Description Usage Arguments Details Value Note Author(s) References Examples

Nosofsky's (1984, 2011) Generalized Context Model; an exemplar-based model of categorization.

1 | ```
stsimGCM(st)
``` |

`st` |
List of model parameters |

Argument `st`

must be a list containing the following required
items: `training_items`

, `tr`

, `nCats`

, `nFeat`

,
`sensitivity`

, `weights`

, `choice_bias`

, `p`

,
`r_metric`

, `mp`

, and `gamma`

`nCats`

- integer indicating the number of categories

`nFeat`

- integer indicating the number of stimulus dimensions

`tr`

- the stimuli presented to the model, for which the choice
probabilities will be predicted. `tr`

has to be a matrix or
dataframe with one row for each stimulus. `tr`

requires the
following columns.

`x1, x2, ...`

- columns for each dimension carrying the
corresponding values (have to be coded as numeric values) for each
exemplar (trial) given in the row. Columns have to start with
`x1`

ascending with dimensions `..., x2, x3, ...`

at
adjacent columns.

`tr`

may have any number of additional columns with any desired
name and position, e.g. for readability. As long as the feature columns
`x1, x2, ...`

are given as defined (i.e. not scattered, across
the range of matrix columns), the output is not affected by optional
columns.

`training_items`

- all unique exemplars assumed to be stored in
memory; has to be a matrix or dataframe with one row for each exemplar.
The rownames have to start with 1 in ascending order.
`training_items`

requires the following columns:

`x1, x2, ...`

- columns for each feature dimension carrying
the corresponding values (have to be coded as numeric values) for each
exemplar (row). Columns have to start with `x1`

ascending with
dimensions `..., x2, x3, ...`

at adjacent columns.

`cat1, cat2, ...`

- columns that indicate the category
assignment of each exemplar (row). For example, if the exemplar in row
2 belongs to category 1 the corresponding cell of `cat1`

has to
be set to `1`

, else `0`

. Columns have to start with
`cat1`

ascending with categories `..., cat2, cat3, ...`

at adjacent columns.

`mem`

- (optional) one column that indicates whether an exemplar
receives an extra memory weight, yes = `1`

, no = `0`

.
For each exemplar (row) in the `training_items`

with `mem`

set to `0`

the corresponding memory strength parameter is set to 1.
When `mem`

for an exemplar is set to `1`

the memory strength
parameter is set as defined in `mp`

, see below.

`training_items`

may have any number of additional columns with any
desired name and position, e.g. for readability. As long as the feature
columns `x1, x2, ...`

and `cat1, cat2, ...`

are given as
defined (i.e. not scattered, across the range of matrix columns), the
output is not affected by optional columns.

NOTE: The current model can be implemented as a prototype model if
the `training_items`

only carry one row for each category representing
the values of the corresponding prototypes (e.g. see Minda & Smith, (2011).

`mp`

- memory strength parameter (optional). Can take any numeric
value between -Inf and +Inf. The default is 1, i.e. all exemplars have
the same memory strength. There are two ways of specifying `mp`

,
i.e. either *globally* or *exemplar specific*:

When *globally* setting `mp`

to a single integer,
e.g. to 5, then all exemplars in `training_items`

with `mem`

= 1 will receive a memory strength 5 times higher than the memory strengths
for the remaining exemplars.

For setting *exemplar specific* memory strengths `mp`

has to be
a vector of length n, where n is the overall number of of exemplars with
`mem`

= 1 in the `training_items`

. The order of memory strengths
defined in this vector exactly follows their row-wise ascending order of
appearence in the `training_items`

. E.g. if there are two exemplars
with `mem`

= 1 in the `training_items`

, the first one in row 2
and the second one in row 10, then setting `mp`

to c(3,2) will result
in assigning a memory strength of 3 to the first exemplar (in row 2) and
a memory strength of 2 to the second exemplar (in row 10). The memory
strengths for all other exemplars will be set to 1. See Note 1.

`sensitivity`

- sensitivity parameter c; can take any value
between 0 (all exemplars are equally similar) and +infinity
(towards being insensitive to large differences). There are two ways
of specifying `sensitivity`

, i.e. either *globally* or
*exemplar specific*: When *globally* setting `sensitivity`

to a single value, e.g. `sensitivity`

=3, then the same parameter is
applied to all exemplars. On the other hand, *exemplar specific*
sensitivity parameters can be used by defining `sensitivity`

as
a vector of length n, where n is the number of rows in
`training_items`

. The `sensitivity`

vector values then represent
the sensitivity parameters for all exemplars in `training_items`

at
the corresponding row positions. E.g. if there are 3 exemplars (rows) in
`training_items`

, then setting `sensitivity`

to `c(1,1,3)`

assigns `sensitivity`

= 1 to the first two exemplars, and
`sensitivity`

= 3 for the third exemplar. See Note 2.

`weights`

- dimensional attention weights. Order corresponds
to the definitions of `x1, x2, ...`

in `tr`

and
`training_items`

. Has to be a vector with length n-1 , where n
equals to `nFeat`

dimension weights, e.g. of length 2 when
there are three features, leaving out the *last* dimension. A
constraint in the GCM is that all attentional weights sum to 1. Thus,
the sum of n-1 weights should be equal to or smaller than 1, too. The
last n-th weight then is computed within the model with: 1 - (sum of
n-1 feature weights). When setting the weights to 1/`nFeat`

=
equal weights. See Note 3.

`choice_bias`

- Category choice biases. Has to be a vector with
length n-1, where n equals to `nCats`

category biases, leaving out
the last category bias, under the constraint that all biases sum to 1.
Order corresponds to the definitions of `cat1, cat2`

