[Koglist] [CEU Cogsci Talks] Reminder: CCC Colloquium Monday, July 1, Fahd Yazin and Gargi Majumdar

Dear All, The CEU Department of Cognitive Science and the Center for Cognitive Computation (CCC) invites you to the following two talks, both held on Monday, July 1. 1. Time: Monday, July 1, 10.00 Speaker: Fahd Yazin, University of Edinburgh Location: CEU Budapest site (1051 Budapest, Nádor u. 15.) N15. building, room 104. Zoom: Meeting ID: 941 3729 1638<https://ceu-edu.zoom.us/j/94137291638?pwd=FXMAybMTbPTewr4ZZHhvrAauvGz32l.1> Passcode: 337577 Title: Will to Predict: How models are created which create our world Abstract : Where do predictions come from in the brain? How do these predictions form and shape the models of the world we construct in our mind, driving our subjective experience? Known by various names across fields—mental models, situation models, cognitive maps— I explore the nature of these world models and predictions in sculpting our subjective experience using naturalistic stimuli. Next, I uncover the mechanisms by which these models are formed and deployed for inference using an unsupervised learning task of a probabilistic virtual world. I show how the Default-Mode Network (DMN) and specifically its Prefrontal sectors segment our environment into abstract, specialized domains - Spatial, Referential and Temporal - through a tripartite architecture, assembling top-down predictions tailored for each domain. This fragments subjective experience, which is unified globally through a multithreaded integration between its prefrontal and parietal core nodes. Computationally, the prefrontal cortex constructs these models by simulating internal data, adaptively adjusting it to match the external reality. Once built, humans compare low-dimensional summaries of these internal replicas to the external sensory information, amounting to rapid model selection. Formalizing the origin of top-down predictions as being computationally equivalent to predictive inference through Bayesian sampling, I discuss specifically how the prefrontal cortex discovers models and model parameters jointly from the data using internally synthesized data, and generally how the DMN utilizes these to form our subjective experience. ************************************************************************************* 2. Time: Monday, July 1., 15.00 Speaker: Gargi Majumdar, University of Hamburg Location: CEU Budapest site (1051 Budapest, Nádor u. 15.) N15. building, room 104. Zoom: Meeting ID: 958 1739 2309<https://ceu-edu.zoom.us/j/95817392309?pwd=mRqp5ZpV4vTJlXLLN8a8M9Os3U1BY2.1> Passcode: 454588 Title: Navigating the unknown in emotion dynamics Abstract: An enigmatic phenomenon of the mind that drives almost all our actions and has attracted scientists and sages in its quest for centuries is emotion. Divergent views on the definition of emotion combined with various psychological and computational models have populated the field of emotion research for a long time. Most of the proposed theoretical models are yet to be exploited empirically to robustly explain our affective experiences. Combining the theoretical tenets of the Bayesian Brain hypothesis and predictive coding, I aimed to investigate whether prediction uncertainty can robustly track the temporal dynamics of complex emotions, along with formulating insights into deviations or disruptions of the process. Accordingly, using behavioral and large-scale neuroimaging data, I show how optimal representation of uncertainty can drive the temporal dynamics of our emotions. Crucially, this uncertainty naturally emerges as a continuous, hierarchical inference from the fluctuating valence while watching a movie. Extending this framework further, I explored whether age-associated idiosyncratic changes in emotional processing can stem from misrepresentation and miscomputation uncertainty. Finally, I investigated the dynamic interplay between rest and emotion and how intermittent anticipatory or ruminative resting periods change our emotional experiences. Please, be informed that video/photo recording might take place at the event and the edited version of the video material might be published to communicate or promote CEU PU's activities. Please, find our Privacy Notice here<https://www.ceu.edu/privacy>cy>. Best regards, Ildikó Varga Department Coordinator (Budapest) Department of Cognitive Science [cid:31b5dce3-7d78-41d3-9b22-60f0412fcbbf] H-1051 Budapest Nádor u. 15. FT room 404. tel: +36-1 327-3000 2941 http://www.ceu.edu<http://www.ceu.edu/> http://cognitivescience.ceu.edu<http://cognitivescience.ceu.edu/> ________________________________ From: Ildiko Zsoka Varga Sent: Wednesday, June 26, 2024 10:10 AM To: &#39;talks(a)cogsci.ceu.edu (talks(a)cogsci.ceu.edu)&#39; &lt;talks(a)cogsci.ceu.edu&gt; Subject: CCC Colloquium Monday, July 1, Fahd Yazin and Gargi Majumdar Dear All, The CEU Department of Cognitive Science and the Center for Cognitive Computation (CCC) invites you to the following two talks, both held on Monday, July 1. 