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A definition of Mind Force would be as the dynamical hyperstructure formed by networks of synchronized oscillators coupled in fields spanning trough heterogeneous domains. Mind Force theory is based on the integration of 4 main pillar-theories: Complexity theory; Synchronization theory; Network theory; Field theory. Mind Force is the result of the causal power of collective phenomena and patterns. Precursors and conditions for Mind Force theory are examined and a possible pathway for modeling is proposed. [ABSTRACT FROM AUTHOR]

Cell membrane's fatty acids (FAs) have been carefully investigated in neurons and platelets in order to study a possible connection to psychopathologies. An important link between the FA distribution and membrane dynamics appears to emerge with the cytoskeleton dynamics. Microtubules (MTs) in particular have been implicated in some recent quantum consciousness models and analyses. The recently proposed quantum model of Craddock et al. (2014) states that MTs possess structural and functional characteristics that are consistent with collective quantum coherent excitations in the aromatic groups of their tryptophan residues. These excitations are consistent with a clocking mechanism on a sub-nanosecond scale. This mechanism and analogous phenomena in light-harvesting complexes in plants and bacteria, are induced by photons and have been touted as evidence of quantum processes in biology. A possible source of intra-cellular photons could be membrane lipid peroxidation processes, so the FA profile could then be linked to the bio-photon emission. The model presented here suggests new ways to understand the role serotonin plays in relation to FAs. In plants, tryptophan conversion of light to exciton energy can participate in the directional orientation of leaves toward sunlight. Since serotonin is structurally similar to tryptophan, in the human brain, neurons could use tryptophan to capture photons and also use serotonin to initiate movement toward the source of light. Hence, we postulate two possible new roles for serotonin: (1) as an antioxidant, in order to counter-balance the oxidative effect of FAs, and (2) to participate in quantum interactions with MTs, in the same way as anesthetics and psychoactive compounds have been recently shown to act. In this latter case, the FA profile could provide an indirect measure of serotonin levels. [ABSTRACT FROM AUTHOR]

The attempt to employ quantum principles for modeling cognition has enabled the introduction of several new concepts in psychology, such as the uncertainty principle, incompatibility, entanglement, and superposition. For many commentators, this is an exciting opportunity to question existing formal frameworks (notably classical probability theory) and explore what is to be gained by employing these novel conceptual tools. This is not to say that major empirical challenges are not there. For example, can we definitely prove the necessity for quantum, as opposed to classical, models? Can the distinction between compatibility and incompatibility inform our understanding of differences between human and nonhuman cognition? Are quantum models less constrained than classical ones? Does incompatibility arise as a limitation, to avoid the requirements from the principle of unicity, or is it an inherent (or essential?) characteristic of intelligent thought? For everyday judgments, do quantum principles allow more accurate prediction than classical ones? Some questions can be confidently addressed within existing quantum models. A definitive resolution of others will have to anticipate further work. What is clear is that the consideration of quantum cognitive models has enabled a new focus on a range of debates about fundamental aspects of cognition. [ABSTRACT FROM PUBLISHER]

The “Orch OR” theory suggests that quantum computations in brain neuronal dendritic-somatic microtubules regulate axonal firings to control conscious behavior. Within microtubule subunit proteins, collective dipoles in arrays of contiguous amino acid electron clouds enable “quantum channels” suitable for topological dipole “qubits” able to physically represent cognitive values, for example, those portrayed by Pothos & Busemeyer (P&B) as projections in abstract Hilbert space. [ABSTRACT FROM AUTHOR]

We focus on two issues: (1) an unusual, counterintuitive prediction that quantum probability (QP) theory appears to make regarding multiple sequential judgments, and (2) the extent to which QP is an appropriate and comprehensive benchmark for assessing judgment. These issues highlight how QP theory can fall prey to the same problems of arbitrariness that Pothos & Busemeyer (P&B) discuss as plaguing other models. [ABSTRACT FROM AUTHOR]

We support the authors' claims, except that we point out that also quantum structure different from quantum probability abundantly plays a role in human cognition. We put forward several elements to illustrate our point, mentioning entanglement, contextuality, interference, and emergence as effects, and states, observables, complex numbers, and Fock space as specific mathematical structures. [ABSTRACT FROM AUTHOR]

