Category Archives: Cognitive Sciences

It seems that vectors can help in the path toward symbols for ANNs

September 6, 2024 09:30 ,

Steven T. Piantadosi, Dyana C.Y. Muller, Joshua S. Rule, Karthikeya Kaushik, Mark Gorenstein, Elena R. Leib, Emily Sanford, Why concepts are (probably) vectors, Trends in Cognitive Sciences, Volume 28, Issue 9, 2024, Pages 844-856 DOI: 10.1016/j.tics.2024.06.011.

For decades, cognitive scientists have debated what kind of representation might characterize human concepts. Whatever the format of the representation, it must allow for the computation of varied properties, including similarities, features, categories, definitions, and relations. It must also support the development of theories, ad hoc categories, and knowledge of procedures. Here, we discuss why vector-based representations provide a compelling account that can meet all these needs while being plausibly encoded into neural architectures. This view has become especially promising with recent advances in both large language models and vector symbolic architectures. These innovations show how vectors can handle many properties traditionally thought to be out of reach for neural models, including compositionality, definitions, structures, and symbolic computational processes.

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Cognitive evidences of the need of abstraction (==”modularity”) in achieving AI

September 6, 2024 08:52 ,

Schilling, M., Hammer, B., Ohl, F.W. et al. Modularity in Nervous Systems—a Key to Efficient Adaptivity for Deep Reinforcement Learning, Cogn Comput 16, 2358–2373 (2024) DOI: 10.1007/s12559-022-10080-w.

Modularity as observed in biological systems has proven valuable for guiding classical motor theories towards good answers about action selection and execution. New challenges arise when we turn to learning: Trying to scale current computational models, such as deep reinforcement learning (DRL), to action spaces, input dimensions, and time horizons seen in biological systems still faces severe obstacles unless vast amounts of training data are available. This leads to the question: does biological modularity also hold an important key for better answers to obtain efficient adaptivity for deep reinforcement learning? We review biological experimental work on modularity in biological motor control and link this with current examples of (deep) RL approaches. Analyzing outcomes of simulation studies, we show that these approaches benefit from forms of modularization as found in biological systems. We identify three different strands of modularity exhibited in biological control systems. Two of them—modularity in state (i) and in action (ii) spaces—appear as a consequence of local interconnectivity (as in reflexes) and are often modulated by higher levels in a control hierarchy. A third strand arises from chunking of action elements along a (iii) temporal dimension. Usually interacting in an overarching spatio-temporal hierarchy of the overall system, the three strands offer major “factors” decomposing the entire modularity structure. We conclude that modularity with its above strands can provide an effective prior for DRL approaches to speed up learning considerably and making learned controllers more robust and adaptive.

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Reducing dimensionality of brain-body state dynamics

September 6, 2024 08:10 ,

Daniel S. Kluger, Micah G. Allen, Joachim Gross, Brain–body states embody complex temporal dynamics, Trends in Cognitive Sciences, Volume 28, Issue 8, 2024, Pages 695-698 DOI: 10.1016/j.tics.2024.05.003.

We propose a computational framework for high-dimensional brain–body states as transient embodiments of nested internal and external dynamics governed by interoception. Unifying recent theoretical work, we suggest ways to reduce arbitrary state complexity to an observable number of features in order to accurately predict and intervene in pathological trajectories.

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RL in periodic scenarios

July 18, 2024 12:42 ,

A. Aniket and A. Chattopadhyay, Online Reinforcement Learning in Periodic MDP, IEEE Transactions on Artificial Intelligence, vol. 5, no. 7, pp. 3624-3637, July 2024 DOI: 10.1109/TAI.2024.3375258.

We study learning in periodic Markov decision process (MDP), a special type of nonstationary MDP where both the state transition probabilities and reward functions vary periodically, under the average reward maximization setting. We formulate the problem as a stationary MDP by augmenting the state space with the period index and propose a periodic upper confidence bound reinforcement learning-2 (PUCRL2) algorithm. We show that the regret of PUCRL2 varies linearly with the period N and as O(TlogT−−−−−√) with the horizon length T . Utilizing the information about the sparsity of transition matrix of augmented MDP, we propose another algorithm [periodic upper confidence reinforcement learning with Bernstein bounds (PUCRLB) which enhances upon PUCRL2, both in terms of regret ( O(N−−√) dependency on period] and empirical performance. Finally, we propose two other algorithms U-PUCRL2 and U-PUCRLB for extended uncertainty in the environment in which the period is unknown but a set of candidate periods are known. Numerical results demonstrate the efficacy of all the algorithms.

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Setting up goals, even unproductive or unuseful ones, can help in building cognition

July 5, 2024 07:51 ,

Junyi Chu, Joshua B. Tenenbaum, Laura E. Schulz, In praise of folly: flexible goals and human cognition, Trends in Cognitive Sciences, Volume 28, Issue 7, 2024, Pages 628-642 DOI: 10.1016/j.tics.2024.03.006.

Humans often pursue idiosyncratic goals that appear remote from functional ends, including information gain. We suggest that this is valuable because goals (even prima facie foolish or unachievable ones) contain structured information that scaffolds thinking and planning. By evaluating hypotheses and plans with respect to their goals, humans can discover new ideas that go beyond prior knowledge and observable evidence. These hypotheses and plans can be transmitted independently of their original motivations, adapted across generations, and serve as an engine of cultural evolution. Here, we review recent empirical and computational research underlying goal generation and planning and discuss the ways that the flexibility of our motivational system supports cognitive gains for both individuals and societies.

