Biology subjects

Manenti, G. L.

Publications and source records attributed to Manenti, G. L..

3 recordsLinked to original sources

bioRxiv2025

The Sweet Spot of Plasticity in Perceptual Learning Dependson Training Variability

Perceptual learning improves sensory discrimination, yet the brain must balance specificity and generalization, especially in variable environments. To investigate how variability shapes perceptual learning, we used functional magnetic resonance imaging while human subjects performed an orientation discrimination task with low or high task-irrelevant variability in spatial frequency. Behaviorally, both training regimes improved performance, but only high variability training enabled transfer to a novel location. Retinotopic orientation representations in early visual areas changed similarly across groups, indicating that variability does not alter local sensory representations. In contrast, functional connectivity differed: low variability training enhanced task-dependent coupling between the intraparietal sulcus and V2, while high variability training differentially affected connectivity with hV4. These results support a "sweet spot" model of perceptual learning, in which the locus of learning flexibly depends on the training environment. But rather than modifying sensory representations, variability reconfigures network-level readout, enabling specific or generalizable improvements in perception.

neuroscience↗

bioRxiv2025

BELIEFS: A Hierarchical Theory of Mind Model based on StrategyInference

Theory of Mind (ToM) refers to the ability to infer another agents latent mental states, such as intentions, beliefs, and strategies, to predict their behavior. A core feature of ToM is its recursive structure: individuals reason not only about what others think, but also about what others think about them. Existing computational models typically assume that ToM Level-0 (L0) agents rely on a fixed heuristic (e.g., Win-Stay Lose-Shift, WSLS), an assumption that fails to capture the diversity of non-mentalizing strategies humans actually use. Here we introduce BELIEFS, a probabilistic ToM framework that infers latent L0 strategies directly from behavior using a Hidden Markov Model (HMM) that enables flexible tracking of elemental strategies and dynamic switches between them without relying on a single predefined heuristics. A second HMM tracks the beliefs about the Opponents ToM level and dynamic changes therein. We evaluated BELIEFS across four classic dyadic games (Matching Pennies, Prisoners Dilemma, Bach or Stravinsky, Stag Hunt) under varying learning rates and volatility of strategy switches. Predictive performance, quantified via cumulative negative log-likelihood (NLL) of the opponents choices, was compared against chance and a WSLS-based ToM model, with BELIEFS consistently achieving superior accuracy across conditions. Strategy inference was assessed using trial-wise confusion matrices and Cohens {kappa}, revealing robust above-chance classification. Additionally, separability of ToM levels across games indicated that competitive games are particularly informative for distinguishing recursive reasoning from deterministic L0 strategies. The model also successfully tracked the opponents recursive reasoning depth (i.e. ToM level) by distinguishing action sequences generated by L0 versus L1 opponents. Parameter-recovery analyses confirmed reliable estimation of core transition parameters. Together, these results show that BELIEFS provides a flexible, computationally grounded account of human ToM, jointly inferring surface-level strategies and recursive reasoning, with applications to modeling adaptive behavior in dynamic interactive environments.

neuroscience↗

bioRxiv2022

Input variability taps on invariant representations to enable generalization in visual perceptual learning

Stimulus and location specificity are long considered hallmarks of visual perceptual learning. This renders visual perceptual learning distinct from other forms of learning, where generalization can be more easily attained, and unsuitable for practical applications, where generalization is key. Based on hypotheses derived from the structure of the visual system, we test here whether stimulus variability can unlock generalization in perceptual learning. We train subjects in orientation discrimination, while we vary the amount of variability in a task-irrelevant feature, spatial frequency. We find that independently of task difficulty, this manipulation enables generalization of learning to new stimuli and locations, while not negatively affecting the overall amount of learning on the task. We then use deep neural networks to investigate how variability unlocks generalization. We find that networks develop invariance to the task-irrelevant feature when trained with variable inputs. The degree of learned invariance strongly predicts generalization. A reliance on invariant representations can explain variability-induced generalization in visual perceptual learning, suggests new targets for understanding the neural basis of perceptual learning in high-order visual cortex, and presents an easy to implement modification of common training paradigms that may benefit practical applications.

neuroscience↗