Structure Learning in Motor Control:A Deep Reinforcement Learning Model

Motor adaptation displays a structure-learning effect: adaptation to a new perturbation occurs more quickly when the subject has prior exposure to perturbations with related structure. Although this `learning-to-learn' effect is well documented, its underlying computational mechanisms are poorly understood. We present a new model of motor structure learning, approaching it from the point of view of deep reinforcement learning. Previous work outside of motor control has shown how recurrent neural networks can account for learning-to-learn effects. We leverage this insight to address motor learning, by importing it into the setting of model-based reinforcement learning. We apply the resulting processing architecture to empirical findings from a landmark study of structure learning in target-directed reaching (Braun et al., 2009), and discuss its implications for a wider range of learning-to-learn phenomena.Comment: 39th Annual Meeting of the Cognitive Science Society, to appea

Generating descriptive text from functional brain images

Recent work has shown that it is possible to take brain images of a subject acquired while they saw a scene and reconstruct an approximation of that scene from the images. Here we show that it is also possible to generate _text_ from brain images. We began with images collected as participants read names of objects (e.g., ``Apartment'). Without accessing information about the object viewed for an individual image, we were able to generate from it a collection of semantically pertinent words (e.g., "door," "window"). Across images, the sets of words generated overlapped consistently with those contained in articles about the relevant concepts from the online encyclopedia Wikipedia. The technique described, if developed further, could offer an important new tool in building human computer interfaces for use in clinical settings

Neural correlates of cognitive dissonance and choice-induced preference change

According to many modern economic theories, actions simply reflect an individual's preferences, whereas a psychological phenomenon called “cognitive dissonance” claims that actions can also create preference. Cognitive dissonance theory states that after making a difficult choice between two equally preferred items, the act of rejecting a favorite item induces an uncomfortable feeling (cognitive dissonance), which in turn motivates individuals to change their preferences to match their prior decision (i.e., reducing preference for rejected items). Recently, however, Chen and Risen [Chen K, Risen J (2010) J Pers Soc Psychol 99:573–594] pointed out a serious methodological problem, which casts a doubt on the very existence of this choice-induced preference change as studied over the past 50 y. Here, using a proper control condition and two measures of preferences (self-report and brain activity), we found that the mere act of making a choice can change self-report preference as well as its neural representation (i.e., striatum activity), thus providing strong evidence for choice-induced preference change. Furthermore, our data indicate that the anterior cingulate cortex and dorsolateral prefrontal cortex tracked the degree of cognitive dissonance on a trial-by-trial basis. Our findings provide important insights into the neural basis of how actions can alter an individual's preferences