Multinomial Processing Tree Research Articles

Dissociating Automatic Associations: Comparing Two Implicit Measurements of Race Bias.

Weak correspondence across different implicit bias tasks may arise from the contribution of unique forms of automatic and controlled processes to response behavior. Here, we examined the correspondence between estimates of automatic and controlled processing derived from two sequential priming tasks with identical structure and timing designed to separately measure stereotypic (Weapons Identification Task; WIT) and evaluative (Affective Priming Task; APT) associations. Across two studies using predominantly White samples, three consistent patterns emerged in the data: (1) stereotypic bias was stronger for Black targets, whereas evaluative bias was stronger for White targets; (2) overall response accuracy bias correlated modestly across the two tasks; and (3) multinomial processing tree estimates of controlled processing corresponded much more strongly than estimates of automatic processing. These findings support models positing distinct learning and memory systems for different forms of race bias, and suggest that these differing forms contribute to estimates of automatic associations.

Is knowledge reliance in source guessing a cognitive trait? Examining stability across time and domain.

When people need to infer the source of information in the absence of memory, they may rely on general knowledge (e.g., stereotypes) to guess the source. Prior research documented task-related determinants and individual differences of stereotype reliance in source guessing, but little is known about the underling nature of this process. In two experiments, we tested whether a cognitive trait could account for the knowledge reliance in source guessing. Participants performed two distinct study-test cycles of a classical source-monitoring paradigm in which two person sources present stereotypical information that in a later test phase had to be attributed to its origin. In Experiment 1, both tasks used item material from the same knowledge domain (age stereotypes) and were either separated by 10 minutes or 7 days. In Experiment 2, we used item material from two different knowledge domains (Task 1: age stereotypes; Task 2: gender stereotypes). Although cross-task correlations of source-guessing parameters from Bayesian-hierarchical multinomial processing tree model analyses showed only weak positive correlations, absolute source guessing remained fairly stable within individuals across time (Experiment 1) and knowledge domains (Experiment 2). Considering statistical challenges of the assessment of relative stability via correlations, we suggest based on the stricter absolute stability criterion that source guessing rather encompasses trait-like features. We discuss implications regarding the generalizability and nature of source guessing in comparison to other cognitive processes involved in source attribution, which were highly stable in both experiments.

Revisiting the divide between deontology and utilitarianism in moral dilemma judgment: A multinomial modeling approach.

We investigate the psychological bases underlying moral dilemma judgment with the help of multinomial processing tree modeling, and consider how determinants of dilemma judgment should best be conceptualized. We argue that, for conceptual as well as empirical reasons, norms and consequences should be considered as more intimately linked with one another than their systematic juxtaposition in dilemma research may suggest, and propose that norm-endorsement should be viewed through a consequentialist lens as well. Investigating this proposal, we introduce a variant of the CNI model of moral dilemma judgment, which focuses on the use of proscriptive norms only. In five experiments, we validate this proCNI model and assess the parameters' sensitivity to different types of consequences and personal involvement. Our findings suggest that the parameter representing "norms" is sensitive to consequences as well, such that norms do not guide moral judgment unless they are expected to produce tangible consequences. Thus, present research suggests that the split between norms and "consequences" (or "deontology" and "utilitarianism") as determinants of judgment is artificial. We suggest that, ultimately, viewing dilemma judgments through a consequentialist lens may be a useful approach for advancing theoretical development in the field of dilemma research. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Testing Interactions in Multinomial Processing Tree Models.

Multinomial processing tree (MPT) models allow testing hypotheses on latent psychological processes that underlie human behavior. However, past applications of this model class have mainly been restricted to the analysis of main effects. In this paper, we adopt the interaction concept as defined in log-linear models and show why it is appropriate for MPT models. We then explain how to implement and test ordinal and disordinal two-way interaction hypotheses in MPT models. We also show how our method generalizes to higher-order interactions involving three or more factors. An empirical example from source memory and aging demonstrates the applicability of this method and allows for directly testing the associative deficit theory that age differences are larger in associative (e.g., source) memory as opposed to item memory. Throughout the paper, we explain how most analytic steps can be easily implemented in the freely available software multiTree.

Are there two independent evaluative conditioning effects in relational paradigms? Dissociating the effects of CS-US pairings and their meaning

ABSTRACTRecent research into evaluative conditioning (EC) shows that information about the relationship between the conditioned and unconditioned stimuli can exert strong effects on the size and direction of the EC effect. Additionally, the co-occurrence of these stimuli seems to exert an orthogonal effect on evaluations. This finding has been interpreted as support for two independent types of EC effects. However, previous research devoted to this question relied on aggregated evaluative measures, allowing for alternative interpretations. In four experiments, we developed and validated a multinomial processing tree model that distinguishes effects of the pairings from effects of the meaning of the pairings. Our findings suggest that two independent EC effects contribute to overall evaluative change in a relational EC paradigm. The model that we developed offers a helpful method for future research in that it allows for an assessment of the effects of manipulations on processes rather than overall performance on an evaluative measure.

