Category Archives: Cognitive Sciences

Short letter with evidences of the use of models in mammal decision making, relating it to reinforcement learning

October 9, 2025 08:29 ,

Ivo Jacobs, Tomas Persson, Peter Gärdenfors, Model-based animal cognition slips through the sequence bottleneck, Trends in Cognitive Sciences, Volume 29, Issue 10, 2025, Pages 872-873, 10.1016/j.tics.2025.06.009.

In a recent article in TiCS, Lind and Jon-And argued that the sequence memory of animals constitutes a cognitive bottleneck, the ‘sequence bottleneck’, and that mental simulations require faithful representation of sequential information. They therefore concluded that animals cannot perform mental simulations, and that behavioral and neurobiological studies suggesting otherwise are best interpreted as results of associative learning. Through examples of predictive maps, cognitive control, and active sleep, we illustrate the overwhelming evidence that mammals and birds make model-based simulations, which suggests the sequence bottleneck to be more limited in scope than proposed by Lind and Jon-And […]

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Model-based RL that addresses the problem of building models that can produce off-distribution data more safely

September 25, 2025 05:13 ,

X. -Y. Liu et al., DOMAIN: Mildly Conservative Model-Based Offline Reinforcement Learning, IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 55, no. 10, pp. 7142-7155, Oct. 2025, 10.1109/TSMC.2025.3578666.

Model-based reinforcement learning (RL), which learns an environment model from the offline dataset and generates more out-of-distribution model data, has become an effective approach to the problem of distribution shift in offline RL. Due to the gap between the learned and actual environment, conservatism should be incorporated into the algorithm to balance accurate offline data and imprecise model data. The conservatism of current algorithms mostly relies on model uncertainty estimation. However, uncertainty estimation is unreliable and leads to poor performance in certain scenarios, and the previous methods ignore differences between the model data, which brings great conservatism. To address the above issues, this article proposes a mildly conservative model-based offline RL algorithm (DOMAIN) without estimating model uncertainty, and designs the adaptive sampling distribution of model samples, which can adaptively adjust the model data penalty. In this article, we theoretically demonstrate that the Q value learned by the DOMAIN outside the region is a lower bound of the true Q value, the DOMAIN is less conservative than previous model-based offline RL algorithms, and has the guarantee of safety policy improvement. The results of extensive experiments show that DOMAIN outperforms prior RL algorithms and the average performance has improved by 1.8% on the D4RL benchmark.

Related: 10.1109/TSMC.2025.3583392
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Accelerating recognition processes of images through NNs that do not vary their weights

September 11, 2025 09:17 ,

Yanli Yang, A brain-inspired projection contrastive learning network for instantaneous learning, Engineering Applications of Artificial Intelligence, Volume 158, 2025 , 10.1016/j.engappai.2025.111524.

The biological brain can learn quickly and efficiently, while the learning of artificial neural networks is astonishing time-consuming and energy-consuming. Biosensory information is quickly projected to the memory areas to be identified or to be signed with a label through biological neural networks. Inspired by the fast learning of biological brains, a projection contrastive learning model is designed for the instantaneous learning of samples. This model is composed of an information projection module for rapid information representation and a contrastive learning module for neural manifold disentanglement. An algorithm instance of projection contrastive learning is designed to process some machinery vibration signals and is tested on several public datasets. The test on a mixed dataset containing 1426 training samples and 14,260 testing samples shows that the running time of our algorithm is approximately 37 s and that the average processing time is approximately 2.31 ms per sample, which is comparable to the processing speed of a human vision system. A prominent feature of this algorithm is that it can track the decision-making process to provide an explanation of outputs in addition to its fast running speed.

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A review of cognitive costs of decision making

September 4, 2025 08:36 ,

Christin Schulze, Ada Aka, Daniel M. Bartels, Stefan F. Bucher, Jake R. Embrey, Todd M. Gureckis, Gerald Häubl, Mark K. Ho, Ian Krajbich, Alexander K. Moore, Gabriele Oettingen, Joan D.K. Ongchoco, Ryan Oprea, Nicholas Reinholtz, Ben R. Newell, A timeline of cognitive costs in decision-making, Trends in Cognitive Sciences, Volume 29, Issue 9, 2025, Pages 827-839, 10.1016/j.tics.2025.04.004.

