Prestimulus default mode activity influences depth of processing and recognition in an emotional memory task

Low self-referential thoughts are associated with better concentration, which leads to deeper encoding and increases learning and subsequent retrieval. There is evidence that being engaged in externally rather than internally focused tasks is related to low neural activity in the default mode network (DMN) promoting open mind and the deep elaboration of new information. Thus, reduced DMN activity should lead to enhanced concentration, comprehensive stimulus evaluation including emotional categorization, deeper stimulus processing, and better long-term retention over one whole week. In this fMRI study, we investigated brain activation preceding and during incidental encoding of emotional pictures and on subsequent recognition performance. During fMRI, 24 subjects were exposed to 80 pictures of different emotional valence and subsequently asked to complete an online recognition task one week later. Results indicate that neural activity within the medial temporal lobes during encoding predicts subsequent memory performance. Moreover, a low activity of the default mode network preceding incidental encoding leads to slightly better recognition performance independent of the emotional perception of a picture. The findings indicate that the suppression of internally-oriented thoughts leads to a more comprehensive and thorough evaluation of a stimulus and its emotional valence. Reduced activation of the DMN prior to stimulus onset is associated with deeper encoding and enhanced consolidation and retrieval performance even one week later. Even small prestimulus lapses of attention influence consolidation and subsequent recognition performance. Hum Brain Mapp, 2015. © 2015 Wiley Periodicals, Inc.

A common mechanism underlying food choice and social decisions

People make numerous decisions every day including perceptual decisions such as walking through a crowd, decisions over primary rewards such as what to eat, and social decisions that require balancing own and others’ benefits. The unifying principles behind choices in various domains are, however, still not well understood. Mathematical models that describe choice behavior in specific contexts have provided important insights into the computations that may underlie decision making in the brain. However, a critical and largely unanswered question is whether these models generalize from one choice context to another. Here we show that a model adapted from the perceptual decision-making domain and estimated on choices over food rewards accurately predicts choices and reaction times in four independent sets of subjects making social decisions. The robustness of the model across domains provides behavioral evidence for a common decision-making process in perceptual, primary reward, and social decision making.

Guidance of treatment decisions in risk-adapted primary radiotherapy for prostate cancer using multiparametric magnetic resonance imaging: a single center experience.

BACKGROUND Magnetic resonance imaging (MRI) of the prostate is considered to be the most precise noninvasive staging modality for localized prostate cancer. Multiparametric MRI (mpMRI) dynamic sequences have recently been shown to further increase the accuracy of staging relative to morphological imaging alone. Correct radiological staging, particularly the detection of extraprostatic disease extension, is of paramount importance for target volume definition and dose prescription in highly-conformal curative radiotherapy (RT); in addition, it may affect the risk-adapted duration of additional antihormonal therapy. The purpose of our study was to analyze the impact of mpMRI-based tumor staging in patients undergoing primary RT for prostate cancer. METHODS A total of 122 patients admitted for primary RT for prostate cancer were retrospectively analyzed regarding initial clinical and computed tomography-based staging in comparison with mpMRI staging. Both tumor stage shifts and overall risk group shifts, including prostate-specific antigen (PSA) level and the Gleason score, were assessed. Potential risk factors for upstaging were tested in a multivariate analysis. Finally, the impact of mpMRI-based staging shift on prostate RT and antihormonal therapy was evaluated. RESULTS Overall, tumor stage shift occurred in 55.7% of patients after mpMRI. Upstaging was most prominent in patients showing high-risk serum PSA levels (73%), but was also substantial in patients presenting with low-risk PSA levels (50%) and low-risk Gleason scores (45.2%). Risk group changes occurred in 28.7% of the patients with consequent treatment adaptations regarding target volume delineation and duration of androgen deprivation therapy. High PSA levels were found to be a significant risk factor for tumor upstaging and newly diagnosed seminal vesicle infiltration assessed using mpMRI. CONCLUSIONS Our findings suggest that mpMRI of the prostate leads to substantial tumor upstaging, and can considerably affect treatment decisions in all patient groups undergoing risk-adapted curative RT for prostate cancer.

Adapting a driving simulator to study pedestrians' street-crossing decisions: A feasibility study

The decision when to cross a street safely is a challenging task that poses high demands on perception and cognition. Both can be affected by normal aging, neurodegenerative disorder, and brain injury, and there is an increasing interest in studying street-crossing decisions. In this article, we describe how driving simulators can be modified to study pedestrians' street-crossing decisions. The driving simulator's projection system and the virtual driving environment were used to present street-crossing scenarios to the participants. New sensors were added to measure when the test person starts to cross the street. Outcome measures were feasibility, usability, task performance, and visual exploration behavior, and were measured in 15 younger persons, 15 older persons, and 5 post-stroke patients. The experiments showed that the test is feasible and usable, and the selected difficulty level was appropriate. Significant differences in the number of crashes between young participants and patients (p = .001) as well as between healthy older participants and patients (p = .003) were found. When the approaching vehicle's speed is high, significant differences between younger and older participants were found as well (p = .038). Overall, the new test setup was well accepted, and we demonstrated that driving simulators can be used to study pedestrians' street-crossing decisions.

Decision strategies for policy decisions under uncertainties: The case of mitigation measures addressing methane emissions from ruminants

Decision strategies aim at enabling reasonable decisions in cases of uncertain policy decision problems which do not meet the conditions for applying standard decision theory. This paper focuses on decision strategies that account for uncertainties by deciding whether a proposed list of policy options should be accepted or revised (scope strategies) and whether to decide now or later (timing strategies). They can be used in participatory approaches to structure the decision process. As a basis, we propose to classify the broad range of uncertainties affecting policy decision problems along two dimensions, source of uncertainty (incomplete information, inherent indeterminacy and unreliable information) and location of uncertainty (information about policy options, outcomes and values). Decision strategies encompass multiple and vague criteria to be deliberated in application. As an example, we discuss which decision strategies may account for the uncertainties related to nutritive technologies that aim at reducing methane (CH4) emissions from ruminants as a means of mitigating climate change, limiting our discussion to published scientific information. These considerations not only speak in favour of revising rather than accepting the discussed list of options, but also in favour of active postponement or semi-closure of decision-making rather than closure or passive postponement.

Rethinking ERP-Outsourcing Decisions for Leveraging Technological and Preserving Business Knowledge

During the selection, implementation and stabilization phases, as well as the operations and optimization phase of an ERP system (ERP-lifecycle), numerous companies consider to utilize the support of an external service provider. This paper analyses how different categories of knowledge influence the sourcing decision of crucial tasks within the ERP lifecycle. Based on a review of the IS outsourcing literature, essential knowledge-related determinants for the IS outsourcing decision are presented and aggregated in a structural model. It will be hypothesized that internal deficits in technological knowledge in comparison to external vendors as well as the specificity of the synthesis of special technological and specific business knowledge have a profound impact on the outsourcing decision. Then, a classification framework will be developed which facilitates the assignment of various tasks within the ERP lifecycle to their respective knowledge categories and knowledge carriers which might be internal or external stakeholders. The configuaration task will be used as an example to illustrate how the structural model and the classification framework may be applied to evaluate the outsourcing of tasks within the ERP lifecycle.