scholarly journals Global semantic similarity effects in recognition memory: Insights from BEAGLE representations and the diffusion decision model

2020 ◽  
Vol 111 ◽  
pp. 104071 ◽  
Author(s):  
Adam F. Osth ◽  
Kevin D. Shabahang ◽  
Douglas J.K. Mewhort ◽  
Andrew Heathcote
Keyword(s):  
Recognition Memory ◽  
Semantic Similarity ◽  
Decision Model ◽  
Diffusion Decision Model

scholarly journals Global semantic similarity effects in recognition memory: Insights from BEAGLE representations and the diffusion decision model

2019 ◽  
Author(s):  
Adam F Osth ◽  
Kevin Shabahang ◽  
Douglas Mewhort ◽  
Andrew Heathcote
Keyword(s):  
Recognition Memory ◽  
Semantic Similarity ◽  
Decision Model ◽  
Response Times ◽  
Semantic Structure ◽  
Prior Work ◽  
Similarity Relations ◽  
Global Similarity ◽  
Diffusion Decision Model ◽  
Taxonomic Categories

Recognition memory models posit that performance is impaired as the similarity between the probe cue and the contents of memory is increased (global similarity). Global similarity predictions have been commonly tested using category length designs, in which the number of items from a common taxonomic or associative category is manipulated. Prior work has demonstrated that increases in the length of associative categories show clear detriments on performance, but that result is found only inconsistently for taxonomic categories. In this work, we explored global similarity predictions using representations from the BEAGLE model (Jones & Mewhort, 2007). BEAGLE’s two types of word representations, item and order vectors, exhibit similarity relations that resemble relations among associative and taxonomic category members, respectively. Global similarity among item and order vectors was regressed onto drift rates in the diffusion decision model (DDM: Ratcliff, 1978), which simultaneously accounts for both response times and accuracy. We implemented this model in a hiearchical Bayesian framework across seven datasets with lists composed of unrelated words. Results indicated clear deficits due to global similarity among item vectors, suggesting that lists of unrelated words exhibit semantic structure that impairs performance. However, there were relatively small influences of global similarity among the order vectors. These results are consistent with prior work suggesting associative similarity causes stronger performance impairments than taxonomic similarity.

scholarly journals A reinforcement learning diffusion decision model for value-based decisions

2019 ◽  
Vol 26 (4) ◽  
pp. 1099-1121 ◽  
Author(s):  
Laura Fontanesi ◽  
Sebastian Gluth ◽  
Mikhail S. Spektor ◽  
Jörg Rieskamp
Keyword(s):  
Reinforcement Learning ◽  
Decision Model ◽  
Diffusion Decision Model

scholarly journals Diffusion Decision Model: Current Issues and History

2016 ◽  
Vol 20 (4) ◽  
pp. 260-281 ◽  
Author(s):  
Roger Ratcliff ◽  
Philip L. Smith ◽  
Scott D. Brown ◽  
Gail McKoon
Keyword(s):  
Decision Model ◽  
Diffusion Decision Model

scholarly journals CHaRTr: An R toolbox for modeling Choices and Response Times in decision-making tasks

2019 ◽  
Author(s):  
Chandramouli Chandrasekaran ◽  
Guy E. Hawkins
Keyword(s):  
Decision Making ◽  
Decision Model ◽  
Response Times ◽  
Research Effort ◽  
Perceptual Decision Making ◽  
Low Pass ◽  
Decision Making Models ◽  
Collapsing Boundaries ◽  
Decision Making Model ◽  
Diffusion Decision Model

AbstractDecision-making is the process of choosing and performing actions in response to sensory cues so as to achieve behavioral goals. A sophisticated research effort has led to the development of many mathematical models to describe the response time (RT) distributions and choice behavior of observers performing decision-making tasks. However, relatively few researchers use these models because it demands expertise in various numerical, statistical, and software techniques. Although some of these problems have been surmounted in existing software packages, the packages have often focused on the classical decision-making model, the diffusion decision model. Recent theoretical advances in decision-making that posit roles for “urgency”, time-varying decision thresholds, noise in various aspects of the decision-formation process or low pass filtering of sensory evidence, have proven to be challenging to incorporate in a coherent software framework that permits quantitative evaluations among these competing classes of decision-making models. Here, we present a toolbox —Choices and Response Times in R, orCHaRTr— that provides the user the ability to implement and test a wide variety of decision-making models ranging from classic through to modern versions of the diffusion decision model, to models with urgency signals, or collapsing boundaries. Earlier versions ofCHaRTrhave been instrumental in a number of recent studies of humans and monkeys performing perceptual decision-making tasks. We also provide guidance on how to extend the toolbox to incorporate future developments in decision-making models.

scholarly journals Bayesian analysis of the piecewise diffusion decision model

2017 ◽  
Vol 50 (2) ◽  
pp. 730-743 ◽  
Author(s):  
William R. Holmes ◽  
Jennifer S. Trueblood
Keyword(s):  
Bayesian Analysis ◽  
Decision Model ◽  
Diffusion Decision Model

scholarly journals Estimating across-trial variability parameters of the Diffusion Decision Model: Expert advice and recommendations

2018 ◽  
Vol 87 ◽  
pp. 46-75 ◽  
Author(s):  
Udo Boehm ◽  
Jeffrey Annis ◽  
Michael J. Frank ◽  
Guy E. Hawkins ◽  
Andrew Heathcote ◽  
...  
Keyword(s):  
Decision Model ◽  
Expert Advice ◽  
Diffusion Decision Model

Sleep Deprivation and Racial Bias in the Decision to Shoot: A Diffusion Model Analysis

2020 ◽  
pp. 194855062093272
Author(s):  
David J. Johnson ◽  
Michelle E. Stepan ◽  
Joseph Cesario ◽  
Kimberly M. Fenn
Keyword(s):  
Sleep Deprivation ◽  
Diffusion Model ◽  
Decision Model ◽  
Racial Bias ◽  
Model Analysis ◽  
The Impact ◽  
Shooting Decisions ◽  
Process Level ◽  
Diffusion Decision Model ◽  
Extract Information

The current study examines the effect of sleep deprivation and caffeine use on racial bias in the decision to shoot. Participants deprived of sleep for 24 hr (vs. rested participants) made more errors in a shooting task and were more likely to shoot unarmed targets. A diffusion decision model analysis revealed sleep deprivation decreased participants’ ability to extract information from the stimuli, whereas caffeine impacted the threshold separation, reflecting decreased caution. Neither sleep deprivation nor caffeine moderated anti-Black racial bias in shooting decisions or at the process level. We discuss how our results clarify discrepancies in past work testing the impact of fatigue on racial bias in shooting decisions.

Sign in / Sign up

Export Citation Format

Share Document