Toward a model-based cognitive neuroscience of mind wandering

Published version also available at http://dx.doi.org/10.1016/j.neuroscience.2015.09.053People often ‘‘mind wander” during everyday tasks, temporarily losing track of time, place, or current task goals. In laboratory-based tasks, mind wandering is often associated with performance decrements in behavioral variables and changes in neural recordings. Such empirical associations provide descriptive accounts of mind wandering – howit affects ongoing task performance – but fail to provide true explanatory accounts – why it affects task performance. In this perspectives paper, we consider mind wandering as a neural state or process that affects the parameters of quantitative cognitive process models, which in turn affect observed behavioral performance. Our approach thus uses cognitive process models to bridge the explanatory divide between neural and behavioral data. We provide an overview of two general frameworks for developing a model-based cognitive neuroscience of mind wandering. The first approach uses neural data to segment observed performance into a discrete mixture of latent task-related and task-unrelated states, and the second regresses single-trial measures of neural activity onto structured trial-by-trial variation in the parameters of cognitive process models. We discuss the relative merits of the two approaches, and the research questions they can answer, and highlight that both approaches allow neural data to provide additional constraint on the parameters of cognitive models, which will lead to a more precise account of the effect of mind wandering on brain and behavior. We conclude by summarizing prospects for mind wandering as conceived within a model-based cognitive neuroscience framework, highlighting the opportunities for its continued study and the benefits that arise from using well-developed quantitative techniques to study abstract theoretical constructs

Trace metals in aerosol at Terra Nova Bay, Antarctica.

Atmospheric particulate with an aerodynamic diameter o10 mm (PM10) was sampled continuously during the austral summers of 2000–2001 and 2001–2002 at a coastal site near to the Italian base of Terra Nova, Antarctica. Li, Pb, Cd, U, Ba, Bi, Cs, Rb, Tl, Sr, Al, V, Fe, Cu, Mn, Zn, Co, Ag were determined by inductively coupled sector field mass spectroscopy (ICP-SFMS) after sample digestion by a combination of HF, HNO3, and H2O2 in ultraclean conditions. Quality control of the analytical procedure was carried out by blank control, by evaluating the limits of detection, recoveries and repeatability. Concentrations found are extremely low for most metals, confirming the high purity of Antarctic aerosol. Principal Component Analysis (PCA) highlights high correlations among Pb, Cr, Bi, Cu and Zn concentration values and among Li, U, Ba, Cs, Rb, Al, V, Fe, Mn, Co concentration values permitting the identification of two principal source groups, namely crustal dust and human emission activities. Elements of anthropogenic origins (Pb, Cr, Cu, Zn) were highly enriched with respect to their crustal composition

Placebo Intervention Enhances Reward Learning in Healthy Individuals

According to the placebo-reward hypothesis, placebo is a reward-anticipation process that increases midbrain dopamine (DA) levels. Reward-based learning processes, such as reinforcement learning, involves a large part of the DA-ergic network that is also activated by the placebo intervention. Given the neurochemical overlap between placebo and reward learning, we investigated whether verbal instructions in conjunction with a placebo intervention are capable of enhancing reward learning in healthy individuals by using a monetary reward-based reinforcement-learning task. Placebo intervention was performed with non-invasive brain stimulation techniques. In a randomized, triple-blind, cross-over study we investigated this cognitive placebo effect in healthy individuals by manipulating the participants’ perceived uncertainty about the intervention’s efficacy. Volunteers in the purportedly low- and high-uncertainty conditions earned more money, responded more quickly and had a higher learning rate from monetary rewards relative to baseline. Participants in the purportedly high- uncertainty conditions showed enhanced reward learning, and a model-free computational analysis revealed a higher learning rate from monetary rewards compared to the purportedly low-uncertainty and baseline conditions. Our results indicate that the placebo response is able to enhance reward learning in healthy individuals, opening up exciting avenues for future research in placebo effects on other cognitive functions