# slpRW: Rescorla-Wagner (1972) associative learning model. In catlearn: Formal Psychological Models of Categorization and Learning

 slpRW R Documentation

## Rescorla-Wagner (1972) associative learning model.

### Description

Rescorla & Wagner's (1972) theory of Pavlovian conditioning.

### Usage

```
slpRW(st, tr, xtdo = FALSE)

```

### Arguments

 `st` List of model parameters `tr` Matrix of training items `xtdo` Boolean specifying whether to include extended information in the output (see below)

### Details

The contents of this help file are relatively brief; a more extensive tutorial on using slpRW can be found in Spicer et al. (n.d.).

The function operates as a stateful list processor (slp; see Wills et al., 2017). Specifically, it takes a matrix (tr) as an argument, where each row represents a single training trial, while each column represents the different types of information required by the model, such as the elemental representation of the training stimuli, and the presence/absence of an outcome. It returns the output activation on each trial (a.k.a. sum of associative strengths of cues present on that trial), as a vector. The slpRW function also returns the final state of the model - a vector of associative strengths between each stimulus and the outcome representation.

Argument `st` must be a list containing the following items:

`lr` - the learning rate (fixed for a given simulation). In order to calculate lr, calculate the product of Rescorla-Wagner parameters alpha and beta. For example, if you want alpha = 0.1 and beta = 0.2, set lr = 0.02. If you want different elements to differ in salience (different alpha values) use the input activations (x1, x2, ..., see below) to represent element-specific salience. For example, if alpha_A = 0.4, alpha_X = 0.2, and beta = 0.1, then set lr = 0.1, and the activations of A and B to 0.4 and 0.2, respectively.

`w` - a vector of initial associative strengths. If you are not sure what to use here, set all values to zero.

`colskip` - the number of optional columns to be skipped in the tr matrix. colskip should be set to the number of optional columns you have added to the tr matrix, PLUS ONE. So, if you have added no optional columns, colskip=1. This is because the first (non-optional) column contains the control values (details below).

Argument `tr` must be a matrix, where each row is one trial presented to the model. Trials are always presented in the order specified. The columns must be as described below, in the order described below:

`ctrl` - a vector of control codes. Available codes are: 0 = normal trial; 1 = reset model (i.e. set associative strengths (weights) back to their initial values as specified in w (see above)); 2 = Freeze learning. Control codes are actioned before the trial is processed.

`opt1, opt2, ...` - any number of preferred optional columns, the names of which can be chosen by the user. It is important that these columns are placed after the control column, and before the remaining columns (see below). These optional columns are ignored by the slpRW function, but you may wish to use them for readability. For example, you might choose to include columns such as block number, trial number and condition. The argument colskip (see above) must be set to the number of optional columns plus one.

`x1, x2, ...` - activation of any number of input elements. There must be one column for each input element. Each row is one trial. In simple applications, one element is used for each stimulus (e.g. a simulation of blocking (Kamin, 1969), A+, AX+, would have two inputs, one for A and one for X). In simple applications, all present elements have an activation of 1 and all absence elements have an activation of 0. However, slpRW supports any real number for activations, e.g. one might use values between 0 and 1 to represent differing cue saliences.

`t` - Teaching signal (a.k.a. lambda). Traditionally, 1 is used to represent the presence of the outcome, and 0 is used to represent the absence of the outcome, although slpRW suports any real values for lambda.

Argument `xtdo` (eXTenDed Output) - if set to TRUE, function will return associative strength for the end of each trial (see Value).

### Value

Returns a list containing two components (if xtdo = FALSE) or three components (if xtdo = TRUE, xout is also returned):

 `suma` Vector of output activations for each trial `st` Vector of final associative strengths `xout` Matrix of associative strengths at the end of each trial

### Author(s)

Stuart Spicer, Lenard Dome, Andy Wills

### References

Kamin, L.J., (1969) Predictability, surprise, attention and conditioning. In Campbell, B.A. & Church, R.M. (eds.), Punishment and Aversive Behaviour. New York: Appleton-Century-Crofts, 1969, pp.279-296.

Rescorla, R. A., & Wagner, A. R. (1972). A theory of Pavlovian conditioning: Variations in the effectiveness of reinforcement and nonreinforcement. In A. H. Black & W. F. Prokasy (Eds.), Classical conditioning II: Current research and theory (pp. 64-99). New York: Appleton-Century-Crofts.

Spicer, S.G., 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.

Wills, A.J., O'Connell, G., Edmunds, C.E.R., & Inkster, A.B.(2017). Progress in modeling through distributed collaboration: Concepts, tools, and category-learning examples. Psychology of Learning and Motivation, 66, 79-115.

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