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Information-Theoretic Analysis
Dynamical Analysis
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Psychology
Cognitive Science

Information Processing and Dynamics in Minimally Cognitive Agents

Profile image of Paul WilliamsPaul Williams
https://doi.org/10.1111/COGS.12142
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Abstract

There has been considerable debate in the literature about the relative merits of information processing vs. dynamical approaches to understanding cognitive processes. In this paper, we explore the relationship between these two styles of explanation using a model agent evolved to solve a relational categorization task. Specifically, we separately analyze the operation of this agent using the mathematical tools of information theory and dynamical systems theory. Information-theoretic analysis reveals how task-relevant information flows through the system to be combined into a categorization decision. Dynamical analysis reveals the key geometrical and temporal interrelationships underlying the categorization decision. Finally, we propose a framework for directly relating these two different styles of explanation and discuss the possible implications of our analysis for some of the ongoing debates in cognitive science.

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Cognitive dynamical models as minimal models
Travis Holmes

Synthese, 2020

The debate over the explanatory nature of cognitive models has been waged mostly between two factions: the mechanists and the dynamical systems theorists. The former hold that cognitive models are explanatory only if they satisfy a set of mapping criteria, particularly the 3M/3M* requirement. The latter have argued, pace the mechanists, that some cognitive models are both dynamical and constitute covering-law explanations. In this paper, I provide a minimal model interpretation of dynamical cognitive models, arguing that this both provides needed clarity to the mechanist versus dynamicist divide in cognitive science and also paves the way towards further insights about scientific explanation generally.

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Cognition Without Neural Representation: Dynamics of a Complex System
Inês Hipólito

Frontiers in Psychology, 2022

This paper proposes an account of neurocognitive activity without leveraging the notion of neural representation. Neural representation is a concept that results from assuming that the properties of the models used in computational cognitive neuroscience (e.g., information, representation, etc.) must literally exist the system being modelled (e.g., the brain). Computational models are important tools to test a theory about how the collected data (e.g., behavioural or neuroimaging) has been generated. While the usefulness of computational models is unquestionable, it does not follow that neurocognitive activity should literally entail the properties construed in the model (e.g., information, representation). While this is an assumption present in computationalist accounts, it is not held across the board in neuroscience. In the last section, the paper offers a dynamical account of neurocognitive activity with Dynamical Causal Modelling (DCM) that combines dynamical systems theory (DS...

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Related topics

  • Cognitive Science
  • Artificial Intelligence
  • Dynamical Systems
  • Information Theory
  • Situated Cognition
  • Embodied Cognition
  • Systems Neuroscience
  • Behavioral Neuroscience

    • Cited by

    JIDT: An Information-Theoretic Toolkit for Studying the Dynamics of Complex Systems
    Joseph Lizier

    Frontiers in Robotics and AI, 2014

    Complex systems are increasingly being viewed as distributed information processing systems, particularly in the domains of computational neuroscience, bioinformatics and Artificial Life. This trend has resulted in a strong uptake in the use of (Shannon) information-theoretic measures to analyse the dynamics of complex systems in these fields. We introduce the Java Information Dynamics Toolkit (JIDT): a Google code project which provides a standalone, (GNU GPL v3 licensed) open-source code implementation for empirical estimation of information-theoretic measures from time-series data. While the toolkit provides classic information-theoretic measures (e.g. entropy, mutual information, conditional mutual information), it ultimately focusses on implementing higherlevel measures for information dynamics. That is, JIDT focusses on quantifying information storage, transfer and modification, and the dynamics of these operations in space and time. For this purpose, it includes implementations of the transfer entropy and active information storage, their multivariate extensions and local or pointwise variants. JIDT provides implementations for both discrete and continuous-valued data for each measure, including various types of estimator for continuous data (e.g. Gaussian, box-kernel and Kraskov-Stögbauer-Grassberger) which can be swapped at run-time due to Java's object-oriented polymorphism. Furthermore, while written in Java, the toolkit can be used directly in MATLAB, GNU Octave and Python. We present the principles behind the code design, and provide several examples to guide users.

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    infotheory: A C++/Python package for multivariate information theoretic analysis
    Madhavun Candadai

    Journal of Open Source Software

    This paper introduces infotheory: a package written in C++ and usable from Python and C++, for multivariate information theoretic analyses of discrete and continuous data. It is open-source () and details on how to install it and use it are available on its website. This package allows the user to study the relationship between components of a complex system simply from the data recorded during its operation, using the tools of information theory. Specifically, this package enables the measurement of entropy and mutual information, and also allows the user to perform partial information decomposition of mutual information into unique, redundant, and synergistic information quantities.

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    Of what is “minimal cognition” the half-baked version?
    Pamela Lyon

    Adaptive Behavior

    ''Minimal cognition'' is used in certain sectors of the cognitive sciences to make a kind of ontological claim that may be unique in the biological sciences: that a function operating in organisms living today is not a fully fledged version of that function (the nature of which remains unspecified), but, rather, exhibits the minimal requirements for whatever it is, properly conceived. Evidence suggests that elsewhere in the life sciences, deployment of minimizing qualifiers relative to a biological function appears largely restricted to two scenarios: first, attenuated functioning and, second, evolution of the function, real or synthetic. The article argues that ''minimal cognition'' and ''proto-cognitive'' were introduced at the turn of this century by cognitive researchers seeking to learn directly from evolved behavior, ecology and physiology. A terminological straitjacket imposed on the central object of cognitive science at its beginning necessitated the move. An alternative terminology is proposed, based on a phyletically neutral definition of cognition as a biological function; a candidate mechanism is explored; and a bacterial example presented. On this story, cognition is like respiration: ubiquitously present, from unicellular life to blue whales and every form of life in between, and for similar reasons: staying alive requires it.

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    Extended Neural Metastability in an Embodied Model of Sensorimotor Coupling
    Xabier E. Barandiaran

    Frontiers in Systems Neuroscience, 2016

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    From chemical soup to computing circuit: transforming a contiguous chemical medium into a logic gate network by modulating its external conditions
    Juan Mercader

    Journal of The Royal Society Interface

    It has been shown that it is possible to transform a well-stirred chemical medium into a logic gate simply by varying the chemistry’s external conditions (feed rates, lighting conditions, etc.). We extend this work, showing that the same method can be generalized to spatially extended systems. We vary the external conditions of a well-known chemical medium (a cubic autocatalytic reaction–diffusion model), so that different regions of the simulated chemistry are operating under particular conditions at particular times. In so doing, we are able to transform the initially uniform chemistry, not just into a single logic gate, but into a functionally integrated network of diverse logic gates that operate as a basic computational circuit known as a full-adder.

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