The feedback-related negativity reflects the binary evaluation of good versus bad outcomes

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Milieu-adopted in vitro and in vivo differentiation of mesenchymal tissues derived from different adult human CD34-negative progenitor cell clones

Adult mesenchymal stem cells with multilineage differentiation potentially exist in the bone marrow, but have also been isolated from the peripheral blood. The differentiation of stem cells after leaving their niches depends predominately on the local milieu and its new microenvironment, and is facilitated by soluble factors but also by the close cell-cell interaction in a three-dimensional tissue or organ system. We have isolated CD34-negative, mesenchymal stem cell lines from human bone marrow and peripheral blood and generated monoclonal cell populations after immortalization with the SV40 large T-antigen. The cultivation of those adult stem cell clones in an especially designed in vitro environment, including self-constructed glass capillaries with defined growth conditions, leads to the spontaneous establishment of pleomorphic three-dimensional cell aggregates ( spheroids) from the monoclonal cell population, which consist of cells with an osteoblast phenotype and areas of mineralization along with well-vascularized tissue areas. Modifications of the culture conditions favored areas of bone-like calcifications. After the transplantation of the at least partly mineralized human spheroids into different murine soft tissue sites but also a dorsal skinfold chamber, no further bone formation could be observed, but angiogenesis and neovessel formation prevailed instead, enabling the transplanted cells and cell aggregates to survive. This study provides evidence that even monoclonal adult human CD34-negative stem cells from the bone marrow as well as peripheral blood can potentially differentiate into different mesenchymal tissues depending on the local milieu and responding to the needs within the microenvironment. Copyright (C) 2005 S. Karger AG, Basel

It's worse than you thought : the feedback negativity and violations of reward prediction in gambling tasks

The reinforcement learning theory suggests that the feedback negativity should be larger when feedback is unexpected. Two recent studies found, however, that the feedback negativity was unaffected by outcome probability. To further examine this issue, participants in the present studies made reward predictions on each trial of a gambling task where objective reward probability was indicated by a cue. In Study 1, participants made reward predictions following the cue, but prior to their gambling choice; in Study 2, predictions were made following their gambling choice. Predicted and unpredicted outcomes were associated with equivalent feedback negativities in Study 1. In Study 2, however, the feedback negativity was larger for unpredicted outcomes. These data suggest that the magnitude of the feedback negativity is sensitive to violations of reward prediction, but that this effect may depend on the close coupling of prediction and outcome

Validating Predictions of Unobserved Quantities

The ultimate purpose of most computational models is to make predictions, commonly in support of some decision-making process (e.g., for design or operation of some system). The quantities that need to be predicted (the quantities of interest or QoIs) are generally not experimentally observable before the prediction, since otherwise no prediction would be needed. Assessing the validity of such extrapolative predictions, which is critical to informed decision-making, is challenging. In classical approaches to validation, model outputs for observed quantities are compared to observations to determine if they are consistent. By itself, this consistency only ensures that the model can predict the observed quantities under the conditions of the observations. This limitation dramatically reduces the utility of the validation effort for decision making because it implies nothing about predictions of unobserved QoIs or for scenarios outside of the range of observations. However, there is no agreement in the scientific community today regarding best practices for validation of extrapolative predictions made using computational models. The purpose of this paper is to propose and explore a validation and predictive assessment process that supports extrapolative predictions for models with known sources of error. The process includes stochastic modeling, calibration, validation, and predictive assessment phases where representations of known sources of uncertainty and error are built, informed, and tested. The proposed methodology is applied to an illustrative extrapolation problem involving a misspecified nonlinear oscillator

A few things I learnt from Jurgen Moser

A few remarks on integrable dynamical systems inspired by discussions with Jurgen Moser and by his work.Comment: An article for the special issue of "Regular and Chaotic Dynamics" dedicated to 80-th anniversary of Jurgen Mose