Category Archives: Psycho-physiological Bases Of Engineering

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

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.

It seems that the human brain working memory uses pointers

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.

On the two-ways of learning language in humans: both abstracting detailed knowledge and refining still-only-abstract one

Susan Goldin-Meadow, Inbal Arnon, Whole-to-part development in language creation, Trends in Cognitive Sciences, Volume 29, Issue 1, 2025, Pages 12-14, DOI: 10.1016/j.tics.2024.09.015.

Children approach language by learning parts and constructing wholes. But they can also first learn wholes and then discover parts. We demonstrate this understudied yet impactful process in children creating language without input. Whole-to-part learning thus need not be driven by hard-to-segment input and is a bias that children bring to language.

On the limited throughput of the human cognition and its implications, e.g., in Engineering

Jieyu Zheng1, and Markus Meister, The unbearable slowness of being: Why do we live at 10 bits/s?, Neuron (2024), DOI: 10.1016/j.neuron.2024.11.008.

This article is about the neural conundrum behind the slowness of human behavior. The information throughput of a human being is about 10 bits/s. In comparison, our sensory systems gather data at 10 bits/s. The stark contrast between these numbers remains unexplained and touches on fundamental aspects of brain function: what neural substrate sets this speed limit on the pace of our existence? Why does the brain need billions of neurons to process 10 bits/s? Why can we only think about one thing at a time? The brain seems to operate in two distinct modes: the ‘‘outer’’ brain handles fast high-dimensional sensory and motor signals, whereas the ‘‘inner’’ brain processes the reduced few bits needed to control behavior. Plausible explanations exist for the large neuron numbers in the outer brain, but not for the inner brain, and we propose new research directions to remedy this.

A good review of allostasis and control theory applied to physiology

Eli Sennesh, Jordan Theriault, Dana Brooks, Jan-Willem van de Meent, Lisa Feldman Barrett, Karen S. Quigley, Interoception as modeling, allostasis as control, Biological Psychology, Volume 167, 2022 DOI: 10.1016/j.biopsycho.2021.108242.

The brain regulates the body by anticipating its needs and attempting to meet them before they arise – a process called allostasis. Allostasis requires a model of the changing sensory conditions within the body, a process called interoception. In this paper, we examine how interoception may provide performance feedback for allostasis. We suggest studying allostasis in terms of control theory, reviewing control theory’s applications to related issues in physiology, motor control, and decision making. We synthesize these by relating them to the important properties of allostatic regulation as a control problem. We then sketch a novel formalism for how the brain might perform allostatic control of the viscera by analogy to skeletomotor control, including a mathematical view on how interoception acts as performance feedback for allostasis. Finally, we suggest ways to test implications of our hypotheses.

An inspiring formalization of the latest models of human emotions into RL

Aviv Emanuel, Eran Eldar, Emotions as Computations, Neuroscience & Biobehavioral Reviews, Volume 144, January 2023 DOI: 10.1016/j.neubiorev.2022.104977.

Emotions ubiquitously impact action, learning, and perception, yet their essence and role remain widely debated. Computational accounts of emotion aspire to answer these questions with greater conceptual precision informed by normative principles and neurobiological data. We examine recent progress in this regard and find that emotions may implement three classes of computations, which serve to evaluate states, actions, and uncertain prospects. For each of these, we use the formalism of reinforcement learning to offer a new formulation that better accounts for existing evidence. We then consider how these distinct computations may map onto distinct emotions and moods. Integrating extensive research on the causes and consequences of different emotions suggests a parsimonious one-to-one mapping, according to which emotions are integral to how we evaluate outcomes (pleasure & pain), learn to predict them (happiness & sadness), use them to inform our (frustration & content) and others’ (anger & gratitude) actions, and plan in order to realize (desire & hope) or avoid (fear & anxiety) uncertain outcomes.

The seminal work on the “firstly cooperate, then repeat other’s actions” strategy in game theory

Robert Axelrod; William D. Hamilton, The Evolution of Cooperation, Science, New Series, Vol. 211, No. 4489. (Mar. 27, 1981), pp. 1390-1396 https://ee.stanford.edu/~hellman/Breakthrough/book/pdfs/axelrod.pdf.

Cooperation in organisms, whether bacteria or primates, has been a
difficulty for evolutionary theory since Darwin. On the assumption that interactions
between pairs of individuals occur on a probabilistic basis, a model is developed
based on the concept of an evolutionarily stable strategy in the context of the
Prisoner’s Dilemma game. Deductions from the model, and the results of a computer
tournament show how cooperation based on reciprocity can get started in an asocial
world, can thrive while interacting with a wide range of other strategies, and can resist
invasion once fully established. Potential applications include specific aspects of
territoriality, mating, and disease.

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

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.

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

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.

Reducing dimensionality of brain-body state dynamics

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.