705 research outputs found
Phenomenoconnectomics and the Neural Correlates of Altered Consciousness. An interview with Timo Torsten Schmidt
No full textIn this interview, Timo Torsten Schmidt provides details about his efforts to compile a comprehensive database of all psychometric measures gathered from controlled experiments investigating altered states of consciousness (ASCs) induced by pharmacological and non-pharmacological methods. He also introduces the paradigm of Phenomenoconnectomics which aims to systematically investigate the phenomenology and functional connectivity of ASCs to identify commonalities and differences, to ultimately identify the necessary neuronal correlates of specific experiences as they occur during ASCs. He explains some key findings of his own neuroscientific research on the neural correlates of consciousness under the influence of non-pharmacological manipulations, such as Ganzfeld exposure and flicker light stimulation-induced visual illusory percepts. Finally, we touch upon the current limitations of psychometric methods in their ability to capture the full diversity of the phenomenal space and future plans to overcome these caveats through Open Science initiatives that support harm reduction efforts
Interest rate convexity and the volatility smile
No full textWhen pricing the convexity effect in irregular interest rate derivatives such as, e.g., Libor-in-arrears or CMS, one often ignores the volatility smile, which is quite pronounced in the interest rate options market. This note solves the problem of convexity by replicating the irregular interest flow or option with liquidly traded options with different strikes thereby taking into account the volatility smile. This idea is known among practitioners for pricing CMS caps. We approach the problem on a more general scale and apply the result to various examples. --interest rate options,volatility smile,convexity,,option replication
Update of the Altered States Database (ASDB): 2022-12-31
No full textThe Altered States Database (ASDB) is an open science project, containing psychometric questionnaire data on altered states of consciousness experiences induced by diverse means. The database was first described in: Schmidt, T. T., & Berkemeyer, H. (2018). The Altered States Database: Psychometric Data of Altered States of Consciousness. In Front. Psychol. and an upgrade to cohere to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) 2020 standards has been reported in: Prugger, J., Derdiyok, E., Dinkelacker, J., Costines, C., & Schmidt, T. T. (2022). The Altered States Database: Psychometric data from a systematic literature review. In Sci. Data. Here, we report the update to include data until 2022-12-31. A systematic literature review according to PRISMA guidelines was conducted in which 431 items were screened, and data from 23 eligible journal articles was extracted. The complete data is available on Open Science Framework (OSF): https://osf.io/8mbru
Neural basis of somatosensory target detection - Data
No full textLog evidence maps and Bayesian Model Selection results for the study:Schröder, P., Schmidt, T. T., & Blankenburg, F. (2019). Neural basis of somatosensory target detection independent of uncertainty, relevance, and reports. eLife, 8, e43410. https://doi.org/10.7554/eLife.43410.001GLMs modelling stimulus intensity, detection probability, target detection, uncertainty, and report were compared. For direct inspection of the exceedance probability maps see https://neurovault.org/collections/4496/</div
Credit dynamics in a first passage time model with jumps
No full textThe payoff of many credit derivatives depends on the level of credit spreads. In particular, the payoff of credit derivatives with a leverage component is sensitive to jumps in the underlying credit spreads. In the framework of first passage time models we extend the model introduced in [Overbeck and Schmidt, 2005] to address these issues. In the extended a model, a credit quality process is driven by an Itô integral with respect to a Brownian motion with stochastic volatility. Using a representation of the credit quality process as a time-changed Brownian motion, we derive formulas for conditional default probabilities and credit spreads. An example for a volatility process is the square root of a Lévy-driven Ornstein-Uhlenbeck process. We show that jumps in the volatility translate into jumps in credit spreads. We examine the dynamics of the OS-model and the extended model and provide examples. --gap risk,credit spreads,credit dynamics,first passage time models,Lévy processes,general Ornstein-Uhlenbeck processes
