Category Archives: Psycho-physiological Bases Of Engineering

Including selective attention and cortical magnification to improve computer vision

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.

On the limitations of cognitive control from the human psychological perspective

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.

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

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.

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

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”.

Survey of Cognitive Offloading

Evan F. Risko, Sam J. Gilbert, Cognitive Offloading, Trends in Cognitive Sciences, Volume 20, Issue 9, 2016, Pages 676-688, ISSN 1364-6613, DOI: 10.1016/j.tics.2016.07.002.

If you have ever tilted your head to perceive a rotated image, or programmed a smartphone to remind you of an upcoming appointment, you have engaged in cognitive offloading: the use of physical action to alter the information processing requirements of a task so as to reduce cognitive demand. Despite the ubiquity of this type of behavior, it has only recently become the target of systematic investigation in and of itself. We review research from several domains that focuses on two main questions: (i) what mechanisms trigger cognitive offloading, and (ii) what are the cognitive consequences of this behavior? We offer a novel metacognitive framework that integrates results from diverse domains and suggests avenues for future research.

A nive review of reinforcement learning from the perspective of its physiological foundations and its application to Robotics

Cornelius Weber, Mark Elshaw, Stefan Wermter, Jochen Triesch and Christopher Willmot, Reinforcement Learning Embedded in Brains and Robots, Reinforcement Learning: Theory and Applications, Book edited by Cornelius Weber, Mark Elshaw and Norbert Michael Mayer, ISBN 978-3-902613-14-1, pp.424, January 2008, I-Tech Education and Publishing, Vienna, Austria. (Local copy)

On how the calculus of utility of actions drives many human behaviours

Julian Jara-Ettinger, Hyowon Gweon, Laura E. Schulz, Joshua B. Tenenbaum, The Naïve Utility Calculus: Computational Principles Underlying Commonsense Psychology, Trends in Cognitive Sciences, Volume 20, Issue 8, 2016, Pages 589-604, ISSN 1364-6613, DOI: 10.1016/j.tics.2016.05.011.

We propose that human social cognition is structured around a basic understanding of ourselves and others as intuitive utility maximizers: from a young age, humans implicitly assume that agents choose goals and actions to maximize the rewards they expect to obtain relative to the costs they expect to incur. This \u2018naïve utility calculus\u2019 allows both children and adults observe the behavior of others and infer their beliefs and desires, their longer-term knowledge and preferences, and even their character: who is knowledgeable or competent, who is praiseworthy or blameworthy, who is friendly, indifferent, or an enemy. We review studies providing support for the naïve utility calculus, and we show how it captures much of the rich social reasoning humans engage in from infancy.

Evidences that the brain encodes numbers on an internal continous line and that the zero value is also represented

Luca Rinaldi, Luisa Girelli, A Place for Zero in the Brain, Trends in Cognitive Sciences, Volume 20, Issue 8, 2016, Pages 563-564, ISSN 1364-6613, DOI: 10.1016/j.tics.2016.06.006.

It has long been thought that the primary cognitive and neural systems responsible for processing numerosities are not predisposed to encode empty sets (i.e., numerosity zero). A new study challenges this view by demonstrating that zero is translated into an abstract quantity along the numerical continuum by the primate parietofrontal magnitude system.

Interesting hypothesis about how cognitive abilities can be modelled with closed control loops that run in parallel -using hierarchies of abstraction and prediction-, traditionally used just for low-level behaviours

Giovanni Pezzulo, Paul Cisek, Navigating the Affordance Landscape: Feedback Control as a Process Model of Behavior and Cognition, Trends in Cognitive Sciences, Volume 20, Issue 6, June 2016, Pages 414-424, ISSN 1364-6613, DOI: 10.1016/j.tics.2016.03.013.

We discuss how cybernetic principles of feedback control, used to explain sensorimotor behavior, can be extended to provide a foundation for understanding cognition. In particular, we describe behavior as parallel processes of competition and selection among potential action opportunities (‘affordances’) expressed at multiple levels of abstraction. Adaptive selection among currently available affordances is biased not only by predictions of their immediate outcomes and payoffs but also by predictions of what new affordances they will make available. This allows animals to purposively create new affordances that they can later exploit to achieve high-level goals, resulting in intentional action that links across multiple levels of control. Finally, we discuss how such a ‘hierarchical affordance competition’ process can be mapped to brain structure.

Physiological evidences that visual attention is based on predictions

Martin Rolfs, Martin Szinte, Remapping Attention Pointers: Linking Physiology and Behavior, Trends in Cognitive Sciences, Volume 20, Issue 6, 2016, Pages 399-401, ISSN 1364-6613, DOI: 10.1016/j.tics.2016.04.003.

Our eyes rapidly scan visual scenes, displacing the projection on the retina with every move. Yet these frequent retinal image shifts do not appear to hamper vision. Two recent physiological studies shed new light on the role of attention in visual processing across saccadic eye movements.