Choice history effects in mice and humans improve reward harvesting efficiency.

Choice history effects describe how future choices depend on the history of past choices. In experimental tasks this is typically framed as a bias because it often diminishes the experienced reward rates. However, in natural habitats, choices made in the past constrain choices that can be made in the future. For foraging animals, the probability of earning a reward in a given patch depends on the degree to which the animals have exploited the patch in the past. One problem with many experimental tasks that show choice history effects is that such tasks artificially decouple choice history from its consequences on reward availability over time. To circumvent this, we use a variable interval (VI) reward schedule that reinstates a more natural contingency between past choices and future reward availability. By examining the behavior of optimal agents in the VI task we discover that choice history effects observed in animals serve to maximize reward harvesting efficiency. We further distil the function of choice history effects by manipulating first- and second-order statistics of the environment. We find that choice history effects primarily reflect the growth rate of the reward probability of the unchosen option, whereas reward history effects primarily reflect environmental volatility. Based on observed choice history effects in animals, we develop a reinforcement learning model that explicitly incorporates choice history over multiple time scales into the decision process, and we assess its predictive adequacy in accounting for the associated behavior. We show that this new variant, known as the double trace model, has a higher performance in predicting choice data, and shows near optimal reward harvesting efficiency in simulated environments. These results suggests that choice history effects may be adaptive for natural contingencies between consumption and reward availability. This concept lends credence to a normative account of choice history effects that extends beyond its description as a bias.

Clinical Impact of Rare and Compound Mutations of Epidermal Growth Factor Receptor in Patients With Non-Small-Cell Lung Cancer.

BACKGROUND: Standard therapy of advanced non-small-cell lung cancer harboring an activating mutation in the epidermal growth factor receptor (EGFR) gene is treatment with tyrosine kinase inhibitors (TKI). However, for rare and compound mutations of the EGFR gene, the clinical evidence of TKI therapy is still unclear. PATIENTS AND METHODS: A total of 2906 lung cancer samples were analyzed for EGFR mutations during routine analysis between 2010 and 2017. The samples have been investigated by Sanger sequencing and since 2014 by next-generation sequencing. RESULTS: We detected EGFR mutations in 408 specimens (14%). Among these, we found 41 samples with rare and 22 with compound mutations. In these 63 samples, 56 different rare EGFR mutations occurred. Information about the clinical outcome was available for 37. Among those with rare mutations, only one patient harboring the mutation p.G874D had disease that responded to first-generation TKI therapy. In contrast, the disease of all patients with compound mutations responded to first- or second-generation TKI therapy. Furthermore, we collected data on clinical relevance regarding TKI therapy from different databases and from an additional literature search, and only found data for 36 of the 56 detected rare mutations. CONCLUSION: Information about the clinical outcome of patients with rare and compound EGFR mutations remains limited. At present, second- and third-generation TKIs are available, which may represent new treatment strategies for these patients. Therefore, it is becoming increasingly important to maintain databases concerning rare EGFR mutations.