Category Archives: Psycho-physiological Bases Of Engineering

Imitating physiological processes for achieving robot-human social interaction

April 26, 2024 09:31 ,

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.

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

April 3, 2024 13:06 ,

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.

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

April 3, 2024 13:03 ,

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.

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

February 9, 2024 09:34 ,

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.

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Further support for a multi-tool approach for consciusness

December 5, 2023 10:12 ,

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.

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Emergence of number meaning from sensorimotor experiences

November 24, 2023 17:48 ,

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.

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Review of emotions in AI

November 17, 2023 09:51 ,

G. Assun��o, B. Patr�o, M. Castelo-Branco and P. Menezes, An Overview of Emotion in Artificial Intelligence, IEEE Transactions on Artificial Intelligence, vol. 3, no. 6, pp. 867-886, Dec. 2022 DOI: 10.1109/TAI.2022.3159614.

The field of artificial intelligence (AI) has gained immense traction over the past decade, producing increasingly successful applications as research strives to understand and exploit neural processing specifics. Nonetheless emotion, despite its demonstrated significance to reinforcement, social integration, and general development, remains a largely stigmatized and consequently disregarded topic by most engineers and computer scientists. In this article, we endorse emotion\u2019s value for the advancement of artificial cognitive processing, as well as explore real-world use cases of emotion-augmented AI. A schematization is provided on the psychological-neurophysiologic basics of emotion in order to bridge the interdisciplinary gap preventing emulation and integration in AI methodology, as well as exploitation by current systems. In addition, we overview three major subdomains of AI greatly benefiting from emotion, and produce a systematic survey of meaningful yet recent contributions to each area. To conclude, we address crucial challenges and promising research paths for the future of emotion in AI with the hope that more researchers will develop an interest for the topic and find it easier to develop their own contributions.

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Normal blindness to visible objects seems to be the result of limited-capacity prediction mechanisms in the brain

November 3, 2023 09:05 ,

Jeremy M. Wolfe, Anna Kosovicheva, Benjamin Wolfe, Normal blindness: when we Look But Fail To See, Trends in Cognitive Sciences, Volume 26, Issue 9, 2022, Pages 809-819 DOI: 10.1016/j.tics.2022.06.006.

Humans routinely miss important information that is \u2018right in front of our eyes\u2019, from overlooking typos in a paper to failing to see a cyclist in an intersection. Recent studies on these \u2018Looked But Failed To See\u2019 (LBFTS) errors point to a common mechanism underlying these failures, whether the missed item was an unexpected gorilla, the clearly defined target of a visual search, or that simple typo. We argue that normal blindness is the by-product of the limited-capacity prediction engine that is our visual system. The processes that evolved to allow us to move through the world with ease are virtually guaranteed to cause us to miss some significant stimuli, especially in important tasks like driving and medical image perception.

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On the existence of multiple fundamental “languages” in the brain that use discrete symbols and a few basic structures

November 3, 2023 09:03 ,

Stanislas Dehaene, Fosca Al Roumi, Yair Lakretz, Samuel Planton, Mathias Sabl�-Meyer, Symbols and mental programs: a hypothesis about human singularity, Trends in Cognitive Sciences, Volume 26, Issue 9, 2022, Pages 751-766 DOI: 10.1016/j.tics.2022.06.010.

Natural language is often seen as the single factor that explains the cognitive singularity of the human species. Instead, we propose that humans possess multiple internal languages of thought, akin to computer languages, which encode and compress structures in various domains (mathematics, music, shape\u2026). These languages rely on cortical circuits distinct from classical language areas. Each is characterized by: (i) the discretization of a domain using a small set of symbols, and (ii) their recursive composition into mental programs that encode nested repetitions with variations. In various tasks of elementary shape or sequence perception, minimum description length in the proposed languages captures human behavior and brain activity, whereas non-human primate data are captured by simpler nonsymbolic models. Our research argues in favor of discrete symbolic models of human thought.

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Unexpected consequences of training smarthome systems with reinforcement learning: effects on human behaviours

October 27, 2023 06:02 ,

S. Suman, A. Etemad and F. Rivest, TPotential Impacts of Smart Homes on Human Behavior: A Reinforcement Learning Approach, IEEE Transactions on Artificial Intelligence, vol. 3, no. 4, pp. 567-580, Aug. 2022 DOI: 10.1109/TAI.2021.3127483.

Smart homes are becoming increasingly popular as a result of advances in machine learning and cloud computing. Devices, such as smart thermostats and speakers, are now capable of learning from user feedback and adaptively adjust their settings to human preferences. Nonetheless, these devices might in turn impact human behavior. To investigate the potential impacts of smart homes on human behavior, we simulate a series of hierarchical-reinforcement learning-based human models capable of performing various activities\u2014namely, setting temperature and humidity for thermal comfort inside a Q-Learning-based smart home model. We then investigate the possibility of the human models\u2019 behaviors being altered as a result of the smart home and the human model adapting to one another. For our human model, the activities are based on hierarchical-reinforcement learning. This allows the human to learn how long it must continue a given activity and decide when to leave it. We then integrate our human model in the environment along with the smart home model and perform rigorous experiments considering various scenarios involving a model of a single human and models of two different humans with the smart home. Our experiments show that with the smart home, the human model can exhibit unexpected behaviors such as frequent changing of activities and an increase in the time required to modify the thermal preferences. With two human models, we interestingly observe that certain combinations of models result in normal behaviors, while other combinations exhibit the same unexpected behaviors as those observed from the single human experiment.

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