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Simultaneous learning and filtering without delusions: a Bayes-optimal combination of Predictive Inference and Adaptive Filtering.

Frontiers in computational neuroscience

Kneissler J, Drugowitsch J, Friston K, Butz MV.
PMID: 25983690
Front Comput Neurosci. 2015 Apr 30;9:47. doi: 10.3389/fncom.2015.00047. eCollection 2015.

Predictive coding appears to be one of the fundamental working principles of brain processing. Amongst other aspects, brains often predict the sensory consequences of their own actions. Predictive coding resembles Kalman filtering, where incoming sensory information is filtered to...

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Kneissler J, Drugowitsch J, Friston K, et al. Simultaneous learning and filtering without delusions: a Bayes-optimal combination of Predictive Inference and Adaptive Filtering. Front Comput Neurosci. 2015;9:47doi: 10.3389/fncom.2015.00047.
Kneissler, J., Drugowitsch, J., Friston, K., & Butz, M. V. (2015). Simultaneous learning and filtering without delusions: a Bayes-optimal combination of Predictive Inference and Adaptive Filtering. Frontiers in computational neuroscience, 947. https://doi.org/10.3389/fncom.2015.00047
Kneissler, Jan, et al. "Simultaneous learning and filtering without delusions: a Bayes-optimal combination of Predictive Inference and Adaptive Filtering." Frontiers in computational neuroscience vol. 9 (2015): 47. doi: https://doi.org/10.3389/fncom.2015.00047
Kneissler J, Drugowitsch J, Friston K, Butz MV. Simultaneous learning and filtering without delusions: a Bayes-optimal combination of Predictive Inference and Adaptive Filtering. Front Comput Neurosci. 2015 Apr 30;9:47. doi: 10.3389/fncom.2015.00047. eCollection 2015. PMID: 25983690; PMCID: PMC4415408.
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Minimal self-models and the free energy principle.

Frontiers in human neuroscience

Limanowski J, Blankenburg F.
PMID: 24062658
Front Hum Neurosci. 2013 Sep 12;7:547. doi: 10.3389/fnhum.2013.00547.

The term "minimal phenomenal selfhood" (MPS) describes the basic, pre-reflective experience of being a self (Blanke and Metzinger, 2009). Theoretical accounts of the minimal self have long recognized the importance and the ambivalence of the body as both part...

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Limanowski J, Blankenburg F. Minimal self-models and the free energy principle. Front Hum Neurosci. 2013;7:547doi: 10.3389/fnhum.2013.00547.
Limanowski, J., & Blankenburg, F. (2013). Minimal self-models and the free energy principle. Frontiers in human neuroscience, 7547. https://doi.org/10.3389/fnhum.2013.00547
Limanowski, Jakub, and Blankenburg, Felix. "Minimal self-models and the free energy principle." Frontiers in human neuroscience vol. 7 (2013): 547. doi: https://doi.org/10.3389/fnhum.2013.00547
Limanowski J, Blankenburg F. Minimal self-models and the free energy principle. Front Hum Neurosci. 2013 Sep 12;7:547. doi: 10.3389/fnhum.2013.00547. PMID: 24062658; PMCID: PMC3770917.
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A formal model of interpersonal inference.

Frontiers in human neuroscience

Moutoussis M, Trujillo-Barreto NJ, El-Deredy W, Dolan RJ, Friston KJ.
PMID: 24723872
Front Hum Neurosci. 2014 Mar 25;8:160. doi: 10.3389/fnhum.2014.00160. eCollection 2014.

INTRODUCTION: We propose that active Bayesian inference-a general framework for decision-making-can equally be applied to interpersonal exchanges. Social cognition, however, entails special challenges. We address these challenges through a novel formulation of a formal model and demonstrate its psychological...

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Moutoussis M, Trujillo-Barreto NJ, El-Deredy W, et al. A formal model of interpersonal inference. Front Hum Neurosci. 2014;8:160doi: 10.3389/fnhum.2014.00160.
Moutoussis, M., Trujillo-Barreto, N. J., El-Deredy, W., Dolan, R. J., & Friston, K. J. (2014). A formal model of interpersonal inference. Frontiers in human neuroscience, 8160. https://doi.org/10.3389/fnhum.2014.00160
Moutoussis, Michael, et al. "A formal model of interpersonal inference." Frontiers in human neuroscience vol. 8 (2014): 160. doi: https://doi.org/10.3389/fnhum.2014.00160
Moutoussis M, Trujillo-Barreto NJ, El-Deredy W, Dolan RJ, Friston KJ. A formal model of interpersonal inference. Front Hum Neurosci. 2014 Mar 25;8:160. doi: 10.3389/fnhum.2014.00160. eCollection 2014. PMID: 24723872; PMCID: PMC3971175.
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The anatomy of choice: active inference and agency.

