Category Archives: Psycho-physiological Bases Of Engineering

On how the simplification on physics made in computer games for real-time execution can explain the simplification on physics made by infants when understanding the world

August 17, 2017 11:39 ,

Tomer D. Ullman, Elizabeth Spelke, Peter Battaglia, Joshua B. Tenenbaum, Mind Games: Game Engines as an Architecture for Intuitive Physics, Trends in Cognitive Sciences, Volume 21, Issue 9, 2017, Pages 649-665, DOI: 10.1016/j.tics.2017.05.012.

We explore the hypothesis that many intuitive physical inferences are based on a mental physics engine that is analogous in many ways to the machine physics engines used in building interactive video games. We describe the key features of game physics engines and their parallels in human mental representation, focusing especially on the intuitive physics of young infants where the hypothesis helps to unify many classic and otherwise puzzling phenomena, and may provide the basis for a computational account of how the physical knowledge of infants develops. This hypothesis also explains several ‘physics illusions’, and helps to inform the development of artificial intelligence (AI) systems with more human-like common sense.

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Reinterpretation of evolutionary processes as algorithms for Bayesian inference

June 30, 2017 11:56 ,

Jordan W. Suchow, David D. Bourgin, Thomas L. Griffiths, Evolution in Mind: Evolutionary Dynamics, Cognitive Processes, and Bayesian Inference, Trends in Cognitive Sciences, Volume 21, Issue 7, July 2017, Pages 522-530, ISSN 1364-6613, DOI: 10.1016/j.tics.2017.04.005.

Evolutionary theory describes the dynamics of population change in settings affected by reproduction, selection, mutation, and drift. In the context of human cognition, evolutionary theory is most often invoked to explain the origins of capacities such as language, metacognition, and spatial reasoning, framing them as functional adaptations to an ancestral environment. However, evolutionary theory is useful for understanding the mind in a second way: as a mathematical framework for describing evolving populations of thoughts, ideas, and memories within a single mind. In fact, deep correspondences exist between the mathematics of evolution and of learning, with perhaps the deepest being an equivalence between certain evolutionary dynamics and Bayesian inference. This equivalence permits reinterpretation of evolutionary processes as algorithms for Bayesian inference and has relevance for understanding diverse cognitive capacities, including memory and creativity.

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Evidences that the human brain has quantifying properties -i.e., ability to discriminate between sets of different sizes- as a result of evolution, but that numerical cognition is a result of culture

June 30, 2017 11:05 ,

Rafael E. Núñez, Is There Really an Evolved Capacity for Number?, Trends in Cognitive Sciences, Volume 21, Issue 6, June 2017, Pages 409-424, ISSN 1364-6613, DOI: 10.1016/j.tics.2017.03.005.

Humans and other species have biologically endowed abilities for discriminating quantities. A widely accepted view sees such abilities as an evolved capacity specific for number and arithmetic. This view, however, is based on an implicit teleological rationale, builds on inaccurate conceptions of biological evolution, downplays human data from non-industrialized cultures, overinterprets results from trained animals, and is enabled by loose terminology that facilitates teleological argumentation. A distinction between quantical (e.g., quantity discrimination) and numerical (exact, symbolic) cognition is needed: quantical cognition provides biologically evolved preconditions for numerical cognition but it does not scale up to number and arithmetic, which require cultural mediation. The argument has implications for debates about the origins of other special capacities – geometry, music, art, and language.

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A summary of the Clarion cognitive architecture

June 27, 2017 11:07 ,

Ron Sun, Anatomy of the Mind: a Quick Overview, Cognitive Computation, February 2017, Volume 9, Issue 1, pp 1–4, DOI: 10.1007/s12559-016-9444-2.

The recently published book, “Anatomy of the Mind,” explains psychological (cognitive) mechanisms, processes, and functionalities through a comprehensive computational theory of the human mind—that is, a cognitive architecture. The goal of the work has been to develop a unified framework and then to develop process-based mechanistic understanding of psychological phenomena within the unified framework. In this article, I will provide a quick overview of the work.

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Approach to explain gaze: gaze is directed to task- and goal-relevant scene regions

June 21, 2017 12:12 ,

John M. Henderson, Gaze Control as Prediction, Trends in Cognitive Sciences, Volume 21, Issue 1, January 2017, Pages 15-23, ISSN 1364-6613, DOI: 10.1016/j.tics.2016.11.003.

The recent study of overt attention during complex scene viewing has emphasized explaining gaze behavior in terms of image properties and image salience independently of the viewer’s intentions and understanding of the scene. In this Opinion article, I outline an alternative approach proposing that gaze control in natural scenes can be characterized as the result of knowledge-driven prediction. This view provides a theoretical context for integrating and unifying many of the disparate phenomena observed in active scene viewing, offers the potential for integrating the behavioral study of gaze with the neurobiological study of eye movements, and provides a theoretical framework for bridging gaze control and other related areas of perception and cognition at both computational and neurobiological levels of analysis.

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Demonstration that a theory of cortical function (“predictive coding”) can perform bayesian inference in some tasks, with a nice related work of physiological foundations of probability distribution representation in neurons and of bayesian inference

June 21, 2017 11:57 ,

M. W. Spratling, A neural implementation of Bayesian inference based on predictive coding, Connection Science, Volume 28, 2016 – Issue 4, DOI: 10.1080/09540091.2016.1243655.

