AFC Lab Talk Series

Together with the Cognitive and Affective Regulation Laboratory (CARLA) we host a virtual talk series together — now, typically on Mondays. Our aim: to support early-career researchers and underrepresented groups by providing a platform for their work and increasing networking opportunities.

If you'd like to give a talk, drop us a message and we'll get it organised.

You can access the talks via this Zoom Link.

Mon, 23 Sept 2024
10:30
Comparing supervised learning dynamics: Deep neural networks match human data efficiency but show a generalisation lag
University of Bern
Recent research has seen many behavioral comparisons between humans and deep neural networks (DNNs) in the domain of image classification. Often, comparison studies focus on the end-result of the learning process by measuring and comparing the similarities in the representations of object categories once they have been formed. However, the process of how these representations emerge—that is, the behavioral changes and intermediate stages observed during the acquisition—is less often directly and empirically compared. In this talk, I'm going to report a detailed investigation of the learning dynamics in human observers and various classic and state-of-the-art DNNs. We develop a constrained supervised learning environment to align learning-relevant conditions such as starting point, input modality, available input data and the feedback provided. Across the whole learning process we evaluate and compare how well learned representations can be generalized to previously unseen test data. Comparisons across the entire learning process indicate that DNNs demonstrate a level of data efficiency comparable to human learners, challenging some prevailing assumptions in the field. However, our results also reveal representational differences: while DNNs' learning is characterized by a pronounced generalisation lag, humans appear to immediately acquire generalizable representations without a preliminary phase of learning training set-specific information that is only later transferred to novel data.
Mon, 30 Sept 2024
10:30
Face matching and decision making: The influence of framing, task presentation and criterion placement
University of Kent
Many situations rely on the accurate identification of people with whom we are unfamiliar. For example, security at airports or in police investigations require the identification of individuals from photo-ID. Yet, the identification of unfamiliar faces is error prone, even for practitioners who routinely perform this task. Indeed, even training protocols often yield no discernible improvement. The challenge of unfamiliar face identification is often thought of as a perceptual problem; however, this assumption ignores the potential role of decision-making and its contributing factors (e.g., criterion placement). In this talk, I am going to present a series of experiments that investigate the role of decision-making in face identification.
Tue, 01 Oct 2024
12:00
How Generative AI is Revolutionizing the Software Developer Industry
Università della Svizzera Italiana
Generative AI is fundamentally transforming the software development industry by improving processes such as software testing, bug detection, bug fixes, and developer productivity. This talk explores how AI-driven techniques, particularly large language models (LLMs), are being utilized to generate realistic test scenarios, automate bug detection and repair, and streamline development workflows. As these technologies evolve, they promise to improve software quality and efficiency significantly. The discussion will cover key methodologies, challenges, and the future impact of generative AI on the software development lifecycle, offering a comprehensive overview of its revolutionary potential in the industry.