M-Theory on the Orbifold C^2/Z_N

We construct M-theory on the orbifold C^2/Z_N by coupling 11-dimensional supergravity to a seven-dimensional Yang-Mills theory located on the orbifold fixed plane. It is shown that the resulting action is supersymmetric to leading non-trivial order in the 11-dimensional Newton constant. This action provides the starting point for a reduction of M-theory on G_2 spaces with co-dimension four singularities.Comment: 33 pages, Late

Measures of metacognition on signal-detection theoretic models

Analysing metacognition, specifically knowledge of accuracy of internal perceptual, memorial or other knowledge states, is vital for many strands of psychology, including determining the accuracy of feelings of knowing, and discriminating conscious from unconscious cognition. Quantifying metacognitive sensitivity is however more challenging than quantifying basic stimulus sensitivity. Under popular signal detection theory (SDT) models for stimulus classification tasks, approaches based on type II receiver-operator characteristic (ROC) curves or type II d-prime risk confounding metacognition with response biases in either the type I (classification) or type II (metacognitive) tasks. A new approach introduces meta-d′: the type I d-prime that would have led to the observed type II data had the subject used all the type I information. Here we (i) further establish the inconsistency of the type II d-prime and ROC approaches with new explicit analyses of the standard SDT model, and (ii) analyse, for the first time, the behaviour of meta-d′ under non-trivial scenarios, such as when metacognitive judgments utilize enhanced or degraded versions of the type I evidence. Analytically, meta-d′ values typically reflect the underlying model well, and are stable under changes in decision criteria; however, in relatively extreme cases meta-d′ can become unstable. We explore bias and variance of in-sample measurements of meta-d′ and supply MATLAB code for estimation in general cases. Our results support meta-d′ as a useful measure of metacognition, and provide rigorous methodology for its application. Our recommendations are useful for any researchers interested in assessing metacognitive accuracy

Granger causality analysis in neuroscience and neuroimaging

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Optimal learning rules for discrete synapses

There is evidence that biological synapses have a limited number of discrete weight states. Memory storage with such synapses behaves quite differently from synapses with unbounded, continuous weights, as old memories are automatically overwritten by new memories. Consequently, there has been substantial discussion about how this affects learning and storage capacity. In this paper, we calculate the storage capacity of discrete, bounded synapses in terms of Shannon information. We use this to optimize the learning rules and investigate how the maximum information capacity depends on the number of synapses, the number of synaptic states, and the coding sparseness. Below a certain critical number of synapses per neuron (comparable to numbers found in biology), we find that storage is similar to unbounded, continuous synapses. Hence, discrete synapses do not necessarily have lower storage capacity

An integration of integrated information theory with fundamental physics

To truly eliminate Cartesian ghosts from the science of consciousness, we must describe consciousness as an aspect of the physical. Integrated Information Theory states that consciousness arises from intrinsic information generated by dynamical systems; however existing formulations of this theory are not applicable to standard models of fundamental physical entities. Modern physics has shown that fields are fundamental entities, and in particular that the electromagnetic field is fundamental. Here I hypothesize that consciousness arises from information intrinsic to fundamental fields. This hypothesis unites fundamental physics with what we know empirically about the neuroscience underlying consciousness, and it bypasses the need to consider quantum effects

Integrated technology: does it affect learner outcomes?

Because technology has become prevalent in classrooms, this study was undertaken to test whether the use of integrated technology, specifically computers and online activities, affects learner outcomes in a classroom setting. The outcomes from classes taught using integrated technology were compared to classes taught with traditional teaching strategies. Students in a 7th grade life-science class were given pre-tests and post-tests to determine their learning gains on the topics of genetics and photosynthesis. Each class was assigned different activities based on the subject. Each unit was covered in four 90 minute periods. When one set of classes was using integrated technology for a topic, the other set was using traditional methods of learning such as notes, discussions and book work. The integrated technology had no detectable effect on learner outcomes. There were no significant difference between mean learning gains and the different variables tested: class size, gender and teaching styles. However, there did appear to be a positive effect on the students’ behavior and attitude for learning the material. The technology-based methods did not detract from student learning. Over a more extended time frame, implementation of technology-based methods in the classroom may increase learning gains and/or foster increases in engagement and class attendance

Collaborative Speculation and Overvaluation: Evidence from Social Media

I use data from StockTwits and Twitter to provide evidence that investor attention on social media in the period before earnings is related to short-term overvaluation, consistent with bullish investors herding around common information. In the 2 to 60 days after earnings, returns for companies in the highest quintile of pre-earnings announcement investor attention are 4.2 percent lower than those of companies in the lowest quintile. I find evidence that the negative post-earnings drift result found in this study is related to investors waiting until after earnings are announced to enact costly arbitrage strategies. I further examine intra- and inter-network herding and find evidence that social media influences investors beyond the population of active users. This study contributes to prior literature on herding, social media, and speculation and arbitrage