scholarly journals Challenging the classical notion of time in cognition: a quantum perspective

2014 ◽  
Vol 281 (1781) ◽  
pp. 20133056 ◽  
Author(s):  
James M. Yearsley ◽  
Emmanuel M. Pothos
Keyword(s):  
Quantum Theory ◽  
Time Dependence ◽  
Mental Representations ◽  
Cognitive Models ◽  
Cognitive Systems ◽  
Cognitive Modelling ◽  
Time Points ◽  
Perfect Knowledge ◽  
Interesting Conclusion ◽  
Cognitive Realism

All mental representations change with time. A baseline intuition is that mental representations have specific values at different time points, which may be more or less accessible, depending on noise, forgetting processes, etc. We present a radical alternative, motivated by recent research using the mathematics from quantum theory for cognitive modelling. Such cognitive models raise the possibility that certain possibilities or events may be incompatible, so that perfect knowledge of one necessitates uncertainty for the others. In the context of time-dependence, in physics, this issue is explored with the so-called temporal Bell (TB) or Leggett–Garg inequalities. We consider in detail the theoretical and empirical challenges involved in exploring the TB inequalities in the context of cognitive systems. One interesting conclusion is that we believe the study of the TB inequalities to be empirically more constrained in psychology than in physics. Specifically, we show how the TB inequalities, as applied to cognitive systems, can be derived from two simple assumptions: cognitive realism and cognitive completeness. We discuss possible implications of putative violations of the TB inequalities for cognitive models and our understanding of time in cognition in general. Overall, this paper provides a surprising, novel direction in relation to how time should be conceptualized in cognition.

Understanding Memories of the Past in the Context of Different Complex Neural Network Architectures

2022 ◽  
pp. 1-27
Author(s):  
Clifford Bohm ◽  
Douglas Kirkpatrick ◽  
Arend Hintze
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Mental Representations ◽  
Network Architectures ◽  
Information Theoretic ◽  
Time Points ◽  
The Past ◽  
Black Boxes ◽  
Past Experiences ◽  
Computational Systems

Abstract Deep learning (primarily using backpropagation) and neuroevolution are the preeminent methods of optimizing artificial neural networks. However, they often create black boxes that are as hard to understand as the natural brains they seek to mimic. Previous work has identified an information-theoretic tool, referred to as R, which allows us to quantify and identify mental representations in artificial cognitive systems. The use of such measures has allowed us to make previous black boxes more transparent. Here we extend R to not only identify where complex computational systems store memory about their environment but also to differentiate between different time points in the past. We show how this extended measure can identify the location of memory related to past experiences in neural networks optimized by deep learning as well as a genetic algorithm.

scholarly journals Toward a quantum theory of tachyon fields

2016 ◽  
Vol 31 (09) ◽  
pp. 1650041 ◽  
Author(s):  
Charles Schwartz
Keyword(s):  
Quantum Theory ◽  
Mass Shell ◽  
Group Representation ◽  
Lorentz Invariance ◽  
Critical Role ◽  
Wave Functions ◽  
Time Points ◽  
Dirac Equations ◽  
Consistent Manner ◽  
Klein Gordon

We construct momentum space expansions for the wave functions that solve the Klein–Gordon and Dirac equations for tachyons, recognizing that the mass shell for such fields is very different from what we are used to for ordinary (slower than light) particles. We find that we can postulate commutation or anticommutation rules for the operators that lead to physically sensible results: causality, for tachyon fields, means that there is no connection between space–time points separated by a timelike interval. Calculating the conserved charge and four-momentum for these fields allows us to interpret the number operators for particles and antiparticles in a consistent manner; and we see that helicity plays a critical role for the spinor field. Some questions about Lorentz invariance are addressed and some remain unresolved; and we show how to handle the group representation for tachyon spinors.

scholarly journals Towards computational models of multilingual sentence processing

2019 ◽  
Author(s):  
Stefan L. Frank
Keyword(s):  
Computational Linguistics ◽  
Sentence Processing ◽  
Computational Models ◽  
State Of The Art ◽  
Cognitive Models ◽  
The State ◽  
Cognitive Modelling ◽  
Current Review ◽  
Multiple Languages ◽  
Human Sentence Processing

Although computational models can simulate aspects of human sentence processing, research on this topic has remained almost exclusively limited to the single language case. The current review presents an overview of the state of the art in computational cognitive models of sentence processing, and discusses how recent sentence-processing models can be used to study bi- and multilingualism. Recent results from cognitive modelling and computational linguistics suggest that phenomena specific to bilingualism can emerge from systems that have no dedicated components for handling multiple languages. Hence, accounting for human bi-/multilingualism may not require models that are much more sophisticated than those for the monolingual case.