Quantum probability (QP) theory can be seen as a type of vector symbolic architecture (VSA): mental states are vectors storing structured information and manipulated using algebraic operations. Furthermore, the operations needed by QP match those in other VSAs. This allows existing biologically realistic neural models to be adapted to provide a mechanistic explanation of the cognitive phenomena described in the target article by Pothos & Busemeyer (P&B). [ABSTRACT FROM PUBLISHER]

Quantum probability models may supersede existing probabilistic models because they account for behaviour inconsistent with classical probability theory that are attributable to normal limitations of cognition. This intriguing position, however, may overstate weaknesses in classical probability theory by underestimating the role of current knowledge states and may under-employ available knowledge about the limitations of cognitive processes. In addition, flexibility in model specification has risks for the use of quantum probability. [ABSTRACT FROM PUBLISHER]

Although the quantum probability (QP) can be useful to model the context effect, it is not relevant to the order effect, conjunction fallacy, and other related biases. Although the issue of potentiality, which is the intuition behind QP, is involved in the context effect, it is not involved in the other biases. [ABSTRACT FROM PUBLISHER]

I argue that the overly simplistic scenarios discussed by Pothos & Busemeyer (P&B) establish at best that quantum probability theory (QPT) is a logical possibility allowing distinct predictions from classical probability theory (CPT). The article fails, however, to provide convincing evidence for the proposal that QPT offers unique insights regarding cognition and the nature of human rationality. [ABSTRACT FROM AUTHOR]

Quantum probability (QP) provides a new perspective for cognitive science. However, one must be clear about the outcome the QP model is predicting. We discuss this concern in reference to modeling the subjective probabilities given by people as opposed to modeling the choice proportions of people. These two models would appear to have different cognitive assumptions. [ABSTRACT FROM PUBLISHER]

Pothos & Busemeyer (P&B) argue that quantum probability (QP) provides a descriptive model of behavior and can also provide a rational analysis of a task. We discuss QP models using Marr's levels of analysis, arguing that they make most sense as algorithmic level theories. We also highlight the importance of having clear interpretations for basic mechanisms such as interference. [ABSTRACT FROM AUTHOR]

Pothos & Busemeyer (P&B) argue convincingly that quantum probability offers an improvement over classical Bayesian probability in modeling the empirical data of cognitive science. However, a weakness related to restrictions on the dimensionality of incompatible physical observables flows from the authors' “agnosticism” regarding quantum processes in neural substrates underlying cognition. Addressing this problem will require either future research findings validating quantum neurophysics or theoretical expansion of the uncertainty principle as a new, neurocognitively contextualized, “local” symmetry. [ABSTRACT FROM AUTHOR]

A lot of research in cognition and decision making suffers from a lack of formalism. The quantum probability program could help to improve this situation, but we wonder whether it would provide even more added value if its presumed focus on outcome models were complemented by process models that are, ideally, informed by ecological analyses and integrated into cognitive architectures. [ABSTRACT FROM AUTHOR]

Faced with probabilistic relationships between causes and effects, quantum theory assumes that deterministic causes do not exist, and that only incomplete probabilistic expressions of knowledge are possible. As in its application to physics, this fundamental epistemological stance severely limits the ability of quantum theory to provide insight and understanding in human cognition. [ABSTRACT FROM AUTHOR]

According to the foundations of economic theory, agents have stable and coherent “global” preferences that guide their choices among alternatives. However, people are constrained by information-processing and memory limitations and hence have a propensity to avoid cognitive load. We propose that this in turn will encourage them to respond to “local” preferences and goals influenced by context and memory representations. [ABSTRACT FROM AUTHOR]

We highlight methodological and theoretical limitations of the authors' Dirac formalism and suggest the von Neumann open systems approach as a resolution. The open systems framework is a generalization of classical probability and we hope it will allow cognitive scientists to extend quantum probability from perception, categorization, memory, decision making, and similarity judgments to phenomena in learning and development. [ABSTRACT FROM AUTHOR]

We concentrate on two aspects of the article by Pothos & Busemeyer (P&B): the relationship between classical and quantum probability and quantum probability as a basis for rational decisions. We argue that the mathematical relationship between classical and quantum probability is not quite what the authors claim. Furthermore, it might be premature to regard quantum probability as the best practical rational scheme for decision making. [ABSTRACT FROM AUTHOR]