Recent research from economics, psychology, cognitive science, computer science, and marketing is increasingly interested in the idea that people face cognitive costs when making decisions. Reviewing and synthesizing this research, we develop a framework of cognitive costs that organizes concepts along a temporal dimension and maps out when costs occur in the decision-making process and how they impact decisions. Our unifying framework broadens the scope of research on cognitive costs to a wider timeline of cognitive processing. We identify implications and recommendations emerging from our framework for intervening on behavior to tackle some of the most pressing issues of our day, from improving health and saving decisions to mitigating the consequences of climate change.

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Social learning is compatible with reward-based decision making

September 4, 2025 08:33 ,

David Schultner, Lucas Molleman, Björn Lindström, Reward is enough for social learning, Trends in Cognitive Sciences, Volume 29, Issue 9, 2025, Pages 787-789, 10.1016/j.tics.2025.06.012.

Adaptive behaviour relies on selective social learning, yet the mechanisms underlying this capacity remain debated. A new account demonstrates that key strategies can emerge through reward-based learning of social features, explaining the widely observed flexibility of social learning and illuminating the cognitive basis of cultural evolution.

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Improving the generalization of robotic RL by inspiration in the humman motion control system

September 4, 2025 08:28 ,

P. Zhang, Z. Hua and J. Ding, A Central Motor System Inspired Pretraining Reinforcement Learning for Robotic Control, IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 55, no. 9, pp. 6285-6298, Sept. 2025, 10.1109/TSMC.2025.3577698.

Robots typically encounter diverse tasks, bringing a significant challenge for motion control. Pretraining reinforcement learning (PRL) enables robots to adapt quickly to various tasks by exploiting reusable skills. The existing PRL methods often rely on datasets and human expert knowledge, struggle to discover diverse and dynamic skills, and exhibit generalization and adaptability to different types of robots and downstream tasks. This article proposes a novel PRL algorithm based on the central motor system mechanisms, which can discover diverse and dynamic skills without relying on data and expert knowledge, effectively enabling robots to tackle different types of downstream tasks. Inspired by the cerebellum’s role in balance control and skill storage within the central motor system, an intrinsic fused reward is introduced to explore dynamic skills and eliminate dependence on data and expert knowledge during pretraining. Drawing from the basal ganglia’s function in motor programming, a discrete skill encoding method is designed to increase the diversity of discovered skills, improving the performance of complex robots in challenging environments. Furthermore, incorporating the basal ganglia’s role in motor regulation, a skill activity function is proposed to generate skills at varying dynamic levels, thereby improving the adaptability of robots in multiple downstream tasks. The effectiveness of the proposed algorithm has been demonstrated through simulation experiments on four different morphological robots across multiple downstream tasks.

Posted in: Applications of reinforcement learning to robots, Psycho-physiological bases of engineering , Tagged: ,

How biology uses primary rewards coming from basic physiological signals and proxy rewards, more immediate (predicting primary rewards) as shaping rewards

September 3, 2025 16:18 ,

Lilian A. Weber, Debbie M. Yee, Dana M. Small, and Frederike H. Petzschner, The interoceptive origin of reinforcement learning, IEEE Robotics and Automation Letters, vol. 10, no. 8, pp. 7723-7730, Aug. 2025, 10.1016/j.tics.2025.05.008.

Rewards play a crucial role in sculpting all motivated behavior. Traditionally, research on reinforcement learning has centered on how rewards guide learning and decision-making. Here, we examine the origins of rewards themselves. Specifically, we discuss that the critical signal sustaining reinforcement for food is generated internally and subliminally during the process of digestion. As such, a shift in our understanding of primary rewards as an immediate sensory gratification to a state-dependent evaluation of an action’s impact on vital phys- iological processes is called for. We integrate this perspective into a revised reinforcement learning framework that recognizes the subliminal nature of bio-logical rewards and their dependency on internal states and goals.