The Somatotopy of Mental Tactile Imagery
No full textTo what degree mental imagery (MI) bears on the same neuronal processes as perception has been a central question in the neurophysiological study of imagery. Sensory-recruitment models suggest that imagery of sensory material heavily relies on the involvement of sensory cortices. Empirical evidence mainly stems from the study of visual imagery and suggests that it depends on the mentally imagined material whether hierarchically lower regions are recruited. However, evidence from other modalities is necessary to infer generalized principles. In this fMRI study we used the somatotopic organization of the primary somatosensory cortex (SI) to test in how far MI of tactile sensations activates topographically sensory brain areas. Participants (N = 19) either perceived or imagined vibrotactile stimuli on their left or right thumbs or big toes. The direct comparison to a corresponding perception condition revealed that SI was somatotopically recruited during imagery. While stimulus driven bottom-up processing induced activity throughout all SI subareas, i.e., BA1, BA3a, BA3b, and BA2 defined by probabilistic cytoarchitectonic maps, top-down recruitment during imagery was limited to the hierarchically highest subarea BA2
Taktile Mentale Repräsentationen
No full text1\. Introduction 1.1 The study of mental content representations 1.2 Models
and controversies of the representation of mental content 1.3 Mental content
representations in the somatosensory system 1.4 Overall aim of this thesis 2\.
Summary and discussion of experiments Study 1: Somatotopic recruitment of SI
subregions during simple tactile imagery Study 2: Recruitment of primary
somatosensory cortex during fine-grained tactile imagery Study 3: The
‘tactospatial sketchpad’: Tactile working memory of spatial stimulus
properties Study 4: Parametric vibrotactile working memory codes investigated
with EEG Study 5: Parametric vibrotactile working memory codes investigated
with fMRI MVPA Study 6: Overlap of parametric vibrotactile working memory with
visual working memory Study 7: Overlap of parametric vibrotactile working
memory with auditory working memory 3\. General discussion 3.1 Empirical
findings on tactile mental representations 3.2 Predictive coding as a
framework for content representations 3.3 Methodological and interpretative
limitations 3.4 OutlookThe human ability to mentally represent and manipulate information in the
absence of sensory stimulation is key for any higher cognitive functions.
Empirical neuroscientific research on mental imagery (MI) and working memory
(WM) addresses the question of how our brain represents various types of
mental contents. Critically, most research stems from studies in the visual
modality, leaving open the question of whether findings, models and theories
generalize to other modalities. In my work I focused on the mental
representation of tactile contents. To empirically address what brain regions
code different types of mental content, two fMRI studies on MI, one WM EEG
study and four fMRI WM decoding studies were conducted. We found that
posterior parietal regions and primary somatosensory cortex code spatial
features of tactile stimuli. In contrast, when participants memorized more
abstract stimulus features such as vibratory frequency, intensity or duration,
the prefrontal cortex was found to exhibit multivariate parametric codes
specific to the mental content. This finding was also replicated in the visual
and auditory modalities. These results support the view that the abstractness
of a mental representation determines which brain regions exhibit content-
specific codes, where the gradient of abstractness stretches from sensory to
categorical or parametric content types. This gradient maps onto the
hierarchical organization of the cortex. In parallel, predictive brain
