S and ωo were determined from FCS experiments using Alexa Fluor 488 hydrazide as a diffusion standard.
As a consequence, reaction times and choice probabilities only allow us to infer these fraction, but not the absolute bound magnitudes independent of the diffusion standard deviations.
Currently, our model can only predict changes in scaled decision boundaries, which conflate actual boundary levels with the diffusion standard deviation.
Furthermore, we cannot even relate the estimated fraction of bound over diffusion standard deviation, ϴ j / σ j (c), across conditions j, as we assume the diffusion standard deviation σ j (c) to be a function of the coherence of the visual flow field.
Our diffusion model necessitates the use of a scaled bound, which is the constant actual bound per modality divided by the diffusion standard deviation that depends on optic flow coherence.
Second, fixing these bounds ϴj in the model to be the same across conditions j does not sufficiently constrain this model, as we can rescale the diffusion standard deviations σ j to achieve arbitrary factions ϴ j / σ j (c).
Instead, we only ever fitted the fraction of the bound over the diffusion standard deviation per condition, that is, ϴ vis / σ vis (c), ϴ vest / σ vest, and ϴ comb /σ comb (c).
The method uses concentration change due to lateral diffusion, internal standards, and two-dimensional detection to record concentrations from different line scans obtained throughout the separation [ 150].
The immaturity and slow diffusion of standards for interoperability and data portability are key barriers to the integration and exchange of structured data among PHRs and the range of relevant entities that provide and finance health care.