Quantum probability (QP) theory provides an alternative account of empirical phenomena in decision making that classical probability (CP) theory cannot explain. Cognitive architectures combine probabilistic mechanisms with symbolic knowledge-based representations (e.g., heuristics) to address effects that motivate QP. They provide simple and natural explanations of these phenomena based on general cognitive processes such as memory retrieval, similarity-based partial matching, and associative learning. [ABSTRACT FROM AUTHOR]

Quantum probability theory offers a viable alternative to classical probability, although there are some ambiguities inherent in transferring the quantum formalism to a less determined realm. A number of physicists are now looking at the applicability of quantum ideas to the assessment of physics learning, an area particularly suited to quantum probability ideas. [ABSTRACT FROM AUTHOR]

There is another meeting place for quantum physics and psychology, both within and outside of cognitive modeling. In physics it is known as the issue of classical (probabilistic) determinism, and in psychology it is known as the issue of selective influences. The formalisms independently developed in the two areas for dealing with these issues turn out to be identical, opening ways for mutually beneficial interactions. [ABSTRACT FROM AUTHOR]

Pothos & Busemeyer's (P&B's) query about whether quantum probability can provide a foundation for the cognitive modeling embodies so many underlying implications that the subject is far from exhausted. In this brief commentary, however, I suggest that the conceptual thresholds of the meaningful learning give rise to a typical Boltzmann's weighting measure, which indicates a statistical verisimilitude of quantum behavior in the human cognitive ensemble. [ABSTRACT FROM AUTHOR]

Typically, human decision making is emotionally “hot” and does not conform to “cold” classical probability (CP) theory. As quantum probability (QP) theory emphasises order, context, superimposition states, and nonlinear dynamic effects, one of its major strengths may be its power to unify formal modeling and realistic psychological theory (e.g., information uncertainty, anxiety, and indecision, as seen in the Prisoner's Dilemma). [ABSTRACT FROM AUTHOR]

We propose a way in which Pothos & Busemeyer (P&B) could strengthen their position. Taking a dynamic stance, we consider cognitive tests as functions that transfer a given input state into the state after testing. Under very general conditions, it can be shown that testable properties in cognition form an orthomodular lattice. Gleason's theorem then yields the conceptual necessity of quantum probabilities (QP). [ABSTRACT FROM AUTHOR]

The Hilbert space formalism is a powerful language to express many cognitive phenomena. Here, relevant concepts from signal detection theory are recast in that language, allowing an empirically testable extension of the quantum probability formalism to psychophysical measures, such as detectability and discriminability. [ABSTRACT FROM AUTHOR]

Pothos & Busemeyer (P&B) argue how key concepts of quantum probability, for example, order/context, interference, superposition, and entanglement, can be used in cognitive modeling. Here, we suggest that these concepts can be extended to analyze neurophysiological measurements of cognitive tasks in humans, especially in functional neuroimaging investigations of large-scale brain networks. [ABSTRACT FROM AUTHOR]

Use of quantum probability as a top-down model of cognition will be enhanced by consideration of the underlying complex-valued wave function, which allows a better account of interference effects and of the structure of learned and ad hoc question operators. Furthermore, the treatment of incompatible questions can be made more quantitative by analyzing them as non-commutative operators. [ABSTRACT FROM AUTHOR]

Quantum theory is a powerful framework for probabilistic modeling of cognition. Strong empirical evidence suggests the context- and order-dependent representation of human judgment and decision-making processes, which falls beyond the scope of classical Bayesian probability theories. However, considering behavior as the output of underlying neurobiological processes, a fundamental question remains unanswered: Is cognition a probabilistic process at all? [ABSTRACT FROM AUTHOR]

Evolution would favor organisms that can make recurrent decisions in accordance with classical probability (CP) theory, because such choices would be optimal in the long run. This is illustrated by the base-rate fallacy and probability matching, where nonhumans choose optimally but humans do not. Quantum probability (QP) theory may be able to account for these species differences in terms of orthogonal versus nonorthogonal representations. [ABSTRACT FROM AUTHOR]