Impaired learning from errors in cannabis users: dorsal anterior cingulate cortex and hippocampus hypoactivity
Drug and alcohol dependence, 155, 175-182, 2015
The chronic use of cannabis has been associated with error processing dysfunction, in particular, hypoactivity in the dorsal anterior cingulate cortex (dACC) during the processing of cognitive errors. Given the role of such activity in influencing post-error adaptive behaviour, we hypothesised that chronic cannabis users would have significantly poorer learning from errors.
Fifteen chronic cannabis users (four females, mean age = 22.40 years, SD = 4.29) and 15 control participants (two females, mean age = 23.27 years, SD = 3.67) were administered a paired associate learning task that enabled participants to learn from their errors, during fMRI data collection.
Compared with controls, chronic cannabis users showed (i) a lower recall error-correction rate and (ii) hypoactivity in the dACC and left hippocampus during the processing of error-related feedback and re-encoding of the correct response. The difference in error-related dACC activation between cannabis users and healthy controls varied as a function of error type, with the control group showing a significantly greater difference between corrected and repeated errors than the cannabis group.
The present results suggest that chronic cannabis users have poorer learning from errors, with the failure to adapt performance associated with hypoactivity in error-related dACC and hippocampal regions. The findings highlight a consequence of performance monitoring dysfunction in drug abuse and the potential consequence this cognitive impairment has for the symptom of failing to learn from negative feedback seen in cannabis and other forms of dependence.
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Bongard's work focuses on understanding the general nature of cognition, regardless of whether it is found in humans, animals or robots. This unique approach focuses on the role that morphology and evolution plays in cognition. Addressing these questions has taken him into the fields of biology, psychology, engineering and computer science.
Danforth is an applied mathematician interested in modeling a variety of physical, biological, and social phenomenon. He has applied principles of chaos theory to improve weather forecasts as a member of the Mathematics and Climate Research Network, and developed a real-time remote sensor of global happiness using messages from Twitter: the Hedonometer. Danforth co-runs the Computational Story Lab with Peter Dodds, and helps run UVM's reading group on complexity.
Laurent studies the interaction of structure and dynamics. His research involves network theory, statistical physics and nonlinear dynamics along with their applications in epidemiology, ecology, biology, and sociology. Recent projects include comparing complex networks of different nature, the coevolution of human behavior and infectious diseases, understanding the role of forest shape in determining stability of tropical forests, as well as the impact of echo chambers in political discussions.
Hines' work broadly focuses on finding ways to make electric energy more reliable, more affordable, with less environmental impact. Particular topics of interest include understanding the mechanisms by which small problems in the power grid become large blackouts, identifying and mitigating the stresses caused by large amounts of electric vehicle charging, and quantifying the impact of high penetrations of wind/solar on electricity systems.
Bagrow's interests include: Complex Networks (community detection, social modeling and human dynamics, statistical phenomena, graph similarity and isomorphism), Statistical Physics (non-equilibrium methods, phase transitions, percolation, interacting particle systems, spin glasses), and Optimization(glassy techniques such as simulated/quantum annealing, (non-gradient) minimization of noisy objective functions).