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Bank of Japan policymakers fretted over stimulus side effects at June meeting

The Japan Times

Bank of Japan policymakers said it was necessary to monitor the negative economic impact of continuing monetary stimulus amid stubbornly weak inflation, minutes of their June meeting showed Friday. The minutes suggest members of the central bank's Policy Board recognized a need to make policy more flexible, setting the stage for their decision earlier this week to allow long-term yields to rise higher and tweak the bank's asset purchases. Many board members pointed out that it was "important to continue to conduct a multifaceted monitoring and assessment of the positive effects and side effects that could arise from the continuation of powerful monetary easing" at the meeting held on June 14 and 15. One person said the BOJ should make sure its policy does not cause "severe distortions" to economic and financial conditions, while another said that it was necessary to "consider the possible countermeasures against the side effects before they materialized." The minutes are released after approval by board members at the following policy meeting and do not attribute comments to individual speakers.


Want to Make a Lie Seem True? Say It Again. And Again. And Again

WIRED

You only use 10 percent of your brain. Eating carrots improves your eyesight. Crime in the United States is at an all-time high. But the facts don't actually matter: People repeat them so often that you believe them. Welcome to the "illusory truth effect," a glitch in the human psyche that equates repetition with truth.


Sequential effects: Superstition or rational behavior?

Neural Information Processing Systems

In a variety of behavioral tasks, subjects exhibit an automatic and apparently sub-optimal sequential effect: they respond more rapidly and accurately to a stimulus if it reinforces a local pattern in stimulus history, such as a string of repetitions or alternations, compared to when it violates such a pattern. This is often the case even if the local trends arise by chance in the context of a randomized design, such that stimulus history has no predictive power. In this work, we use a normative Bayesian framework to examine the hypothesis that such idiosyncrasies may reflect the inadvertent engagement of fundamental mechanisms critical for adapting to changing statistics in the natural environment. We show that prior belief in non-stationarity can induce experimentally observed sequential effects in an otherwise Bayes-optimal algorithm. The Bayesian algorithm is shown to be well approximated by linear-exponential filtering of past observations, a feature also apparent in the behavioral data. We derive an explicit relationship between the parameters and computations of the exact Bayesian algorithm and those of the approximate linear-exponential filter. Since the latter is equivalent to a leaky-integration process, a commonly used model of neuronal dynamics underlying perceptual decision-making and trial-to-trial dependencies, our model provides a principled account of why such dynamics are useful. We also show that near-optimal tuning of the leaky-integration process is possible, using stochastic gradient descent based only on the noisy binary inputs. This is a proof of concept that not only can neurons implement near-optimal prediction based on standard neuronal dynamics, but that they can also learn to tune the processing parameters without explicitly representing probabilities.


Sequential effects: Superstition or rational behavior?

Neural Information Processing Systems

In a variety of behavioral tasks, subjects exhibit an automatic and apparently sub-optimal sequential effect: they respond more rapidly and accurately to a stimulus if it reinforces a local pattern in stimulus history, such as a string of repetitions or alternations, compared to when it violates such a pattern. This is often the case even if the local trends arise by chance in the context of a randomized design, such that stimulus history has no predictive power. In this work, we use a normative Bayesian framework to examine the hypothesis that such idiosyncrasies may reflect the inadvertent engagement of fundamental mechanisms critical for adapting to changing statistics in the natural environment. We show that prior belief in non-stationarity can induce experimentally observed sequential effects in an otherwise Bayes-optimal algorithm. The Bayesian algorithm is shown to be well approximated by linear-exponential filtering of past observations, a feature also apparent in the behavioral data.


Nocebo effects can make you feel pain

Science

The mysterious phenomenon known as the nocebo effect describes negative expectancies. This is in contrast to positive expectancies that trigger placebo effects (1). In evolutionary terms, nocebo and placebo effects coexist to favor perceptual mechanisms that anticipate threat and dangerous events (nocebo effects) and promote appetitive and safety behaviors (placebo effects). In randomized placebo-controlled clinical trials, patients that receive placebos often report side effects (nocebos) that are similar to those experienced by patients that receive the investigational treatment (2). Information provided during the informed consent process and divulgence of adverse effects contribute to nocebo effects in clinical trials (1).