memnet (version 0.1.0)

one_fluency_steps: Verbal fluency step counter

Description

Generates verbal fluency data using a switcher-random walk process and counts the number of steps required to produce n unique responses.

Usage

one_fluency_steps(adj_list, n, pjump = 0, type = 0L)

Arguments

adj_list

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

n

integer specifying the number of productions.

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

Integer vector containing the indices of the fluency productions. Indices refer to the row of the item in the original adjacency matrix. See get_adjlist.

Details

Function produces verbal fluency data via a switcher random walk process that traverses the network by selecting a neighbor with probability 1-pjump or jumps to a random place in the network with probability pjump. Where the random walk process enters the network and where it jumps to is further controlled by type. Neighbors are always selected uniformly.

In contrast to fluency and ffluency, returns the number of steps required to produce a sequence of unique productions, rather than the productions itself.

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
one_fluency_steps(get_adjlist(network), 10)

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

# }