fluency_steps: Verbal fluency step counter

In memnet: Network Tools for Memory Research

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.

Details

For details see one_fluency_steps.

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.

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

# 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)

memnet documentation built on May 2, 2019, 9:35 a.m.