On-line for CiNet members: Satoshi Hirose: “i-test: alternative to t-test for the decoding accuracy evaluation”

Friday Lunch Seminar

January 22, 2021
12:15 〜 13:00
(On-line for CiNet members only)

Talk Title: “i-test: alternative to t-test for the decoding accuracy evaluation”

Satoshi Hirose
Brain Networks and Communication Labortory
Center for Information and Neural Networks (CiNet)
National Institute of Information and Communications Technology (NICT)

Host PI: Eiichi Naito

In fMRI decoding studies we often test the mean decoding accuracy across participants against the chance-level accuracy (e.g., one-sample Student t-test or permutation test) to verify whether brain activation includes information about the label (e.g., experimental condition, cognitive content). However, the significant results for such tests only indicate that “there are some people in the population whose fMRI data carry information about the experimental condition.” (Allefeld et al., 2016) Thus, such tests failed to infer whether the effect is typical in the population. In this study, I propose the statistical test “information prevalence inference using the i-th order statistic (i-test).” The i-test has high statistical power to provide an inference regarding the typical effect in the population. In the i-test, the i-th lowest sample decoding accuracy (the i-th order statistic) is compared to the null distribution to test whether the proportion of higher-than-chance decoding accuracy in the population (information prevalence) is larger than the threshold. Thus, a significant result of the i-test can be interpreted as “a majority of the population has information about the label in the brain.”

Allefeld, C., Görgen, K., Haynes, J.D., 2016. Valid population inference for information-based imaging: From the second-level t-test to prevalence inference. Neuroimage.