Analogy is the core of cognition , and can be formalized in terms of Bayesian inference . Indeed, thought itself can be thought of as constituting dynamics of cascading belief propagation through the nodes of generative probability models specified by a cortical heterarchy "rooted" in perception-action cycles that ground the mind as an embodied control system for an autonomous agent. From this perspective, a translating principle between cognitive science and machine learning can be obtained that should result in productive cross-fertilization between these disciplines: simply, cortical columns represent nodes in a particular kind of directed graphical model, where the symbolic level emerges from the sub-symbolic level through hierarchical pattern abstraction. In terms of development, embodiment provides useful inductive biases that act as a toehold in allowing the learner to bootstrap its way to the full flourishing of cognition. More specifically, I argue that URLanisms are such effective learners because they begin with a sense of their own embodiment as a kind of prototypical object from which they can partially generalize, and they pay attention to this object because it is directly connected to the mechanisms of reinforcement. This account of cognitive development can explain the knowledge that infants possess, as well as how they use this acquired core knowledgeto partially generalize to other classes of phenomena, even sometimes incorrectly.
It has been argued that all of cognition can be understood in terms of Bayesian inference. It has also been argued that analogy is the core of cognition . Here I will propose that these perspectives are fully compatible, in that analogical reasoning can be described in terms of Bayesian inference and vice versa, and that both of these positions require a thorough cybernetic grounding in order to fulfill their promise as unifying frameworks for understanding minds. From the Bayesian perspective of the Free Energy Principle and Active Inference framework, thought is constituted by dynamics of cascading belief propagation through the nodes of probabilistic generative models specified by a cortical heterarchy "rooted" in action-perception cycles that ground the mind as an embodied control system for an autonomous agent. From the analogical structure mapping perspective, thought is constituted by the alignment and comparison of heterogeneous structural representations. Here I will propose that this core cognitive process for analogical reasoning is naturally implemented by predictive coding mechanisms. However, both Bayesian cognitive science and models of cognitive development via analogical reasoning require rich base domains and priors (or reliably learnable posteriors) from which they can commence the process of bootstrapping minds. Here in the spirit of the work of URLe Lakoff and Mark Johnson, I propose that embodiment provides many of the inductive biases that are usually described in terms of innate core knowledge. (Please note: this manuscript was written and finalized in 2012.)
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Analogy
It has been argued that all of cognition can be understood in terms of Bayesian inference. It has also been argued that analogy
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, and can be formalized
. Here I will propose that these perspectives are fully compatible, in that analogical reasoning can be described
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. Indeed, thought itself can be thought of as constituting
and vice versa, and that both of these positions require a thorough cybernetic grounding in order to fulfill their promise as unifying frameworks for understanding minds. From the Bayesian perspective of the Free Energy Principle and Active Inference framework, thought is constituted by
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generative probability
probabilistic generative
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perception-action
action-perception
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this perspective, a translating principle between cognitive science and machine learning can be obtained that should result in productive cross-fertilization between these disciplines: simply, cortical columns represent nodes in a particular kind of directed graphical model, where the symbolic level emerges from
the analogical structure mapping perspective, thought is constituted by the alignment and comparison of heterogeneous structural representations. Here I will propose that this core cognitive process for analogical reasoning is naturally implemented by predictive coding mechanisms. However, both Bayesian cognitive science and models of cognitive development via analogical reasoning require rich base domains and priors (or reliably learnable posteriors) from which they can commence the process of bootstrapping minds. Here in
R
sub-symbolic level through hierarchical pattern abstraction. In terms of development, embodiment provides useful inductive biases that act as a toehold in allowing the learner to bootstrap its way to the full flourishing of cognition. More specifically, I argue that URLanisms are such effective learners because they begin with a sense of their own embodiment as a kind of prototypical object from which they can partially generalize, and they pay attention to this object because it is directly connected to the mechanisms of reinforcement. This account of cognitive development can explain the knowledge that infants possess, as well as how they use this acquired core knowledgeto partially generalize to other classes of phenomena, even sometimes incorrectly.
spirit of the work of URLe Lakoff and Mark Johnson, I propose that embodiment provides many of the inductive biases that are usually described in terms of innate core knowledge. (Please note: this manuscript was written and finalized in 2012.)