Category Archives: Cognitive Sciences

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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How to improve statistical results obtained from limited set-ups through active sampling, and a nice review of possible pitfalls in conducting statistical research (and a mention to “pre-registration” of hypothesis and plans to be peer-reviewed before submitting the results)

June 27, 2017 08:42 ,

Romy Lorenz, Adam Hampshire, Robert Leech, Neuroadaptive Bayesian Optimization and Hypothesis Testing, Trends in Cognitive Sciences, Volume 21, Issue 3, March 2017, Pages 155-167, ISSN 1364-6613, DOI: 10.1016/j.tics.2017.01.006.

Cognitive neuroscientists are often interested in broad research questions, yet use overly narrow experimental designs by considering only a small subset of possible experimental conditions. This limits the generalizability and reproducibility of many research findings. Here, we propose an alternative approach that resolves these problems by taking advantage of recent developments in real-time data analysis and machine learning. Neuroadaptive Bayesian optimization is a powerful strategy to efficiently explore more experimental conditions than is currently possible with standard methodology. We argue that such an approach could broaden the hypotheses considered in cognitive science, improving the generalizability of findings. In addition, Bayesian optimization can be combined with preregistration to cover exploration, mitigating researcher bias more broadly and improving reproducibility.

Posted in: Cognitive sciences, Probability and statistics , Tagged: ,

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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Subgraph matching (isomorphism) using GPUs for managing commonsense knowledge, and a short list of other graph problems that have had benefit from multiprocessing

June 21, 2017 10:48 ,

Ha-Nguyen Tran, Erik Cambria, Amir Hussain, Towards GPU-Based Common-Sense Reasoning: Using Fast Subgraph Matching, Cognitive Computation, December 2016, Volume 8, Issue 6, pp 1074–1086, DOI: 10.1007/s12559-016-9418-4.

Common-sense reasoning is concerned with simulating cognitive human ability to make presumptions about the type and essence of ordinary situations encountered every day. The most popular way to represent common-sense knowledge is in the form of a semantic graph. Such type of knowledge, however, is known to be rather extensive: the more concepts added in the graph, the harder and slower it becomes to apply standard graph mining techniques.In this work, we propose a new fast subgraph matching approach to overcome these issues. Subgraph matching is the task of finding all matches of a query graph in a large data graph, which is known to be a non-deterministic polynomial time-complete problem. Many algorithms have been previously proposed to solve this problem using central processing units. Here, we present a new graphics processing unit-friendly method for common-sense subgraph matching, termed GpSense, which is designed for scalable massively parallel architectures, to enable next-generation Big Data sentiment analysis and natural language processing applications.We show that GpSense outperforms state-of-the-art algorithms and efficiently answers subgraph queries on large common-sense graphs.

Posted in: Artificial Intelligence, Graph theory , Tagged: ,

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.

Posted in: Computer vision, Psycho-physiological bases of engineering , Tagged:

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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State of the art of symbolic planning, particularly the one that optimizes some cost, and a novel approach

June 20, 2017 11:22 ,

Álvaro Torralba, Vidal Alcázar, Peter Kissmann, Stefan Edelkamp, Efficient symbolic search for cost-optimal planning, Artificial Intelligence, Volume 242, January 2017, Pages 52-79, ISSN 0004-3702, DOI: 10.1016/j.artint.2016.10.001.

In cost-optimal planning we aim to find a sequence of operators that achieve a set of goals with minimum cost. Symbolic search with Binary Decision Diagrams (BDDs) performs efficient state space exploration in terms of time and memory. This is crucial in optimal settings, in which large parts of the state space must be explored in order to prove optimality. However, the development of accurate heuristics for explicit-state search in recent years have left symbolic search techniques in a secondary place. In this article we propose two orthogonal improvements for symbolic search planning. On the one hand, we analyze and compare different methods for image computation in order to efficiently perform the successor generation on symbolic search. Image computation is the main bottleneck of symbolic search algorithms so an efficient computation is paramount for efficient symbolic search planning. On the other hand, we study how to use state-invariant constraints to prune states in symbolic search. This is essential in regression search but it is yet to be exploited in symbolic search planners. Experiments with symbolic bidirectional uniform-cost search and symbolic A ⁎ search with PDBs show remarkable performance improvements on most IPC benchmark domains. Overall, with the help of our improvements, symbolic bidirectional search outperforms explicit-state search with state-of-the-art heuristics such as LM-cut across many different domains.

Posted in: Artificial Intelligence , Tagged:

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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