scienceofintelligence.de

Dafna Burema; Mattis Jacobs (Science of Intelligence), “Workshop: Discussing Ethically Problematic “Incidents” of AI Systems”
2022-06-09 10:00 - 11:00 (Europe/Berlin)
(In-person talk at Science of Intelligence)
Workshop: Discussing ethically problematic “incidents” of AI systems
In the Thursday morning talk, we discuss ethically problematic incidents of AI that we selected from the AIAAIC Repository. In total, we discuss four cases.
In a first step, we present a brief overview what occurred in the respective cases, based on media coverage. The audience is then invited to give a briefly assess the case: what went wrong? Which ethical values or principles were involved? How could the incident have been prevented? Who is responsible for what has happened and who could have prevented it?
In a second step, we present how the respective case was assessed in the ethics-related academic literature and compare the assessment of the participants with the expert’s assessment. We conclude by inviting the participants to reflect on their own projects and potential problematic outcomes of their research.

Photo by Ehud Neuhaus on Unsplah

Max Wolf, “Fishy Twin Studies and the Origin of Personality Differences”
2022-06-02 16:00 - 17:30
Abstract:
I will discuss research on a powerful biological model system, the natural clonal fish Amazon molly. Mothers in this female-only species produce genetically identical offspring, allowing us to employ an experimental twin study approach to address some of the most fundamental questions associated with behaviour and the development of behaviour. Do twins separated directly after birth into identical environments develop personality differences? When and how do such differences develop? And how can this unique model system be used to understand the development of intelligent behaviour?
The Zoom Link will be sent the day before the lecture.

Florian Blume, Martin Maier, Doris Pischedda, Olga Wudarczyk-Markett and Murat Kirtay (Science of Intelligence)
2022-06-02 10:00 - 11:00
Abstract:
Social interaction and communication are supported by the integration of multimodal signals. One crucial social cue when interacting with other humans are facial expressions. In Project 8, we study how people not only read information from faces, but how they read meaning into faces based on context and prior knowledge. Incorporating sources of information in addition to what is actually visible (top-down processing) supports efficient, robust, context-adaptive visual perception. Neural networks designed to recognize facial expressions largely ignore such contextual information and are therefore inherently misaligned with human social perception. Closing this gap promises to make synthetic face processing at the same time more intelligent, useful for human-machine interaction, and ethically responsible.
Successful social interaction relies on additional social factors and cognitive processes including partner co-representation (i.e., the representation of the partner’s actions alongside one’s own actions), emotion processing, theory of mind (i.e., the ability to consider mental states – such as beliefs, desires, intentions – to predict people’s behaviour) and trust. In project 9, we study processes underlying social communication in humans and assess potential changes in these processes when the interaction partner is an artificial agent. We use this knowledge to implement similar mechanisms in our robots and assess how this affects their performance along other dimensions, such as trust or scaffolding. Our ultimate goal is to create robots with higher social intelligence that can interact smoothly with humans and other agents.

Photo by Yuyeung Lau on Unsplash

SCIoI+MoA CollActive Materials Event, “Woraus Ist Deine Zukunft Gemacht?”: IdeenLabor, Kombucha Workshop, and Family Activities!
2022-05-20 14:00 - 2022-05-21 18:00 | Mall Anders, Wilma Arkaden, Wilmersdorfer Str. 46, Berlin, 10627, Germany
What do sensitive robots, gripping plants and creative bacteria have in common? Let’s find out together! In “CollActive Materials” experimental laboratory, a joint project of SCIoI and MoA, research and society come together to explore the materials of the future: Can they be more active or more intelligent than the materials of today? Come by to discover surprisingly living materials, and tell us your ideas and wishes for the future.