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Including “fear” and “curiosity” in RL applied to robot navigation

July 4, 2025 09:07 ,

D. Hu, L. Mo, J. Wu and C. Huang, “Feariosity”-Guided Reinforcement Learning for Safe and Efficient Autonomous End-to-End Navigation, IEEE Robotics and Automation Letters, vol. 10, no. 8, pp. 7723-7730, Aug. 2025, 10.1109/LRA.2025.3577523.

End-to-end navigation strategies using reinforcement learning (RL) can improve the adaptability and autonomy of Autonomous ground vehicles (AGVs) in complex environments. However, RL still faces challenges in data efficiency and safety. Neuroscientific and psychological research shows that during exploration, the brain balances between fear and curiosity, a critical process for survival and adaptation in dangerous environments. Inspired by this scientific insight, we propose the “Feariosity” model, which integrates fear and curiosity model to simulate the complex psychological dynamics organisms experience during exploration. Based on this model, we developed an innovative policy constraint method that evaluates potential hazards and applies necessary safety constraints while encouraging exploration of unknown areas. Additionally, we designed a new experience replay mechanism that quantifies the threat and unknown level of data, optimizing their usage probability. Extensive experiments in both simulation and real-world scenarios demonstrate that the proposed method significantly improves data efficiency, asymptotic performance during training. Furthermore, it achieves higher success rates, driving efficiency, and robustness in deployment. This also highlights the key role of mimicking biological neural and psychological mechanisms in improving the safety and efficiency through RL.

Posted in: Applications of reinforcement learning to control engineering, Psycho-physiological bases of engineering , Tagged: ,

It seems that human predictive brain works by predicting at the abstract level, not at the sensory level

July 4, 2025 09:02 ,

Kaitlyn M. Gabhart, Yihan (Sophy) Xiong, André M. Bastos, Predictive coding: a more cognitive process than we thought?, Trends in Cognitive Sciences, Volume 29, Issue 7, 2025, Pages 627-640, 10.1016/j.tics.2025.01.012.

In predictive coding (PC), higher-order brain areas generate predictions that are sent to lower-order sensory areas. Top-down predictions are compared with bottom-up sensory data, and mismatches evoke prediction errors. In PC, the prediction errors are encoded in layer 2/3 pyramidal neurons of sensory cortex that feed forward. The PC model has been tested with multiple recording modalities using the global–local oddball paradigm. Consistent with PC, neuroimaging studies reported prediction error responses in sensory and higher-order areas. However, recent studies of neuronal spiking suggest that genuine prediction errors emerge in prefrontal cortex (PFC). This implies that predictive processing is a more cognitive than sensory-based mechanism – an observation that challenges PC and better aligns with a framework we call predictive routing (PR).

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Evidences of the dimensionality reduction and augmentation performed by the brain

July 4, 2025 08:58 ,

Casper Kerrén, Daniel Reznik, Christian F. Doeller, Benjamin J. Griffiths, Exploring the role of dimensionality transformation in episodic memory, Trends in Cognitive Sciences, Volume 29, Issue 7, 2025, Pages 614-626, 10.1016/j.tics.2025.01.007.

Episodic memory must accomplish two adversarial goals: encoding and storing a multitude of experiences without exceeding the finite neuronal structure of the brain, and recalling memories in vivid detail. Dimensionality reduction and expansion (‘dimensionality transformation’) enable the brain to meet these demands. Reduction compresses sensory input into simplified, storable codes, while expansion reconstructs vivid details. Although these processes are essential to memory, their neural mechanisms for episodic memory remain unclear. Drawing on recent insights from cognitive psychology, systems neuroscience, and neuroanatomy, we propose two accounts of how dimensionality transformation occurs in the brain: structurally (via corticohippocampal pathways) and functionally (through neural oscillations). By examining cross-species evidence, we highlight neural mechanisms that may support episodic memory and identify crucial questions for future research.

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