6 papers
Grounded world models in biological organisms and future embodied AI
Giovanni Pezzulo, Davide Nuzzi, Marco D'Alessandro +3
Recent advances in generative and embodied AI have been driven by large-scale predictive learning over multimodal data. However, the resulting systems remain largely based on passi…
Gravity Prior and Temporal Horizon Shape Interceptive Behavior under Active Inference
Marta Russo, Antonella Maselli, Federico Maggiore +1
Accurate interception of moving objects, such as catching a ball, requires the nervous system to overcome sensory delays, noise, and environmental dynamics. One key challenge is pr…
Sensory robustness through top-down feedback and neural stochasticity in recurrent vision models
Antonino Greco, Marco D'Alessandro, Karl J. Friston +2
Biological systems leverage top-down feedback for visual processing, yet most artificial vision models succeed in image classification using purely feedforward or recurrent archite…
Predictive Strategies for the Control of Complex Motor Skills: Recent Insights into Individual and Joint Actions
Marta Russo, Antonella Maselli, Dagmar Sternad +1
Humans perform exquisite sensorimotor skills, both individually and in teams, from athletes performing rhythmic gymnastics to everyday tasks like carrying a cup of coffee. The "pre…
Transitive inference as probabilistic preference learning
Francesco Mannella, Giovanni Pezzulo
Transitive Inference (TI) is a cognitive task that assesses an organism's ability to infer novel relations between items based on previously acquired knowledge. TI is known for exh…
Neural representation in active inference: using generative models to interact with -- and understand -- the lived world
Giovanni Pezzulo, Leo D'Amato, Francesco Mannella +4
This paper considers neural representation through the lens of active inference, a normative framework for understanding brain function. It delves into how living organisms employ…