159,573 research outputs found
The quantum measurement problem and physical reality: a computation theoretic perspective
Is the universe computable? If yes, is it computationally a polynomial place?
In standard quantum mechanics, which permits infinite parallelism and the
infinitely precise specification of states, a negative answer to both questions
is not ruled out. On the other hand, empirical evidence suggests that
NP-complete problems are intractable in the physical world. Likewise,
computational problems known to be algorithmically uncomputable do not seem to
be computable by any physical means. We suggest that this close correspondence
between the efficiency and power of abstract algorithms on the one hand, and
physical computers on the other, finds a natural explanation if the universe is
assumed to be algorithmic; that is, that physical reality is the product of
discrete sub-physical information processing equivalent to the actions of a
probabilistic Turing machine. This assumption can be reconciled with the
observed exponentiality of quantum systems at microscopic scales, and the
consequent possibility of implementing Shor's quantum polynomial time algorithm
at that scale, provided the degree of superposition is intrinsically, finitely
upper-bounded. If this bound is associated with the quantum-classical divide
(the Heisenberg cut), a natural resolution to the quantum measurement problem
arises. From this viewpoint, macroscopic classicality is an evidence that the
universe is in BPP, and both questions raised above receive affirmative
answers. A recently proposed computational model of quantum measurement, which
relates the Heisenberg cut to the discreteness of Hilbert space, is briefly
discussed. A connection to quantum gravity is noted. Our results are compatible
with the philosophy that mathematical truths are independent of the laws of
physics.Comment: Talk presented at "Quantum Computing: Back Action 2006", IIT Kanpur,
India, March 200
Modeling practical thinking
Intellectualists about knowledge how argue that knowing how to do something is knowing the content of a proposition (i.e, a fact). An important component of this view is the idea that propositional knowledge is translated into behavior when it is presented to the mind in a peculiarly practical way. Until recently, however, intellectualists have not said much about what it means for propositional knowledge to be entertained under thought's practical guise. Carlotta Pavese fills this gap in the intellectualist view by modeling practical modes of thought after Fregean senses. In this paper, I take up her model and the presuppositions it is built upon, arguing that her view of practical thought is not positioned to account for much of what human agents are able to do
The challenges of purely mechanistic models in biology and the minimum need for a 'mechanism-plus-X' framework
Ever since the advent of molecular biology in the 1970s, mechanical models have become the dogma in the field, where a "true" understanding of any subject is equated to a mechanistic description. This has been to the detriment of the biomedical sciences, where, barring some exceptions, notable new feats of understanding have arguably not been achieved in normal and disease biology, including neurodegenerative disease and cancer pathobiology. I argue for a "mechanism-plus-X" paradigm, where mainstay elements of mechanistic models such as hierarchy and correlation are combined with nomological principles such as general operative rules and generative principles. Depending on the question at hand and the nature of the inquiry, X could range from proven physical laws to speculative biological generalizations, such as the notional principle of cellular synchrony. I argue that the "mechanism-plus-X" approach should ultimately aim to move biological inquiries out of the deadlock of oft-encountered mechanistic pitfalls and reposition biology to its former capacity of illuminating fundamental truths about the world
Can hierarchical predictive coding explain binocular rivalry?
Hohwy et al.’s (2008) model of binocular rivalry (BR) is taken as a classic illustration of predictive coding’s explanatory power. I revisit the account and show that it cannot explain the role of reward in BR. I then consider a more recent version of Bayesian model averaging, which recasts the role of reward in (BR) in terms of optimism bias. If we accept this account, however, then we must reconsider our conception of perception. On this latter view, I argue, organisms engage in what amounts to policy-driven, motivated perception
Ten reasons why a thermalized system cannot be described by a many-particle wave function
It is widely believed that the underlying reality behind statistical
mechanics is a deterministic and unitary time evolution of a many-particle wave
function, even though this is in conflict with the irreversible, stochastic
nature of statistical mechanics. The usual attempts to resolve this conflict
for instance by appealing to decoherence or eigenstate thermalization are
riddled with problems. This paper considers theoretical physics of thermalized
systems as it is done in practise and shows that all approaches to thermalized
systems presuppose in some form limits to linear superposition and
deterministic time evolution. These considerations include, among others, the
classical limit, extensivity, the concepts of entropy and equilibrium, and
symmetry breaking in phase transitions and quantum measurement. As a
conclusion, the paper argues that the irreversibility and stochasticity of
statistical mechanics should be taken as a true property of nature. It follows
that a gas of a macroscopic number of atoms in thermal equilibrium is best
represented by a collection of wave packets of a size of the order of the
thermal de Broglie wave length, which behave quantum mechanically below this
scale but classically sufficiently far beyond this scale. In particular, these
wave packets must localize again after scattering events, which requires
stochasticity and indicates a connection to the measurement process.Comment: Drastically rewritten version, with more explanations, with three new
reasons added and three old ones merged with other parts of the tex
Perceptual Consciousness, Short-Term Memory, and Overflow: Replies to Beck, Orlandi and Franklin, and Phillips
A reply to commentators -- Jake Beck, Nico Orlandi and Aaron Franklin, and Ian Phillips -- on our paper "Does perceptual consciousness overflow cognitive access?"
Laws, Causation and Dynamics at Different Levels
I have two main aims. The first is general, and more philosophical (Section 2). The second is specific, and more closely related to physics (Sections 3 and 4).
The first aim is to state my general views about laws and causation at different `levels'. The main task is to understand how the higher levels sustain notions of law and causation that `ride free' of reductions to the lower level or levels. I endeavour to relate my views to those of other symposiasts.
The second aim is to give a framework for describing dynamics at different levels, emphasising how the various levels' dynamics can mesh or fail to mesh. This framework is essentially that of elementary dynamical systems theory. The main idea will be, for simplicity, to work with just two levels, dubbed `micro' and `macro' which are related by coarse-graining. I use this framework to describe, in part, the first four of Ellis' five types of top-down causation
Perceptual Consciousness and Cognitive Access from the Perspective of Capacity-Unlimited Working Memory
Theories of consciousness divide over whether perceptual consciousness is rich or
sparse in specific representational content and whether it requires cognitive access.
These two issues are often treated in tandem because of a shared assumption that
the representational capacity of cognitive access is fairly limited. Recent research
on working memory challenges this shared assumption. This paper argues that
abandoning the assumption undermines post-cue-based “overflow” arguments,
according to which perceptual conscious is rich and does not require cognitive
access. Abandoning it also dissociates the rich/sparse debate from the access
question. The paper then explores attempts to reformulate overflow theses in ways
that don’t require the assumption of limited capacity. Finally, it discusses the
problem of relating seemingly non-probabilistic perceptual consciousness to the
probabilistic representations posited by the models that challenge conceptions of
cognitive access as capacity-limited
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