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Keynote speakers
Individuating Cognitive Capacities in Terms of Cognitive Homology
How should scientists carve up the cognitive domain to generate good predictions, explanations, and models of cognition? Based on join work with the philosopher and developmental psychologist Mariel Goddu, I argue that cognitive categories should be constructed the same way that biological categories are: in terms of homology. I will make use of a recent account of Character Identity Mechanisms (DiFrisco, Wagner and Love 2020) to make sense of the notion of “cognitive homology.” The consequence of this notion is that brain structures and the organism’s ongoing interactions with the environment turn out to be crucial for individuating cognitive homologies, and thus for individuating cognitive capacities.
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Transforming Body Perceptions through the Senses: Innovative Neuroscientific Approaches and Applications
Body perceptions are crucial for individuals’ motor, social, and emotional functioning. Importantly, neuroscientific research shows that body perceptions are continually updated through sensorimotor information. This talk will showcase our group’s research on how sensory feedback, particularly sound related to one’s body and actions, can modify body perception, leading to Body Transformation Experiences. I will discuss how these findings contribute to the design of innovative body-centered technologies to address people’s needs and support behavior change. Additionally, beyond such practical applications, these technologies serve as valuable tools for examining multisensory influences on body perception. Our ERC-funded project, BODYinTRANSIT, aims to establish a framework for individualized sensorial manipulation of body perceptions with long-lasting effects in everyday use contexts. The framework stands on four scientific pillars to induce, measure, support, personalize, and preserve body transformations: neuroscience of multisensory body perception; data modeling of the links between body perception, behavior, and emotion; wearable-based embodied multisensory interaction design; and field studies in real-life contexts with diverse user groups. Finally, I will identify challenges and opportunities in this research field.
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Human Autonomy in an AI World
Human autonomy is a pillar of contemporary ethics and politics, particularly in liberal democracies like the UK, as well as in biomedical ethics. Psychological research robustly shows that a personal sense of autonomy is essential to wellbeing and sustained motivation. In technology design, such felt autonomy also underpins user adoption, engagement, and satisfaction. But today, human autonomy is coming under new threat by AI-driven technologies. Meanwhile, current AI research and policy, questions of safety, fairness, or explainability have received far more attention than how AI may impact autonomy – let alone how to design AI in an autonomy-supporting fashion. In this talk I will describe a vision of a socio-technical future where evidence-based and legitimate design and regulatory guidelines ensure that algorithmic environments safeguard and support human autonomy.
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Rafael A. Calvo is Professor at Imperial College London (UK). His focus is on the design of systems that support wellbeing in areas of mental health, medicine and education, and on the ethical challenges raised by new technologies.
Integration and Transfer of Action and Language Knowledge in Learning Robots
The integration of action and language knowledge and skills is a pivotal element in the realm of human intelligence and stands as one of the most compelling challenges in scientific inquiry. In my presentation I will review the body of evidence and insights collected by attempting to design learning robots capable of understanding and using language and operating in a physical environment. I will particularly highlight the contribution of foundational models and the integration of passive observational learning and active embodied learning modalities. Furthermore, I will examine the merits of learning methods that foster the simultaneous development of diverse competencies indirectly by focusing on the optimization of a single learning objective.
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Discovering Cognitive Structure using
Large-Scale Social Data and Artificial Intelligence
What can we learn about the structure of individual minds, human or artificial, using large-scale social data, such as the textual or visual data flowing through search engines and social media platforms? In this keynote, I present a diverse range of studies showing that large-scale social data can reveal striking insights into the mind, ranging from the structure of embodied cognition to the psychological biases that drive the formation of stereotypes. I will give special attention to presenting the results of a study we recently published in Nature which demonstrates how combining large-scale image and text data from online sources, analyzed via artificial intelligence, can reveal the latent multimodal structure of gender stereotypes. I will then share ongoing work that builds on these results by revealing the multimodal structure of intersectional stereotypes (e.g., gendered ageism) not only in human minds, but also in the judgments and associations formed by generative AI. Importantly, I will emphasize that big data and artificial intelligence are useful not only for testing existing theories about cognitive structure, but also for discovering and testing new theories. As an example, I will discuss ongoing work that harnesses this suite of algorithmic methodologies to unveil deep connections between the representational structure of gender and the concreteness and abstractness of concepts across domains, using visual and textual data, as well as behavioral outputs from AI. Opportunities for further advancing the integration of computer science, cognitive science, and cultural sociology will be discussed.