Register now, and bring the kids too! The event is in German.
PRESS RELEASE:

Lebendige Materialien entdecken
Pop-Up Materiallabor „Woraus ist deine Zukunft gemacht?“ im Mall Anders Lernlabor in der WILMA Shoppen
Am 20. und 21. Mai 2022 lädt das Experimentallabor für Wissenschaftskommunikation „CollActive Materials“ in das Mall Anders Lernlabor in der WILMA Shoppen ein. Rund um die Frage „Woraus ist deine Zukunft gemacht?“ können Erwachsene und Kinder in Workshops, Experimenten und Gesprächsrunden ungeahnt lebendige Materialien entdecken. Ganz zentral dabei: Welche dieser lebendigen Materialien helfen uns dabei, eine nachhaltigere Zukunft zu bauen? Das Pop-Up Materiallabor markiert den Startschuss für CollActive Materials, das als gemeinsames Projekt der Berliner Exzellenzcluster „Matters of Activity“ und „Science of Intelligence“ einen Dialog zwischen Wissenschaft und Öffentlichkeit zu dieser dringlichen Frage initiieren möchte.
Was haben einfühlsame Roboter, zupackende Pflanzen und kreative Bakterien gemeinsam? Als lebendige Materialien und Objekte könnten sie Zukunft ganz anders werden lassen, als sie sich heute viele vorstellen. Statt aus starren und passiven Materialien wie Stahl und Beton könnten nachhaltige Zukünfte aus aktiven und intelligenten Materialien gemacht sein: Materialien, die reagieren, die wachsen, sich verändern – Materialien mit Eigensinn.
In den Berliner Forschungsclustern „Matters of Activity“ und „Science of Intelligence“ sind aktive und intelligente Materialien und deren Beiträge zu einer nachhaltigeren Zukunft bereits zentrale Themen. Wissenschaftler*innen der beiden Verbünde erforschen beispielsweise, was Menschen von der Architektur von bakteriellen Filmen lernen können, wie ein Schwarm aus Mini-Robotern in Zukunft gemeinsam Entscheidungen treffen könnte oder wie Baumrinden in Zukunft als flexibles Bau-Material genutzt werden könnten.
Doch Wissen, Wünsche und Ideen rund um neuartige Materialien gibt es nicht nur in der Wissenschaft, sondern auch – in vielen verschiedenen Formen – in der Gesellschaft. Um miteinander Wissen und Ideen zu teilen und lebendige Materialien gemeinsam zu erkunden, startet nun das Experimentallabor „CollActive Materials“. In dem dreijährigen Projekt von „Matters of Activity“ und „Science of Intelligence“ sollen Menschen aus Gesellschaft und Wissenschaft gemeinsam über die Materialien von morgen spekulieren und neue Zukunftsvisionen rund um lebendige Materialien schaffen.
Den Beginn dieser neuen Form des „Wissens durch Machen“ macht das Projekt mit dem Pop-Up-Materiallabor in der Mall Anders. An diesem Ort, mitten in der Öffentlichkeit, lädt das Projekt zum gemeinsamen Lernen, Ausprobieren und Entdecken, zum Besprechen und Verhandeln von lebendigen Materialien ein. In einem Kombucha-Workshop, Gesprächsrunden und einem Kinderlabor wird gemeinsam getestet: Wie könnten sie denn aussehen, die Zukünfte der lebendigen Materialien?
Kontakt
Dr. Kristin Werner, Koordinatorin CollActive Materials
Kristin.werner@hu-berlin.de
0176/32445222

Karoline Wiesner (University of Potsdam) “Information Entropy in Complex Systems”
2022-05-19 16:00 - 17:30
Lecture by Prof. Karoline Wiesner, Dept. of Physics and Astronomy, University of Potsdam

Abstract:
I begin with an introduction to the nature of complex systems. From a foundational perspective, I will present a systematic description of their features. This will be followed by a brief discussion of the role of information theory in the analysis of complex systems. In particular, we distinguish between information processing — in the sense of encoding and transmitting a symbolic representation — and the formation of correlations (pattern formation / self-organisation). I will give our perspective on why information theory is a prevalent tool in the study of complex systems. If time permits, I will give an outlook on current, unpublished work in this area.

karowiesner.org
This talk will take place in person.

Tilman Geishauer, "Virtual Reality - From Research to Market"
2022-05-19 10:00 - 11:00 | SCIoI, MAR Building, Marchstr. 23, Berlin
Abstract: Tilman Geishauser is currently working at Somareality to create a virtual reality focused product out of a cognitive load algorithm based on pupillometry that has been in development for 8 years at Research Studios Austria. In the past he invented one of Germanys most promising VR tools: Halocline Layout. He lead his team at what is now Halocline up until product launch. In his presentations he will talk about making products for virtual reality and about making products in cooperation with research institutions and universities.

