Supervised and Unsupervised STratified Adaptive Incremental Network (Love, Medin & Gureckis, 2004)
slpSUSTAIN(st, tr, xtdo = FALSE, ties = "random")List of model parameters
Matrix of training items
Boolean specifying whether to include extended information in the output (see below)
Model behaviour where multiple clusters have the same
highest activations. Options are: random, first, see
below.
Returns a list with the following items if xtdo = FALSE:
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 tr, see below. In the
case of unsupervised learning, probabilities are calculated for all
dimensions (as there is no queried dimension for unsupervised
learning).
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 tr, see below.
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 tr description, below).
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 tr description, below).
If xtdo = TRUE, xtdo is returned instead of probs:
A matrix that includes probs, and also includes the
number of the winning cluster and its output activation after cluster
competition, Eq. 6 in Love et al. (2004). The last column contains the
recognition scores for the current stimulus from Eq. A6 in Love and
Gureckis (2007); this measure represents the model's overall familiarity
with the stimulus.
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, always non-negative.
beta - Cluster competition parameter, always non-negative.
d - Decision consistency parameter, always non-negative.
eta - Learning rate parameter, see Note 1.
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, cluster = 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 as
w = 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].
maxcat - optional. If set, maxcat is an integer specifying the
maximum number of clusters to be recruited during unsupervised
learning. A similar restriction has been used by Love et al. (2004) to
simulate an unsupervised free-sorting task from Experiment 1 in Medin,
Wattenmaker, & Hampson (1987). In this experiment, participants needed
to sort items into two predefined categories. This parameter will only
be used during unsupervised learning. If it is not set, or if it is
set to 0, there is no maximum to the number of clusters that can be
created.
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, Eq. 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].
Argument ties can be either random or first. It specifies
how the model behaves in the event, when there are multiple winning clusters
with the same activations (see Note):
random - The model randomly selects one cluster from the ones
that have the same activations. To increase the reproducibility of
your simulation, set a specific random seed seed before calling
slpSUSTAIN (use e.g.set.seed).
first - The model selects the cluster that was first recruited
from the clusters that have the same activations. Up to and including
version 0.7.1 of catlearn, this was the default behaviour of
slpSUSTAIN.
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.
Medin, D. L., Wattenmaker, W. D., & Hampson, S. E. (1987). Family resemblance, conceptual cohesiveness, and category construction. Cognitive Psychology, 19(2), 242-279.
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.