alpha_v¶
Specifies the learning rate for v-values (the associative strength between stimulus element - behavior pairs), used in the updating of the v-values. See The mechanisms.
The parameter alpha_v
can be set to certain values for certain particular element-behavior pairs.
It can also be specified as a single value, in which case the same learning rate is used for all
element-behavior pairs. Wildcards (*) may be used to set several values at once.
Syntax¶
alpha_v = e1->b1: v1, e1->b2: v2, ..., en->bn: vn, default: d
alpha_v = v1
alpha_v = e1->*: v1, default: d
alpha_v = *->b1: v1, default: d
alpha_v = *->*: v1
where v1,v2,...,vn
and d
are scalar expressions.
Description¶
alpha_v = e1->b1: v1, e1->b2: v2, ..., en->bn: vn, default: d
sets the learning rate for
v(e1->b1) to v1,
v(e1->b2) to v2, …,
v(en->bn) to vn,
and the learning rate for all other v-values to d.
The specification is independent of the list order:
alpha_v = e1->b1:v1, e1->b2:v2, default:d
is the same as
alpha_v = e1->b2:v2, default:d, e1->b1:v1
.default
need not be specified if all possible combinationselement->behavior
are present in the list. For example,stimulus_elements = e1, e2 behaviors = b1, b2 alpha_v = e1->b1:v11, e1->b2:v12, e2->b1:v21, e2->b2:v22
alpha_v = v1
sets the learning rate for all v-values to v1.
alpha_v = v1
is the same asalpha_v = default: v1
.
Dependencies¶
The properties
stimulus_elements
andbehaviors
must be specified beforealpha_v
.Each stimulus element used in the specification of
alpha_v
must be present in the parameterstimulus_elements
.Each behavior used in the specification of
alpha_v
must be present in the parameterbehaviors
.Applicable in all mechanisms except the Rescorla-Wagner mechanism. (Use
alpha_vss
in Rescorla-Wagner.)
Examples¶
@variables x = 1
alpha_v = element1->behavior1: x, element1->behavior2: x+1, default:x+2
sets the learning rate for v(element1->behavior1) to 1 and for v(element1->behavior1) to 2, and for the remaining possible element-behavior pairs to 3.
alpha_v = element1->behavior1: 0.5, default:0
sets the learning rate for v(element1->behavior1) to 0.5, and for the remaining possible element-behavior pairs to 3.
alpha_v = 0.1
sets the learning rate for all v-values to 0.1.