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Review of the current methologies for achieving continuous learning, and its biological bases

June 28, 2024 08:32 ,

Buddhi Wickramasinghe, Gobinda Saha , and Kaushik Roy, Continual Learning: A Review of Techniques, Challenges, and Future Directions, IEEE TRANSACTIONS ON ARTIFICIAL INTELLIGENCE, VOL. 5, NO. 6, JUNE 2024 DOI: 10.1109/TAI.2023.3339091.

Continual learning (CL), or the ability to acquire, process, and learn from new information without forgetting acquired knowledge, is a fundamental quality of an intelligent agent. The human brain has evolved into gracefully dealing with ever-changing circumstances and learning from experience with the help of complex neurophysiological mechanisms. Even though artificial intelligence takes after human intelligence, traditional neural networks do not possess the ability to adapt to dynamic environments. When presented with new information, an artificial neural network (ANN) often completely forgets its prior knowledge, a phenomenon called catastrophic forgetting or catastrophic interference. Incorporating CL capabilities into ANNs is an active field of research and is integral to achieving artificial general intelligence. In this review, we revisit CL approaches and critically examine their strengths and limitations. We conclude that CL approaches should look beyond mitigating catastrophic forgetting and strive for systems that can learn, store, recall, and transfer knowledge, much like the human brain. To this end, we highlight the importance of adopting alternative brain-inspired data representations and learning algorithms and provide our perspective on promising new directions where CL could play an instrumental role.

See also: doi: 10.1109/TAI.2024.3355879

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Thermodynamics as a way of identifying hierarchies

June 7, 2024 11:26 ,

Morten L. Kringelbach, Yonatan Sanz Perl, Gustavo Deco, The Thermodynamics of Mind, Trends in Cognitive Sciences, Volume 28, Issue 6, 2024, Pages 568-581 DOI: 10.1016/j.tics.2024.03.009.

To not only survive, but also thrive, the brain must efficiently orchestrate distributed computation across space and time. This requires hierarchical organisation facilitating fast information transfer and processing at the lowest possible metabolic cost. Quantifying brain hierarchy is difficult but can be estimated from the asymmetry of information flow. Thermodynamics has successfully characterised hierarchy in many other complex systems. Here, we propose the ‘Thermodynamics of Mind’ framework as a natural way to quantify hierarchical brain orchestration and its underlying mechanisms. This has already provided novel insights into the orchestration of hierarchy in brain states including movie watching, where the hierarchy of the brain is flatter than during rest. Overall, this framework holds great promise for revealing the orchestration of cognition.

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Reducing discovered skills in DRL to the essential ones, modelling skills with SMDP Q-learning

June 7, 2024 10:38 ,

Shuai Qing, Fei Zhu, Refine to the essence: Less-redundant skill learning via diversity clustering, Engineering Applications of Artificial Intelligence, Volume 133, Part A, 2024 DOI: 10.1016/j.engappai.2024.107981.

In reinforcement learning, skill is a potentially conditional policy that solves tasks in a hierarchically controlled manner. Progress on skill discovery helps agents learn a set of diverse and useful skills without external supervision to tackle complex tasks with sparse rewards. Although most of the studies have aimed to maximize the diversity of skills discovered, the distinguishability between skills diminishes as the number of skills increases, leading to a subset of similar and redundant skills. To tackle this problem, a method called Refine to the Essence of Skills (RE-Skill) is proposed, which aims at learning skills with less redundancy. RE-Skill integrates the concepts of cluster analysis and policy distillation, clustering similar skills together based on their unique features, learning the most optimal performance within each cluster, and filtering out similar skills that involve excessive and intricate actions, thereby reducing redundancy among skills. By refining clusters of similar skills into less-redundant independent skills, RE-Skill demonstrates superior performance compared to other skill discovery algorithms and shows how these less-redundant skills effectively address downstream tasks, indicating that RE-Skill is able to extend its efficacy to engineering applications in robot control and obstacle training tasks within complex environments.

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An alternative conceptual basis for curiosity / motivation

May 9, 2024 08:21 ,

Francesco Poli, Jill X. O’Reilly, Rogier B. Mars, Sabine Hunnius, Curiosity and the dynamics of optimal exploration, Trends in Cognitive Sciences, Volume 28, Issue 5, 2024, DOI: 10.1016/j.tics.2024.02.001.

What drives our curiosity remains an elusive and hotly debated issue, with multiple hypotheses proposed but a cohesive account yet to be established. This review discusses traditional and emergent theories that frame curiosity as a desire to know and a drive to learn, respectively. We adopt a model-based approach that maps the temporal dynamics of various factors underlying curiosity-based exploration, such as uncertainty, information gain, and learning progress. In so doing, we identify the limitations of past theories and posit an integrated account that harnesses their strengths in describing curiosity as a tool for optimal environmental exploration. In our unified account, curiosity serves as a ‘common currency’ for exploration, which must be balanced with other drives such as safety and hunger to achieve efficient action.

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A novel RL setting for non-Markovian systems

April 26, 2024 09:38 ,

Ronen I. Brafman, Giuseppe De Giacomo, Regular decision processes, Artificial Intelligence, Volume 331, 2024 DOI: 10.1016/j.artint.2024.104113.

We introduce and study Regular Decision Processes (RDPs), a new, compact model for domains with non-Markovian dynamics and rewards, in which the dependence on the past is regular, in the language theoretic sense. RDPs are an intermediate model between MDPs and POMDPs. They generalize k-order MDPs and can be viewed as a POMDP in which the hidden state is a regular function of the entire history. In factored RDPs, transition and reward functions are specified using formulas in linear temporal logics over finite traces, or using regular expressions. This allows specifying complex dependence on the past using intuitive and compact formulas, and building models of partially observable domains without specifying an underlying state space.

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