scholarly journals RESTORING TIME DEPENDENCE INTO QUANTUM COSMOLOGY

2012 ◽  
Vol 21 (11) ◽  
pp. 1242011 ◽  
Author(s):  
AHARON DAVIDSON ◽  
BEN YELLIN
Keyword(s):  
Quantum Theory ◽  
Time Dependence ◽  
Quantum Cosmology ◽  
Scale Factor ◽  
Time Dependent ◽  
Hamiltonian Constraint ◽  
Canonical Variables ◽  
Explicit Time Dependence ◽  
Dirac Brackets ◽  
Lapse Function

Mini superspace cosmology treats the scale factor a(t), the lapse function n(t) and an optional dilation field ϕ(t) as canonical variables. While pre-fixing n(t) means losing the Hamiltonian constraint, pre-fixing a(t) is serendipitously harmless at this level. This suggests an alternative to the Hartle–Hawking approach, where the pre-fixed a(t) and its derivatives are treated as explicit functions of time, leaving n(t) and a now mandatory ϕ(t) to serve as canonical variables. The naive gauge pre-fix a(t) = const . is clearly forbidden, causing evolution to freeze altogether; so pre-fixing the scale factor, say a(t) = t, necessarily introduces explicit time dependence into the Lagrangian. Invoking Dirac's prescription for dealing with constraints, we construct the corresponding mini superspace time-dependent total Hamiltonian and calculate the Dirac brackets, characterized by {n, ϕ}D ≠ 0, which are promoted to commutation relations in the quantum theory.

Explaining Religious Ideas: Elements of a Cognitive Approach

Numen ◽  
1992 ◽  
Vol 39 (1) ◽  
pp. 27-57 ◽  
Author(s):  
Pascal Boyer
Keyword(s):  
Mental Representations ◽  
Belief Systems ◽  
Cognitive Systems ◽  
Social Categories ◽  
Cognitive Approach ◽  
Ethnographic Data ◽  
Anthropological Approach ◽  
Religious Systems ◽  
Argument Proceeds ◽  
Cognitive Salience

AbstractThis paper outlines an anthropological approach to religious representations that is grounded in recent findings and hypotheses in cognitive psychology. The argument proceeds in four points. First, the main goal of this framework is to account for the recurrence of certain types of mental representations in religious systems. Recurrent features are not necessarily universal. They are the outcome of cognitive systems that make certain representations easier to acquire than others. Second, a cognitive approach must take into account the diversity of religious representations. It is argued here that religious systems bring together ontological assumptions, causal claims, episode types and social categories. These four "repertoires" may have different functional properties, and may therefore be acquired and represented in different ways. Third, universal features of tacit, intuitive systems may impose strong constraints on the variability of religious ideas. This is illustrated on the basis of ethnographic data. Finally, the type of representations one finds in religious belief-systems consists in conjectures, the cognitive salience of which is variable and should be evaluated in precise terms.

Artificial Neural Networks and Cognitive Modelling

2011 ◽  
pp. 161-166
Author(s):  
Amanda J.C. Sharkey
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Computational Models ◽  
Cognitive Models ◽  
Cognitive Modelling ◽  
Connectionist Models ◽  
Category Formation ◽  
Connectionist Approach ◽  
Artificial Neural ◽  
The Impact

In their heyday, artificial neural networks promised a radically new approach to cognitive modelling. The connectionist approach spawned a number of influential, and controversial, cognitive models. In this article, we consider the main characteristics of the approach, look at the factors leading to its enthusiastic adoption, and discuss the extent to which it differs from earlier computational models. Connectionist cognitive models have made a significant impact on the study of mind. However connectionism is no longer in its prime. Possible reasons for the diminution in its popularity will be identified, together with an attempt to identify its likely future. The rise of connectionist models dates from the publication in 1986 by Rumelhart and McClelland, of an edited work containing a collection of connectionist models of cognition, each trained by exposure to samples of the required tasks. These volumes set the agenda for connectionist cognitive modellers and offered a methodology that subsequently became the standard. Connectionist cognitive models have since been produced in domains including memory retrieval and category formation, and (in language) phoneme recognition, word recognition, speech perception, acquired dyslexia, language acquisition, and (in vision) edge detection, object and shape recognition. More than twenty years later the impact of this work is still apparent.

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