Adaptive memory: enhanced source memory for animate entities

ABSTRACTThe animacy effect refers to enhanced memory for animate over inanimate items. In two studies, we examined whether this memory advantage generalises to source memory. A multinomial processing tree model was used to disentangle item recognition, source memory, and guessing processes. In Study 1, animate and inanimate words were presented at different spatial locations on the screen. Animacy was associated with enhanced source memory for the spatial locations of the items. In Study 2, pseudowords were associated with animate and inanimate properties. Replicating previous results, the pseudowords were better remembered when they were associated with animate properties than when they were associated with inanimate properties. What is more, participants had enhanced source memory for the association between the pseudowords and the animate properties. The results strengthen the idea that animate items are associated with richer mnemonic representations than inanimate items.

Tree Inference: Response time in Multinomial Processing Trees, representation and uniqueness of parameters

Multinomial Processing Trees are widely used in Psychology to model probabilities of responses in classes such as correct and incorrect. Some models include an additional measure such as response time, but predictions can become complex. Here we develop testable predictions using a method traditional for response times: manipulating experimental factors that selectively influence processes. In a Multinomial Processing Tree, each vertex represents a process, such as memory retrieval. An arc descending from a vertex represents a possible process outcome; for example, success or failure. Each arc has associated with it the probability the outcome it represents will occur. We assume that also associated with each arc is the time required for the outcome to occur. A factor that changes parameter values on arcs descending from a single vertex selectively influences that vertex. Suppose each of two factors selectively influences a different vertex in a Multinomial Processing Tree. There are only two ways the two vertices can be arranged. Either there is a path from one vertex to the other, and parameters on some arc on this path are changed by a factor, or there is not. Here we consider two Multinomial Processing Trees, important representatives of the two vertex arrangements. For each we develop testable necessary and sufficient conditions. Parameter values in the Multinomial Processing Trees may not be unique. If two sets of parameter values both lead to the same predictions, the values are related by admissible transformations, which we derive along with degrees of freedom.

False Memory for Orthographically Related Words: Research in the Simplified Conjoint Recognition Paradigm

Abstract The aim of this study was to define the processes underlying false recognition memory for orthographically related words by using the simplified conjoint recognition paradigm (Stahl & Klauer, 2009), which is grounded in the fuzzy trace theory. We considered 2 accounts. The first assumes that false memory stems from the perceptual feature overlap between targets and distractors represented in the gist memory parameter. The second account assumes that false memory for orthographically related distractors results from the misattribution of verbatim information, which is reflected in the phantom recollection parameter. Data from two experiments were analyzed with multinomial processing tree modeling to estimate the contribution of latent processes to performance in recognition memory tests. These analyses supported the feature overlap account, suggesting that gist trace encodes both semantic and orthographic patterns. We also showed that a boost in false memories due to the adding of semantic associates to a list of orthographically related words stems from an increase in phantom recollection. We discussed further directions in theoretical and empirical research on the gist and verbatim traces’ contribution to false recognition of perceptually related stimuli.

Age differences in hindsight bias: A meta-analysis.

After people have learned a fact or the outcome of an event, they often overestimate their ability to have known the correct answer beforehand. This hindsight bias has two sources: an impairment in direct recall of the original (i.e., uninformed) judgment after presentation of the correct answer (recollection bias) and a reconstruction of the original judgment that is biased toward the correct answer (reconstruction bias). Research on how cognitive aging affects these two sources of hindsight bias has produced mixed results. To synthesize the available findings, we conducted a meta-analysis of nine studies (N = 366 young, N = 368 older adults). We isolated the probabilities of recollection, recollection bias, and reconstruction bias with a Bayesian, three-level hierarchical implementation of the multinomial processing tree model of hindsight bias (Erdfelder & Buchner, 1998). Additionally, we quantified the magnitude of bias in the reconstructed judgment. Overall, older adults were less likely to recollect their original judgment than young adults, and thus had to reconstruct it more frequently. Importantly, whereas outcome knowledge impaired recollection of the original judgment (i.e., recollection bias) to a similar extent in both age groups, outcome knowledge was more likely to distort reconstruction of the original judgment (i.e., reconstruction bias) in older adults. In addition, the magnitude of bias in the reconstructed judgments was slightly larger in older than in young adults. Our results provide the basis for a targeted investigation of the mechanisms driving these age differences. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Adaptive memory: Source memory is positively associated with adaptive social decision making

