This paper concerns
learning theory
of {\em recursive real-valued functions\/}
that are one of the formulations for the computable real function.
Hirowatari et al. (2005) have introduced
the finite prediction of recursive real-valued functions,
which is based on a finite prediction machine
that is a procedure to request finite examples
of a recursive real-valued function f
and a datum of a real number x,
and to output a datum of a real number
as the value of f(x).
In this paper,
we newly establish the interaction of
the criterion RealFP for finite prediction of recursive real-valued functions
and the criteria RealEx, RealCons, RealFin and RealNum!
for inductive inference of recursive real-valued functions.