in the
`training_items`

. The sum of n-1 choice biases has to be equal
to or smaller than 1. Setting the weights to 1/`nCats`

= no
choice bias. The bias for the last category then is computed in the
model with: 1 - (sum of `nCats`

-1 choice biases). See Note 3.

`gamma`

- decision constant/ response scaling. Can take any
value between 0 (towards more probabilistic) and +infinity (towards
deterministic choices). Nosofsky (2011) suggests setting gamma higher
than 1 when individual participants' data are considered. See Note 2.

`r_metric`

- distance metric. Set to 1 (city-block) or 2
(Euclidean). See Nosofsky (2011), and Note 4, for more details.

`p`

- similarity gradient. Set to 1 (exponential) or 2 (Gaussian).
See Nosofsky (2011), for more details.

A matrix of probabilities for category responses (columns) for each
stimulus (rows) presented to the model (e.g. test trials). Stimuli
and categories are in the same order as presented to the model in
`st`

, see below.

1. Please note that setting mp = 1 or e.g. mp = 5 globally, will yield identical response probabilities. Crucially, memory strength is indifferent from the category choice bias parameter, if (and only if) mp's vary between categories, without varying within categories. Thus, the memory strength parameter can therefore be interpreted in terms of an exemplar choice bias (potentially related to categorization accuracy). In addition, if exemplar specific mp's are assigned during parameter fitting, one might want to calculate the natural log of the corresponding estimates, enabling direct comparisons between mp's indicating different directions, e.g. -log(.5) = log(2), for loss and gain, respectively, which are equal regarding their extent into different directions.

2. Theoretically, increasing global sensitivity indicates that categorization mainly relies on the most similar exemplars, usually making choices less probabilistic. Thus sensitivity c is likely to be correlated with gamma. See Navarro (2007) for a detailed discussion. However, it is possible to assume exemplar specific sensitivities, or specificity. Then, exemplars with lower sensitivity parameters will have a stronger impact on stimulus similarity and thus categorization behavior for stimuli. See Rodrigues & Murre (2007) for a related study.

3. Setting only the n-1 instead of all n feature weights (or bias parameters) eases model fitting procedures, in which the last weight always is a linear combination of the n-1 weights.

4. See Tversky & Gati (1982) for further info on r. In brief summary, r=2 (usually termed Euclidean), then a large difference on only one feature outweighs small differences on all features. In contrast, if r=1 (usually termed City-Block or Manhattan distance) both aspects contribute to an equal extent to the distance. Thus, r = 2 comes with the assumption that small differences in all features may be less recognized, than a large noticable differences on one feature, which may be depend on confusability of the stimuli or on the nature of the given task domain (perceptual or abstract).

Rene Schlegelmilch, Andy Wills

Minda, J. P., & Smith, J. D. (2011). Prototype models of categorization: Basic formulation, predictions, and limitations. Formal approaches in categorization, 40-64.

Navarro, D. J. (2007). On the interaction between exemplar-based concepts and a response scaling process. Journal of Mathematical Psychology, 51(2), 85-98.

Nosofsky, R. M. (1984). Choice, similarity, and the context theory
of classification. *Journal of Experimental Psychology:
Learning, memory, and cognition, 10*(1), 104.

Nosofsky, R. M. (2011). The generalized context model: An exemplar
model of classification. In Pothos, E.M. & Wills, A.J. *Formal
approaches in categorization*. Cambridge University Press.

Rodrigues, P. M., & Murre, J. M. (2007). Rules-plus-exception tasks:
A problem for exemplar models?. *Psychonomic Bulletin & Review,
14*(4), 640-646.

Tversky, A., & Gati, I. (1982). Similarity, separability, and the
triangle inequality. *Psychological review, 89*(2), 123.

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## Three Categories with 2 Training Items each, and repeatedly presented
## transfer/test items (from nosof94train()). Each item has three
## features with two (binary) values: memory strength (st$mp and
## 'mem' column in st$training_items are optional) is
## equal for all exemplars
st<-list(
sensitivity = 3,
weights = c(.2,.3),
choice_bias = c(1/3),
gamma = 1,
mp = 1,
r_metric = 1,
p = 1,
nCats = 2,
nFeat=3
)
## training item definitions
st$training_items <- as.data.frame(
t(matrix(cbind(c(1,0,1,1,1,0,0),c(1,1,0,2,1,0,0),
c(0,1,0,5,0,1,0),c(0,0,1,1,0,1,0)),
ncol=4, nrow=7,
dimnames=list(c("stim","x1", "x2", "x3",
"cat1", "cat2", "mem"),
c(1:4)))))
st$tr <- nosof94train()
## get the resulting predictions for the test items
## columns of the output correspond to category numbers as defined
## above rows correspond to the column indices of the test_items
stsimGCM(st)
## columns of the output correspond to category numbers as defined
## above rows correspond to the column indices of the test_items
## Example 2
## Same (settings) as above, except: memory strength is 5 times higher
## for for some exemplars
st$mp<-5
## which exemplars?
## training item definitions
st$training_items <- as.data.frame(
t(matrix(cbind(c(1,0,1,1,1,0,1),c(1,1,0,2,1,0,0),
c(0,1,0,5,0,1,0),c(0,0,1,1,0,1,1)),
ncol=4, nrow=7,
dimnames=list(c("stim","x1", "x2", "x3",
"cat1", "cat2", "mem"),
c(1:4)))))
## exemplars in row 1 and 4 will receive a memory strength of 5
## get predictions
stsimGCM(st)
## Example 3
## Same (settings) as above, except: memory strength is item specific
## for the two exemplars i.e. memory strength boost is not the same
## for both exemplars (3 for the first in row 1, and 5 for the
## second exemplar in row 4)
st$mp<-c(3,5)
## get predictions
stsimGCM(st)
``` |

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