Frontiers in human neuroscience

Friston K, Schwartenbeck P, Fitzgerald T, Moutoussis M, Behrens T, Dolan RJ.
PMID: 24093015
Front Hum Neurosci. 2013 Sep 25;7:598. doi: 10.3389/fnhum.2013.00598. eCollection 2013.

This paper considers agency in the setting of embodied or active inference. In brief, we associate a sense of agency with prior beliefs about action and ask what sorts of beliefs underlie optimal behavior. In particular, we consider prior...

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Friston K, Schwartenbeck P, Fitzgerald T, et al. The anatomy of choice: active inference and agency. Front Hum Neurosci. 2013;7:598doi: 10.3389/fnhum.2013.00598.
Friston, K., Schwartenbeck, P., Fitzgerald, T., Moutoussis, M., Behrens, T., & Dolan, R. J. (2013). The anatomy of choice: active inference and agency. Frontiers in human neuroscience, 7598. https://doi.org/10.3389/fnhum.2013.00598
Friston, Karl, et al. "The anatomy of choice: active inference and agency." Frontiers in human neuroscience vol. 7 (2013): 598. doi: https://doi.org/10.3389/fnhum.2013.00598
Friston K, Schwartenbeck P, Fitzgerald T, Moutoussis M, Behrens T, Dolan RJ. The anatomy of choice: active inference and agency. Front Hum Neurosci. 2013 Sep 25;7:598. doi: 10.3389/fnhum.2013.00598. eCollection 2013. PMID: 24093015; PMCID: PMC3782702.
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A bayesian foundation for individual learning under uncertainty.

Frontiers in human neuroscience

Mathys C, Daunizeau J, Friston KJ, Stephan KE.
PMID: 21629826
Front Hum Neurosci. 2011 May 02;5:39. doi: 10.3389/fnhum.2011.00039. eCollection 2011.

Computational learning models are critical for understanding mechanisms of adaptive behavior. However, the two major current frameworks, reinforcement learning (RL) and Bayesian learning, both have certain limitations. For example, many Bayesian models are agnostic of inter-individual variability and involve...

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Mathys C, Daunizeau J, Friston KJ, et al. A bayesian foundation for individual learning under uncertainty. Front Hum Neurosci. 2011;5:39doi: 10.3389/fnhum.2011.00039.
Mathys, C., Daunizeau, J., Friston, K. J., & Stephan, K. E. (2011). A bayesian foundation for individual learning under uncertainty. Frontiers in human neuroscience, 539. https://doi.org/10.3389/fnhum.2011.00039
Mathys, Christoph, et al. "A bayesian foundation for individual learning under uncertainty." Frontiers in human neuroscience vol. 5 (2011): 39. doi: https://doi.org/10.3389/fnhum.2011.00039
Mathys C, Daunizeau J, Friston KJ, Stephan KE. A bayesian foundation for individual learning under uncertainty. Front Hum Neurosci. 2011 May 02;5:39. doi: 10.3389/fnhum.2011.00039. eCollection 2011. PMID: 21629826; PMCID: PMC3096853.
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An aberrant precision account of autism.

Frontiers in human neuroscience

Lawson RP, Rees G, Friston KJ.
PMID: 24860482
Front Hum Neurosci. 2014 May 14;8:302. doi: 10.3389/fnhum.2014.00302. eCollection 2014.

Autism is a neurodevelopmental disorder characterized by problems with social-communication, restricted interests and repetitive behavior. A recent and thought-provoking article presented a normative explanation for the perceptual symptoms of autism in terms of a failure of Bayesian inference (Pellicano...

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Lawson RP, Rees G, Friston KJ. An aberrant precision account of autism. Front Hum Neurosci. 2014;8:302doi: 10.3389/fnhum.2014.00302.
Lawson, R. P., Rees, G., & Friston, K. J. (2014). An aberrant precision account of autism. Frontiers in human neuroscience, 8302. https://doi.org/10.3389/fnhum.2014.00302
Lawson, Rebecca P, et al. "An aberrant precision account of autism." Frontiers in human neuroscience vol. 8 (2014): 302. doi: https://doi.org/10.3389/fnhum.2014.00302
Lawson RP, Rees G, Friston KJ. An aberrant precision account of autism. Front Hum Neurosci. 2014 May 14;8:302. doi: 10.3389/fnhum.2014.00302. eCollection 2014. PMID: 24860482; PMCID: PMC4030191.
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Uncertainty in perception and the Hierarchical Gaussian Filter.

