If episodic memory is constructive, experienced time is also a construct. We develop an event-based formalism that replaces the traditional objective, agent-independent notion of time with a constructive, agent-dependent notion of time. We show how to make this agent-dependent time entropic and hence well-defined. We use sheaf-theoretic techniques to render agent-dependent time functorial and to construct episodic memories as sequences of observed and constructed events with well-defined limits that maximise the consistency of categorisations assigned to objects appearing in memories. We then develop a condensed formalism that represents episodic memories as pure constructs from single events. We formulate an empirical hypothesis that human episodic memory implements a particular time-symmetric constructive functor, and discuss possible experimental tests.

Recent theories developing broad notions of context and its effects on inference are becoming increasingly important in fields as diverse as cognitive psychology, information science and quantum information theory and computing. Here we introduce a novel and general approach to the characterisation of contextuality using the techniques of Chu spaces and Channel Theory viewed as general theories of information flow. This involves introducing three essential components into the formulism: events, conditions and measurement systems. Incorporating these factors in relationship to conditional probabilities leads to information flows both in the setting of Chu spaces and Channel Theory. The latter provides a representation of semantic content using local logics from which conditionals can be derived. We employ these features to construct cone-cocone diagrams, commutativity of which enforces inferential coherence. With these we build a scale-free architecture incorporating a Bayesian-like hierarchical structure, in which there is an interpretation of active inference and Markov blankets. We compare this architecture with other theories of contextuality which we briefly review. We also show that this development of ideas conveniently accommodates negative probabilities, leading to the notion of signed information flow, and address how quantum contextuality can be interpreted within this model. Finally, we relate contextuality to the Frame Problem, another way of characterising a fundamental limitation on the observational and inferential capabilities of finite agents.

Chu Spaces and Channel Theory are well-established areas of investigation in the general context of category theory when applied to semantically-based information flow. In this Part I of a two-part work, we review a range of related concepts and examples showing how these methods can be applied to logic and computer science, including Formal Concept Analysis, distributed systems and ontology development. We also discuss spatial coarse-graining in relationship to information, and in this direction we establish some basic simplicial and categorical techniques which will supplement the other methods of this Part I when they are applied to characterise visual object identification and the inference of mereological (i.e. part-whole) complexity in Part II.

In this Part II of a two-part work, we proceed from the survey of concepts and techniques of Chu spaces and Channel Theory in Part I to the characterisation of human visual object identification, beginning with the construction of uncategorised object files and proceeding through categorisation, individual object identification and the tracking of object identity through time. We investigate the relationship between abstraction and mereological categorisation, particularly as these affect object-identity tracking. This we accomplish in terms of information flow that is semantically structured in terms of local logics, providing an inferential mechanism for object identification and tracking. We introduce categorical Cone-Cocone Diagrams to explicitly capture a bidirectional duality between ‘token’ roles and ‘type’ roles, and show that all representations can be considered to play both roles. We discuss the emergence of mereotopology from the representation of classifications with underlying simplicial complexes, and briefly explore the emergence of geometric relations and interactions between objects. Throughout we discuss the empirical support for the utility of this descriptive mechanism, particularly as a scale-free organisation that is applicable to cognition and to AI systems in general, and raise open issues and problems.

A time‐dependent centrality metric for disciplinary coauthorship graphs, the “Nobel number” for a discipline, is introduced. A researcher's Nobel number for a given discipline in a given year is defined as the researcher's average coauthorship distance to that discipline's Nobel laureates in that year. Plotting Nobel numbers over several decades provides a quantitative as well as visual indication of a researcher's proximity to the intuitive “center” of a discipline as defined by recognized scientific achievement. It is shown that the Nobel number distributions for physics of several researchers both within and outside of physics are surprisingly flat over the five‐decade span from 1951 to 2000. A model in which Nobel laureates are typically connected by short coauthorship paths both intergenerationally and between subdisciplines reproduces such flat Nobel number distributions.

It is shown that the paradoxes of quantum theory can be traced to an assumption that observers can know which physical degrees of freedom are causally responsible for each of their experiences. The untenability of this assumption is demonstrated, and a quantum theory without it is proposed. Removing this assumption from quantum theory sheds new light on the nature of semantics, and perhaps also on the nature of freedom.

