The RWiener Package: an R Package Providing Distribution Functions for the Wiener Diffusion Model

Wabersich, Dominik; Vandekerckhove, Joachim

doi:10.32614/rj-2014-005

Cited by 58 publications

(54 citation statements)

References 11 publications

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“…For each trial, observed payoffs were used together with learning parameters to update the expected rewards for the next trial. Expected rewards were then used together with decision parameters to generate choices for the next trial with the rwiener function from the RWiener package (Wabersich & Vandekerckhove, 2014). This procedure was performed 100 times to account for posterior uncertainty, each time drawing a parameter combination from a random position in the individuals’ joint posterior distribution.…”

Section: Methodsmentioning

confidence: 99%

The drift diffusion model as the choice rule in reinforcement learning

2016

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Current reinforcement-learning models often assume simplified decision processes that do not fully reflect the dynamic complexities of choice processes. Conversely, sequential-sampling models of decision making account for both choice accuracy and response time, but assume that decisions are based on static decision values. To combine these two computational models of decision making and learning, we implemented reinforcement-learning models in which the drift diffusion model describes the choice process, thereby capturing both within- and across-trial dynamics. To exemplify the utility of this approach, we quantitatively fit data from a common reinforcement-learning paradigm using hierarchical Bayesian parameter estimation, and compared model variants to determine whether they could capture the effects of stimulant medication in adult patients with attention-deficit hyper-activity disorder (ADHD). The model with the best relative fit provided a good description of the learning process, choices, and response times. A parameter recovery experiment showed that the hierarchical Bayesian modeling approach enabled accurate estimation of the model parameters. The model approach described here, using simultaneous estimation of reinforcement-learning and drift diffusion model parameters, shows promise for revealing new insights into the cognitive and neural mechanisms of learning and decision making, as well as the alteration of such processes in clinical groups.

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Section: Methodsmentioning

confidence: 99%

The drift diffusion model as the choice rule in reinforcement learning

2016

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“…An ER above 3 indicates moderate to substantial evidence for our hypothesis, below 0.3 indicates moderate to substantial evidence for the null hypothesis, and anything in between is inconclusive evidence (Morey, Rouder & Jamil, 2014). The models were implemented through the brms (Bürkner, 2017) and RWiener (Wabersich & Vandekerckhove, 2014) packages in RStudio v1.1.46, following the procedures of the tutorial written by Singmann (2017).…”

Section: Discussionmentioning

confidence: 99%

Wait for it! Stronger influence of context on categorical perception in Danish than Norwegian

Ishkhanyan¹,

Højen²,

Fusaroli³

et al. 2019

Preprint

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Speech input is often noisy and ambiguous. Yet listenersusually do not have difficulties understanding it. A keyhypothesis is that in speech processing acoustic-phoneticbottom-up processing is complemented by top-downcontextual information. This context effect is larger when theambiguous word is only separated from a disambiguating word by a few syllables compared to many syllables, suggesting that there is a limited time window for processing acoustic-phonetic information with the help of context. Here, we argue that the relative weight of bottom-up and top-down processes may be different for languages that have different phonological properties. We report an experiment comparing two closely related languages, Danish and Norwegian. We show that Danish speakers do indeed rely on context more than Norwegian speakers do. These results highlight the importance of investigating cross-linguistic differences in speech processing, suggesting that speakers of different languages may develop different language processing strategies.

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“…Drift diffusion model. We estimated a hierarchical Wiener diffusion model (Wabersich & Vandekerckhove, 2014;Wiecki, Sofer, & Frank, 2013) to estimate the joint effects of the experimental manipulation on responses and response times. We used a response coded model, where the upper boundary response was defined as "smile" and the lower boundary response was defined as "frown".…”

Section: Reversal Using Descriptive Data and T-testsmentioning

confidence: 99%

The face value of feedback: Facial behavior is shaped by goals and punishments during interaction with dynamic faces

Yi¹,

Pärnamets²,

Olsson³

2020

Preprint

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Responding appropriately to others’ facial expressions is key to successful social functioning and, depending on the identity of your interactive partner, it might save you from harm. Despite the large body of work on face perception and spontaneous responses to static faces, little is known about responses to faces in dynamic, naturalistic situations, and no study has investigated how people’s goal directed responses to faces are influenced by learning during dyadic interactions. Addressing this question, we developed a novel method based on online integration of electromyography (EMG) signals from the participants’ face (corrugator supercilii and zygomaticus major). In this experiment (N = 58), participants’ EMG signals were recorded during their interaction with dynamic faces that either displayed happy or angry facial expressions. The participants’ task was to either reciprocate (congruent) or respond in the opposite manner (incongruent). Incorrect facial expressions were punished with a mild electrical shock to the participant’s wrist. Our results validated our new method, showing that participants learned to optimize their facial behavior over time, and replicated earlier findings of faster and more accurate responses in congruent vs. incongruent conditions. Moreover, participants performed better on trials when confronted with smiling, as compared to frowning, faces, suggesting it might be easier to adapt facial responses to positively associated expressions. Our results introduce a new method to study learning and decision-making in dynamic interactive social situations, in which there is a need to gradually adapt facial mimicry to both social and non-social reinforcements.

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The RWiener Package: an R Package Providing Distribution Functions for the Wiener Diffusion Model

Cited by 58 publications

References 11 publications

The drift diffusion model as the choice rule in reinforcement learning

The drift diffusion model as the choice rule in reinforcement learning

Wait for it! Stronger influence of context on categorical perception in Danish than Norwegian

The face value of feedback: Facial behavior is shaped by goals and punishments during interaction with dynamic faces

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