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The Critique Of Pure Reason

This chapter contains sections titled: 1 Introduction, 2 Connectionist Representation and Tensor Product Binding: Definition and Examples, 3 Tensor Product Representation: Properties, 4 Conclusion

Tensor Product Variable Binding and the Representation of Symbolic Structures in Connectionist Systems

The History of StatisticsThe History of Statistics in the 17th and 18th CenturiesThe Politics of Large NumbersStatistics on the TableFiguring Out The PastDicing with DeathHow to Lie with StatisticsAnnotated Readings in the History of StatisticsPast, Present, and Future of Statistical ScienceCartoon Guide to StatisticsStatistics in PsychologyFisher, Neyman, and the Creation of Classical StatisticsHistory of the Mathematical Theory of Probability from the Time of Pascal to that of LaplaceMeasurement and Statistics on Science and TechnologyThe Science of ConjectureDisciplining StatisticsThe Seven Pillars of Statistical WisdomA History of Mathematical Statistics from 1750 to 1930The History of StatisticsThe History of StatisticsIntroducing StatisticsA History of Probability and Statistics and Their Applications before 1750Making It CountStatistics in the 21st CenturyModern Statistics for Modern BiologyClassic Problems of ProbabilityStatistical ThoughtThe Rise of Statistical Thinking, 1820-1900Strength in Numbers: The Rising of Academic Statistics Departments in the U. S.Mathematical StatisticsStatistics on the TableStatistics and the German State, 1900-1945Statistics, Public Debate and the State, 1800–1945Modern Interdisciplinary University Statistics EducationUncertaintyProbability and StatisticsHistory, Theory, and Technique of StatisticsArthur L BowleyHandbook of Forensic StatisticsClassic Topics on the History of Modern Mathematical Statistics

The History of Statistics. The Measurement of Uncertainty before 1900.

Afterword The impact of “The cognitive basis for linguistic structures”

The purpose of this article is to begin the process of engaging the international research community in developing what can be called a standard model of the mind, where the mind we have in mind here is human-like. The notion of a standard model has its roots in physics, where over more than a half-century the international community has developed and tested a standard model that combines much of what is known about particles. This model is assumed to be internally consistent, yet still have major gaps. Its function is to serve as a cumulative reference point for the field while also driving efforts to both extend and break it.

/pdf/a-standard-model-of-the-mind-toward-a-common-computational-f5mhxm3hk5.pdf

A Standard Model of the Mind: Toward a Common Computational Framework across Artificial Intelligence, Cognitive Science, Neuroscience, and Robotics

We present analysis of existing memory models, examining how models represent knowledge, structure memory, learn, make decisions, and predict reaction times. On the basis of this analysis, we propose a theoretical framework that characterizes memory modelling in terms of six key decisions: (1) choice of knowledge representation scheme, (2) choice of data structure, (3) choice of associative architecture, (4) choice of learning rule, (5) choice of time variant process, and (6) choice of response decision criteria. This framework is both descriptive and prescriptive: we intend to both describe the state of the literature and outline what we believe is the most fruitful space of possibilities for the development of future memory models.

A framework for computational models of human memory

We present a new cognitive architecture that combines two neurobiologically plausible, computational models: (1) a variant of predictive processing known as neural generative coding (NGC) and (2) hyperdimensional / vector-symbolic models of human memory. We draw inspiration from well-known cognitive architectures such as ACT-R, Soar, Leabra, and Spaun/Nengo. Our cognitive architecture, the COGnitive Neural GENerative system (CogNGen), is in broad agreement with these architectures, but provides a level of detail between ACT-R’s high-level, symbolic description of human cognition and Spaun’s low-level neurobiological description. CogNGen creates the groundwork for developing agents that learn continually from diverse tasks and model human performance at larger scales than what is possible with existent cognitive architectures. We aim to develop a cognitive architecture that has the power of modern machine learning techniques while retaining long-term memory, single-trial learning, transfer-learning, planning, and other capacities associated with high-level cognition. We test CogNGen on a set of maze-learning tasks, including mazes that test short-term memory and planning, and ﬁnd that the addition of vector-symbolic models of memory improves the ability of the NGC reinforcement learning model to master the maze task. Future work includes testing CogNGen on more tasks and exploring methods for efﬁciently scaling hyperdimensional memory models to lifetime learning.

CogNGen: Constructing the Kernel of a Hyperdimensional Predictive Processing Cognitive Architecture

Computational memory models can explain the behaviour of human memory in diverse experimental paradigms—whether it be recall or recognition, short-term or long-term retention, implicit or explicit learning. Simulation has led to parsimonious theories of memory, but at a cost of a profusion of competing models. As different models focus on different phenomena, there is no best model. However, the models share many characteristics, indicating wide agreement on the mathematics of how memory works in the brain. On the basis of an analysis of computational memory models, we argue that these models can be understood in terms of a single neurally-plausible computational and theoretical framework. We present a proof of concept neural implementation, integration with the ACT-R cognitive architecture, and demonstrate model performance on procedural, declarative, episodic, and semantic learning tasks. This research aims to advance cognitive psychology towards a single integrated, computational model of human memory that can account for human performance on diverse experimental tasks, that can be implemented at a neural level of detail, and can be scaled to modelling arbitrarily long-term learning.

The Memory Tesseract: Developing a Unified Framework for Modelling Memory and Cognition

The task of turning undergrads into academics requires teaching them to reason about the world in a more complex way. We present the Argument Complexity Scale, a tool for analyzing the complexity of argu-mentation, based on the Integrative Complexity and Conceptual Complexity Scales from, respectively, political psychology and personality theory. Argument Complexity classifies arguments based on acknowl-edgement and consideration of conflicting evidence or conflicting frameworks for judging the issue, use of frameworks for evaluating evidence, and use of meta-frameworks for evaluating frameworks. We discuss how the Argument Complexity Scale can be used to teach undergraduate students to reason and write like academics by providing the scaffolding for forming complex argumentation.

/pdf/argument-complexity-teaching-undergraduates-to-make-better-45omy2x00l.pdf

Argument Complexity: Teaching Undergraduates to Make Better Arguments.

What role does the study of natural language play in the task of developing a unified theory and common model of cognition? Language is perhaps the most complex behaviour that humans exhibit, and, as such, is one of the most difficult problems for understanding human cognition. Linguistic theory can both inform and be informed by unified models of cognition. We discuss (1) how computational models of human cognition can provide insight into how humans produce and comprehend language and (2) how the problem of modelling language processing raises questions and creates challenges for widely used computational models of cognition. Evidence from the literature suggests that behavioural phenomena, such as recency and priming effects, and cognitive constraints, such as working memory limits, affect how language is produced by humans in ways that can be predicted by computational cognitive models. But just as computational models can provide new insights into language, language can serve as a test for these models. For example, simulating language learning requires the use of more powerful machine learning techniques, such as deep learning and vector symbolic architectures, and language comprehension requires a capacity for on-the-fly situational model construction. In sum, language plays an important role in both shaping the development of a common model of the mind, and, in turn, the theoretical understanding of language stands to benefit greatly from the development of a common model.

https://ojs.library.carleton.ca/index.php/cmcb/article/download/2592/2254

Matthew A. Kelly

Papers

A framework for computational models of human memory

CogNGen: Constructing the Kernel of a Hyperdimensional Predictive Processing Cognitive Architecture

The Memory Tesseract: Developing a Unified Framework for Modelling Memory and Cognition

Argument Complexity: Teaching Undergraduates to Make Better Arguments.

How Language Processing can Shape a Common Model of Cognition