Description Usage Arguments Details Value Author(s) References
Supervised and Unsupervised STratified Adaptive Incremental Network (Love, Medin & Gureckis, 2004)
1 | slpSUSTAIN(st, tr, xtdo = FALSE)
|
st |
List of model parameters |
tr |
Matrix of training items |
xtdo |
Boolean specifying whether to include extended information in the output (see below) |
This function works as a stateful list processor (slp; see Wills et al., 2017). It takes a matrix (tr) as an argument, where each row represents a single training trial, while each column represents some information required by the model, such as the stimulus representation, indications of supervised/unsupervised learning, etc (details below).
Argument st
must be a list containing the following items:
r
- Attentional focus parameter
beta
- Cluster competition parameter
d
- Decision consistency parameter
eta
- Learning rate parameter
tau
- Threshold parameter for cluster recruitment under
unsupervised learning conditions (Love et al., 2004, Eq. 11). If every
trial is a supervised learning trial, set tau to 0. slpSUSTAIN can
accomodate both supervised and unsupervised learning within the same
simulation, using the ctrl
column in tr
(see below).
lambda
- Vector containing the initial receptive field tuning
value for each stimulus dimension; the order corresponds to the order
of dimensions in tr
, below. For a stimulus with three
dimensions, where all receptive fields are equally tuned, lambda = [1,
1, 1].
cluster
- A matrix of the initial positions of each recruited
cluster. If set to NA then, each time the network is reset, a single
cluster will be created, centered on the stimulus presented on the
current trial.
w
- A matrix of initial connection weights. If set to NA then,
each time the network is reset, zero-strength weights to a single
cluster will be created.
dims
- Vector containing the length of each dimension
(excluding category dimension, see tr
, below), i.e the number
of nominal spaces in the representation of each dimension. For Figure
1 of Love et al. (2004), dims = [2, 2, 2].
colskip
- Number of optional columns skipped in tr
,
PLUS ONE. So, if there are no optional columns, set colskip to 1.
Argument tr
must be a matrix, where each row is one trial
presented to the model. Columns are always presented in the order
specified below:
ctrl
- A vector of control codes. The control codes are
processed prior to the trial and prior to updating cluster's position,
lambdas and weights (Love et al., 2004, Equ. 12, 13 and 14,
respectively). The available values are:
0 = do supervised learning.
1 = reset network and then do supervised learning.
2 = freeze supervised learning.
3 = do unsupervised learning.
4 = reset network and then do unsupervised learning.
5 = freeze unsupervised learning
'Reset network' means revert w
, cluster
,and
lambda
back to the values passed in st
.
Unsupervised learning in slpSUSTAIN
is at an early stage of
testing, as we have not yet established any CIRP for unsupervised
learning.
opt1, opt2, ...
- optional columns, which may have any names
you wish, and you may have as many as you like, but they must be
placed after the ctrl column, and before the remaining columns (see
below). These optional columns are ignored by this function, but you
may wish to use them for readability. For example, you might include
columns for block number, trial number, and stimulus ID number.
x1, x2, y1, y2, y3, ...
- Stimulus representation. The
columns represent the kth nominal value for ith dimension. It's a
'padded' way to represent stimulus dimensions and category membership
(as category membership in supervised learning is treated as an
additional dimension) with varying nominal length, see McDonnell &
Gureckis (2011), Fig. 10.2A. All dimensions for the trial are
represented in this single row. For example, if for the presented
stimulus, dimension 1 is [0 1] and dimension 2 is [0 1 0] with
category membership [0 1], then the input representation is [0 1 0 1 0
0 1].
t
- Indicator of supervised vs. unsupervised learning. The
values can be either 1 = supervised learning or 0 = unsupervised
learning. It can also vary trial by trial, within the same simulation.
Returns a list with the following items if xtdo = FALSE
:
probs |
Matrix of probabilities of making each response within
the queried dimension (e.g. column 1 = category A; column 2 = category
B), see Love et al. (2004, Eq. 8). Each row is a single trial and
columns are in the order presented in |
lambda |
Vector of the receptive field tunings for each stimulus
dimension, after the final trial. The order of dimensions corresponds
to the order they are presented in |
w |
Matrix of connection weights, after the final
trial. Each row is a separate cluster, reported in order of
recruitment (first row is the first cluster to be recruited). The
columns correspond to the columns on the input representation
presented (see |
cluster |
Matrix of recruited clusters, with their positions in
stimulus space. Each row is a separate cluster, reported in order of
recruitment. The columns correspond to the columns on the input
representation presented (see |
If xtdo = TRUE
, xtdo
is returned instead of
probs
:
xtdo |
A matrix that includes |
Lenard Dome, Andy Wills
Love, B. C., & Gureckis, T.M. (2007). Models in Search of a Brain. Cognitive, Affective, & Behavioral Neuroscience, 7, 90-108.
Love, B. C., Medin, D. L., & Gureckis, T. M. (2004). SUSTAIN: a network model of category learning. Psychological Review, 111, 309-332.
McDonnell, J. V., & Gureckis, T. M. (2011). Adaptive clustering models of categorization. In E. M. Pothos & A. J. Wills (Eds.), Formal Approaches in Categorization, pp. 220-252.
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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