Predictive coding (PC) is a leading theory of cortical function that has previously been shown to explain a great deal of neurophysiological and psychophysical data. Here it is shown that PC can perform almost exact Bayesian inference when applied to computing with population codes. It is demonstrated that the proposed algorithm, based on PC, can: decode probability distributions encoded as noisy population codes; combine priors with likelihoods to calculate posteriors; perform cue integration and cue segregation; perform function approximation; be extended to perform hierarchical inference; simultaneously represent and reason about multiple stimuli; and perform inference with multi-modal and non-Gaussian probability distributions. PC thus provides a neural network-based method for performing probabilistic computation and provides a simple, yet comprehensive, theory of how the cerebral cortex performs Bayesian inference.

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Including selective attention and cortical magnification to improve computer vision

June 21, 2017 10:43 ,

Ala Aboudib, Vincent Gripon, Gilles Coppin, A Biologically Inspired Framework for Visual Information Processing and an Application on Modeling Bottom-Up Visual Attention, Cognitive Computation, December 2016, Volume 8, Issue 6, pp 1007–1026, DOI: 10.1007/s12559-016-9430-8.

An emerging trend in visual information processing is toward incorporating some interesting properties of the ventral stream in order to account for some limitations of machine learning algorithms. Selective attention and cortical magnification are two such important phenomena that have been the subject of a large body of research in recent years. In this paper, we focus on designing a new model for visual acquisition that takes these important properties into account.We propose a new framework for visual information acquisition and representation that emulates the architecture of the primate visual system by integrating features such as retinal sampling and cortical magnification while avoiding spatial deformations and other side effects produced by models that tried to implement these two features. It also explicitly integrates the notion of visual angle, which is rarely taken into account by vision models. We argue that this framework can provide the infrastructure for implementing vision tasks such as object recognition and computational visual attention algorithms.To demonstrate the utility of the proposed vision framework, we propose an algorithm for bottom-up saliency prediction implemented using the proposed architecture. We evaluate the performance of the proposed model on the MIT saliency benchmark and show that it attains state-of-the-art performance, while providing some advantages over other models.

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On the limitations of cognitive control from the human psychological perspective

June 20, 2017 12:44 ,

Tarek Amer, Karen L. Campbell, Lynn Hasher, Cognitive Control As a Double-Edged Sword, Trends in Cognitive Sciences, Volume 20, Issue 12, 2016, Pages 905-915, ISSN 1364-6613, DOI: 10.1016/j.tics.2016.10.002.

Cognitive control, the ability to limit attention to goal-relevant information, aids performance on a wide range of laboratory tasks. However, there are many day-to-day functions which require little to no control and others which even benefit from reduced control. We review behavioral and neuroimaging evidence demonstrating that reduced control can enhance the performance of both older and, under some circumstances, younger adults. Using healthy aging as a model, we demonstrate that decreased cognitive control benefits performance on tasks ranging from acquiring and using environmental information to generating creative solutions to problems. Cognitive control is thus a double-edged sword – aiding performance on some tasks when fully engaged, and many others when less engaged.

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A proposal that explains why the human brain seems bayesian but finds difficulties in using probabilities: because it uses sampling

June 20, 2017 11:26 ,

Adam N. Sanborn, Nick Chater, Bayesian Brains without Probabilities, Trends in Cognitive Sciences, Volume 20, Issue 12, 2016, Pages 883-893, ISSN 1364-6613, DOI: 10.1016/j.tics.2016.10.003.

Bayesian explanations have swept through cognitive science over the past two decades, from intuitive physics and causal learning, to perception, motor control and language. Yet people flounder with even the simplest probability questions. What explains this apparent paradox? How can a supposedly Bayesian brain reason so poorly with probabilities? In this paper, we propose a direct and perhaps unexpected answer: that Bayesian brains need not represent or calculate probabilities at all and are, indeed, poorly adapted to do so. Instead, the brain is a Bayesian sampler. Only with infinite samples does a Bayesian sampler conform to the laws of probability; with finite samples it systematically generates classic probabilistic reasoning errors, including the unpacking effect, base-rate neglect, and the conjunction fallacy.

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How hierarchical reinforcement learning resembles human creativity, i.e., matching the psychological aspects with the engineering ones

June 20, 2017 10:26 ,

Thomas R. Colin, Tony Belpaeme, Angelo Cangelosi, Nikolas Hemion, Hierarchical reinforcement learning as creative problem solving, Robotics and Autonomous Systems, Volume 86, 2016, Pages 196-206, ISSN 0921-8890, DOI: 10.1016/j.robot.2016.08.021.

Although creativity is studied from philosophy to cognitive robotics, a definition has proven elusive. We argue for emphasizing the creative process (the cognition of the creative agent), rather than the creative product (the artifact or behavior). Owing to developments in experimental psychology, the process approach has become an increasingly attractive way of characterizing creative problem solving. In particular, the phenomenon of insight, in which an individual arrives at a solution through a sudden change in perspective, is a crucial component of the process of creativity. These developments resonate with advances in machine learning, in particular hierarchical and modular approaches, as the field of artificial intelligence aims for general solutions to problems that typically rely on creativity in humans or other animals. We draw a parallel between the properties of insight according to psychology and the properties of Hierarchical Reinforcement Learning (HRL) systems for embodied agents. Using the Creative Systems Framework developed by Wiggins and Ritchie, we analyze both insight and HRL, establishing that they are creative in similar ways. We highlight the key challenges to be met in order to call an artificial system “insightful”.

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