Publications by Year: 2008

The recognition that computational ideas from reinforcement learning are relevant to the study of neural circuits has taken the cognitive neuroscience community by storm. A central tenet of these models is that discrepancies between actual and expected outcomes can be used for learning. Neural correlates of such prediction-error signals have been observed now in midbrain dopaminergic neurons, striatum, amygdala and even prefrontal cortex, and models incorporating prediction errors have been invoked to explain complex phenomena such as the transition from goal-directed to habitual behavior. Yet, like any revolution, the fast-paced progress has left an uneven understanding in its wake. Here, we provide answers to ten simple questions about prediction errors, with the aim of exposing both the strengths and the limitations of this active area of neuroscience research. ©2008 Elsevier Ltd. All rights reserved.

Fear learning is a rapid and persistent process that promotes defense against threats and reduces the need to relearn about danger. However, it is also important to flexibly readjust fear behavior when circumstances change. Indeed, a failure to adjust to changing conditions may contribute to anxiety disorders. A central, yet neglected aspect of fear modulation is the ability to flexibly shift fear responses from one stimulus to another if a once-threatening stimulus becomes safe or a once-safe stimulus becomes threatening. In these situations, the inhibition of fear and the development of fear reactions co-occur but are directed at different targets, requiring accurate responding under continuous stress. To date, research on fear modulation has focused mainly on the shift from fear to safety by using paradigms such as extinction, resulting in a reduction of fear. The aim of the present study was to track the dynamic shifts from fear to safety and from safety to fear when these transitions occur simultaneously. We used functional neuroimaging in conjunction with a fear-conditioning reversal paradigm. Our results reveal a unique dissociation within the ventromedial prefrontal cortex between a safe stimulus that previously predicted danger and a "naive" safe stimulus. We show that amygdala and striatal responses tracked the fear-predictive stimuli, flexibly flipping their responses from one predictive stimulus to another. Moreover, prediction errors associated with reversal learning correlated with striatal activation. These results elucidate how fear is readjusted to appropriately track environmental changes, and the brain mechanisms underlying the flexible control of fear.

Reinforcement learning provides both qualitative and quantitative frameworks for understanding and modeling adaptive decision-making in the face of rewards and punishments. Here we review the latest dispatches from the forefront of this field, and map out some of the territories where lie monsters. ©2008 Elsevier Ltd. All rights reserved.

A critical problem in daily decision making is how to choose actions now in order to bring about rewards later. Indeed, many of our actions have long-term consequences, and it is important to not be myopic in balancing the pros and cons of different options, but rather to take into account both immediate and delayed consequences of actions. Failures to do so may be manifest as persistent, maladaptive decision-making, one example of which is addiction where behavior seems to be driven by the immediate positive experiences with drugs, despite the delayed adverse consequences. A recent study by Takahashi et al. (2007) investigated the effects of cocaine sensitization on decision making in rats and showed that drug use resulted in altered representations in the ventral striatum and the dorsolateral striatum, areas that have been implicated in the neural instantiation of a computational solution to optimal long-term actions selection called the Actor/Critic framework. In this Focus article we discuss their results and offer a computational interpretation in terms of drug-induced impairments in the Critic. We first survey the different lines of evidence linking the subparts of the striatum to the Actor/Critic framework, and then suggest two possible scenarios of breakdown that are suggested by Takahashi et al.'s (2007) data. As both are compatible with the current data, we discuss their different predictions and how these could be empirically tested in order to further elucidate (and hopefully inch towards curing) the neural basis of drug addiction.