Biology subjects

Menghi, N.

Publications and source records attributed to Menghi, N..

6 recordsLinked to original sources

bioRxiv2025

Impact of Task Similarity and Training Regimes on Cognitive Transfer and Interference

Learning depends not only on the content of what we learn, but also on how we learn and on how experiences are structured over time. To investigate how task similarity and training regime interact during learning, we trained participants on spatial and conceptual learning tasks that shared either similar or distinct underlying structures, using either interleaved or blocked regimes. Interleaving the two tasks hindered performance when their structures were similar, compared to when they were different. In contrast, blocked training produced the opposite effect: it improved performance and facilitated transfer across similar tasks. This effect, however, emerged only when participants first learned the conceptual task, followed by the spatial task, suggesting an asymmetric interaction between task order and structural similarity. We also replicated our results using a neural network model, providing converging evidence for the computational principles governing the interplay between training regime and structural similarity in multi-task learning.

neuroscience↗

bioRxiv2025

Neural Oscillations Coordinate Continuous Error Correction During Force Control

Effective motor control depends on the brains ability to monitor performance and make continuous corrections. While many studies focus on discrete errors, everyday actions often require ongoing feedback-based adjustments. Here, we used an isometric force control task with EEG to investigate the neural dynamics supporting real-time error correction. Participants maintained a constant grip force with or without continuous visual feedback. With feedback, behavior showed [~]6 Hz rhythmic fluctuations, consistent with active correction. These fluctuations were mirrored in EEG activity across theta, beta, and alpha bands--oscillations linked to performance monitoring, updating, and attentional control. Without feedback, performance decayed linearly, and the corresponding neural signatures were reduced. These findings suggest that continuous sensory feedback engages a dynamic feedback loop involving distinct neural processes that support adaptive behavior. Our results highlight the importance of oscillatory activity in tracking and correcting moment-to-moment fluctuations in force, offering insight into the neural basis of feedback-loop force control.

neuroscience↗

bioRxiv2025

The effects of task similarity during representation learning in brains and neural networks

The complexity of our environment poses significant challenges for adaptive behavior. Recognizing shared structures across tasks can theoretically improve learning through generalization. However, how such shared representations emerge and influence performance remains poorly understood. Contrary to expectations, our findings revealed that individuals trained on tasks with similar low-dimensional structures performed worse than those trained on dissimilar tasks. Magnetoencephalography revealed correlated neural representations in the samestructure group and anticorrelated ones in the different-structure group. Crucially, practice reduced this performance gap and shifted the neural representations of the tasks in the samestructure group towards anticorrelation, like those in the different-structure group. A neural network model trained on similar tasks replicated these findings: tasks with similar structures require more iterations to orthogonalize their representations. These results highlight a complex interplay between task similarity, neural dynamics, and behavior, challenging traditional assumptions about learning and generalization.

neuroscience↗

bioRxiv2022

Cluster-Based Inference for Memory-Based Cognition

This paper proposes a model of hippocampal-based category learning using a cluster-based inference framework which produces two systems (i) an extendable cluster-based memory module (CMM) that provides representations of learning episodes with strong pattern separation, and supports online decision making during that learning, (ii) cluster-based task modules (CTMs) which consolidate clusters learnt by CMM to support higher-accuracy decision making in the mid-term. Learning in CMMs optimises the joint probability of stimuli and category labels, whereas learning in CTMs optimises the probability of categories given stimuli. The latter system learns from the former via a process of "cluster consolidation". We apply the model to data from a behavioral learning task and find that, as well as improving categorisation performance, cluster consolidation decreases recognition scores for old cues but increases them for novel cues. This model-based perspective explains forgetting and false memory effects as serving future categorisation performance. The paper also expresses a view that models of memorybased cognition should provide human-level performance on complex categorisation tasks, and do so with minimal labelled data. In working toward this goal we therefore compared the accuracy of CMM- and CTM-based decision making to standard Softmax Regression approaches on benchmark machine learning datasets. This produced mixed results. We found some significant improvements of CMM over Softmax Regression and of CTM over CMM. Overall, our framework reconciles complementary learning systems theory with more recent findings from cognitive neuroscience of generative replay and hippocampal-based generalisation.

neuroscience↗

bioRxiv2020

Multitask Learning over Shared Subspaces

This paper uses constructs from machine learning to define pairs of learning tasks that either shared or did not share a common subspace. Human subjects then learnt these tasks using a feedback-based approach. We found, as hypothesised, that subject performance was significantly higher on the second task if it shared the same subspace as the first. Additionally, accuracy was positively correlated over subjects learning same-subspace tasks, and negatively correlated for those learning different-subspace tasks. These results were compared to the behaviour of a Neural Network model trained using sequential Bayesian learning. Human performance was found to be consistent with a minimal capacity variant of this model. Networks with an increased representational capacity, and networks without Bayesian learning, did not show these transfer effects. We propose that the concept of shared subspaces provides a useful framework for the experimental study of human multitask and transfer learning. Author summaryHow does knowledge gained from previous experience affect learning of new tasks ? This question of "Transfer Learning" has been addressed by teachers, psychologists, and more recently by researchers in the fields of neural networks and machine learning. Leveraging constructs from machine learning, we designed pairs of learning tasks that either shared or did not share a common subspace. We compared the dynamics of transfer learning in humans with those of a multitask neural network model, finding that human performance was consistent with a minimal capacity variant of the model. Learning was boosted in the second task if the same subspace was shared between tasks. Additionally, accuracy between tasks was positively correlated but only when they shared the same subspace. Our results highlight the roles of subspaces, showing how they could act as a learning boost if shared, and be detrimental if not.

animal behavior and cognition↗

bioRxiv2018

Action-specific feature processing in the human visual cortex

Sensorimotor integration involves feedforward and reentrant processing of sensory input. Grasp-related motor activity precedes and is thought to influence visual object processing. Yet, while the importance of reentrant feedback is well established in perception, the top-down modulations for action and the neural circuits involved in this process have received less attention. Do action-specific intentions influence the processing of visual information in the human cortex? Using a cue-separation fMRI paradigm, we found that action-specific instruction (manual alignment vs. grasp) influences the cortical processing of object orientation several seconds after the object had been viewed. This influence occurred as early as in the primary visual cortex and extended to ventral and dorsal visual stream areas. Importantly, this modulation was unrelated to non-specific action planning. Further, the primary visual cortex showed stronger functional connectivity with frontal-parietal areas and the inferior temporal cortex during the delay following orientation processing for align than grasping movements, strengthening the idea of reentrant feedback from dorsal visual stream areas involved in action. To our knowledge, this is the first demonstration that intended manual actions have such an early, pervasive, and differential influence on the cortical processing of vision.

neuroscience↗