scholarly journals The Diffusion Decision Model: Theory and Data for Two-Choice Decision Tasks

2008 ◽  
Vol 20 (4) ◽  
pp. 873-922 ◽  
Author(s):  
Roger Ratcliff ◽  
Gail McKoon
Keyword(s):  
Cognitive Processing ◽  
Decision Model ◽  
Drift Rate ◽  
Response Times ◽  
Quality Of Information ◽  
Starting Point ◽  
Choice Decision ◽  
Response Time Distributions ◽  
Diffusion Decision Model

The diffusion decision model allows detailed explanations of behavior in two-choice discrimination tasks. In this article, the model is reviewed to show how it translates behavioral data—accuracy, mean response times, and response time distributions—into components of cognitive processing. Three experiments are used to illustrate experimental manipulations of three components: stimulus difficulty affects the quality of information on which a decision is based; instructions emphasizing either speed or accuracy affect the criterial amounts of information that a subject requires before initiating a response; and the relative proportions of the two stimuli affect biases in drift rate and starting point. The experiments also illustrate the strong constraints that ensure the model is empirically testable and potentially falsifiable. The broad range of applications of the model is also reviewed, including research in the domains of aging and neurophysiology.

Identifying adverse drug reactions and drug-induced diseases using network-based drug mapping

2015 ◽  
Vol 13 (01) ◽  
pp. 1540007 ◽  
Author(s):  
Alban Shoshi ◽  
Venus Ogultarhan ◽  
Tobias Hoppe ◽  
Benjamin Kormeier ◽  
Ulrich Müller ◽  
...  
Keyword(s):  
Adverse Drug Reactions ◽  
Prescription Drugs ◽  
Medical Doctor ◽  
Drug Reactions ◽  
Quality Of Information ◽  
Drug Induced ◽  
Starting Point ◽  
Molecular Information ◽  
Multiple Drugs

Drugs are essential for the prevention and treatment of diseases. However, co-administration of multiple drugs may cause serious adverse drug reactions, which are usually known but sometimes unknown. Package inserts of prescription drugs are supposed to contain risks and side effects, but such information is not necessarily complete. At the core of efforts to improve prescription quality, there is reliance on the extent and quality of information used for decision of a medical doctor. To address this on-going need, GraphSAW provides users a comprehensive view on drug-related pharmacological and molecular information. The features of GraphSAW allow users to analyze drug cocktails for adverse drug reactions and drug-induced diseases. Network visualization by drug mapping enables exploring associative networks of drugs, pathways, and diseases to fully understand effects of drugs in an intuitive way. GraphSAW is meant to be a platform and starting point for health professionals and researchers for educational and scientific research in order to achieve substantial improvements in patient safety.

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.

Investigating Cognitive Effort and Response Quality of Question Formats in Web Surveys Using Paradata

Field Methods ◽  
2017 ◽  
Vol 29 (4) ◽  
pp. 365-382 ◽  
Author(s):  
Jan Karem Höhne ◽  
Stephan Schlosser ◽  
Dagmar Krebs
Keyword(s):  
Cognitive Processing ◽  
Response Times ◽  
Social Research ◽  
Cognitive Effort ◽  
Web Surveys ◽  
Web Based ◽  
Response Quality ◽  
Identical Response ◽  
Question Formats

Measuring attitudes and opinions employing agree/disagree (A/D) questions is a common method in social research because it appears to be possible to measure different constructs with identical response scales. However, theoretical considerations suggest that A/D questions require a considerable cognitive processing. Item-specific (IS) questions, in contrast, offer content-related response categories, implying less cognitive processing. To investigate the respective cognitive effort and response quality associated with A/D and IS questions, we conducted a web-based experiment with 1,005 students. Cognitive effort was assessed by response times and answer changes. Response quality, in contrast, was assessed by different indicators such as dropouts. According to our results, single IS questions require higher cognitive effort than single A/D questions in terms of response times. Moreover, our findings show substantial differences in processing single and grid questions.

scholarly journals Estimating Across-Trial Variability Parameters of the Diffusion Decision Model: Expert Advice and Recommendations

2018 ◽  
Author(s):  
Udo Boehm ◽  
Jeff Annis ◽  
Michael Frank ◽  
Guy Hawkins ◽  
Andrew Heathcote ◽  
...  
Keyword(s):  
Response Time ◽  
Decision Model ◽  
Simulated Data ◽  
Expert Advice ◽  
Collaborative Project ◽  
Wide Range ◽  
Problems And Solutions ◽  
Response Time Distributions ◽  
Diffusion Decision Model ◽  
Comprehensive Reference

For many years the Diffusion Decision Model (DDM) has successfully accounted for behavioral data from a wide range of domains. Important contributors to the DDM’s success are the across-trial variability parameters, which allow the model to account for the various shapes of response time distributions encountered in practice. However, several researchers have pointed out that estimating the variability parameters can be a challenging task. Moreover, the numerous fitting methods for the DDM each come with their own associated problems and solutions. This often leaves users in a difficult position. In this collaborative project we invited researchers from the DDM community to apply their various fitting methods to simulated data and provide advice and expert guidance on estimating the DDM’s between-trial variability parameters using these methods. Our study establishes a comprehensive reference resource and describes methods that can help to overcome the challenges associated with estimating the DDM’s across-trial variability parameters.

