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Bias was quantified by fitting the drift-diffusion model to the behavioral data.
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Authors: In the manuscript, we fit the drift-diffusion models to parameters of the infotaxis trajectories.
For each task, we fitted the drift-diffusion model (DDM; Ratcliff 1978) to the behavioral data and used the starting point parameter from the model to measure the magnitude of choice bias (Fig. 1).
By fitting the full drift diffusion model to the data, we showed that the effects of modality mainly affected drift rate and nondecision time, with lower drift rates and longer nondecision times for the auditory task.
The procedure was performed by fitting the linear drift to the whole run and subtracting it from the voxel intensities.
We propose a different hazard function and show that our hazard model fits nicely to the drift-diffusion model with respect to the psychophysical functions.
Langmuir isotherms parameters are obtained along with the estimation of the effective pore diffusion coefficient (Dpe) by fitting the batch adsorption kinetics experiments with the pore diffusion model.
The absolute absorption spectrum is then extracted by fitting the corrected reflectance spectrum to a diffusion reflectance model [ 29, 30].
The kinetic and equilibrium parameters (K D ) values were calculated by fitting the raw sensorgram with the 1 1 drifting baseline-binding model by using Biacore T100 evaluation software.
The mechanism of release kinetics was evaluated by fitting the permeation data to the zero-order and Higuchi diffusion models.
The diffusion mechanism was analysed with intraparticulate diffusion model, while the equilibrium adsorption behaviour was analysed by fitting the equilibrium data with six isotherm models.
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