Auditorium | CUBE 1

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Jul 19 @ 15:00 CEST - Jul 19 @ 18:30 CEST

In-person session

Join us at the 2024 WoMP-PRIME professional development symposium where we merge self-promotion strategies with a focus on professional growth and empowerment. Delve into conversation tailored to navigating the job market (industry, government, and academia), building a robust network, and mastering the art of grant and award applications. Together, we'll dismantle barriers, celebrate diverse voices, and equip you with the tools and confidence to excel in your career aspirations while championing inclusivity and representation.

Jul 20 @ 12:00 CEST - Jul 20 @ 13:00 CEST

In-person session

Modelling real-world visual perception with deep learning Deep neural networks (DNNs) have emerged as powerful computer algorithms that achieve human-level performance on challenging tasks such as object recognition and language comprehension. This has opened up a new research field in which explanations of brain function are sought in comparisons between DNNs and human behaviour and brain activations. In this talk I will discuss why and how this computational modelling approach could indeed help us explain the brain, focusing specifically on the case of human visual perception of naturalistic real-world images and videos.

Jul 20 @ 16:00 CEST - Jul 20 @ 17:00 CEST

Public session

A discussion with Rich Shiffrin about his career as a mathematical psychologist. The discussion will be led by E.J. Wagenmakers.

Jul 21 @ 14:00 CEST - Jul 21 @ 15:00 CEST

Public session

Reclaiming AI as a theoretical tool for cognitive science The idea that human cognition is, or can be understood as, a form of computation is a useful conceptual tool for cognitive science. It was a foundational assumption during the birth of cognitive science as a multidisciplinary field, with Artificial Intelligence (AI) as one of its contributing fields. One conception of AI in this context is as a provider of computational tools (frameworks, concepts, formalisms, models, proofs, simulations, etc.) that support theory building in cognitive science. The contemporary field of AI, however, has taken the theoretical possibility of explaining human cognition as a form of computation to imply the practical feasibility of realising human(-like or -level) cognition in factual computational systems; and, the field frames this realisation as a short-term inevitability. Yet, as we formally prove herein, creating systems with human(-like or -level) cognition is intrinsically computationally intractable. This means that any factual AI systems created in the short-run are at best decoys. When we think these systems capture something deep about ourselves and our thinking, we induce distorted and impoverished images of ourselves and our cognition. In other words, AI in current practice is deteriorating our theoretical understanding of cognition rather than advancing and enhancing it. The situation could be remediated by releasing the grip of the currently dominant view on AI and by returning to the idea of AI as a theoretical tool for cognitive science. In reclaiming this older idea of AI, however, it is important not to repeat conceptual mistakes of the past (and present) that brought us to where we are today. This is joint work with Olivia Guest, Federico Adolfi, Ronald de Haan, Antonina Kolokolova, and Patricia Rich. The full paper is available here (https://osf.io/preprints/psyarxiv/4cbuv).

Jul 21 @ 16:40 CEST - Jul 21 @ 17:40 CEST

Public session

This is the annual Society for Mathematical Psychology Open Business meeting. All are welcome to attend. We cover updates on Society business, updates from our Journal editors and announce all the awards.

Jul 22 @ 14:00 CEST - Jul 22 @ 15:00 CEST

Public session

Models and Meaning: Coming Out as a Mathematical Psychologist I love introducing myself to laypeople as a "mathematical psychologist" because they often view the term as an inherent contradiction between the strict formal world of mathematical and computational models on the one hand and the fuzzy ineffable realm of the mind on the other. By embracing that apparent conflict, I think mathematical psychology is in a unique position to help advance the science of cognition beyond the recent crises which have exposed issues in our experimental and statistical methods. Mathematical psychology has already played a large part in developing tools that expand access to Bayesian statistics and psychometrics, but I think it has an even more valuable part to play in advancing the psychological theories our methods are meant to test---by imbuing formal models with meaning and enabling meaning to be expressed via formal models. In this talk, I consider a number of issues I have encountered in developing mathematical models to express cognitive theories. These issues manifest, like mathematical psychology itself, as tensions between seemingly opposing ends of a continuum. These include some specific issues related to my own areas of research, like the tension between episodic and semantic memory; the tension between associations and the items they bind together; and the tension between neural and cognitive levels of description. They also include broader issues like the tension between statistical/descriptive models and causal/mechanistic models; the tension between quantitative fit and explanatory power; and the tension between models as psychometric tools versus expressions of theory. I make no attempt to resolve these tensions. Instead, I argue that the value of mathematical psychology lies in providing the language to articulate these tensions and to enable researchers to decide for themselves where they fall along these various continua in a given scientific context---to express what they mean in terms of models and to use models to help them explore what they mean.