</ref><ref>Koerding KP, Ku S & Wolpert DM (2004). 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	==Neural coding==		==Neural coding==

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	{{DEFAULTSORT:Bayesian approaches to brain function}}		{{DEFAULTSORT:Bayesian approaches to brain function}}
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	[[Category:Cognitive neuroscience]]		[[Category:Cognitive neuroscience]]
	[[Category:Bayesian statistics\|Brain]]		[[Category:Bayesian statistics\|Brain]]