66,938 research outputs found
Formalizing Neurath's ship:Approximate algorithms for online causal learning
Higher-level cognition depends on the ability to learn models of the world.
We can characterize this at the computational level as a structure-learning
problem with the goal of best identifying the prevailing causal relationships
among a set of relata. However, the computational cost of performing exact
Bayesian inference over causal models grows rapidly as the number of relata
increases. This implies that the cognitive processes underlying causal learning
must be substantially approximate. A powerful class of approximations that
focuses on the sequential absorption of successive inputs is captured by the
Neurath's ship metaphor in philosophy of science, where theory change is cast
as a stochastic and gradual process shaped as much by people's limited
willingness to abandon their current theory when considering alternatives as by
the ground truth they hope to approach. Inspired by this metaphor and by
algorithms for approximating Bayesian inference in machine learning, we propose
an algorithmic-level model of causal structure learning under which learners
represent only a single global hypothesis that they update locally as they
gather evidence. We propose a related scheme for understanding how, under these
limitations, learners choose informative interventions that manipulate the
causal system to help elucidate its workings. We find support for our approach
in the analysis of four experiments
A Novel Machine Learning Classifier Based on a Qualia Modeling Agent (QMA)
This dissertation addresses a problem found in supervised machine learning (ML) classification, that the target variable, i.e., the variable a classifier predicts, has to be identified before training begins and cannot change during training and testing. This research develops a computational agent, which overcomes this problem. The Qualia Modeling Agent (QMA) is modeled after two cognitive theories: Stanovich\u27s tripartite framework, which proposes learning results from interactions between conscious and unconscious processes; and, the Integrated Information Theory (IIT) of Consciousness, which proposes that the fundamental structural elements of consciousness are qualia. By modeling the informational relationships of qualia, the QMA allows for retaining and reasoning-over data sets in a non-ontological, non-hierarchical qualia space (QS). This novel computational approach supports concept drift, by allowing the target variable to change ad infinitum without re-training while achieving classification accuracy comparable to or greater than benchmark classifiers. Additionally, the research produced a functioning model of Stanovich\u27s framework, and a computationally tractable working solution for a representation of qualia, which when exposed to new examples, is able to match the causal structure and generate new inferences
Causal Dependence Tree Approximations of Joint Distributions for Multiple Random Processes
We investigate approximating joint distributions of random processes with
causal dependence tree distributions. Such distributions are particularly
useful in providing parsimonious representation when there exists causal
dynamics among processes. By extending the results by Chow and Liu on
dependence tree approximations, we show that the best causal dependence tree
approximation is the one which maximizes the sum of directed informations on
its edges, where best is defined in terms of minimizing the KL-divergence
between the original and the approximate distribution. Moreover, we describe a
low-complexity algorithm to efficiently pick this approximate distribution.Comment: 9 pages, 15 figure
Modelling the Developing Mind: From Structure to Change
This paper presents a theory of cognitive change. The theory assumes that the fundamental causes of cognitive change reside in the architecture of mind. Thus, the architecture of mind as specified by the theory is described first. It is assumed that the mind is a three-level universe involving (1) a processing system that constrains processing potentials, (2) a set of specialized capacity systems that guide understanding of different reality and knowledge domains, and (3) a hypecognitive system that monitors and controls the functioning of all other systems. The paper then specifies the types of change that may occur in cognitive development (changes within the levels of mind, changes in the relations between structures across levels, changes in the efficiency of a structure) and a series of general (e.g., metarepresentation) and more specific mechanisms (e.g., bridging, interweaving, and fusion) that bring the changes about. It is argued that different types of change require different mechanisms. Finally, a general model of the nature of cognitive development is offered. The relations between the theory proposed in the paper and other theories and research in cognitive development and cognitive neuroscience is discussed throughout the paper
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