14 papers · ranked by Valyu relevance
Sebastian Hellmann, Michael Zehetleitner, Manuel Rausch
The modeling of response times using sequential sampling models has a long history. Because choices, confidence judgments, and reaction times are closely linked in perceptual decisions, it seems only natural to simultaneously model these three outcome variables of a decision. In the package dynConfiR, we implemented…
Jake Spicer, Yun-Xiao Li, Lucas Castillo, Johanna K. Falbén + 2 more
Recent decision-making models have explained behaviour using mental sampling mechanisms, but there is still little agreement on the specific sampling process, such as whether sampling rates match true probabilities. Here, we seek to trace the sampling process using generation tasks: in two experiments using general…
Douglas G. Lee, Giovanni Pezzulo
Sequential sampling models of choice, such as the drift-diffusion model (DDM), are frequently fit to empirical data to account for a variety of effects related to accuracy/consistency, response time (RT), and sometimes confidence. However, no model in this class has been shown to account for the phenomenon known as…
Chao Zhang, Arlette van Wissen, Ron Dotsch, Daniël Lakens + 1 more
Habits often conflict with goal-directed behaviors and this phenomenon continues to attract interests from neuroscientists, experimental psychologists, and applied health psychologists. Recent computational models explain habit-goal conflicts as the competitions between two learning systems, arbitrated by a central…
Fulvia Mecatti, Charalambos Sismanidis, Emanuela Furfaro, Pier Luigi Conti
'Pier Luigi Conti'] A new class of sampling strategies is proposed that can be applied to population-based surveys targeting a rare trait that is unevenly spread over an area of interest. Our proposal is characterised by the ability to tailor the data collection to specific features and challenges of the survey at…
Max Hinne
Bayesian inference is becoming an increasingly popular framework for statistics in the behavioral sciences. However, its application is hampered by its computational intractability - almost all Bayesian analyses require a form of approximation. While some of these approximate inference algorithms, such as Markov chain…
Angelika M. Stefan, Felix D. Schönbrodt, Nathan J. Evans, Eric-Jan Wagenmakers
'Eric-Jan Wagenmakers'] In a sequential hypothesis test, the analyst checks at multiple steps during data collection whether sufficient evidence has accrued to make a decision about the tested hypotheses. As soon as sufficient information has been obtained, data collection is terminated. Here, we compare two sequential…
Andrew A. Manderson, Robert J. B. Goudie
When statistical analyses consider multiple data sources, Markov melding provides a method for combining the source-specific Bayesian models. Markov melding joins together submodels that have a common quantity. One challenge is that the prior for this quantity can be implicit, and its prior density must be estimated.…
Elena L. Grigorenko, Jian-Qiao Zhu, Joakim Sundh, Jake Spicer + 2 more
'Nick Chater' 'Adam N. Sanborn'] Normative models of decision-making that optimally transform noisy (sensory) information into categorical decisions qualitatively mismatch human behavior. Indeed, leading computational models have only achieved high empirical corroboration by adding task-specific assumptions that…
Yoonji Kim, Oksana A. Chkrebtii, Sebastian A. Kurtek
In many modern applications, discretely-observed data may be naturally understood as a set of functions. Functional data often exhibit two confounded sources of variability: amplitude (y-axis) and phase (x-axis). The extraction of amplitude and phase, a process known as registration, is essential in exploring the…
Brandon K. Ashinoff, Justin Buck, Michael Woodford, Guillermo Horga + 1 more
'Samuel J. Gershman'] Base-rate neglect is a pervasive bias in judgment that is conceptualized as underweighting of prior information and can have serious consequences in real-world scenarios. This bias is thought to reflect variability in inferential processes but empirical support for a cohesive theory of base-rate…
Clémence Alméras, Valerian Chambon, Valentin Wyart
Exploring novel environments through sequential sampling is essential for efficient decision-making under uncertainty. In the laboratory, human exploration has been studied in situations where it is traded against reward maximisation. By design, these ‘explore-exploit’ dilemmas confound the behavioural characteristics…
Tom Edinburgh, Ari Ercole, Stephen Eglen, Alessandro Barbiero
Multilevel linear models allow flexible statistical modelling of complex data with different levels of stratification. Identifying the most appropriate model from the large set of possible candidates is a challenging problem. In the Bayesian setting, the standard approach is a comparison of models using the model…
Shaoyang Guo, Qian Sun, Xiaoyu Li
Introduction The No-U-Turn Sampler (NUTS), widely applied in psychometrics via the Stan platform, lacks algorithm-level systematic introduction for item response theory (IRT) models and tailored optimizations for specific models. This study systematically explicates the NUTS algorithm for the 4-parameter normal ogive…