Michael Pauen (Science of Intelligence), "Detecting Higher Cognitive States in Natural and Artificial Intelligence"
2022-05-05 16:00 - 17:30
More details to follow.

Ralf Kurvers at “Meet the Scientist”, Humboldt Forum
2022-05-05 16:00 - 17:00
“Should I listen to you – or not?”
Should I listen to you? Or should I ignore you when making up my mind? And what factors shape my decision-making? Questions like these are the field of behavioral researchers from the Cluster of Excellence “Science of Intelligence”. Here, psychologists, computer scientists, biologists, neuroscientists, educational scientists, philosophers, and physicists work together to find out more about collective intelligence and human rationality.

One of these researchers is Ralf Kurvers. He has developed an interactive survey for the exhibition “After Nature” that aims at finding out more about decision-making processes in social environments – particularly, the value of “social information”. Questions he is trying to answer are, for instance: Do younger individuals use more social information than older generations? Are there differences across cultures in their use of social information? And is it, in general, good or bad to use social information?
In the series “Meet the Scientist”, Ralf Kurvers presents data collected in the Humboldt Lab over the last six months. He will give insights into his findings and the fascinating field of collective psychology.
Register here! https://www.museumsportal-berlin.de/en/events/soll-ich-auf-dich-hoeren-oder-nicht-neue-for[…]-zur-kollektiven-entscheidungsfindung-mit-ralf-kurvers/

Rebecca Lazarides (Science of Intelligence), “From Understanding Learners’ Adaptive Motivation and Emotion to Designing Social Learning Companions”
2022-05-05 10:00 - 11:00 | MAR 2.057
Abstract:
High motivation and positive emotions are important prerequisites for effective knowledge transfer, while negative emotions can impede the acquisition of new knowledge. This talk presents our current state of research in SCIoI’s project 06 that aims at identifying adaptive teaching strategies of humans, and to synthesize them with the goal to test whether these strategies can be applied successfully by synthetic agents. To this end, we conduct 3 experiments in which (a) we aim to build a model of adaptive teaching using an emotionally-and performance adaptive intelligent tutoring system (ITS) and to compare it to a cognitively-adaptive ITS in its effects on human emotional experience and learning performance; (b) we integrate the model of the emotionally-adaptive ITS in embodied agents (physical robots: Pepper and Cozmo) and compare its effectiveness in knowledge transfer to the traditional ‘on-screen’ form by examining humans’ learning outcomes across conditions; c) we test the adaptive mechanism in the social interaction of two robotic agents – a learner robot (Nao) and a tutor robot (Pepper) – in a scaffolding situation were both are equipped with simple emotional expression / recognition mechanisms. The talks provides an overview about our recent results, their implications and perspectives for future research.

Benjamin Lang at "An excellent Science Slam" (in German)
2022-04-30 15:00 - 17:00
Prepare for an extraordinary Science Slam! Young scientists from four Clusters of Excellence of the Berlin University Alliance present their research work in the learning lab Mall Anders in the Wilma Shoppen mall. They only have 15 minutes to present, and everything is allowed: costumes, props, movies, PowerPoint presentations or experimental setups. After the presentations, the audience can meet the slammers and have a few drinks with them.
Among the speakers, SCIoI’s Benjamin Lang will talk about intelligence, its definition and challenges.

Girls' Day 2022
2022-04-28 10:00 - 13:00
On Girls’ Day 2022 (28 April 22), SCIoI members Jinan Allan and Marah Halawa will give an overview of their life as women in science, including ambitions and possible challenges. The brief introduction will be followed by visits to the cluster’s labs.
More information here.