mechanisms also rely on the hierarchical interaction of bottom-up and top-down
processes. I will suggest mechanisms for how these well-established
hierarchical processing principles relate to the representation of mental
contents.Die menschliche Fähigkeit, in Abwesenheit von sensorischer Stimulation
Informationen mental zu repräsentieren und weiterzuverarbeiten, stellt eine
Schlüsselfunktion für höhere kognitive Aufgaben dar. Empirische,
neurowissenschaftliche Untersuchungen zur mentalen Imagination und zum
Arbeitsgedächtnis beschäftigen sich mit der Frage, wie unser Gehirn
unterschiedliche Typen mentaler Inhalte repräsentiert. Hierzu konzentrierte
sich die Forschung bisher meist auf die visuelle Modalität. Dies lässt die
Frage unbeantwortet, ob sich die Resultate sowie Modelle und Theorien auf
andere Modalitäten generalisieren lassen. Der Schwerpunkt meiner Arbeit liegt
auf der mentalen Repräsentation von taktilen Inhalten. Welche Gehirnregionen
unterschiedliche Typen mentaler Inhalte kodieren, wurde empirisch in zwei fMRI
Studien zu mentaler Imagination, einer EEG Arbeitsgedächtnisstudie sowie vier
fMRI Arbeitsgedächtnis-Dekodierungsstudien untersucht. Dabei fanden wir
heraus, dass posterior parietale Areale und der primäre somatosensorische
Cortex räumliche Eigenschaften taktiler Reize kodiert. Wenn Probanden im
Gegensatz dazu abstraktere Reizeigenschaften, wie die Vibrationsfrequenz,
Reizintensität oder Reizdauer erinnerten, fanden wir stimulusspezifische,
multivariate, parametrische Codes im präfrontalen Cortex. Dieses Ergebnis
konnten wir in der visuellen und auditorischen Modalität replizieren. Unsere
Ergebnisse stützen die Sichtweise, dass die Abstraktheit von mentalen Inhalten
bestimmt, welche Gehirnregionen inhaltsspezifische Codes zeigen. Dabei umfasst
die Abstraktheit mentaler Repräsentationen sensorische bis hin zu kategorialen
oder parametrischen Inhaltstypen. Dieser Gradient bildet sich auf die
hierarchische Organisation des Cortex ab. Gleichermaßen basieren prädiktive
Gehirnmechanismen auf der hierarchischen Interaktion von Bottom-up und Top-
down Prozessen. Ich werde Mechanismen vorschlagen, wie diese etablierten,
hierarchischen Prozessierungsprinzipien in Beziehung zur Repräsentation von
mentalen Inhalten stehen können
On the cost of delayed currency fixing announcements
No full textIn Foreign Exchange Markets vanilla and barrier options are traded frequently. The market standard is a cutoff time of 10:00 a.m. in New York for the strike of vanillas and a knock-out event based on a continuously observed barrier in the inter bank market. However, many clients, particularly from Italy, prefer the cutoff and knock-out event to be based on the fixing published by the European Central Bank on the Reuters Page ECB37. These barrier options are called discretely monitored barrier options. While these options can be priced in several models by various techniques, the ECB source of the fixing causes two problems. First of all, it is not tradable, and secondly it is published with a delay of about 10 - 20 minutes. We examine here the effect of these problems on the hedge of those options and consequently suggest a cost based on the additional uncertainty encountered. --exotic options,currency fixings
The brain as a generative model: information-theoretic surprise in learning and action
No full textOur environment is rich with statistical regularities, such as a sudden cold gust of wind indicating a potential change in weather. A combination of theoretical work and empirical evidence suggests that humans embed this information in an internal representation of the world. This generative model is used to perform probabilistic inference, which may be approximated through surprise minimization. This process rests on current beliefs enabling predictions, with expectation violation amounting to surprise. Through repeated interaction with the world, beliefs become more accurate and grow more certain over time. Perception and learning may be accounted for by minimizing surprise of current observations, while action is proposed to minimize expected surprise of future events. This framework thus shows promise as a common formulation for different brain functions.