1. Time: Monday, July 1, 10.00 Speaker: Fahd Yazin, University of Edinburgh Location: CEU Budapest site (1051 Budapest, Nádor u. 15.) N15. building, room 104. Zoom: Meeting ID: 941 3729 1638<https://ceu-edu.zoom.us/j/94137291638?pwd=FXMAybMTbPTewr4ZZHhvrAauvGz32l.1> Passcode: 337577 Title: Will to Predict: How models are created which create our world Abstract : Where do predictions come from in the brain? How do these predictions form and shape the models of the world we construct in our mind, driving our subjective experience? Known by various names across fields—mental models, situation models, cognitive maps— I explore the nature of these world models and predictions in sculpting our subjective experience using naturalistic stimuli. Next, I uncover the mechanisms by which these models are formed and deployed for inference using an unsupervised learning task of a probabilistic virtual world. I show how the Default-Mode Network (DMN) and specifically its Prefrontal sectors segment our environment into abstract, specialized domains - Spatial, Referential and Temporal - through a tripartite architecture, assembling top-down predictions tailored for each domain. This fragments subjective experience, which is unified globally through a multithreaded integration between its prefrontal and parietal core nodes. Computationally, the prefrontal cortex constructs these models by simulating internal data, adaptively adjusting it to match the external reality. Once built, humans compare low-dimensional summaries of these internal replicas to the external sensory information, amounting to rapid model selection. Formalizing the origin of top-down predictions as being computationally equivalent to predictive inference through Bayesian sampling, I discuss specifically how the prefrontal cortex discovers models and model parameters jointly from the data using internally synthesized data, and generally how the DMN utilizes these to form our subjective experience. ************************************************************************************* 2. Time: Monday, July 1., 15.00 Speaker: Gargi Majumdar, University of Hamburg Location: CEU Budapest site (1051 Budapest, Nádor u. 15.) N15. building, room 104. Zoom: Meeting ID: 958 1739 2309<https://ceu-edu.zoom.us/j/95817392309?pwd=mRqp5ZpV4vTJlXLLN8a8M9Os3U1BY2.1> Passcode: 454588 Title: Navigating the unknown in emotion dynamics Abstract: An enigmatic phenomenon of the mind that drives almost all our actions and has attracted scientists and sages in its quest for centuries is emotion. Divergent views on the definition of emotion combined with various psychological and computational models have populated the field of emotion research for a long time. Most of the proposed theoretical models are yet to be exploited empirically to robustly explain our affective experiences. Combining the theoretical tenets of the Bayesian Brain hypothesis and predictive coding, I aimed to investigate whether prediction uncertainty can robustly track the temporal dynamics of complex emotions, along with formulating insights into deviations or disruptions of the process. Accordingly, using behavioral and large-scale neuroimaging data, I show how optimal representation of uncertainty can drive the temporal dynamics of our emotions. Crucially, this uncertainty naturally emerges as a continuous, hierarchical inference from the fluctuating valence while watching a movie. Extending this framework further, I explored whether age-associated idiosyncratic changes in emotional processing can stem from misrepresentation and miscomputation uncertainty. Finally, I investigated the dynamic interplay between rest and emotion and how intermittent anticipatory or ruminative resting periods change our emotional experiences. Please, be informed that video/photo recording might take place at the event and the edited version of the video material might be published to communicate or promote CEU PU's activities. Please, find our Privacy Notice here<https://www.ceu.edu/privacy>cy>. Best regards, Ildikó Varga Department Coordinator (Budapest) Department of Cognitive Science [cid:f4adf602-06ae-43f0-9f96-2b79c5cbdf6d] H-1051 Budapest Nádor u. 15. FT room 404. tel: +36-1 327-3000 2941 http://www.ceu.edu<http://www.ceu.edu/> http://cognitivescience.ceu.edu<http://cognitivescience.ceu.edu/> ______________________________________________ Subscribe by sending an empty mail to talks-subscribe(a)cogsci.ceu.edu Unsubscribe by sending an empty mail to talks-unsubscribe(a)cogsci.ceu.edu