Effects of parietal TMS on somatosensory judgments challenge interhemispheric rivalry accounts

Interplay between the cerebral hemispheres is vital for coordinating perception and behavior. One influential account holds that the hemispheres engage in rivalry, each inhibiting the other. In the somatosensory domain, a seminal paper claimed to demonstrate such interhemispheric rivalry, reporting improved tactile detection sensitivity on the right hand after transcranial magnetic stimulation (TMS) to the right parietal lobe (Seyal, Ro, & Rafal, 1995). Such improvement in tactile detection ipsilateral to TMS could follow from interhemispheric rivalry, if one assumes that TMS disrupted cortical processing under the coil and thereby released the other hemisphere from inhibition. Here we extended the study by Seyal et al. (1995) to determine the effects of right parietal TMS on tactile processing for either hand, rather than only the ipsilateral hand. We performed two experiments applying TMS in the context of median-nerve stimulation; one experiment required somatosensory detection, the second somatosensory intensity discrimination. We found different TMS effects on detection versus discrimination, but neither set of results followed the prediction from hemispheric rivalry that enhanced performance for one hand should invariably be associated with impaired performance for the other hand, and vice-versa. Our results argue against a strict rivalry interpretation, instead suggesting that parietal TMS can provide a pedestal-like increment in somatosensory response

Bonsai Trees in Your Head: How the Pavlovian System Sculpts Goal-Directed Choices by Pruning Decision Trees

When planning a series of actions, it is usually infeasible to consider all potential future sequences; instead, one must prune the decision tree. Provably optimal pruning is, however, still computationally ruinous and the specific approximations humans employ remain unknown. We designed a new sequential reinforcement-based task and showed that human subjects adopted a simple pruning strategy: during mental evaluation of a sequence of choices, they curtailed any further evaluation of a sequence as soon as they encountered a large loss. This pruning strategy was Pavlovian: it was reflexively evoked by large losses and persisted even when overwhelmingly counterproductive. It was also evident above and beyond loss aversion. We found that the tendency towards Pavlovian pruning was selectively predicted by the degree to which subjects exhibited sub-clinical mood disturbance, in accordance with theories that ascribe Pavlovian behavioural inhibition, via serotonin, a role in mood disorders. We conclude that Pavlovian behavioural inhibition shapes highly flexible, goal-directed choices in a manner that may be important for theories of decision-making in mood disorders

Arithmetic and local circuitry underlying dopamine prediction errors

Predictions help guide learning. As we encounter objects in our environment, we make predictions about their value. When outcomes match our predictions, learning is not required. When outcomes are unexpected, however, we update our predictions to reflect our experience. Dopamine neurons are thought to facilitate this process by encoding reward prediction error, or the difference between actual and predicted reward. Despite decades of work on prediction errors and their role in learning, little is known about how they are calculated in the brain. To determine how dopamine neurons calculate prediction error, I combined optogenetic manipulations with extracellular recordings while mice engaged in classical conditioning. In Chapter 1, I demonstrate that dopamine neurons perform subtraction, a computation that is ideal for reward learning but rarely observed in the brain. Furthermore, by carefully examining how individual dopamine neurons respond to various sizes of reward and levels of expectation, I reveal striking homogeneity from neuron to neuron. All dopamine neurons appear to follow the same function, just scaled up or down. This universal template ensures robust information coding, allowing each dopamine neuron to accurately calculate reward prediction error and broadcast this information to other brain areas vital for learning. In Chapter 2, I attempt to uncover the inputs that dopamine neurons use to calculate prediction errors. In particular, I test the hypothesis that a group of inhibitory neurons surrounding dopamine neurons in the midbrain may provide information about expected reward. By selectively exciting and inhibiting these nearby neurons, I discover that they indeed play a causal role in prediction errors, inhibiting dopamine neurons when reward is expected. Together, my results help uncover the arithmetic and local circuitry underlying dopamine prediction errors

Learning what to approach.

Most decisions share a common goal: maximize reward and minimize punishment. Achieving this goal requires learning which choices are likely to lead to favorable outcomes. Dopamine is essential for this process, enabling learning by signaling the difference between what we expect to get and what we actually get. Although all animals appear to use this dopamine prediction error circuit, some do so more than others, and this neural heterogeneity correlates with individual variability in behavior. In this issue of PLOS Biology, Lee and colleagues show that manipulating a simple task parameter can bias the animals' behavioral strategy and modulate dopamine release, implying that how we learn is just as flexible as what we learn