27,879 research outputs found
Unconscious Inference Theories of Cognitive Acheivement
This chapter argues that the only tenable unconscious inferences theories of cognitive achievement are ones that employ a theory internal technical notion of representation, but that once we give cash-value definitions of the relevant notions of representation and inference, there is little left of the ordinary notion of representation. We suggest that the real value of talk of unconscious inferences lies in (a) their heuristic utility in helping us to make fruitful predictions, such as about illusions, and (b) their providing a high-level description of the functional organization of subpersonal faculties that makes clear how they equip an agent to navigate its environment and pursue its goals
Disentangling causal webs in the brain using functional Magnetic Resonance Imaging: A review of current approaches
In the past two decades, functional Magnetic Resonance Imaging has been used
to relate neuronal network activity to cognitive processing and behaviour.
Recently this approach has been augmented by algorithms that allow us to infer
causal links between component populations of neuronal networks. Multiple
inference procedures have been proposed to approach this research question but
so far, each method has limitations when it comes to establishing whole-brain
connectivity patterns. In this work, we discuss eight ways to infer causality
in fMRI research: Bayesian Nets, Dynamical Causal Modelling, Granger Causality,
Likelihood Ratios, LiNGAM, Patel's Tau, Structural Equation Modelling, and
Transfer Entropy. We finish with formulating some recommendations for the
future directions in this area
An analysis of the Buddhist doctrines of karma and rebirth in the Visuddhimagga
In the Visuddhimagga, there is movement from an early Buddhist
phenominalist epistemology towards essentialist ontology based in
rationality and abstraction. The reductionist methodology of the
Abhidhamma and reactions to it brought forth a theory of momentariness not
found in early Buddhism. Abhidhamma reductionism and the concept of
phenomenal dhammas led to a conception of momentary time-points and
the incorporation of a cinematic model of temporal consciousness as a direct
consequence of momentariness. Essentialism was incorporated into the
Visuddhimagga precisely because of Buddhaghosa’s commitment to
momentariness. This is seen in Buddhaghosa’s treatment of karma and
rebirth. Karma, particularly death-threshold karma, receives more emphasis
in the Visuddhimagga than was previously found in the Suttas. This is due to
the need to explain the continuity of the process of karmic rebirth in light of
the theory of momentariness, making it necessary for Buddhaghosa to
synthesise momentariness with the tri-temporal existence of the
Sarvāstivādins
Model-free reconstruction of neuronal network connectivity from calcium imaging signals
A systematic assessment of global neural network connectivity through direct
electrophysiological assays has remained technically unfeasible even in
dissociated neuronal cultures. We introduce an improved algorithmic approach
based on Transfer Entropy to reconstruct approximations to network structural
connectivities from network activity monitored through calcium fluorescence
imaging. Based on information theory, our method requires no prior assumptions
on the statistics of neuronal firing and neuronal connections. The performance
of our algorithm is benchmarked on surrogate time-series of calcium
fluorescence generated by the simulated dynamics of a network with known
ground-truth topology. We find that the effective network topology revealed by
Transfer Entropy depends qualitatively on the time-dependent dynamic state of
the network (e.g., bursting or non-bursting). We thus demonstrate how
conditioning with respect to the global mean activity improves the performance
of our method. [...] Compared to other reconstruction strategies such as
cross-correlation or Granger Causality methods, our method based on improved
Transfer Entropy is remarkably more accurate. In particular, it provides a good
reconstruction of the network clustering coefficient, allowing to discriminate
between weakly or strongly clustered topologies, whereas on the other hand an
approach based on cross-correlations would invariantly detect artificially high
levels of clustering. Finally, we present the applicability of our method to
real recordings of in vitro cortical cultures. We demonstrate that these
networks are characterized by an elevated level of clustering compared to a
random graph (although not extreme) and by a markedly non-local connectivity.Comment: 54 pages, 8 figures (+9 supplementary figures), 1 table; submitted
for publicatio
Structure or Noise?
We show how rate-distortion theory provides a mechanism for automated theory
building by naturally distinguishing between regularity and randomness. We
start from the simple principle that model variables should, as much as
possible, render the future and past conditionally independent. From this, we
construct an objective function for model making whose extrema embody the
trade-off between a model's structural complexity and its predictive power. The
solutions correspond to a hierarchy of models that, at each level of
complexity, achieve optimal predictive power at minimal cost. In the limit of
maximal prediction the resulting optimal model identifies a process's intrinsic
organization by extracting the underlying causal states. In this limit, the
model's complexity is given by the statistical complexity, which is known to be
minimal for achieving maximum prediction. Examples show how theory building can
profit from analyzing a process's causal compressibility, which is reflected in
the optimal models' rate-distortion curve--the process's characteristic for
optimally balancing structure and noise at different levels of representation.Comment: 6 pages, 2 figures;
http://cse.ucdavis.edu/~cmg/compmech/pubs/son.htm
The Philosophy and Neuroscience Movement
A movement dedicated to applying neuroscience to traditional philosophical problems and using philosophical methods to illuminate issues in neuroscience began about twenty-five years ago. Results in neuroscience have affected how we see traditional areas of philosophical concern such as perception, belief-formation, and consciousness. There is an interesting interaction between some of the distinctive features of neuroscience and important general issues in the philosophy of science. And recent neuroscience has thrown up a few conceptual issues that philosophers are perhaps best trained to deal with. After sketching the history of the movement, we explore the relationships between neuroscience and philosophy and introduce some of the specific issues that have arise
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