The key insight behind the adaptive memory framework is that the primary function of remembering is not to help us to relive the past but to inform adaptive behavior in the future. However, the beneficial effects of memory on the individual’s fitness are often difficult to study empirically. In the case of social cooperation, it is comparatively easy to derive testable predictions about the relationship between specific types of memory (e.g., source memory) and specific types of adaptive decision making (e.g., direct reciprocity). In the present study, we examined both the participants’ behaviors in a Prisoner’s Dilemma game and their memory performance in a source-monitoring test. Participants showed evidence of adaptive decision making. Their willingness to cooperate was strongly determined by their partners’ behaviors in previous rounds. Individual parameter estimates of old-new recognition, source memory, and guessing were obtained via hierarchical multinomial processing tree modeling. Source memory was positively associated with adaptive decisions in the Prisoner’s Dilemma game. The better participants’ source memory, the more often they cooperated with cooperators and the less often they cooperated with cheaters. Guessing in the memory test, by contrast, was unrelated to cooperation. The results underline the importance of source memory in adaptive decision making.

Can self-referential information improve directed forgetting? Evidence from a multinomial processing tree model.

A large body of research has shown that self-referential processing can enhance an individual’s memory of information. However, there are many arguments about how self-referential processing affects directed forgetting (DF). In this study, two experiments were designed to investigate the DF effect and its internal psychological mechanism under explicit and implicit referential conditions using the item-method DF paradigm combined with the storage-retrieval MPT model. We compare the difference in the DF effect between self-referential and other-referential conditions and explain the reasons for the difference. Our results suggest that the item-method DF effect is the result of a selective rehearsal mechanism and a retrieval inhibition mechanism working together. Both self-reference and other-reference can cause DF in explicit referential processing or implicit referential processing, although the DF effect is stronger under the self-referential condition. Furthermore, the memory advantage effect of implicit self-referential processing is stronger than that of explicit self-referential processing.

Tree inference: Uniqueness of multinomial processing trees representing response time when two factors selectively influence processes

Multinomial Processing Trees are widely used to model response probability and sometimes to model response time and other measures. Information about the structure of a Multinomial Processing Tree can be discovered by manipulating experimental factors that selectively influence its vertices. A factor selectively influences a vertex if changing the level of the factor changes values of parameters associated with arcs descending from that vertex, leaving all else invariant. If two factors, each with a finite number of levels, selectively influence two different vertices in a Multinomial Processing Tree, an infinite number of Multinomial Processing Trees can be constructed that account for the response probability and response time. Under relatively unrestrictive assumptions, we show that all are equivalent, for these two factors, to one of two relatively simple Multinomial Processing Trees. We also show that if response probabilities and response times are produced by averaging over a mixture of different trees, the mixture is equivalent to one of the two relatively simple trees.

A Simple Method for Comparing Complex Models: Bayesian Model Comparison for Hierarchical Multinomial Processing Tree Models Using Warp-III Bridge Sampling

Multinomial processing trees (MPTs) are a popular class of cognitive models for categorical data. Typically, researchers compare several MPTs, each equipped with many parameters, especially when the models are implemented in a hierarchical framework. A Bayesian solution is to compute posterior model probabilities and Bayes factors. Both quantities, however, rely on the marginal likelihood, a high-dimensional integral that cannot be evaluated analytically. In this case study, we show how Warp-III bridge sampling can be used to compute the marginal likelihood for hierarchical MPTs. We illustrate the procedure with two published data sets and demonstrate how Warp-III facilitates Bayesian model averaging.

An examination of the processes by which construal level affects the implicit evaluation of goal relevant stimuli.

Author(s): Sherman, Jeffrey; Rees, Heather; Fujita, Kentaro; Han, H Anna; Sklar, Asael Y | Abstract: 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.

Tree inference: Selective influence in multinomial processing trees with supplementary measures such as response time

Multinomial Processing Trees are successful models of response probabilities for many phenomena. Empirical validation is often based on manipulating an experimental factor intended to selectively influence a process represented in a Multinomial Processing Tree, to see whether the factor indeed has an effect on and only on a parameter associated with that process. Response times are rarely included, but have great potential for increasing resolution. We consider Multinomial Processing Trees in which outcomes of processes represented by vertices occur with probabilities (as usual), and also take time. For response time itself, the method of selectively influencing processes is well developed. Established tests are based on response time means and distribution functions. We modify well established tests so they can be applied to Multinomial Processing Trees in which responses fall into two classes, say, correct and incorrect. The new tests are based on response time means and distribution functions, each multiplied by response probability. If two experimental factors selectively influence two different vertices in a two class Multinomial Processing Tree, the tree is equivalent to one of two simple trees. Patterns in response probabilities and times will indicate which of the two trees accounts for the data. In one of the two trees, the selectively influenced vertices are executed in order, in the other they are not. If there are more than two response classes, each class can be tested separately. If the patterns do not occur, no Multinomial Processing Tree exists in which the two experimental factors selectively influence two different vertices. We demonstrate the method with simulated data from a two factor experiment.

