Category Archives: Cognitive Sciences

Hierarchical optimization based on learning from mistakes

June 5, 2025 09:20 ,

L. Zhang, B. Garg, P. Sridhara, R. Hosseini and P. Xie, Learning From Mistakes: A Multilevel Optimization Framework, IEEE Transactions on Artificial Intelligence, vol. 6, no. 6, pp. 1651-1663, June 2025, 10.1109/TAI.2025.3534151.

Bi-level optimization methods in machine learning are popularly effective in subdomains of neural architecture search, data reweighting, etc. However, most of these methods do not factor in variations in learning difficulty, which limits their performance in real-world applications. To address the above problems, we propose a framework that imitates the learning process of humans. In human learning, learners usually focus more on the topics where mistakes have been made in the past to deepen their understanding and master the knowledge. Inspired by this effective human learning technique, we propose a multilevel optimization framework, learning from mistakes (LFM), for machine learning. We formulate LFM as a three-stage optimization problem: 1) the learner learns, 2) the learner relearns based on the mistakes made before, and 3) the learner validates his learning. We develop an efficient algorithm to solve the optimization problem. We further apply our method to differentiable neural architecture search and data reweighting. Extensive experiments on CIFAR-10, CIFAR-100, ImageNet, and other related datasets powerfully demonstrate the effectiveness of our approach. The code of LFM is available at: https://github.com/importZL/LFM.

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The brain is organized to minimize energy consumption while maximizing computation

June 5, 2025 09:12 ,

Sharna D. Jamadar, Anna Behler, Hamish Deery, Michael Breakspear, The metabolic costs of cognition, Trends in Cognitive Sciences, Volume 29, Issue 6, 2025, Pages 541-555, 10.1016/j.tics.2024.11.010.

Cognition and behavior are emergent properties of brain systems that seek to maximize complex and adaptive behaviors while minimizing energy utilization. Different species reconcile this trade-off in different ways, but in humans the outcome is biased towards complex behaviors and hence relatively high energy use. However, even in energy-intensive brains, numerous parsimonious processes operate to optimize energy use. We review how this balance manifests in both homeostatic processes and task-associated cognition. We also consider the perturbations and disruptions of metabolism in neurocognitive diseases.

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A possible explanation of the origin of the concept of number and some arithmetical operations based on language concepts

June 5, 2025 09:09 ,

Stanislas Dehaene, Mathias Sablé-Meyer, Lorenzo Ciccione, Origins of numbers: a shared language-of-thought for arithmetic and geometry?, Trends in Cognitive Sciences, Volume 29, Issue 6, 2025, Pages 526-540, 10.1016/j.tics.2025.03.001.

Concepts of exact number are often thought to originate from counting and the successor function, or from a refinement of the approximate number system (ANS). We argue here for a third origin: a shared language-of-thought (LoT) for geometry and arithmetic that involves primitives of repetition, concatenation, and recursive embedding. Applied to sets, those primitives engender concepts of exact integers through recursive applications of additions and multiplications. Links between geometry and arithmetic also explain the emergence of higher-level notions (squares, primes, etc.). Under our hypothesis, understanding a number means having one or several mental expressions for it, and their minimal description length (MDL) determines how easily they can be mentally manipulated. Several historical, developmental, linguistic, and brain imaging phenomena provide preliminary support for our proposal.

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Detecting novelties in the model of the world within MCTS

May 29, 2025 11:48 ,

Bryan Loyall, Avi Pfeffer, James Niehaus, Michael Harradon, Paola Rizzo, Alex Gee, Joe Campolongo, Tyler Mayer, John Steigerwald, Coltrane: A domain-independent system for characterizing and planning in novel situations, Artificial Intelligence, Volume 345, 2025, 10.1016/j.artint.2025.104336.

AI systems operating in open-world environments must be able to adapt to impactful changes in the world, immediately when they occur, and be able to do this across the many types of changes that can occur. We are seeking to create methods to extend traditional AI systems so that they can (1) immediately recognize changes in how the world works that are impactful to task accomplishment; (2) rapidly characterize the nature of the change using the limited observations that are available when the change is first detected; (3) adapt to the change as well as feasible to accomplish the system’s tasks given the available observations; and (4) continue to improve the characterization and adaptation as additional observations are available. In this paper, we describe Coltrane, a domain-independent system for characterizing and planning in novel situations that uses only natural domain descriptions to generate its novelty-handling behavior, without any domain-specific anticipation of the novelty. Coltrane’s characterization method is based on probabilistic program synthesis of perturbations to programs expressed in a traditional programming language describing domain transition models. Its planning method is based on incorporating novel domain models in an MCTS search algorithm and on automatically adapting the heuristics used. Both a formal external evaluation and our own demonstrations show that Coltrane is capable of accurately characterizing interesting forms of novelty and of adapting its behavior to restore its performance to pre-novelty levels and even beyond.

Posted in: Artificial Intelligence , Tagged: , ,

A possible explanation for the formation of concepts in the human brain

May 8, 2025 11:09 ,

Luca D. Kolibius, Sheena A. Josselyn, Simon Hanslmayr, On the origin of memory neurons in the human hippocampus, Trends in Cognitive Sciences, Volume 29, Issue 5, 2025, Pages 421-433 10.1016/j.tics.2025.01.013.