Both standard formulations of the frame problem and standard solutions implicitly assume that the re-identification of objects as persisting individuals between pre- and post-action contexts is unproblematic. In the case of human beings, this assumption is false: humans dedicate considerable cognitive resources to object re-identification. An analysis of both the phenomenology and neurocognitive implementation of object re-identification is used to show that in humans, all of the information architecturally available to solve the frame problem is in fact deployed for object re-identification. The frame problem is, therefore, equivalent to the object re-identification problem in the case of human problem solving.

What humans can't do: a review of Derek Partridge, The Seductive Computer by Derek Partridge, London, Springer-Verlag London Limited, 2010, 339 pp., US59.95 (paperback), ISBN 978-1849964975 With ju...

The generation of novel conceptual combinations is an effective strategy for solving unfamiliar problems only if it is accompanied by a mechanism that identifies potential solutions as such. Both mechanistic studies and common phenomenology suggest that in humans this mechanism involves, and is experienced as, an attachment of strong positive emotional response to a candidate solution. Thus, key questions are how such responses can be indicators of solution correctness, how and at what stage of problem solving they are generated, and how and at what stage of problem solving they are attached to representations of candidate solutions. These questions were investigated in an aesthetic problem solving task environment requiring the synthesis of visual and verbal components. The phenomenology observed in this environment suggests that generation and attachment of a sufficient emotional response is necessary for binding of nascent visual and verbal representation components to form a candidate solution, and that executive-driven attentional amplification of nascent representations suppresses the attachment of emotional valence. It also suggests that a complex, multivalent emotional response is characteristic of correct solutions. If this proves to be correct, it will require a re-thinking of the role of executive attention and predefined goals in creative problem solving.

When stripped of its everyday familiarity, the virtually constant inner dialogue experienced by virtually everyone presents a mystery: why do we use language to communicate to ourselves. When examined from a design perspective in light of currently plausible cognitive neuroscience, language seems highly non-optimal as an internal communication medium. Other than its role in maintaining the privacy of thought, proposed functions for self-dialogue raise more questions than they answer. Therefore, it is appropriate to question the role played by the familiarity of self-dialogue in shaping our intuitions about consciousness.

(1995). The role of the frame problem in Fodor's modularity thesis: a case study of rationalist cognitive science. Journal of Experimental & Theoretical Artificial Intelligence: Vol. 7, No. 3, pp. 279-289.

A hybrid analog-digital neural processing element with the time-dependent behavior of biological neurons has been developed. The hybrid processing element is designed for VLSI implementation and offers the best attributes of both analog and digital computation. Custom VLSI layout reduces the layout area of the processing element, which in turn increases the expected network density. The hybrid processing element operates at the nanosecond time scale, which enables it to produce real-time solutions to complex spatiotemporal problems found in high-speed signal processing applications. VLSI prototype chips have been designed, fabricated, and tested with encouraging results. Systems utilizing the time-dependent behavior of the hybrid processing element have been simulated and are currently in the fabrication process. Future applications are also discussed.

The gm (gene modeler) program automates the identification of candidate genes in anonymous, genomic DNA sequence data. gm accepts sequence data, organism-specific consensus matrices and codon asymmetry tables, and a set of parameters as input; it returns a set of models describing the structures of candidate genes in the sequence and a corresponding set of predicted amino acid sequences as output, gm is implemented in C, and has been tested on Sun, VAX, Sequent, MIPS and Cray computers. It is capable of analyzing sequences of several kilobases containing multi-exon genes in less than 1 min execution time on a Sun 4/60.

Abstract

Continuous dynamical systems intuitively seem capable of more complex behavior than discrete systems. If analyzed in the framework of the traditional theory of computation, a.continuous dynamical system with countably many quasistable states has at least the computational power of a universal Turing machine. Such an analysis assumes, however, the classical notion of measurement. If measurement is viewed nonclassically, a continuous dynamical system cannot, even in principle, exhibit behavior that cannot be simulated by a universal Turing machine.