Frontiers in human neuroscience

Mathys CD, Lomakina EI, Daunizeau J, Iglesias S, Brodersen KH, Friston KJ, Stephan KE.
PMID: 25477800
Front Hum Neurosci. 2014 Nov 19;8:825. doi: 10.3389/fnhum.2014.00825. eCollection 2014.

In its full sense, perception rests on an agent's model of how its sensory input comes about and the inferences it draws based on this model. These inferences are necessarily uncertain. Here, we illustrate how the Hierarchical Gaussian Filter...

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Mathys CD, Lomakina EI, Daunizeau J, et al. Uncertainty in perception and the Hierarchical Gaussian Filter. Front Hum Neurosci. 2014;8:825doi: 10.3389/fnhum.2014.00825.
Mathys, C. D., Lomakina, E. I., Daunizeau, J., Iglesias, S., Brodersen, K. H., Friston, K. J., & Stephan, K. E. (2014). Uncertainty in perception and the Hierarchical Gaussian Filter. Frontiers in human neuroscience, 8825. https://doi.org/10.3389/fnhum.2014.00825
Mathys, Christoph D, et al. "Uncertainty in perception and the Hierarchical Gaussian Filter." Frontiers in human neuroscience vol. 8 (2014): 825. doi: https://doi.org/10.3389/fnhum.2014.00825
Mathys CD, Lomakina EI, Daunizeau J, Iglesias S, Brodersen KH, Friston KJ, Stephan KE. Uncertainty in perception and the Hierarchical Gaussian Filter. Front Hum Neurosci. 2014 Nov 19;8:825. doi: 10.3389/fnhum.2014.00825. eCollection 2014. PMID: 25477800; PMCID: PMC4237059.
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Neural masses and fields: modeling the dynamics of brain activity.

Frontiers in computational neuroscience

Pinotsis D, Robinson P, Beim Graben P, Friston K.
PMID: 25477813
Front Comput Neurosci. 2014 Nov 17;8:149. doi: 10.3389/fncom.2014.00149. eCollection 2014.

No abstract available.

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Pinotsis D, Robinson P, Beim Graben P, et al. Neural masses and fields: modeling the dynamics of brain activity. Front Comput Neurosci. 2014;8:149doi: 10.3389/fncom.2014.00149.
Pinotsis, D., Robinson, P., Beim Graben, P., & Friston, K. (2014). Neural masses and fields: modeling the dynamics of brain activity. Frontiers in computational neuroscience, 8149. https://doi.org/10.3389/fncom.2014.00149
Pinotsis, Dimitris, et al. "Neural masses and fields: modeling the dynamics of brain activity." Frontiers in computational neuroscience vol. 8 (2014): 149. doi: https://doi.org/10.3389/fncom.2014.00149
Pinotsis D, Robinson P, Beim Graben P, Friston K. Neural masses and fields: modeling the dynamics of brain activity. Front Comput Neurosci. 2014 Nov 17;8:149. doi: 10.3389/fncom.2014.00149. eCollection 2014. PMID: 25477813; PMCID: PMC4237139.
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Virtual reality and consciousness inference in dreaming.

Frontiers in psychology

Hobson JA, Hong CC, Friston KJ.
PMID: 25346710
Front Psychol. 2014 Oct 09;5:1133. doi: 10.3389/fpsyg.2014.01133. eCollection 2014.

This article explores the notion that the brain is genetically endowed with an innate virtual reality generator that - through experience-dependent plasticity - becomes a generative or predictive model of the world. This model, which is most clearly revealed...

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Hobson JA, Hong CC, Friston KJ. Virtual reality and consciousness inference in dreaming. Front Psychol. 2014;5:1133doi: 10.3389/fpsyg.2014.01133.
Hobson, J. A., Hong, C. C., & Friston, K. J. (2014). Virtual reality and consciousness inference in dreaming. Frontiers in psychology, 51133. https://doi.org/10.3389/fpsyg.2014.01133
Hobson, J Allan, et al. "Virtual reality and consciousness inference in dreaming." Frontiers in psychology vol. 5 (2014): 1133. doi: https://doi.org/10.3389/fpsyg.2014.01133
Hobson JA, Hong CC, Friston KJ. Virtual reality and consciousness inference in dreaming. Front Psychol. 2014 Oct 09;5:1133. doi: 10.3389/fpsyg.2014.01133. eCollection 2014. PMID: 25346710; PMCID: PMC4191565.
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Active inference, attention, and motor preparation.

