scholarly journals A Multinomial Processing Tree Model of RC Attachment

2021 ◽  
Author(s):  
Pavel Logacev ◽  
Noyan Dokudan
Keyword(s):  
Tree Model ◽  
Multinomial Processing Tree ◽  
Multinomial Processing Tree Model

scholarly journals Bayesian Inference for Psychology, Part III: Parameter Estimation in Nonstandard Models

2017 ◽  
Author(s):  
Dora Matzke ◽  
Udo Boehm ◽  
Joachim Vandekerckhove
Keyword(s):  
Parameter Estimation ◽  
Broad Class ◽  
Worked Examples ◽  
Psychological Research ◽  
Tree Model ◽  
Multinomial Processing Tree ◽  
Software Packages ◽  
Multinomial Processing Tree Model ◽  
Nonstandard Models ◽  
Memory Experiment

We demonstrate the use of three popular Bayesian software packages that enable researchers to estimate parameters in a broad class of models that are commonly used in psychological research. We focus on WinBUGS, JAGS, and Stan, and show how they can be interfaced from R and MATLAB. We illustrate the use of the packages through two fully worked examples; the examples involve a simple univariate linear regression and fitting a multinomial processing tree model to data from a classic false-memory experiment. We conclude with a comparison of the strengths and weaknesses of the packages. Our example code, data, and this text are available via https://osf.io/ucmaz/.

Resource Availability and Explicit Memory Largely Determine Evaluative Conditioning Effects in a Paradigm Claimed to be Conducive to Implicit Attitude Acquisition

2017 ◽  
Vol 8 (7) ◽  
pp. 758-767 ◽  
Author(s):  
Adrien Mierop ◽  
Mandy Hütter ◽  
Olivier Corneille
Keyword(s):  
Implicit Memory ◽  
Explicit Memory ◽  
Secondary Task ◽  
Resource Depletion ◽  
Evaluative Conditioning ◽  
Explicit Learning ◽  
Tree Model ◽  
Implicit Processes ◽  
Multinomial Processing Tree ◽  
Multinomial Processing Tree Model

In three experiments, we investigated how preventing the explicit encoding of conditioned stimulus–unconditioned stimulus (CS–US) pairings by imposing a secondary task at learning influences evaluative conditioning (EC) effects in a paradigm claimed to be conducive to implicit EC. We additionally used a multinomial processing tree model to examine how the resource depletion manipulation affects explicit and implicit memory contributions to EC. In all experiments, the EC effect largely vanished when a secondary task was employed that severely reduced participants’ explicit memory for the CS–US pairings. Furthermore, no evidence obtained for an implicit memory contribution to EC effects. In conclusion, the present research yields evidence for explicit learning, but no support for the contribution of implicit processes to EC in a paradigm claimed to facilitate implicit EC.

Process Dissociation Procedure: Double Dissociations following Divided Attention and Speeded Responding

1997 ◽  
Vol 50 (2) ◽  
pp. 318-336 ◽  
Author(s):  
M. Doris Dehn ◽  
Johannes Engelkamp
Keyword(s):  
Recognition Task ◽  
Process Dissociation ◽  
Tree Model ◽  
Memory Processes ◽  
Multinomial Processing Tree ◽  
Process Dissociation Procedure ◽  
Multinomial Processing Tree Model ◽  
Automatic Memory Processes ◽  
Automatic Memory ◽  
Double Dissociations

The validity of the process dissociation procedure was examined by manipulating attention and speed of responding in a recognition task. Both manipulations were expected to decrease the probability of controlled memory processes, c, while leaving the probability of automatic memory processes, a, unaffected. In order to estimate c and a, a multinomial processing tree model was fitted to the data. Contrary to expectation, a double dissociation (i.e.a decrease inc coupled with an increase in a) was obtained, suggesting that a does not accurately measure the probability of automatic processes. The results are discussed with reference to the independence assumption in the process dissociation procedure.

scholarly journals An Examination of the Processes by Which Construal Level Affects the Implicit Evaluation of Goal Relevant Stimuli

2018 ◽  
Author(s):  
Heather Rees
Keyword(s):  
Cognitive Process ◽  
Construal Level ◽  
Short Term ◽  
Tree Model ◽  
Low Level ◽  
Multinomial Processing Tree ◽  
Multinomial Processing Tree Model ◽  
High Level

Previous research has found that construal level—how abstractly or concretely people represent events—can impact implicit evaluations. Abstract high-level construal (vs. concrete low-level construal) promotes evaluative responses consistent with global (strongly held, long-term) rather than local (short-term, situational) goals. It remains unclear by what cognitive process(es) this occurs. In this paper, we examine two possibilities. High-level construal might enhance the unintended influence of activated evaluative associations or facilitate the detection and implementation of intentional responses. To examine these possibilities, the current study applies a multinomial processing tree model to data from Fujita and Han (2009). Results suggest that high-level construal facilitates goal-consistent evaluations by increasing both the unintentional influence of activated goal-consistent positive associations and the intentional detection of and implementation of accurate responding to goal-relevant stimuli. These findings extend our understanding of how construal level promotes goal consistent evaluations.

scholarly journals A Multinomial Processing Tree Model of RC Attachment

2021 ◽  
Author(s):  
Pavel Logacev ◽  
Noyan Dokudan
Keyword(s):  
Sentence Processing ◽  
Nuisance Parameters ◽  
Ambiguous Sentence ◽  
Analysis Method ◽  
Tree Model ◽  
Multinomial Processing Tree ◽  
Multinomial Processing Tree Model ◽  
Tree Models ◽  
Comparison Of The Results ◽  
Insight Into

In the field of sentence processing, speakers’ preferred interpretation of ambiguous sentences is often determined using a variant of a discrete choice task, in which participants are asked to indicate their preferred meaning of an ambiguous sentence. We discuss participants’ degree of attentiveness as a potential source of bias and variability in such tasks.We show that it may distort the estimates of the preference of a particular interpretation obtained in such experiments and may thus complicate the interpretation of the results as well as the comparison of the results of several experiments. We propose an analysis method based on multinomial processing tree models (Batchelder and Riefer, 1999) which can correct for this bias and allows for a separation of parameters of theoretical importance from nuisance parameters. We test two variants of the MPT-based model on experimental data from English and Turkish and demonstrate that our method can provide deeper insight into the processes underlying participants’ answering behavior and their interpretation preferences than analyses based on raw percentages.

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