Danny Driess, Manuel Baum, Katharina Hohlbaum, Niek Andresen (Science of Intelligence), “Perspectives on the Gap Between Robotic Models and Observed Biological Behavior”
2022-04-28 10:00 - 11:00
Abstract:

One of SCIoI’s goals is to establish and evaluate synthetic models for biological data. Achieving this goal is clearly beneficial not only to produce more capable artificial behavior, but also to get a better idea about the processes that may underlie biological intelligence. Why can a cockatoo or mouse open a lock box? What information do we need to provide an artificial agent to perform this task? While this goal is attractive in theory, different projects in SCIoI face diverse practical problems on their way to fulfill this vision. In this joint talk we (Katharina Hohlbaum, Niek Andresen, Danny Driess, and Manuel Baum) will discuss this topic from the perspectives of our individual projects. We aim not only to report on the challenges we face, but would also like to discuss ideas to address these challenges with you.

Oliver Brock (Science of Intelligence)
2022-04-21 16:00 - 17:30
More details to follow

C. jessica E. Metcalf, “Evolving Immunity: Responding, Learning, Forgetting “
2022-04-21 10:00 - 11:00
Abstract:
The adaptive immune system must meet the challenge of a constantly changing pool of both dangerous and friendly microbes. Evolution has favored a system that can learn what responses work, and forget responses that are unlikely to be needed again, and can seem both remarkably intelligent and remarkably stupid. I will introduce the concepts evolutionary biologists might use to frame thinking about the evolution of immunity, considering the pace of change in the environment, and patterns of exposure over age

Earlybird UNI-X
2022-04-07 10:00 - 11:00 | On Zoom
Meet Philipp Semmer and Frederic du Bois-Reymond, both partners at the venture capital firm Earlybird UNI-X. They will talk about funding for university spin-offs and why they believe that scientist and researcher should be more excited about entrepreneurship. They will also share their insights on deep tech companies becoming the next generation of unicorns.
The Zoom Link will be sent the day before the lecture.

Global Scientific Exchange Program - Part II
2022-03-31 10:00 - 11:00 | On Zoom
The talks will be held by Emmanuel Ousu Ahenkan and Tatiana Ngoli Moteu Marcos.

The Zoom Link will be sent the day before the lecture.

Global Scientific Exchange Program - Part I
2022-03-24 10:00 - 11:00 | On Zoom
The talks will be held by Arinze Lawrence Folarin, “My 175 days journey in Berlin”; Juliana T.C. Marcos “GSEP Internship: More than a research experience in neuromorphic vision at SCIoI”; and Kiprono Elijah Koech “Action Recognition in a Wildlife Setting – Taken a Leap”.
The Zoom Link will be sent the day before the lecture.

Henning Sprekeler (Science of Intelligence), "Architectural Design Principles for Intelligence: Modularity vs. Integration"
2022-03-17 10:00 - 11:00 | On Zoom
Abstract:
The world is modular. So – intuitively – it seems clear that cognitive systems that deal with the world should benefit from a modular architecture. Simple or less important problems should use less cognitive resources than complex or important problems, which – intuitively – may be achieved by changing the degree of modularity that a system uses. More modular, less power consumption, shallower processing; more integrated, more power consumption, deeper processing. Is this indeed the case? Is it beneficial for a cognitive system to regulate its degree of modularity depending on task demand? Does the human brain do this? How can a system determine which degree of modularity to use? In this talk, we will first provide an update on the project, and then invite everyone for discussion.
The Zoom Link will be sent the day before the lecture.

Mark Nawrot (North Dakota University), “Pursuit Eye Movements in the Perception of Depth From Motion Parallax”
2022-03-10 16:00 - 2022-03-24 17:00 | On Zoom
Abstract:
The brain performs critical calculations on visual information as we swiftly, yet effortlessly, navigate around objects and obstacles in our cluttered environment. Perhaps one of the most important calculations is for the perception of depth using the apparent relative motion of objects in the environment created by our own translation known as motion parallax. This presentation will illustrate how the visual system relies on the combination of retinal image motion with a pursuit eye movement signal to quickly determine the relative depths of objects using motion parallax. Relative depth is accurately modelled with a simple formula known as the motion-pursuit ratio.
The Zoom Link will be sent the day before the lecture.

