Month: May 2017

Anna Konova
Center for Neural Science
New York University

The symptoms of drug addiction imply alterations in decision processes. The integrative field of neuroeconomics, which borrows concepts and methods from economics, psychology, and neuroscience, has identified a “domain general” neural system encompassing the ventromedial prefrontal cortex and the striatum in the computation of subjective value – the basis of idiosyncratic preferences and choice. Indeed, the application of this framework to addiction in both humans and other animals has successfully captured many features of addiction. However, this work has generally considered addiction as a static entity, ignoring addiction’s most elusive (and perhaps most defining) feature – its stereotyped, cyclic nature at the level of the individual, characterized by alternating periods of abstinence and drug use. I will discuss ongoing work in which we aim to better understand two dynamic processes at the transition between abstinence and relapse to drug use: (1) a relatively slower process (in the order of weeks and months) related to risk preferences that tracks drug use vulnerability and which we have modeled with repeated assessments of economic choice behavior through the first months of treatment for opioid addiction; and (2) a faster process (in the order of minutes or hours) related to the motivational state of craving that tracks immediate vulnerability and which we have modeled as a specific, gain-control like shift in the value of the object of craving. Understanding these addiction states as dynamic changes in valuation, we hope, can help identify when additional therapeutic intervention is needed on a timescale that is clinically useful as well as motivate the development of new decision- and valuation-based interventions for breaking the cycle of addiction.

Jordan Taylor
Department of Psychology and Princeton Neuroscience Institute
Princeton University

The most commonly cited human-specific cognitive abilities are generally language, abstract reasoning, and complex sociality. Our motor talents are often left off that list, as such abilities are generally not considered linked to higher cognition. Indeed, the field of sensorimotor learning often seems stuck in the subcortex, with the lens of research focused on cerebellar-driven implicit sensorimotor recalibration. This trend ignores a key element of human motor learning: rapid and flexible cognitive strategizing. Indeed, recent research from our lab, as well as others, has suggested that explicit cognitive strategies may play a bigger role than previously thought. In fact, we find that implicit learning is surprisingly inflexible — showing only small incremental changes that are insensitive to the task — which calls into question its relative importance in motor learning. Our findings suggest that much of motor learning, at least in the short-term, reflects a more complex, cognitive decision-making process which aids in the rapid, flexible selection of movements to achieve precise goals in distal regions of space. Accordingly, models of the multiple processes at work during motor learning, and hypotheses about the putative neural substrates underlying such processes, need to be broadened to accommodate the important contribution of cognitive strategies. Understanding how multiple neural systems contribute to learning should lead to the development of optimal neurorehabilitation protocols either designed to target impaired systems or bias performance to rely on systems that are relatively intact.