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pro vyhledávání: '"William D. Penny"'
In an age where the amount of data collected from brain imaging is increasing constantly, it is of critical importance to analyse those data within an accepted framework to ensure proper integration and comparison of the information collected. This b
Autor:
Karl J. Friston, Christopher D. Frith, Raymond J. Dolan, Cathy J. Price, Semir Zeki, John T. Ashburner, William D. Penny
This updated second edition provides the state of the art perspective of the theory, practice and application of modern non-invasive imaging methods employed in exploring the structural and functional architecture of the normal and diseased human bra
Publikováno v:
PLoS Biology, Vol 15, Iss 1, p e1002588 (2017)
We are remarkably adept at inferring the consequences of our actions, yet the neuronal mechanisms that allow us to plan a sequence of novel choices remain unclear. We used functional magnetic resonance imaging (fMRI) to investigate how the human brai
Externí odkaz:
https://doaj.org/article/ed35c57e1a1e479db23cefcd419bb7be
Autor:
Aaron T. Buss, John P. Spencer, Vincent A. Magnotta, William D. Penny, Theodore J. Huppert, Gregor Schöner
Publikováno v:
Psychological Review. 128:362-395
There is consensus that activation within distributed functional brain networks underlies human thought. The impact of this consensus is limited, however, by a gap that exists between data-driven correlational analyses that specify where functional b
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5bfad2c64fd43ef673346897314544d3
https://doi.org/10.1037/rev0000264
https://doi.org/10.1037/rev0000264
Autor:
Eileanoir B. Johnson, Gabriel Ziegler, William D. Penny, Rachael I. Scahill, Sarah Gregory, Geraint Rees, Sarah J. Tabrizi
Publikováno v:
Biological psychiatry 89(8), 807-816 (2021). doi:10.1016/j.biopsych.2020.11.009
Biological Psychiatry
Biological Psychiatry
Background Characterizing changing brain structure in neurodegeneration is fundamental to understanding long-term effects of pathology and ultimately providing therapeutic targets. It is well established that Huntington’s disease (HD) gene carriers
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::554e778614274ca4601e0c0ca33c1055
Pupil dilation indexes automatic and dynamic inference about the precision of stimulus distributions
Publikováno v:
Journal of Mathematical Psychology
Learning about the statistics of one’s environment is a fundamental requirement of adaptive behaviour. In this experiment we probe whether pupil dilation in response to brief auditory stimuli reflects automatic statistical learning about the underl
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9f064900e2fba7d450e21bff6442062b
https://hdl.handle.net/21.11116/0000-0008-0CA1-5
https://hdl.handle.net/21.11116/0000-0008-0CA1-5
Autor:
Klaas E. Stephan, Stefan Frässle, William D. Penny, Yu Yao, Jakob Heinzle, Sudhir Raman, Eduardo A. Aponte
Publikováno v:
Cognitive Neurodynamics, 16 (1)
Cognitive Neurodynamics
Cognitive Neurodynamics
In generative modeling of neuroimaging data, such as dynamic causal modeling (DCM), one typically considers several alternative models, either to determine the most plausible explanation for observed data (Bayesian model selection) or to account for
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::42da5618471042b0a40b442b55785e4e
https://doi.org/10.1101/2020.12.21.423807
https://doi.org/10.1101/2020.12.21.423807
Autor:
William D Penny, Gerard R Ridgway
Publikováno v:
PLoS ONE, Vol 8, Iss 3, p e59655 (2013)
Statistical Parametric Mapping (SPM) is the dominant paradigm for mass-univariate analysis of neuroimaging data. More recently, a bayesian approach termed Posterior Probability Mapping (PPM) has been proposed as an alternative. PPM offers two advanta
Externí odkaz:
https://doaj.org/article/5a18ce1ad3aa44a098fee9f39130a884
Publikováno v:
PLoS Computational Biology, Vol 17, Iss 7, p e1009092 (2021)
PLoS Computational Biology
PLoS Computational Biology
This paper uses constructs from machine learning to define pairs of learning tasks that either shared or did not share a common subspace. Human subjects then learnt these tasks using a feedback-based approach and we hypothesised that learning would b
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cd4cd465d3bd4233dccf450207a5d122
https://doi.org/10.1101/2020.07.12.199265
https://doi.org/10.1101/2020.07.12.199265
Publikováno v:
Frontiers in Artificial Intelligence, Vol 3 (2020)
Frontiers in Artificial Intelligence
Frontiers in Artificial Intelligence
Probabilistic models of cognition typically assume that agents make inferences about current states by combining new sensory information with fixed beliefs about the past, an approach known as Bayesian filtering. This is computationally parsimonious,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c18a3993e70e732bb870b2dbf4160250
https://ueaeprints.uea.ac.uk/id/eprint/74305/
https://ueaeprints.uea.ac.uk/id/eprint/74305/