memnet (version 0.1.0)

fluency_steps: Verbal fluency step counter

Description

Repeatedly generates verbal fluency data using one_fluency_steps and counts the number of steps required to produce n unique responses.

Usage

fluency_steps(adjlist, n, pjump = 0, type = 0L)

Arguments

adjlist

a list containing row indices of nodes adjacent node to the ith node as created by get_adjlist.

n

integer vector specifying the numbers of production.

pjump

numeric specifying the probability of a jump.

type

integer controlling network start and jump nodes. For type = 0 the process selects the start node and any jump nodes proportional to their degree. For type = 1 the process selects a random node to serve both as the start node and the jump node. For type = 2 the process selects the start and any jump nodes uniformly at random.

Value

List of character vectors containing the indices of the fluency productions. Indices refer to the row of the item in the original adjacency matrix. See get_adjlist.

Details

For details see one_fluency_steps.

References

Wulff, D. U., Hills, T., & Mata, R. (2018, October 29). Structural differences in the semantic networks of younger and older adults. https://doi.org/10.31234/osf.io/s73dp

Goni, J., Martincorena, I., Corominas-Murtra, B., Arrondo, G., Ardanza- Trevijano, S., & Villoslada, P. (2010). Switcher-random-walks: A cognitive- inspired mechanism for network exploration. International Journal of Bifurcation and Chaos, 20(03), 913-922.

Examples

# NOT RUN {
# generate watts strogatz graph
network = grow_ws(n = 100, k = 10)

# count number of steps needed to create sequence
fluency_steps(get_adjlist(network), c(10, 10))

# count number of steps needed to create sequence
# with high jump probability
fluency_steps(get_adjlist(network), c(10, 10), pjump = .5)

# }