Learning to become an expert : reinforcement learning and the acquisition of perceptual expertise

To elucidate the neural mechanisms underlying the development of perceptual expertise, we recorded ERPs while participants performed a categorization task. We found that as participants learned to discriminate computer-generated "blob'' stimuli, feedback modulated the amplitude of the errorrelated negativity (ERN)-an ERP component thought to reflect error evaluation within medial-frontal cortex. As participants improved at the categorization task, we also observed an increase in amplitude of an ERP component associated with object recognition (the N250). The increase in N250 amplitude preceded an increase in amplitude of an ERN component associated with internal error evaluation (the response ERN). Importantly, these electroencephalographic changes were not observed for participants who failed to improve on the categorization task. Our results suggest that the acquisition of perceptual expertise relies on interactions between the posterior perceptual system and the reinforcement learning system involving medial-frontal cortex

Percolation in invariant Poisson graphs with i.i.d. degrees

Let each point of a homogeneous Poisson process in R^d independently be equipped with a random number of stubs (half-edges) according to a given probability distribution mu on the positive integers. We consider translation-invariant schemes for perfectly matching the stubs to obtain a simple graph with degree distribution mu. Leaving aside degenerate cases, we prove that for any mu there exist schemes that give only finite components as well as schemes that give infinite components. For a particular matching scheme that is a natural extension of Gale-Shapley stable marriage, we give sufficient conditions on mu for the absence and presence of infinite components

Bootstrap Percolation on Complex Networks

We consider bootstrap percolation on uncorrelated complex networks. We obtain the phase diagram for this process with respect to two parameters: $f$, the fraction of vertices initially activated, and $p$, the fraction of undamaged vertices in the graph. We observe two transitions: the giant active component appears continuously at a first threshold. There may also be a second, discontinuous, hybrid transition at a higher threshold. Avalanches of activations increase in size as this second critical point is approached, finally diverging at this threshold. We describe the existence of a special critical point at which this second transition first appears. In networks with degree distributions whose second moment diverges (but whose first moment does not), we find a qualitatively different behavior. In this case the giant active component appears for any $f>0$ and $p>0$, and the discontinuous transition is absent. This means that the giant active component is robust to damage, and also is very easily activated. We also formulate a generalized bootstrap process in which each vertex can have an arbitrary threshold.Comment: 9 pages, 3 figure

Latitudinal diversity gradients in Mesozoic non-marine turtles

© 2016 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited. The attached file is the published version of the article

Heterogeneous-k-core versus Bootstrap Percolation on Complex Networks

We introduce the heterogeneous-$k$-core, which generalizes the $k$-core, and contrast it with bootstrap percolation. Vertices have a threshold $k_i$ which may be different at each vertex. If a vertex has less than $k_i$ neighbors it is pruned from the network. The heterogeneous-$k$-core is the sub-graph remaining after no further vertices can be pruned. If the thresholds $k_i$ are $1$ with probability $f$ or $k \geq 3$ with probability $(1-f)$, the process forms one branch of an activation-pruning process which demonstrates hysteresis. The other branch is formed by ordinary bootstrap percolation. We show that there are two types of transitions in this heterogeneous-$k$-core process: the giant heterogeneous-$k$-core may appear with a continuous transition and there may be a second, discontinuous, hybrid transition. We compare critical phenomena, critical clusters and avalanches at the heterogeneous-$k$-core and bootstrap percolation transitions. We also show that network structure has a crucial effect on these processes, with the giant heterogeneous-$k$-core appearing immediately at a finite value for any $f > 0$ when the degree distribution tends to a power law $P(q) \sim q^{-\gamma}$ with $\gamma < 3$.Comment: 10 pages, 4 figure

Punishment and Justice

Should the state punish its disadvantaged citizens who have committed crimes? Duff has recently argued that where disadvantage persists the state loses its authority to hold individuals to account and to punish for criminal wrongdoings. I here scrutinize Duff’s argument for the claim that social justice is a precondition for the legitimacy of state punishment. I sharpen an objection to Duff’s argument: with his framework, we seem unable to block the implausible conclusion that where disadvantage persists the state lacks the authority to punish any citizen for any crime. I then set out an alternative line of argument in support of the claim that social deprivation can threaten the states legitimate punitive authority. I argue that a penal system must incorporate certain proportionality principles, and that these principles cannot both be met where citizens suffer from deprivation