Giorgio L. Manenti

This study shows that high variability training in perceptual learning enables transfer to novel locations, while low variability training enhances task-specific connectivity, supporting a 'sweet spot' model of learning.

BELIEFS is a probabilistic ToM model that infers latent strategies and beliefs from behavior, outperforming WSLS-based models in predicting opponent choices across games.

The paper investigates whether visual perceptual learning is effector-specific by studying decision-making processes in an orientation discrimination task using different effectors.

This paper shows that introducing variability in training improves generalization in visual perceptual learning by promoting invariant representations, without reducing task performance.

This study finds that chronic cocaine users show altered sensitivity to gain information during risky decision-making, leading to increased risk-seeking in unfavorable scenarios.