Frontiers in psychology

Brown H, Friston K, Bestmann S.
PMID: 21960978
Front Psychol. 2011 Sep 21;2:218. doi: 10.3389/fpsyg.2011.00218. eCollection 2011.

Perception is the foundation of cognition and is fundamental to our beliefs and consequent action planning. The Editorial (this issue) asks: "what mechanisms, if any, mediate between perceptual and cognitive processes?" It has recently been argued that attention might...

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Brown H, Friston K, Bestmann S. Active inference, attention, and motor preparation. Front Psychol. 2011;2:218doi: 10.3389/fpsyg.2011.00218.
Brown, H., Friston, K., & Bestmann, S. (2011). Active inference, attention, and motor preparation. Frontiers in psychology, 2218. https://doi.org/10.3389/fpsyg.2011.00218
Brown, Harriet, et al. "Active inference, attention, and motor preparation." Frontiers in psychology vol. 2 (2011): 218. doi: https://doi.org/10.3389/fpsyg.2011.00218
Brown H, Friston K, Bestmann S. Active inference, attention, and motor preparation. Front Psychol. 2011 Sep 21;2:218. doi: 10.3389/fpsyg.2011.00218. eCollection 2011. PMID: 21960978; PMCID: PMC3177296.
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Analyzing effective connectivity with functional magnetic resonance imaging.

Wiley interdisciplinary reviews. Cognitive science

Stephan KE, Friston KJ.
PMID: 21209846
Wiley Interdiscip Rev Cogn Sci. 2010 May;1(3):446-459. doi: 10.1002/wcs.58. Epub 2010 Apr 02.

Functional neuroimaging techniques are used widely in cognitive neuroscience to investigate aspects of functional specialization and functional integration in the human brain. Functional integration can be characterized in two ways, functional connectivity and effective connectivity. While functional connectivity describes...

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Stephan KE, Friston KJ. Analyzing effective connectivity with functional magnetic resonance imaging. Wiley Interdiscip Rev Cogn Sci. 2010;1(3):446-459doi: 10.1002/wcs.58.
Stephan, K. E., & Friston, K. J. (2010). Analyzing effective connectivity with functional magnetic resonance imaging. Wiley interdisciplinary reviews. Cognitive science, 1(3), 446-459. https://doi.org/10.1002/wcs.58
Stephan, Klaas Enno, and Friston, Karl J. "Analyzing effective connectivity with functional magnetic resonance imaging." Wiley interdisciplinary reviews. Cognitive science vol. 1,3 (2010): 446-459. doi: https://doi.org/10.1002/wcs.58
Stephan KE, Friston KJ. Analyzing effective connectivity with functional magnetic resonance imaging. Wiley Interdiscip Rev Cogn Sci. 2010 May;1(3):446-459. doi: 10.1002/wcs.58. Epub 2010 Apr 02. PMID: 21209846; PMCID: PMC3013343.
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Dysconnectivity in the frontoparietal attention network in schizophrenia.

Frontiers in psychiatry

Roiser JP, Wigton R, Kilner JM, Mendez MA, Hon N, Friston KJ, Joyce EM.
PMID: 24399975
Front Psychiatry. 2013 Dec 24;4:176. doi: 10.3389/fpsyt.2013.00176. eCollection 2013.

Cognitive impairment is common in patients with schizophrenia, and even those with relatively preserved function perform worse than healthy volunteers (HVs) on attentional tasks. This is consistent with the hypothesis that connectivity - in the frontoparietal network (FPN) activated...

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Roiser JP, Wigton R, Kilner JM, et al. Dysconnectivity in the frontoparietal attention network in schizophrenia. Front Psychiatry. 2013;4:176doi: 10.3389/fpsyt.2013.00176.
Roiser, J. P., Wigton, R., Kilner, J. M., Mendez, M. A., Hon, N., Friston, K. J., & Joyce, E. M. (2013). Dysconnectivity in the frontoparietal attention network in schizophrenia. Frontiers in psychiatry, 4176. https://doi.org/10.3389/fpsyt.2013.00176
Roiser, Jonathan P, et al. "Dysconnectivity in the frontoparietal attention network in schizophrenia." Frontiers in psychiatry vol. 4 (2013): 176. doi: https://doi.org/10.3389/fpsyt.2013.00176
Roiser JP, Wigton R, Kilner JM, Mendez MA, Hon N, Friston KJ, Joyce EM. Dysconnectivity in the frontoparietal attention network in schizophrenia. Front Psychiatry. 2013 Dec 24;4:176. doi: 10.3389/fpsyt.2013.00176. eCollection 2013. PMID: 24399975; PMCID: PMC3871715.
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Showing 1 to 12 of 28 entries