New multinomial models for the Chechile–Meyer task

Recognition memory models and binary-response ROCs: A comparison by minimum description length

“…The 2HTM assumes that old/new judgments reflect a mixture of responses made in "memory"/"detection" states and in a "guessing" state in which information on the status of the item is not available. Despite the likely oversimplification and misconception of the underlying cognitive processes (Kinchla, 1994), this particular model and its extensions maintain a mathematical tractability that makes them quite useful in several implementations (see Batchelder, Riefer, & Hu, 1994) and extensions (e.g., Chechile, 2004). Three distinct parameter restrictions are considered here for the 2HTM: is included given that it (1) reflects the pattern of results usually found in the literature (e.g., , and (2) reflects the notion that D n captures distractor rejection processes that are, in part, conditional on memory for studied items as captured by D o (e.g., Rotello & Heit, 2000;Strack & Bless, 1994).…”

Analysis of multinomial models under inequality constraints: Applications to measurement theory

“…The current article can be considered as the ''frequentist'' companion to this methodology. Chechile (1998Chechile ( , 2004 presented a population parameter mapping (PPM) methodology that analyzes multinomial models under a variety of constraints. The PPM methodology circumvents many of the traditional obstacles found in the frequentist analysis through the use of Bayesian assumptions, and has been successfully applied to multinomial processing tree models within the context of cognitive storage and retrieval systems (Chechile, 2004).…”

“…Chechile (1998Chechile ( , 2004 presented a population parameter mapping (PPM) methodology that analyzes multinomial models under a variety of constraints. The PPM methodology circumvents many of the traditional obstacles found in the frequentist analysis through the use of Bayesian assumptions, and has been successfully applied to multinomial processing tree models within the context of cognitive storage and retrieval systems (Chechile, 2004). Other Bayesian approaches include constrained latent class analysis (Hoijtink, 1998) and inequality constrained analysis of variance (Klugkist, Laudy, & Hoijtink, 2005).…”

Minimum description length model selection of multinomial processing tree models

“…Multinomial processing tree (MPT) modeling is a statistical methodology for measuring latent cognitive capacities in selected experimental paradigms (Batchelder & Riefer, 1986, 1990Chechile, 2004;Erdfelder et al, 2009;Riefer & Batchelder, 1988, 1991, 1995Riefer, Hu, & Batchelder, 1994). The data structure requires that participants performing a cognitive task make categorical responses to a series of test items.…”