Modelling human choices: MADeM and decision‑making

Research supported by FAPESP 2015/50122-0 and DFG-GRTK 1740/2. RP and AR are also part of the Research, Innovation and Dissemination Center for Neuromathematics FAPESP grant (2013/07699-0). RP is supported by a FAPESP scholarship (2013/25667-8). ACR is partially supported by a CNPq fellowship (grant 306251/2014-0)

Fast, accurate, and stable feature selection using neural networks

Multi-voxel pattern analysis often necessitates feature selection due to the high dimensional nature of neuroimaging data. In this context, feature selection techniques serve the dual purpose of potentially increasing classification accuracy and revealing sets of features that best discriminate between classes. However, feature selection techniques in current, widespread use in the literature suffer from a number of deficits, including the need for extended computational time, lack of consistency in selecting features relevant to classification, and only marginal increases in classifier accuracy. In this paper we present a novel method for feature selection based on a single-layer neural network which incorporates cross-validation during feature selection and stability selection through iterative subsampling. Comparing our approach to popular alternative feature selection methods, we find increased classifier accuracy, reduced computational cost and greater consistency with which relevant features are selected. Furthermore, we demonstrate that importance mapping, a technique used to identify voxels relevant to classification, can lead to the selection of irrelevant voxels due to shared activation patterns across categories. Our method, owing to its relatively simple architecture, flexibility and speed, can provide a viable alternative for researchers to identify sets of features that best discriminate classes

Surprise, value and control in anterior cingulate cortex during speeded decision-making

Activity in the dorsal anterior cingulate cortex (dACC) is observed across a variety of contexts, and its function remains intensely debated in the field of cognitive neuroscience. While traditional views emphasize its role in inhibitory control (suppressing prepotent, incorrect actions), recent proposals suggest a more active role in motivated control (invigorating actions to obtain rewards). Lagging behind empirical findings, formal models of dACC function primarily focus on inhibitory control, highlighting surprise, choice difficulty and value of control as key computations. Although successful in explaining dACC involvement in inhibitory control, it remains unclear whether these mechanisms generalize to motivated control. In this study, we derive predictions from three prominent accounts of dACC and test these with functional magnetic resonance imaging during value-based decision-making under time pressure. We find that the single mechanism of surprise best accounts for activity in dACC during a task requiring response invigoration, suggesting surprise signalling as a shared driver of inhibitory and motivated control. The role of the anterior cingulate cortex (ACC) in decision-making and cognitive control is the subject of a long-standing debate. Vassena et al. tested the dominant accounts in the same paradigm and found that the ACC signals the difference between predicted and actual outcomes

Predicting motivation : computational models of PFC can explain neural coding of motivation and effort-based decision-making in health and disease

Human behavior is strongly driven by the pursuit of rewards. In daily life, however, benefits mostly come at a cost, often requiring that effort be exerted to obtain potential benefits. Medial PFC (MPFC) and dorsolateral PFC (DLPFC) are frequently implicated in the expectation of effortful control, showing increased activity as a function of predicted task difficulty. Such activity partially overlaps with expectation of reward and has been observed both during decision-making and during task preparation. Recently, novel computational frameworks have been developed to explain activity in these regions during cognitive control, based on the principle of prediction and prediction error (predicted response-outcome [PRO] model [Alexander, W. H., & Brown, J. W. Medial prefrontal cortex as an action-outcome predictor. Nature Neuroscience, 14, 1338-1344, 2011], hierarchical error representation [HER] model [Alexander, W. H., & Brown, J. W. Hierarchical error representation: A computational model of anterior cingulate and dorsolateral prefrontal cortex. Neural Computation, 27, 2354-2410, 2015]). Despite the broad explanatory power of these models, it is not clear whether they can also accommodate effects related to the expectation of effort observed in MPFC and DLPFC. Here, we propose a translation of these computational frameworks to the domain of effort-based behavior. First, we discuss how the PRO model, based on prediction error, can explain effort-related activity in MPFC, by reframing effort-based behavior in a predictive context. We propose that MPFC activity reflects monitoring of motivationally relevant variables (such as effort and reward), by coding expectations and discrepancies from such expectations. Moreover, we derive behavioral and neural model-based predictions for healthy controls and clinical populations with impairments of motivation. Second, we illustrate the possible translation to effort-based behavior of the HER model, an extended version of PRO model based on hierarchical error prediction, developed to explain MPFC-DLPFC interactions. We derive behavioral predictions that describe how effort and reward information is coded in PFC and how changing the configuration of such environmental information might affect decision-making and task performance involving motivation

What do palliative care patients and their relatives think about research in palliative care? - a systematic review

Research in palliative care patients has been controversial and is often challenging. It is important to know the views of potentially eligible patients themselves in order to determine the appropriateness of research in the palliative care population and to develop realistic studies that are practical and achievable in this population. This systematic review aims to identify the views of palliative care patients and their families towards research, the factors that are important when considering participation, and the types of research trial they would support or reject