Chaz Firestone (Johns Hopkins University), "Seeing 'How'"
2022-03-03 16:00 - 2022-03-10 17:00 | On Zoom
Abstract:
What is perception? The most intuitive and influential answer to this question has long been the one given by David Marr: To see the world is “to know what is where by looking” – to transform light into representations of objects and their features, located somewhere ins pace. But is this all that perception delivers? Consider a figure composed by pieces of a puzzle; certainly you see some colored shapes, as well as where they are located. Yet, beyond this, you may also see how they relate to one another: A, say, green piece can fit into the others, and even create a new object with a shape of its own.
In this talk, I present evidence that perception extracts relations between objects in much the same way as it processes the objects themselves, and that these relations are abstract, structured, and surprisingly sophisticated. We’ll explore (and experience) the perception of several sophisticated relations between objects, including combining, supporting, containing, covering, and fastening – as well as relational “illusions” in which objects appear to interact with mysteriously invisible entities. Together, this work suggests that we see not only “what” and “where”, but also “how”.
The Zoom Link will be sent the day before the lecture.

Marcel Brass (Science of Intelligence), “The Cognitive Neuroscience of Implementing Novel Instructions”
2022-02-17 16:00 - 17:30
One fundamental difference between human and non-human animals is the ability of humans to instantaneously implement instructed behaviour. While other animals acquire new behaviour via effortful trial-and-error learning or extensive practice, humans can implement novel behaviour based on instructions. This ability is presumably a key aspect of cultural learning. In my talk, I will discuss the neuro-cognitive basis of implementing novel instructions. I will provide evidence for the hypothesis that instruction following requires a reformatting of symbolic/declarative representations into a procedural format. This procedural format is capacity limited and shows characteristics of a ‘prepared reflex’. I will discuss potential implications of these findings for artificial systems.
The Zoom Link will be sent the day before the lecture.

Yuejiang Liu (EPFL University), "Learning Beyond the IID Setting with Robust and Adaptive Representations"
2022-02-17 10:00 - 11:00 | On Zoom
Abstract
Machine learning models have achieved stunning successes in the IID setting. Yet, beyond this setting, existing models still suffer from two grand challenges: brittle under covariate shift and inefficient for knowledge transfer. In this talk, I will introduce three approaches to tackle these challenges, namely self-supervised learning, causal representation learning, and test-time training. More specifically, I will share our recent findings on (i) incorporating prior knowledge of negative examples into representation learning, (ii) promoting causal invariance and structure by making use of data from multiple domains, (iii) exploiting extra information besides model parameters for effective test-time adaptation. I will show how these techniques enable deep neural networks to more robustly generalize and efficiently adapt to new environments in the motion or vision context. I will finally discuss the implications of these results on the design, training, and deployment of deep models for domain generalization and adaptation. Comments and feedback are more than welcome.

Paper Links
Social NCE: Contrastive Learning of Socially-Aware Motion Representations, ICCV’21
TTT++: When Does Self-Supervised Test-Time Training Fail or Thrive? NeurIPS’21
Collaborative Sampling in Generative Adversarial Networks, AAAI’20
Towards Robust and Adaptive Motion Forecasting: A Causal Representation Perspective, Preprint’21 (under review)

Bio
Yuejiang Liu is a PhD student at EPFL, advised by Alexandre Alahi. His research interests center around representation learning and its applications to autonomous agents. He is particularly excited about unsupervised learning for robust generalization and efficient adaptation.

The Zoom Link will be sent the day before the lecture.

Mathilde Caron, “Self-Supervised Learning: How To Learn From Images Without Human Annotations”
2022-02-10 10:00 - 11:00 | On Zoom
Abstract:
Self-supervised learning (SSL) consists in training neural network systems without using any human annotations. Typically, neural networks require large amounts of annotated data, which have limited their applications in fields where accessing these annotations is expensive or difficult. Moreover, manual annotations are biased towards a specific task and towards the annotator’s own biases, which can result in noisy and unreliable signals. Training systems without annotations could lead to better, more generic and robust representations. In this talk, I will present different contributions to the fast-growing field of SSL conducted during my PhD. I will finish by discussing open questions and challenges for the future of SSL.

The Zoom Link will be sent the day before the lecture.

Dimitri Coelho Mollo (Science of Intelligence), "The Concept of Intelligence - A progress report"
2022-01-27 10:00 - 11:00
In this presentation, I will report on the results of my work so far on the concept of intelligence, summarising some of the main points and proposals made, and opening the floor for open discussion about the topic.

