Psychological interpretation of the ex-Gaussian and shifted Wald parameters: A diffusion model analysis

Matzke, Dora; Wagenmakers, Eric‐Jan

doi:10.3758/pbr.16.5.798

Cited by 433 publications

(511 citation statements)

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“…Responses executed only in the 100 ms − -3000 ms time range were included in RT analysis in order to rule out lapses and longer breaks possibly not associated with explorative behavior [47]. Exploration rate was calculated for each participant and each balloon trial (in sum 90 per participant) by dividing the pump number on a given balloon by the number of explorative pumps on that balloon (e.g., if two explorative pumps occurred in a balloon trial where 10 pumps were initiated, the exploration rate on that balloon would be 0.2).…”

Section: Behavioral Datamentioning

confidence: 99%

“…Specifically, the mean adjusted number of pumps [mean number of pumps on balloons that did not explode, This behavioral index defines a pumping response as explorative if the associated response time (RT) exceeded the level of 3 standard deviation (SD) of the participants' own mean RT. Responses executed only in the 100 ms − -3000 ms time range were included in RT analysis in order to rule out lapses and longer breaks possibly not associated with explorative behavior [47]. Exploration rate was calculated for each participant and each balloon trial (in sum 90 per participant) by dividing the pump number on a given balloon by the number of explorative pumps on that balloon (e.g., if two explorative pumps occurred in a balloon trial where 10 pumps were initiated, the exploration rate on that balloon would be 0.2).…”

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Age-related characteristics of risky decision-making and progressive expectation formation

Kardos

Kóbor

Takács

et al. 2016

Behavioural Brain Research

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During daily encounters, it is inevitable that people take risks. Investigating the sequential processing of risk hazards involve expectation formation about outcome contingencies. The present study aimed to explore risk behavior and its neural correlates in sequences of decision making, particularly in old age, which represents a critical period regarding risk-taking propensity. The Balloon Analogue Risk Task was used in an electrophysiological setting with young and elderly age groups. During the task each additional pump on a virtual balloon increased the likelihood of a balloon burst but also increased the chance to collect more reward. Event-related potentials associated with rewarding feedback were analyzed based on the forthcoming decisions (whether to continue or to stop) in order to differentiate between states of expectation towards gain or loss. In the young, the reward positivity ERP component increased as a function of reward contingencies with the largest amplitude for rewarding feedback followed by the decision to stop. In the elderly, however, reward positivity did not reflect the effect of reward structure. Behavioral indices of risk-taking propensity suggest that the performance of the young and the elderly were dissociable only with respect to response times: The elderly was characterized by hesitation and more deliberative decision making throughout the experiment. These findings signify that sequential tracking of outcome contingencies has a key role in cost-efficient action planning and progressive expectation formation.Keywords: Aging; Decision making; Feedback-related ERPs; Reward positivity; Risk-taking behavior Whether we are aware of the fact or not, risk-taking behavior occurs more frequently during our everyday actions than generally thought. Besides those simple and common examples like taking only one more drink before driving, a temporary excursion from one's self-regulatory goals, i.e., risking the long-term goals for short-term advantages such as making a cheat in a diet, can be considered as a more complex form of 'gambling' [1]. Risk-taking behavior can be defined as a decision making propensity motivated by gain reward beside the possible opportunity to lose [2][3][4]. Since natural circumstances usually include continuous fluctuations in risk, the ability of making decisions based on the acquisition of action-outcome contingencies is an essential part of adaptive risk behavior [5]. In particular, old age represents a critical period due to substantial neurocognitive changes having an effect on both frontal cortical and subcortical brain areas known to be involved in decision making [6,7]. The question remains, however, whether there is a specific inability of the acquisition of relevant information behind the possibly reduced decision quality of the elderly.From the perspective of self-report questionnaires, elderly are characterized by higher risk-avoidance tendency [8]. However, regarding risk behavior, results show evidence for both normal and altered patterns of ...

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Section: Behavioral Datamentioning

confidence: 99%

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confidence: 99%

Age-related characteristics of risky decision-making and progressive expectation formation

Kardos

Kóbor

Takács

et al. 2016

Behavioural Brain Research

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“…The figure does not seem to betray any large systematic misfit of the model. 10 See Matzke and Wagenmakers (2009) for plausible ranges of diffusion model parameters, as found in published studies. Note.…”

Section: Example 2: Anova and Random Person-domain Effectsmentioning

confidence: 99%

Hierarchical diffusion models for two-choice response times.

Vandekerckhove

Tuerlinckx

Lee

2011

Psychological Methods

276

324

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Two-choice response times are a common type of data, and much research has been devoted to the development of process models for such data. However, the practical application of these models is notoriously complicated, and flexible methods are largely nonexistent. We combine a popular model for choice response times-the Wiener diffusion process-with techniques from psychometrics in order to construct a hierarchical diffusion model. Chief among these techniques is the application of random effects, with which we allow for unexplained variability among participants, items, or other experimental units. These techniques lead to a modeling framework that is highly flexible and easy to work with. Among the many novel models this statistical framework provides are a multilevel diffusion model, regression diffusion models, and a large family of explanatory diffusion models. We provide examples and the necessary computer code.Keywords: response time, psychometrics, hierarchical, random effects, diffusion model Supplemental materials: http://dx.doi.org/10.1037/a0021765.suppIn his 1957 presidential address at the 65th annual business meeting of the American Psychological Association, Lee Cronbach drew a captivating sketch of the state of psychology at the time. He focused on the two distinct disciplines that then existed in the field of scientific psychology. On the one side, there was the experimental discipline, which concerned itself with the systematic manipulation of conditions in order to observe the consequences. On the other side, there was the correlational discipline, which focused on the study of preexisting differences between individuals or groups. Cronbach saw many potential contributions of these disciplines to one another and argued that the time and opportunity had come for the two dissociated fields to crossbreed: "We are free at last to look up from our own bedazzling treasure, to cast properly covetous glances upon the scientific wealth of our neighbor discipline. Trading has already been resumed, with benefit to both parties" (Cronbach, 1957, p. 675). Two decades onward, Cronbach (1975) saw the hybrid discipline flourishing across several domains.In the area of measurement of psychological processes, there exists a schism similar to the one Cronbach pointed out in his presidential address. Psychological measurement and individual differences are studied in the domain of psychometrics, whereas cognitive processes are the stuff of the more nomothetic mathematical psychology. In both areas, statistical models are used extensively. There are common models based on the (general) linear model, such as analysis of variance (ANOVA) and regression, but we focus on more advanced, nonlinear techniques.Experimental psychology has, for a long time, made use of process models to describe interesting psychological phenomena in various fields. Some famous examples are Sternberg's (1966) sequential exhaustive search model for visual search and memory scanning, Atkinson and Shiffrin's (1968) multistore model for memory,...

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“…The HDDM toolbox includes informative priors to constrain estimates of the model parameters. The priors are based on analyses of 23 published studies (Matzke & Wagenmakers, 2009) and I have used these in the current study.…”

Section: Hierarchical Drift Diffusion Modelling and Data Analysesmentioning

confidence: 99%