Logistic function to convert output activations to rating of outcome probability (see e.g. Gluck & Bower, 1988).
act2probrat(act, theta, beta)Vector of output activations
Scaling constant
Bias constant
Returns a vector of probability ratings.
The contents of this help file are relatively brief; a more extensive tutorial on using act2probrat can be found in Spicer et al. (n.d.).
The function takes the output activation of a learning model
(e.g. slpRW), and converts it into a rating of the subjective
probability that the outcome will occur. It does this separately for
each activation in the vector act. It uses a logistic function
to do this conversion (see e.g. Gluck & Bower, 1988, Equation 7). This
function can produce a variety of monotonic mappings from activation
to probability rating, determined by the value set for the two
constants:
theta is a scaling constant; as its value rises, the function
relating activation to rating becomes less linear and at high values
approximates a step function.
beta is a bias parameter; it is the value of the output
activation that results in an output rating of P = 0.5. For example,
if you wish an output activation of 0.4 to produce a rated probability
of 0.5, set beta to 0.4.
Gluck, M.A. & Bower, G.H. (1988). From conditioning to category learning: An adaptive network model. Journal of Experimental Psychology: General, 117, 227-247.
Spicer, S., Jones, P.M., Inkster, A.B., Edmunds, C.E.R. & Wills, A.J. (n.d.). Progress in learning theory through distributed collaboration: Concepts, tools, and examples. Manuscript in preparation.