The hippocampus is essential for episodic memory, yet its coding mechanism remains debated. In humans, two main theories have been proposed: one suggests that concept neurons represent specific elements of an episode, while another posits a conjunctive code, where index neurons code the entire episode. Here, we integrate new findings of index neurons in humans and other animals with the concept-specific memory framework, proposing that concept neurons evolve from index neurons through overlapping memories. This process is supported by engram literature, which posits that neurons are allocated to a memory trace based on excitability and that reactivation induces excitability. By integrating these insights, we connect two historically disparate fields of neuroscience: engram research and human single neuron episodic memory research.

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On the problem of choice overload for human cognition

May 8, 2025 11:06 ,

Jessie C. Tanner, Claire T. Hemingway, Choice overload and its consequences for animal decision-making, Trends in Cognitive Sciences, Volume 29, Issue 5, 2025, Pages 403-406 10.1016/j.tics.2025.01.003.

Animals routinely make decisions with important consequences for their survival and reproduction, but they frequently make suboptimal decisions. Here, we explore choice overload as one reason why animals may make suboptimal decisions, arguing that choice overload may have important ecological and evolutionary consequences, and propose future directions.

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Improving reward shaping in Deep RL for avoiding user’s biases and boosting learning efficiency

April 24, 2025 08:32 ,

Jiawei Lin, Xuekai Wei, Weizhi Xian, Jielu Yan, Leong Hou U, Yong Feng, Zhaowei Shang, Mingliang Zhou, Continuous reinforcement learning via advantage value difference reward shaping: A proximal policy optimization perspective, Engineering Applications of Artificial Intelligence, Volume 151, 2025 10.1016/j.engappai.2025.110676.

Deep reinforcement learning has shown great promise in industrial applications. However, these algorithms suffer from low learning efficiency because of sparse reward signals in continuous control tasks. Reward shaping addresses this issue by transforming sparse rewards into more informative signals, but some designs that rely on domain experts or heuristic rules can introduce cognitive biases, leading to suboptimal solutions. To overcome this challenge, this paper proposes the advantage value difference (AVD), a generalized potential-based end-to-end exploration reward function. The main contribution of this paper is to improve the agent’s exploration efficiency, accelerate the learning process, and prevent premature convergence to local optima. The method leverages the temporal difference error to estimate the potential of states and uses the advantage function to guide the learning process toward more effective strategies. In the context of engineering applications, this paper proves the superiority of AVD in continuous control tasks within the multi-joint dynamics with contact (MuJoCo) environment. Specifically, the proposed method achieves an average increase of 23.5% in episode rewards for the Hopper, Swimmer, and Humanoid tasks compared with the state-of-the-art approaches. The results demonstrate the significant improvement in learning efficiency achieved by AVD for industrial robotic systems.

Posted in: Reinforcement learning in AI , Tagged: , , ,

When to rely on memories versus sampling sensory information anew to guide behavior

April 3, 2025 06:41 ,

Levi Kumle, Anna C. Nobre, Dejan Draschkow, Sensorimnemonic decisions: choosing memories versus sensory information, Trends in Cognitive Sciences, Volume 29, Issue 4, 2025, Pages 311-313, 10.1016/j.tics.2024.12.010.

We highlight a fundamental psychological function that is central to many of our interactions in the environment – when to rely on memories versus sampling sensory information anew to guide behavior. By operationalizing sensorimnemonic decisions we aim to encourage and advance research into this pivotal process for understanding how memories serve adaptive cognition.

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On the innate ability of vertebrates for number recognition and the one of distinguishing ratios of numbers

March 6, 2025 10:33 ,

Elena Lorenzi, Dmitry Kobylkov, Giorgio Vallortigara, Is there an innate sense of number in the brain?, Cerebral Cortex, Volume 35, Issue 2, February 2025, DOI: 10.1093/cercor/bhaf004.

The approximate number system or «sense of number» is a crucial, presymbolic mechanism enabling animals to estimate quantities, which is essential for survival in various contexts (eg estimating numerosities of social companions, prey, predators, and so on). Behavioral studies indicate that a sense of number is widespread across vertebrates and invertebrates. Specific brain regions such as the intraparietal sulcus and prefrontal cortex in primates, or equivalent areas in birds and fish, are involved in numerical estimation, and their activity is modulated by the ratio of quantities. Data gathered across species strongly suggest similar evolutionary pressures for number estimation pointing to a likely common origin, at least across vertebrates. On the other hand, few studies have investigated the origins of the sense of number. Recent findings, however, have shown that numerosity-selective neurons exist in newborn animals, such as domestic chicks and zebrafish, supporting the hypothesis of an innateness of the approximate number system. Control-rearing experiments on visually naïve animals further support the notion that the sense of number is innate and does not need any specific instructive experience in order to be triggered.

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It seems that the human brain working memory uses pointers

March 6, 2025 10:21 ,

Edward Awh, Edward K. Vogel, Working memory needs pointers, Trends in Cognitive Sciences, Volume 29, Issue 3, 2025, Pages 230-241, DOI: 10.1016/j.tics.2024.12.006.

Cognitive neuroscience has converged on a definition of working memory (WM) as a capacity-limited system that maintains highly accessible representations via stimulus-specific neural patterns. We argue that this standard definition may be incomplete. We highlight the fundamental need to recognize specific instances or tokens and to bind those tokens to the surrounding context. We propose that contextual binding is supported by spatiotemporal ‘pointers’ and that pointers are the source of neural signals that track the number of stored items, independent of their content. These content-independent pointers may provide a productive perspective for understanding item-based capacity limits in WM and the role of WM as a gateway for long-term storage.

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