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.

The S-SH Confusion Test and the Effects of Frequency Lowering

2019 ◽  
Vol 62 (5) ◽  
pp. 1486-1505
Author(s):  
Joshua M. Alexander
Keyword(s):  
Cognitive Processing ◽  
Hearing Aids ◽  
Response Times ◽  
Nonlinear Frequency ◽  
Low Frequencies ◽  
Frequency Compression ◽  
Negative Side ◽  
Minimal Word ◽  
Low Pass ◽  
Negative Side Effects

PurposeFrequency lowering in hearing aids can cause listeners to perceive [s] as [ʃ]. The S-SH Confusion Test, which consists of 66 minimal word pairs spoken by 6 female talkers, was designed to help clinicians and researchers document these negative side effects. This study's purpose was to use this new test to evaluate the hypothesis that these confusions will increase to the extent that low frequencies are altered.MethodTwenty-one listeners with normal hearing were each tested on 7 conditions. Three were control conditions that were low-pass filtered at 3.3, 5.0, and 9.1 kHz. Four conditions were processed with nonlinear frequency compression (NFC): 2 had a 3.3-kHz maximum audible output frequency (MAOF), with a start frequency (SF) of 1.6 or 2.2 kHz; 2 had a 5.0-kHz MAOF, with an SF of 1.6 or 4.0 kHz. Listeners' responses were analyzed using concepts from signal detection theory. Response times were also collected as a measure of cognitive processing.ResultsOverall, [s] for [ʃ] confusions were minimal. As predicted, [ʃ] for [s] confusions increased for NFC conditions with a lower versus higher MAOF and with a lower versus higher SF. Response times for trials with correct [s] responses were shortest for the 9.1-kHz control and increased for the 5.0- and 3.3-kHz controls. NFC response times were also significantly longer as MAOF and SF decreased. The NFC condition with the highest MAOF and SF had statistically shorter response times than its control condition, indicating that, under some circumstances, NFC may ease cognitive processing.ConclusionsLarge differences in the S-SH Confusion Test across frequency-lowering conditions show that it can be used to document a major negative side effect associated with frequency lowering. Smaller but significant differences in response times for correct [s] trials indicate that NFC can help or hinder cognitive processing, depending on its settings.

Disfluent, But Fast

2019 ◽  
Vol 66 (5) ◽  
pp. 346-354
Author(s):  
Štěpán Bahník
Keyword(s):  
Cognitive Processing ◽  
Response Times ◽  
Processing Fluency ◽  
Retrieval Fluency ◽  
Decision Times

Abstract. Processing fluency, a metacognitive feeling of ease of cognitive processing, serves as a cue in various types of judgments. Processing fluency is sometimes evaluated by response times, with shorter response times indicating higher fluency. The present study examined existence of the opposite association; that is, it tested whether disfluency may lead to faster decision times when it serves as a strong cue in judgment. Retrieval fluency was manipulated in an experiment using previous presentation and phonological fluency by varying pronounceability of pseudowords. Participants liked easy-to-pronounce and previously presented words more. Importantly, their decisions were faster for hard-to-pronounce and easy-to-pronounce pseudowords than for pseudowords moderate in pronounceability. The results thus showed an inverted-U shaped relationship between fluency and decision times. The findings suggest that disfluency can lead to faster decision times and thus demonstrate the importance of separating different processes comprising judgment when response times are used as a measure of processing fluency.

PENGARUH KUALITAS INFORMASI DAN KUALITAS LAYANAN TERHADAP SIKAP DALAM PENGGUNAAN SISTEM APLIKASI DENGAN KEMUDAHAN PENGGUNAAN SEBAGAI VARIABEL INTERVENING PADA JASA TRANSPORTASI ONLINE (GOJEK) DI YOGYAKARTA

2019 ◽  
Vol 27 (2) ◽  
pp. 119-133
Author(s):  
Putri Aprilia Isnaini ◽  
Ida Bagus Nyoman Udayana
Keyword(s):  
Social Media ◽  
Quality Of Service ◽  
Information Quality ◽  
Sampling Technique ◽  
Ease Of Use ◽  
Quality Of Services ◽  
Quality Of Information ◽  
Transportation Services ◽  
Application Systems

This writing is done to determine the effect of information quality and service quality on attitudes in the use of application systems with the ease of use of the system as an intervining variable in online transportation services (gojek) in Yogyakarta. The sample in this study is customers who use online motorcycle transportation services in Yogyakarta. The sampling technique uses accidental sampling technique. Data collection is done by distributing online questionnaires through the Goegle form and distributed with social media such as WhatsApp and Instagram on a 1-4 scale to measure 4 indicators. The results of this study show 1) the quality of information affects the ease of use, 2) the quality of service affects the ease of use, 3) the quality of information influences attitudes in use, 4) the quality of services does not affect attitudes in use, and 5) ease of use attitude in use.

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