The work presented here adopts information-theoretic quantities of surprise to investigate both perceptual learning and action. We recorded electroencephalography (EEG) of participants in a somatosensory roving-stimulus paradigm and performed trial-by-trial modeling of cortical dynamics. Bayesian model selection suggests early processing in somatosensory cortices to encode confidence-corrected surprise and subsequently Bayesian surprise. This suggests the somatosensory system to signal surprise of observations and update a probabilistic model learning transition probabilities. We also extended this framework to include audition and vision in a multi-modal roving-stimulus study. Next, we studied action by investigating a sensitivity to expected Bayesian surprise. Interestingly, this quantity is also known as information gain and arises as an incentive to reduce uncertainty in the active inference framework, which can correspond to surprise minimization. In comparing active inference to a classical reinforcement learning model on the two-step decision-making task, we provided initial evidence for active inference to better account for human model-based behaviour. This appeared to relate to participants’ sensitivity to expected Bayesian surprise and contributed to explaining exploration behaviour not accounted for by the reinforcement learning model. Overall, our findings provide evidence for information-theoretic surprise as a model for perceptual learning signals while also guiding human action.Unsere Umwelt ist reich an statistischen Regelmäßigkeiten, wie z. B. ein plötzlicher kalter Windstoß, der einen möglichen Wetterumschwung ankündigt. Eine Kombination aus theoretischen Arbeiten und empirischen Erkenntnissen legt nahe, dass der Mensch diese Informationen in eine interne Darstellung der Welt einbettet. Dieses generative Modell wird verwendet, um probabilistische Inferenz durchzuführen, die durch Minimierung von Überraschungen angenähert werden kann. Der Prozess beruht auf aktuellen Annahmen, die Vorhersagen ermöglichen, wobei eine Verletzung der Erwartungen einer Überraschung gleichkommt. Durch wiederholte Interaktion mit der Welt nehmen die Annahmen mit der Zeit an Genauigkeit und Gewissheit zu. Es wird angenommen, dass Wahrnehmung und Lernen durch die Minimierung von Überraschungen bei aktuellen Beobachtungen erklärt werden können, während Handlung erwartete Überraschungen für zukünftige Beobachtungen minimiert. Dieser Rahmen ist daher als gemeinsame Bezeichnung für verschiedene Gehirnfunktionen vielversprechend.
In der hier vorgestellten Arbeit werden informationstheoretische Größen der Überraschung verwendet, um sowohl Wahrnehmungslernen als auch Handeln zu untersuchen. Wir haben die Elektroenzephalographie (EEG) von Teilnehmern in einem somatosensorischen Paradigma aufgezeichnet und eine trial-by-trial Modellierung der kortikalen Dynamik durchgeführt. Die Bayes'sche Modellauswahl deutet darauf hin, dass frühe Verarbeitung in den somatosensorischen Kortizes confidence corrected surprise und Bayesian surprise kodiert. Dies legt nahe, dass das somatosensorische System die Überraschung über Beobachtungen signalisiert und ein probabilistisches Modell aktualisiert, welches wiederum Wahrscheinlichkeiten in Bezug auf Übergänge zwischen Reizen lernt. In einer weiteren multimodalen Roving-Stimulus-Studie haben wir diesen Rahmen auch auf die auditorische und visuelle Modalität ausgeweitet. Als Nächstes untersuchten wir Handlungen, indem wir die Empfindlichkeit gegenüber der erwarteten Bayesian surprise betrachteten. Interessanterweise ist diese informationstheoretische Größe auch als Informationsgewinn bekannt und stellt, im Rahmen von active inference, einen Anreiz dar, Unsicherheit zu reduzieren. Dies wiederum kann einer Minimierung der Überraschung entsprechen. Durch den Vergleich von active inference mit einem klassischen Modell des Verstärkungslernens (reinforcement learning) bei der zweistufigen Entscheidungsaufgabe konnten wir erste Belege dafür liefern, dass active inference menschliches modellbasiertes Verhalten besser abbildet. Dies scheint mit der Sensibilität der Teilnehmer gegenüber der erwarteten Bayesian surprise zusammenzuhängen und trägt zur Erklärung des Explorationsverhaltens bei, das jedoch nicht vom reinforcement learning-Modell erklärt werden kann. Insgesamt liefern unsere Ergebnisse Hinweise für Formulierungen der informationstheoretischen Überraschung als Modell für Signale wahrnehmungsbasierten Lernens, die auch menschliches Handeln steuern
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