The brain as a generative model: information-theoretic surprise in learning and action

Our 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

Defining a sub-Saharan fertility pattern and a standard for use with the relational Gompertz model

Includes abstract.Includes bibliographical references (leaves 122-124).The relational Gompertz model is often used to obtain fertility estimates for sub-Saharan Africa populations. This indirect estimation technique is dependent on a fertility standard - the Booth standard. This standard was developed in 1979 using a selection of 33 Coale-Trussell schedules congruent with high fertility patterns. However, evidence from 61 Demographic and Health Surveys of sub-Saharan countries shows that fertility has decreased to levels that were considered medium fertility at the time the standard was developed. This raises concerns about the continued relevance of the (high fertility) Booth standard. In particular, the standard would appear to consistently underestimate fertility among African women aged 45-49

Confronting the Demise of a Mother Tongue: The Feasibility of Implementing Language Immersion Programs to Reinvigorate the Taiwanese Language

In Taiwan, where Mandarin is the official language, the survival of Taiwanese, the mother tongue of sixty percent of the island’s inhabitants, is threatened. In this article, the authors discuss data from previous and ongoing research on the role of language and the significance of language loss in the quest for a “Taiwanese identity.” Research shows that the dominance of Mandarin over Taiwanese plus the growing support for English in Taiwan are likely indications that current Mandarin/Taiwanese bilingualism is being replaced by Mandarin/English bilingualism. Canadian, Finnish, Basque and Catalonian models of language immersion programs will be proposed as an alternative to Taiwan’s current language policy. The authors argue that such models, when applied to a significant degree in Taiwan’s primary education system, will contribute to strengthening Taiwanese identity, to defending the right of youngsters to receive their education in their Taiwanese mother tongue, and to creating more effective English language training.Le mandarin, langue officielle de Taiwan, menace la survie du taiwanais, qui est la langue maternelle de soixante pour cent de la population de l’île. Dans le présent article, les auteurs discutent des résultats de leurs recherches précédentes et en cours et montrent la signifiance de la perte de la langue maternelle pour l’identité taiwanaise. Ils montrent également que la suprématie du mandarin sur le taiwanais et l’appui croissant en faveur de l’anglais à Taiwan laissent prévoir l’implantation d’un bilinguisme mandarin/anglais au détriment du bilinguisme mandarin/taiwanais. Les auteurs considèrent que les modèles d’enseignement bilingue immersif mis en place au Canada, en Finlande, dans le pays Basque et en Catalogne, s’ils étaient mis en oeuvre de façon généralisée à Taiwan, pourraient contribuer à renforcer l’identité taiwanaise, à défendre le droit des jeunes à une éducation dans leur langue maternelle et à favoriser l’enseignement plus efficace de l’anglais

Active inference and the two-step task

Sequential decision problems distill important challenges frequently faced by humans. Through repeated interactions with an uncertain world, unknown statistics need to be learned while balancing exploration and exploitation. Reinforcement learning is a prominent method for modeling such behaviour, with a prevalent application being the two-step task. However, recent studies indicate that the standard reinforcement learning model sometimes describes features of human task behaviour inaccurately and incompletely. We investigated whether active inference, a framework proposing a trade-off to the exploration-exploitation dilemma, could better describe human behaviour. Therefore, we re-analysed four publicly available datasets of the two-step task, performed Bayesian model selection, and compared behavioural model predictions. Two datasets, which revealed more model-based inference and behaviour indicative of directed exploration, were better described by active inference, while the models scored similarly for the remaining datasets. Learning using probability distributions appears to contribute to the improved model fits. Further, approximately half of all participants showed sensitivity to information gain as formulated under active inference, although behavioural exploration effects were not fully captured. These results contribute to the empirical validation of active inference as a model of human behaviour and the study of alternative models for the influential two-step task

Gold(I) as an Artificial Cyclase: Short Stereodivergent Syntheses of (−)-Epiglobulol and (−)-4β,7α- and (−)-4α,7α-Aromadendranediols

Three natural aromadendrane sesquiterpenes, (−)‐epiglobulol, (−)‐4β,7α‐aromadendranediol, and (−)‐4α,7α‐aromadendranediol, have been synthesized in only seven steps in 12, 15, and 17 % overall yields, respectively, from (E,E)‐farnesol by a stereodivergent gold(I)‐catalyzed cascade reaction which forms the tricyclic aromadendrane core in a single step. These are the shortest total syntheses of these natural compounds

EEG mismatch responses in a multimodal roving stimulus paradigm provide evidence for probabilistic inference across audition, somatosensation, and vision

The human brain is constantly subjected to a multimodal stream of probabilistic sensory inputs. Electroencephalography (EEG) signatures, such as the mismatch negativity (MMN) and the P3, can give valuable insight into neuronal probabilistic inference. Although reported for different modalities, mismatch responses have largely been studied in isolation, with a strong focus on the auditory MMN. To investigate the extent to which early and late mismatch responses across modalities represent comparable signatures of uni- and cross-modal probabilistic inference in the hierarchically structured cortex, we recorded EEG from 32 participants undergoing a novel tri-modal roving stimulus paradigm. The employed sequences consisted of high and low intensity stimuli in the auditory, somatosensory and visual modalities and were governed by unimodal transition probabilities and cross-modal conditional dependencies. We found modality specific signatures of MMN (~100–200 ms) in all three modalities, which were source localized to the respective sensory cortices and shared right lateralized prefrontal sources. Additionally, we identified a cross-modal signature of mismatch processing in the P3a time range (~300–350 ms), for which a common network with frontal dominance was found. Across modalities, the mismatch responses showed highly comparable parametric effects of stimulus train length, which were driven by standard and deviant response modulations in opposite directions. Strikingly, P3a responses across modalities were increased for mispredicted stimuli with low cross-modal conditional probability, suggesting sensitivity to multimodal (global) predictive sequence properties. Finally, model comparisons indicated that the observed single trial dynamics were best captured by Bayesian learning models tracking unimodal stimulus transitions as well as cross-modal conditional dependencies

Intimal thickness determines the detection of non-calcified plaques by computed tomography angiography

No abstract