Learning in a changing environment

Multiple cue probability learning studies have typically focused on stationary environments. We present three experiments investigating learning in changing environments. A fine-grained analysis of the learning dynamics shows that participants were responsive to both abrupt and gradual changes in cue-outcome relations. We found no evidence that participants adapted to these types of change in qualitatively different ways. Also, in contrast to earlier claims that these tasks are learned implicitly, participants showed good insight into what they learned. By fitting formal learning models, we investigated whether participants learned global functional relationships or made localized predictions from similar experienced exemplars. Both a local (the Associative Learning Model) and a global learning model (the novel Bayesian Linear Filter) fitted the data of the first two experiments. However, the results of Experiment 3, which was specifically designed to discriminate between local and global learning models, provided more support for global learning models. Finally, we present a novel model to account for the cue competition effects found in previous research and displayed by some of our participants

Models of probabilistic category learning in Parkinson's disease: Strategy use and the effects of L-dopa

Probabilistic category learning (PCL) has become an increasingly popular paradigm to study the brain bases of learning and memory. It has been argued that PCL relies on procedural habit learning, which is impaired in Parkinson's disease (PD). However, as PD patients were typically tested under medication, it is possible that levodopa (L-dopa) caused impaired performance in PCL. We present formal models of rule-based strategy switching in PCL, to re-analyse the data from [Jahanshahi, M., Wilkinson, L, Gahir, H., Dharminda, A., & Lagnado, D.A., (2009). Medication impairs probabilistic classification learning in Parkinson's disease. Manuscript submitted for publication] comparing PD patients on and off medication (within subjects) to matched controls. Our analysis shows that PD patients followed a similar strategy switch process as controls when off medication, but not when on medication. On medication, PD patients mainly followed a random guessing strategy, with only few switching to the better Single Cue strategies. PD patients on medication and controls made more use of the optimal Multi-Cue strategy. In addition, while controls and PD patients off medication only switched to strategies which did not decrease performance, strategy switches of PD patients on medication were not always directed as such. Finally, results indicated that PD patients on medication responded according to a probability matching strategy indicative of associative learning, while the behaviour of PD patients off medication and controls was consistent with a rule-based hypothesis testing procedure. (C) 2009 Elsevier Inc. All rights reserved

Spatial Correlation Function of X-ray Selected AGN

We present a detailed description of the first direct measurement of the spatial correlation function of X-ray selected AGN. This result is based on an X-ray flux-limited sample of 219 AGN discovered in the contiguous 80.7 deg^2 region of the ROSAT North Ecliptic Pole (NEP) Survey. Clustering is detected at the 4 sigma level at comoving scales in the interval r = 5-60 h^-1 Mpc. Fitting the data with a power law of slope gamma=1.8, we find a correlation length of r_0 = 7.4 (+1.8, -1.9) h^-1 Mpc (Omega_M=0.3, Omega_Lambda=0.7). The median redshift of the AGN contributing to the signal is z_xi=0.22. This clustering amplitude implies that X-ray selected AGN are spatially distributed in a manner similar to that of optically selected AGN. Furthermore, the ROSAT NEP determination establishes the local behavior of AGN clustering, a regime which is poorly sampled in general. Combined with high-redshift measures from optical studies, the ROSAT NEP results argue that the AGN correlation strength essentially does not evolve with redshift, at least out to z~2.2. In the local Universe, X-ray selected AGN appear to be unbiased relative to galaxies and the inferred X-ray bias parameter is near unity, b_X~1. Hence X-ray selected AGN closely trace the underlying mass distribution. The ROSAT NEP AGN catalog, presented here, features complete optical identifications and spectroscopic redshifts. The median redshift, X-ray flux, and X-ray luminosity are z=0.41, f_X=1.1*10^-13 cgs, and L_X=9.2*10^43 h_70^-2 cgs (0.5-2.0 keV), respectively. Unobscured, type 1 AGN are the dominant constituents (90%) of this soft X-ray selected sample of AGN.Comment: 17 pages, 8 figures, accepted for publication in ApJ, a version with high-resolution figures is available at http://www.eso.org/~cmullis/papers/Mullis_et_al_2004b.ps.gz, a machine-readable version of the ROSAT NEP AGN catalog is available at http://www.eso.org/~cmullis/research/nep-catalog.htm

Treatment of multidrug-resistant tuberculosis in a remote, conflict-affected area of the Democratic Republic of Congo.

The Democratic Republic of Congo is a high-burden country for multidrug-resistant tuberculosis. Médecins Sans Frontières has supported the Ministry of Health in the conflict-affected region of Shabunda since 1997. In 2006, three patients were diagnosed with drug-resistant TB (DR-TB) and had no options for further treatment. An innovative model was developed to treat these patients despite the remote setting. Key innovations were the devolving of responsibility for treatment to non-TB clinicians remotely supported by a TB specialist, use of simplified monitoring protocols, and a strong focus on addressing stigma to support adherence. Treatment was successfully completed after a median of 24 months. This pilot programme demonstrates that successful treatment for DR-TB is possible on a small scale in remote settings