The Zoom Link will be sent the day before the lecture.

Henning Sprekeler (Science of Intelligence), “Harnessing Machine Learning To Model Biological Systems”
2022-01-20 16:00 - 17:30
“Harnessing machine learning to model biological systems”
Abstract:
Classically, models of biological systems follow two different approaches. In bottom-up approaches, biological data are used to constrain a phenomenological model of the system in question, and the model is the studied to identify potential functions or potential consequences of the observations that flow into the model. Top-down approaches, on the other hand, start with a presumed function and ask how this question could be implemented in a biologically inspired architecture. Both approaches have been very successful, but both suffer from their own kind of problems. Bottom-up approaches often suffer from (potentially many) parameters that cannot be sufficiently constrained from data. Top-down approaches were in the past hard to combine with the complexities of the biological system in question. Recent advances in machine learning (ML) software now offer a promising hybrid approach, because they allow to optimize not only neural nets, but basically any dynamical system by gradient descent. I will offer a few examples how we have used ML to study biological systems, ranging from behavioral level (nature vs. nurture) down to the level of neural circuits (role of feedback for invariant sensory processing, and, time permitting, the function of different cell classes).
The Zoom Link will be sent the day before the lecture.

Mengmi Zhang (Harvard Medical School), “A Peek Into How Brain Computations Inspire New Paths in AI and How AI Elucidate Brain Computations”
2022-01-20 10:00 - 11:00 | On Zoom
Abstract:
The fields of neuroscience and AI have a long and intertwined history. From the study of simple and complex cells in visual areas of the brain to the recent success of convolution neural networks in many real-world applications, experimental and theoretical neuroscience has contributed significantly to designing smarter machines. In turn, AI models help us better understand brain computations that underlie biological intelligence. In my talk, I will present several efforts of deciphering brain computations by building computational models and quantifying model behaviors with human benchmarks in visual search and object recognition. Specifically, I divide my presentation into two parts. First, I will present works on predicting eye movement behaviors during visual search tasks. An intriguing property of some classical search tasks is asymmetry such that finding a target A among distractors B can be easier than finding B among A. We elucidate the mechanisms responsible for asymmetry in visual search. Second, I will introduce two works on contextual reasoning in object recognition. We systematically investigated critical properties of where, when, and how context modulates recognition in humans and machines.
Please refer to the following list of papers for details.
CVPR 2020: https://arxiv.org/pdf/1911.07349.pdf
ICCV 2021: https://arxiv.org/pdf/2104.02215.pdf
Neurips 2021: https://arxiv.org/pdf/2106.02953.pdf
Nature Communications 2018: https://www.nature.com/articles/s41467-018-06217-x.pdf
Bio:
Mengmi Zhang is a research scientist and principal investigator in Agency for Science, Technology and Research (A*STAR), Singapore. Prior to this, Dr. Zhang is a postdoc with Gabriel Kreiman at the Harvard Medical School from 2019-2021. She obtained her PhD at the National University of Singapore (2015-2019) and was a visiting graduate student in KreimanLab at the Harvard Medical School (2017-2018). Her research background is multi-disciplinary at the intersection of artificial intelligence and computational neuroscience. She has published multiple papers in top-tier conferences (such as CVPR, ICCV, IROS and NeurIPS) and international science journals (TPAMI, Nature Communications, Nature Human Behaviors). Her papers often come with humorous titles like “Finding any Waldo with zero-shot invariant and efficient visual search” or “When Pigs Fly”. She will become an adjunct assistant professor in the Department of Electrical and Computer Engineering, National University of Singapore from August 2022.

The Zoom Link will be sent the day before the lecture.

