# Abstract The speed‑accuracy tradeoff (SAT) often makes psychophysical data difficult to interpret. Accordingly, the SAT experimental procedure and model were proposed for an integrated account of the speed and accuracy of responses. However, the extensive data collection for an SAT experiment has blocked its popularity. For a quick estimation of SAT function (SATf), we previously developed a Bayesian adaptive SAT method, including an online stimulus selection strategy. By simulations, the method was proved efficient with high accuracy and precision with minimal trials, adequate for practically applying a single condition task. However, it calls for extensions to more general designs with multiple conditions and should be revised to achieve improved estimation performance. It also demands real experimental validation with human participants. In the current study, we suggested an improved method to measure SATfs for multiple task conditions concurrently and to enhance robustness in general designs. The performance was evaluated with simulation studies and a psychophysical experiment using a flanker task. Simulation results revealed that the proposed method with the adaptive stimulus selection strategy efficiently estimated multiple SATfs and improved performance even for cases with an extreme parameter value. In the flanker experiment, SATfs estimated by minimal adaptive trials (1/8 of conventional trials) showed high agreement with those by conventional trials for multiple conditions. These results indicate that the Bayesian adaptive SAT method is reliable and efficient in estimating SATfs in most experimental settings and may apply to SATf estimation in general behavioral research designs. # Files ## Program for the experiment The MATLAB codes for the psychophysical experiment were written and tested under the following settings: - OS: Microsft Windows 10 (x64) - MATLAB: ver XXX (x86) - Psychtoolbox: ver 3.0.17 - Monitor/Screen: 15.6-inch LCD monitor; 60Hz; - Please note that the qSAT folder should be included in your path before running this program (use 'setpath' function in MATLAB). ## The Bayesian Adaptive SAT engine - Files in 'qSAT' folder are for the core algorithm of the Bayesian adaptive SAT method. - You can edit stimulus and parameter space for SAT function in 'qSpace.m'. For example, you may want to adjust the range of lambda parameter by changing 'minL' and 'maxL' variables. - Simulation codes can be found elsewhere (https://osf.io/75sqe/) ## Data Files - The naming convention for data files is *FlankerSAT_v2r3_{observer initials}{session number}_{date and time for file creation}.csv* - Files contain the trial history. Each row represents one trial with the following variables: - column 1: random numbers (which were used to randomize the order of trials) - column 2: congruency (1 for congruent;2 for incogruent) - column 3: direction of target arrow (1 for left; 2 for right) - column 4: direction of flanker arrows (1 for left; 2 for right) - column 5: participant's response (1 for left; 2 for right; 0 for late response) - column 6: RT (in sec) - column 7: correctness of response (0 for incorrect; 1 for correct) - column 8: index numbers for SOA. The actual differences were stored in column 9. - column 9: acutal SOA (in sec) - column 10: block number - column 11: MCS or adaptive block (1 for MCS; 0 for adaptive) - Since later responses were not counted as completed trials, total trials (length of rows in the matrix) would vary across participants*sessions.