Retrospective review.Intraoperative motor evoked potential (MEP) monitoring in spine surgery may assist surgeons in taking corrective measures to prevent neurologic deficits. The efficacy of monitoring MEPs intraoperatively in patients with myelopathy from

When observing the outcome of a choice, people are sensitive to the choices context, such that the experienced value of an option depends on the alternatives: getting $1 when the possibilities were 0 or 1 feels much better than when the possibilities were 1 or 10. Context-sensitive valuation has been documented within reinforcement learning (RL) tasks, in which values are learned from experience through trial and error. Range adaptation, wherein options are rescaled according to the range of values yielded by available options, has been proposed to account for this phenomenon. However, we propose that other mechanisms-reflecting a different theoretical viewpoint-may also explain this phenomenon. Specifically, we theorize that internally defined goals play a crucial role in shaping the subjective value attributed to any given option. Motivated by this theory, we develop a new intrinsically enhanced RL model, which combines extrinsically provided rewards with internally generated signals of goal achievement as a teaching signal. Across 7 different studies (including previously published data sets as well as a novel, preregistered experiment with replication and control studies), we show that the intrinsically enhanced model can explain context-sensitive valuation as well as, or better than, range adaptation. Our findings indicate a more prominent role of intrinsic, goal-dependent rewards than previously recognized within formal models of human RL. By integrating internally generated signals of reward, standard RL theories should better account for human behavior, including context-sensitive valuation and beyond.

When a patient receives a positive test result from a diagnostic test they assume they have the disease. However, the positive predictive value (PPV), ie the probability that they have the disease given a positive test result, is rarely equal to one. To assist their patients, doctors must explain the chance that they do in fact have the disease. However, physicians frequently miscalculate the PPV as the sensitivity and/or misinterpret the PPV, which results in increased anxiety in patients and generates unnecessary tests and consultations. The reasons for this miscalculation as well as three ways to calculate the PPV are reviewed here.

Human learning is often motivated by self-imposed challenges, which guide behavior even in the absence of external rewards. Previous studies have shown that humans can use personal goals to "hack" the definition of reward, warranting an extension of the classic reinforcement learning framework to account for the flexible attribution of value to outcomes according to current goals. However, learning through goal-derived outcomes is less efficient than learning through more established reinforcers, such as numeric points. At least three possible explanations exist for this sort of impairment, or "bug". First, occasional lapses in executive function, which is required to encode and recognize goals, may result in subsequent failure to update values accordingly. Second, the higher working memory load required to encode novel stimuli as desirable outcomes may impair people's ability to update and remember correct stimulus-reward associations. Third, a weaker commitment to arbitrary goals may result in dimmer appetitive signals. By extending existing experimental paradigms that include learning from both familiar rewards and abstract, goal-contingent outcomes and combining them with computational modeling techniques, we find evidence for each of the proposed accounts. While other factors might also play a role in this process, our results provide an initial indication of the key elements supporting (or impairing) the attribution of rewarding properties to otherwise neutral stimuli, which enable humans to better pursue arbitrarily set goals.

Gliomas are routinely graded according to histopathological criteria established by the World Health Organization. Although this classification can be used to understand some of the variance in the clinical outcome of patients, there is still substantial h

Every day, people are exposed to vast amounts of information that can impact how they feel, think about, and act upon the world. Here, we extend the computational reinforcement learning framework to explain how such an impact can shape future decisions to either seek or avoid information. By simulating human behavioral data, we showed that agents are more likely to seek information after exposure to information with a positive net impact on the agent’s affect, cognition, and ability to make good decisions. The more the agent is exposed to this kind of information, the higher the probability that it will seek even more information in the future. On the contrary, decisions to remain ignorant are more likely to occur after repeated exposure to information with a negative net impact. Our model offers a novel computational framework within which maladaptive information-seeking and information-avoidance behaviors can be further investigated.

© 2014 The Author(s) To evaluate metrics that describe changes in apparent diffusion coefficient (ADC) and to examine their association with clinical outcome for patients with newly diagnosed GBM who were participating in a Phase II clinical trial of treat