Lars Lewejohann (Science of Intelligence), “What’s on a Mouse’s Mind? Behavioral Measures To Understand Experiences and Needs of an Animal”
2022-01-06 16:00 - 17:30
What’s on a mouse’s mind? Behavioral measures to understand experiences and needs of an animal
Lars Lewejohann, Freie Universität Berlin, German Federal Institute for Risk Assessment (BfR), German Centre for the Protection of Laboratory Animals (Bf3R)
Mice, as all other living creatures, have adapted to specific living conditions in the course of evolution. From a human point of view, the behavior of animals is therefore not always easy to understand. This applies not only to the question of whether mice are actually capable of behaving intelligently, but also to the question of what is necessary for optimizing animal welfare of laboratory animals. In our work, we are interested in both questions and follow an animal-centered approach and are giving mice their say. Of course mice cannot fill out questionnaires, but we have developed a series of behavioral tests that allow to query the animals. In this lecture I will outline our approach with regard to improving housing and living conditions as well as the implications of using mice as a model species for the science of intelligence.
The Zoom Link will be sent the day before the lecture.

Ruben Arslan (MPI Berlin): “Bad Science vs. Open Science. The Replication Crisis and Possible Ways Out.”
2022-01-06 10:00 - 11:00 | On Zoom
Estimates from large-scale replication projects in psychology suggest that the majority of studies from top journals do not replicate. Using commonly accepted research methods, several academic fields amassed prolific, seemingly coherent literatures on phenomena that do not exist, such as extrasensory perception and depression candidate genes. Throughout the biomedical and life sciences, data detectives keep finding highly cited papers that are riddled with errors invalidating their conclusions. Our textbooks are full of findings that do not replicate or are otherwise in serious doubt.
Academia as a system has issues, but can we use the scientific method to understand and remedy them? A vibrant reform movement is seeking to do so, but it is hard to keep track of all the suggestions to do better and tell fads from truly beneficial reforms. I outline concrete plans and paths that could lead to lasting improvements, such as PCI Registered Reports, the Peer Reviewer’s Openness Initiative, post publication peer review, and guideline and incentive setting at the journal, hiring and funding level.

The Zoom Link will be sent the day before the lecture.

Elke Weber (Princeton University), "Personal and Social Information Search and Integration for Intelligent Decisions on Climate Action"
2021-12-23 10:00 - 11:00 | On Zoom
Abstract:
Some of my past and current research looks at “decisions from experience,” i.e., decisions based on the personally experienced outcomes of past choices, along the lines of reinforcement learning models and how such learning and updating is related to and differs from the way in which people and other intelligent agents use other sources of information, e.g., vicarious feedback (anecdotal/social and/or in the form of statistical distributions of outcomes) or science- or model-based outcome predictions. What happens when these different sources of forecasts of the consequences of choices disagree with each other? How do such conflicts get resolved? How do these different ways of learning and updating over time lie at the basis of the formation and/or modification of social norms? And how can answers to this complex of questions be put to use to motivate greater action on climate change?
The Zoom Link will be sent the day before the lecture.

Lars Chittka (Queen Mary, University of London), “The Mind of a Bee”
2021-12-16 16:00 - 17:30 | TU Berlin
Abstract: Bees have a diverse instinctual repertoire that exceeds in complexity that of most vertebrates. This repertoire allows the social organisation of such feats as the construction of precisely hexagonal honeycombs, an exact climate control system inside their home, the provision of the hive with commodities that must be harvested over a large territory (nectar, pollen, resin, and water), as well as a symbolic communication system that allows them to inform hive members about the location of these commodities. However, the richness of bees’ instincts has traditionally been contrasted with the notion that bees’ small brains allow little behavioural flexibility and learning behaviour. This view has been entirely overturned in recent years, when it was discovered that bees display abilities such as counting, attention, simple tool use, learning by observation and metacognition (knowing their own knowledge). Thus, some scholars now discuss the possibility of consciousness-like phenomena in the bees. These observations raise the obvious question of how such capacities may be implemented at a neuronal level in the miniature brains of insects. We need to understand the neural circuits, not just the size of brain regions, which underlie these feats. Neural network analyses show that cognitive features found in insects, such as numerosity, attention and categorisation-like processes, may require only very limited neuron numbers. Using computational models of the bees’ visual and olfactory systems, we explore whether seemingly advanced cognitive capacities might ‘pop out’ of the properties of relatively basic neural processes in the insect brain’s visual processing area, and their connection with the mushroom bodies, higher order learning centres in the brains of insects.
The Zoom Link will be sent the day before the lecture.
