Table 2.

A summary of the assumptions and requirements for each of the five different structure analysis models suggested in the review.

Model type	Embedding type	Data requirements	Typical hypotheses	Assumptions
Markov chain	Repetition Diversity Ordering	Number of observations required increases greatly as the size of the model grows	Independence of sequence Sequential structure	Stationary transition matrix Sufficient data for maximum likelihood estimator of transition matrix
Hidden Markov model	Repetition Diversity Ordering	Number of observations required increases greatly as the size of the model grows	Non-stationary transitions of observable states Long-range correlations Existence of cognitive states	Sufficient data to estimate hidden states
Network	Combination Ordering	Many unit types	Network metrics have biological meaning Comparison of motifs	The properties of relations between units are meaningful
Formal grammar	Repetition Diversity Ordering	Few requirements	Linguistic hypotheses Deterministic sequences Place in Chomsky hierarchy	Deterministic transition rules
Temporal structure	Overlapping Timing	Timing information exists No need to define units	Production/perception mechanisms Changes with time/effect	Temporal variations are perceived by receiver