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The Fox and the Grapes. The Impact of Neuroimaging Data on Cognitive Ontology
In the early days of classical cognitive science, when the mind was often likened to a computer, cognitive theories developed largely without concern for the ‘hardware’—the brain. Neuroscience was seen as irrelevant to psychological inquiry. However, this began to change in the 1990s with the rise of functional Magnetic Resonance Imaging (fMRI). Neuroscience started to play a significant role in shaping psychological theories, as researchers sought to map specific cognitive functions onto corresponding neural structures. Some proposed that an ideal neurocognitive theory would feature a perfect one-to-one mapping between functions and structures. However, such precise mappings have proven elusive. Instead of neat pairings, we find complex, many-to-many relationships. This raises an important question: how can we reconcile the ideal of one-to-one mappings with the current, entangled status of our knowledge? In this presentation, I will explore four (non-mutually exclusive) approaches that may help us refine our neuro-inspired Cognitive Ontology: (a) We may have chosen the wrong structures or functions, and a one-to-one mapping might be found with the correct selections; (b) The one-to-one mapping might be unattainable, and a probabilistic mapping could be a more realistic goal; (c) It’s possible that the one-to-one mapping exists, but our concepts of ‘functions’ and ‘structures’ need to be redefined; (d) One-to-one mappings may exist, but they might need to be contextualized to specific circumstances.
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The synthetic method in the age of AI
A symposium in honor of Roberto Cordeschi
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From Surrogative Reasoning to Surrogative Simulation
The so-called ‘synthetic method’ is a form of surrogative reasoning, a term used in the philosophy of science to refer to the use of a model M (e.g. a robot) to acquire knowledge about the system T it represents (e.g. a living system). In recent years, a new use of (robotic) models has gained momentum, which can be called ‘surrogative stimulation’. In surrogative stimulation, the model M is not used to learn about T, but to stimulate another system F in order to learn how the latter would react to T (the system represented by the model). The talk aims to clarify how surrogative stimulation differs from the synthetic method so thoroughly studied by Roberto Cordeschi, and how the two can be integrated, using examples from ethorobotics and social robotics.
Edoardo Datteri (University of Milano-Bicocca, Milan)
The Synthetic Method in Cognitive Robotics for Interaction
An important objective in current robotics is the development of robots capable of nuanced and effective human-robot interaction (HRI). Achieving this goal requires a deep understanding of human cognition, and robots can serve as ideal tools for this investigation. By constructing and programming robots, it is possible to test and model the dynamics of human interaction, gaining insights into human cognition through a synthetic and embodied approach. Drawing inspiration from the natural progression of human cognitive skills, a developmental perspective is adopted to design robots that can learn from their direct interactions with the environment and human partners. The integration of memory, motivation, and anticipation within a cognitive architecture enhances robots’ social awareness and autonomous learning capabilities. This approach not only contributes to a deeper understanding of human cognition but also achieves the crucial technological goal of building machines that can dynamically adapt to individual human partners over time, fostering long-term collaboration and interaction.
Alessandra Sciutti (Istituto Italiano di Tecnologia, Genoa)
Synthesizing Autonomy: From Biology to Robots and Back
The development of autonomous robotic systems has predominantly focused on enabling machines to independently complete predefined tasks. However, the emerging field of open-ended learning aims to push the boundaries of autonomy by creating systems capable of operating in unknown and unstructured environments without specific task assignments. In particular, the concept of Intrinsic Motivations (IMs), derived from animal and human psychology, is at the core of the development of a new typology of artificial agents capable of autonomously gathering knowledge and competences through the interaction with the environment. This line of research not only stresses the importance of the cognitive sciences for technological advancements, but also shows how robots and AI in general can be used as models of a feature that we consider essential of what it means to be human. Moreover, regardless of whether robotic autonomy can be equated to human autonomy, open-ended learning systems pose the critical issue of managing and aligning artificial agents that, to maintain the desired autonomy, cannot be pre-programmed or limited, even at their goal-setting level.
Alessandra Sciutti
Vieri Giuliano Santucci (ISTC-CNR, Rome)