A New Model for Acquiescence at the Interface of Psychometrics and Cognitive Psychology

ABSTRACTWhen measuring psychological traits, one has to consider that respondents often show content-unrelated response behavior in answering questionnaires. To disentangle the target trait and two such response styles, extreme responding and midpoint responding, Böckenholt (2012a) developed an item response model based on a latent processing tree structure. We propose a theoretically motivated extension of this model to also measure acquiescence, the tendency to agree with both regular and reversed items. Substantively, our approach builds on multinomial processing tree (MPT) models that are used in cognitive psychology to disentangle qualitatively distinct processes. Accordingly, the new model for response styles assumes a mixture distribution of affirmative responses, which are either determined by the underlying target trait or by acquiescence. In order to estimate the model parameters, we rely on Bayesian hierarchical estimation of MPT models. In simulations, we show that the model provides unbiased estimates of response styles and the target trait, and we compare the new model and Böckenholt’s model in a recovery study. An empirical example from personality psychology is used for illustrative purposes.

Generalized Processing Tree Models: Jointly Modeling Discrete and Continuous Variables.

Multinomial processing tree models assume that discrete cognitive states determine observed response frequencies. Generalized processing tree (GPT) models extend this conceptual framework to continuous variables such as response times, process-tracing measures, or neurophysiological variables. GPT models assume finite-mixture distributions, with weights determined by a processing tree structure, and continuous components modeled by parameterized distributions such as Gaussians with separate or shared parameters across states. We discuss identifiability, parameter estimation, model testing, a modeling syntax, and the improved precision of GPT estimates. Finally, a GPT version of the feature comparison model of semantic categorization is applied to computer-mouse trajectories.

The effects of bilingual language proficiency on recall accuracy and semantic clustering in free recall output: evidence for shared semantic associations across languages

ABSTRACTTwo experiments investigated how well bilinguals utilise long-standing semantic associations to encode and retrieve semantic clusters in verbal episodic memory. In Experiment 1, Spanish-English bilinguals (N = 128) studied and recalled word and picture sets. Word recall was equivalent in L1 and L2, picture recall was better in L1 than in L2, and the picture superiority effect was stronger in L1 than in L2. Semantic clustering in word and picture recall was equivalent in L1 and L2. In Experiment 2, Spanish-English bilinguals (N = 128) and English-speaking monolinguals (N = 128) studied and recalled word sequences that contained semantically related pairs. Data were analyzed using a multinomial processing tree approach, the pair-clustering model. Cluster formation was more likely for semantically organised than for randomly ordered word sequences. Probabilities of cluster formation, cluster retrieval, and retrieval of unclustered items did not differ across languages or language groups. Language proficiency has little if any impact on the utilisation of long-standing semantic associations, which are language-general.

Adding a speed-accuracy trade-off to discrete-state models: A comment on Heck and Erdfelder (2016).

Heck and Erdfelder (2016) developed a model that extends discrete-state multinomial processing tree models to response time (RT) data. Their model is an important advance, but it does not have a mechanism to produce the speed-accuracy trade-off, the bedrock empirical observation that rushed decisions are less accurate. I present a similar model, the "discrete-race" model, with a simple mechanism for the speed-accuracy trade-off. In the model, information that supports detection of the stimulus type is available for some proportion of items and unavailable for others. Both the amount of time needed for detection to succeed and the amount of time that the decision maker waits before guessing are variable from trial to trial. Responses are based on detection when it is available and has a finishing time before the guess time for that trial. In other words, the decision maker sometimes loses opportunities to respond correctly on the basis of detection by first making a guess. These lost opportunities are more common when the guess-time distribution tends to have low wait times, which decreases accuracy. I report simulations showing that the model can accurately recover parameter values and is strongly constrained by the speed-accuracy trade-offs across conditions with different levels of response caution.

ABrox-A user-friendly Python module for approximate Bayesian computation with a focus on model comparison.

We give an overview of the basic principles of approximate Bayesian computation (ABC), a class of stochastic methods that enable flexible and likelihood-free model comparison and parameter estimation. Our new open-source software called ABrox is used to illustrate ABC for model comparison on two prominent statistical tests, the two-sample t-test and the Levene-Test. We further highlight the flexibility of ABC compared to classical Bayesian hypothesis testing by computing an approximate Bayes factor for two multinomial processing tree models. Last but not least, throughout the paper, we introduce ABrox using the accompanied graphical user interface.