act2probrat | R Documentation |

Logistic function to convert output activations to rating of outcome probability (see e.g. Gluck & Bower, 1988).

` act2probrat(act, theta, beta) `

`act` |
Vector of output activations |

`theta` |
Scaling constant |

`beta` |
Bias constant |

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.

Returns a vector of probability ratings.

As this function returns probabilities, the numbers returned are always in the range 0-1. If the data you are fitting use a different range, convert them. For example, if your data are ratings on a 0-10 scale, divide them by 10. If your data are something other than probability estimates (e.g. you asked participants to use negative ratings to indicate preventative relationships), don't use this function unless you are sure it is doing what you intend.

Andy Wills

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*.

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