Category Archives: Psycho-physiological Bases Of Engineering

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

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.

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

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

Thermodynamics as a way of identifying hierarchies

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.

An alternative conceptual basis for curiosity / motivation

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.

Imitating physiological processes for achieving robot-human social interaction

Marcos Maroto-Gómez, Martín Bueno-Adrada, María Malfaz, Álvaro Castro-González, Miguel Ángel Salichs, Human–robot pair-bonding from a neuroendocrine perspective: Modeling the effect of oxytocin, arginine vasopressin, and dopamine on the social behavior of an autonomous robot, Robotics and Autonomous Systems, Volume 176, 2024 DOI: 10.1016/j.robot.2024.104687.

Robots and humans coexist in various social environments. In these contexts, robots predominantly serve as assistants, necessitating communication and understanding capabilities. This paper introduces a biologically inspired model grounded on neuroendocrine substances that facilitate the development of social bonds between robots and individuals. The model simulates the effects of oxytocin, arginine vasopressin, and dopamine on social behavior, acting as modulators for bonding in the interaction between the social robot Mini and its users. Neuroendocrine levels vary in response to circadian rhythms and social stimuli perceived by the robot. If users express care for the robot, a positive bond is established, enhancing human–robot interaction by prompting the robot to engage in cooperative actions such as playing or communicating more frequently. Conversely, mistreating the robot leads to a deterioration of the relationship, causing user rejection. An experimenter-robot interaction scenario illustrates the model’s adaptive mechanisms involving three types of profiles: Friendly, Aversive, and Naive. Besides, a user study with 22 participants was conducted to analyze the differences in Attachment, Social Presence, perceived Anthropomorphism, Likability, and User Experience between a robot randomly selecting its behavior and a robot behaving using the bioinspired pair-bonded method proposed in this contribution. The results show how the pair-bonding with the user regulates the robot’s social behavior in response to user actions. The user study reveals statistical differences favoring the robot using the pair-bonding regulation in Attachment and Social Presence. A qualitative study using an interview-like form suggests the positive effects of creating bonds with bioinspired robots.

What attention is (from a cognitive science point of view)

Wayne Wu, We know what attention is!, Trends in Cognitive Sciences, Volume 28, Issue 4, 2024 DOI: 10.1016/j.tics.2023.11.007.

Attention is one of the most thoroughly investigated psychological phenomena, yet skepticism about attention is widespread: we do not know what it is, it is too many things, there is no such thing. The deficiencies highlighted are not about experimental work but the adequacy of the scientific theory of attention. Combining common scientific claims about attention into a single theory leads to internal inconsistency. This paper demonstrates that a specific functional conception of attention is incorporated into the tasks used in standard experimental paradigms. In accepting these paradigms as valid probes of attention, we commit to this common conception. The conception unifies work at multiple levels of analysis into a coherent scientific explanation of attention. Thus, we all know what attention is.

On how much imagery can be said to be real or not by the human brain

Rebecca Keogh, Reality check: how do we know what’s real?, Trends in Cognitive Sciences, Volume 28, Issue 4, 2024 DOI: 10.1016/j.tics.2023.06.001.

How do we know what is real and what is merely a figment of our imagination? Dijkstra and Fleming tackle this question in a recent study. In contrast to the classic Perky effect, they found that once imagery crosses a ‘reality threshold’, it becomes difficult to distinguish from reality.

On the complexities of RL when it confronts the real (natural) world

Toby Wise, Kara Emery, Angela Radulescu, Naturalistic reinforcement learning, Trends in Cognitive Sciences, Volume 28, Issue 2, 2024, Pages 144-158 DOI: 10.1016/j.tics.2023.08.016.

Humans possess a remarkable ability to make decisions within real-world environments that are expansive, complex, and multidimensional. Human cognitive computational neuroscience has sought to exploit reinforcement learning (RL) as a framework within which to explain human decision-making, often focusing on constrained, artificial experimental tasks. In this article, we review recent efforts that use naturalistic approaches to determine how humans make decisions in complex environments that better approximate the real world, providing a clearer picture of how humans navigate the challenges posed by real-world decisions. These studies purposely embed elements of naturalistic complexity within experimental paradigms, rather than focusing on simplification, generating insights into the processes that likely underpin humans\u2019 ability to navigate complex, multidimensional real-world environments so successfully.

Further support for a multi-tool approach for consciusness

Biyu J. He, Towards a pluralistic neurobiological understanding of consciousness, Trends in Cognitive Sciences, Volume 27, Issue 5, 2023 DOI: 10.1016/j.tics.2023.02.001.

Theories of consciousness are often based on the assumption that a single, unified neurobiological account will explain different types of conscious awareness. However, recent findings show that, even within a single modality such as conscious visual perception, the anatomical location, timing, and information flow of neural activity related to conscious awareness vary depending on both external and internal factors. This suggests that the search for generic neural correlates of consciousness may not be fruitful. I argue that consciousness science requires a more pluralistic approach and propose a new framework: joint determinant theory (JDT). This theory may be capable of accommodating different brain circuit mechanisms for conscious contents as varied as percepts, wills, memories, emotions, and thoughts, as well as their integrated experience.

Emergence of number meaning from sensorimotor experiences

Elena Sixtus, Florian Krause, Oliver Lindemann, Martin H. Fischer, A sensorimotor perspective on numerical cognition, Trends in Cognitive Sciences, Volume 27, Issue 4, 2023, Pages 367-378 DOI: 10.1016/j.tics.2023.01.002.

Numbers are present in every part of modern society and the human capacity to use numbers is unparalleled in other species. Understanding the mental and neural representations supporting this capacity is of central interest to cognitive psychology, neuroscience, and education. Embodied numerical cognition theory suggests that beyond the seemingly abstract symbols used to refer to numbers, their underlying meaning is deeply grounded in sensorimotor experiences, and that our specific understanding of numerical information is shaped by actions related to our fingers, egocentric space, and experiences with magnitudes in everyday life. We propose a sensorimotor perspective on numerical cognition in which number comprehension and numerical proficiency emerge from grounding three distinct numerical core concepts: magnitude, ordinality, and cardinality.