Perceptual robustness of stochastic visual, vibrotactile, and bimodal stimuli

2018 ◽  
Author(s):  
Mercedes B. Villalonga ◽  
Rachel F. Sussman ◽  
Robert Sekuler
Keyword(s):  
Logistic Regression ◽  
Response Times ◽  
Visual Signals ◽  
Optimal Decision ◽  
Parameter Estimates ◽  
Drift Diffusion Model ◽  
Drift Diffusion ◽  
Bimodal Stimulation ◽  
Random Samples ◽  
Rate Information

In a study of perceptual robustness, 23 subjects discriminated between two rates (3 and 6 Hz) at which brief visual, vibrotactile, or concurrent visual and vibrotactile pulses were presented. On two-thirds of the trials, inter-pulse intervals (IPIs) were stochastic, perturbed by random samples from a zero-mean Gaussian distribution. Directional changes in IPIs increased or diminished the likelihood of confusing the pulse rates, making it possible to gauge the influence of successive IPIs on subjects’ judgments. Logistic regression revealed a strong primacy effect: subjects’ decisions were disproportionately influenced by a trial’s initial IPIs. Both response times and drift-diffusion model parameter estimates indicated that information accumulates more rapidly with bimodal stimulation than with either unimodal stimulus. Error analysis suggested consistent reliance on statistically optimal decision criteria. Finally, rate information delivered by vibrotactile signals proved just as robust as information conveyed by visual signals, confirming vibrotactile stimulation’s potential for timely communication.

scholarly journals Feeling the Beat (and Seeing It, Too): Vibrotactile, Visual, and Bimodal Rate Discrimination

2020 ◽  
Vol 33 (1) ◽  
pp. 31-59
Author(s):  
Mercedes B. Villalonga ◽  
Rachel F. Sussman ◽  
Robert Sekuler
Keyword(s):  
Response Times ◽  
Error Rates ◽  
Visual Signals ◽  
Optimal Decision ◽  
Parameter Estimates ◽  
Temporal Domain ◽  
Random Samples ◽  
Rate Discrimination ◽  
Optimal Decision Making ◽  
Analysis Of Error

Abstract Beats are among the basic units of perceptual experience. Produced by regular, intermittent stimulation, beats are most commonly associated with audition, but the experience of a beat can result from stimulation in other modalities as well. We studied the robustness of visual, vibrotactile, and bimodal signals as sources of beat perception. Subjects attempted to discriminate between pulse trains delivered at 3 Hz or at 6 Hz. To investigate signal robustness, we intentionally degraded signals on two-thirds of the trials using temporal-domain noise. On these trials, inter-pulse intervals (IPIs) were stochastic, perturbed independently from the nominal IPI by random samples from zero-mean Gaussian distributions with different variances. These perturbations produced directional changes in the IPIs, which either increased or decreased the likelihood of confusing the two pulse rates. In addition to affording an assay of signal robustness, this paradigm made it possible to gauge how subjects’ judgments were influenced by successive IPIs. Logistic regression revealed a strong primacy effect: subjects’ decisions were disproportionately influenced by a trial’s initial IPIs. Response times and parameter estimates from drift-diffusion modeling showed that information accumulates more rapidly with bimodal stimulation than with either unimodal stimulus alone. Analysis of error rates within each condition suggested consistently optimal decision making, even with increased IPI variability. Finally, beat information delivered by vibrotactile signals proved just as robust as information conveyed by visual signals, confirming vibrotactile stimulation’s potential as a communication channel.

scholarly journals Neural Substrates of the Drift-Diffusion Model in Brain Disorders

2022 ◽  
Vol 15 ◽  
Author(s):  
Ankur Gupta ◽  
Rohini Bansal ◽  
Hany Alashwal ◽  
Anil Safak Kacar ◽  
Fuat Balci ◽  
...  
Keyword(s):  
Diffusion Model ◽  
Response Times ◽  
Autism Spectrum ◽  
Brain Disorders ◽  
Functional Impairments ◽  
Drift Diffusion Model ◽  
Drift Diffusion ◽  
Obsessive Compulsive ◽  
Theoretical Understanding ◽  
Compulsive Disorder

Many studies on the drift-diffusion model (DDM) explain decision-making based on a unified analysis of both accuracy and response times. This review provides an in-depth account of the recent advances in DDM research which ground different DDM parameters on several brain areas, including the cortex and basal ganglia. Furthermore, we discuss the changes in DDM parameters due to structural and functional impairments in several clinical disorders, including Parkinson's disease, Attention Deficit Hyperactivity Disorder (ADHD), Autism Spectrum Disorders, Obsessive-Compulsive Disorder (OCD), and schizophrenia. This review thus uses DDM to provide a theoretical understanding of different brain disorders.

scholarly journals Bayesian confidence for drift diffusion observers in dynamic stimuli tasks

2020 ◽  
Author(s):  
Joshua Calder-Travis ◽  
Rafal Bogacz ◽  
Nick Yeung
Keyword(s):  
Diffusion Model ◽  
Drift Rate ◽  
Response Times ◽  
Time Dependent ◽  
Drift Diffusion Model ◽  
Drift Diffusion ◽  
Dynamic Stimuli ◽  
Computationally Expensive ◽  
Insight Into ◽  
Confidence Data

AbstractMuch work has explored the possibility that the drift diffusion model, a model of response times and choices, could be extended to account for confidence reports. Many methods for making predictions from such models exist, although these methods either assume that stimuli are static over the course of a trial, or are computationally expensive, making it difficult to capitalise on trial-by-trial variability in dynamic stimuli. Using the framework of the drift diffusion model with time-dependent thresholds, and the idea of a Bayesian confidence readout, we derive expressions for the probability distribution over confidence reports. In line with current models of confidence, the derivations allow for the accumulation of “pipeline” evidence which has been received but not processed by the time of response, the effect of drift rate variability, and metacognitive noise. The expressions are valid for stimuli which change over the course of a trial with normally distributed fluctuations in the evidence they provide. A number of approximations are made to arrive at the final expressions, and we test all approximations via simulation. The derived expressions only contain a small number of standard functions, and only require evaluating once per trial, making trial-by-trial modelling of confidence data in dynamic stimuli tasks more feasible. We conclude by using the expressions to gain insight into the confidence of optimal observers, and empirically observed patterns.

scholarly journals Gamification of Two Alternative Forced Choice: Validation based on Drift Diffusion Model and Individual Differences

2019 ◽  
Author(s):  
Esin Turkakin ◽  
Ceyla Karamancı ◽  
Kaan Karamancı ◽  
Fuat Balcı
Keyword(s):  
Individual Differences ◽  
Diffusion Model ◽  
Response Times ◽  
Forced Choice ◽  
Drift Diffusion Model ◽  
Drift Diffusion ◽  
Obsessive Compulsive ◽  
Constant Variance ◽  
Decision Making Processes ◽  
Evidence Integration

Two alternative forced choice (2AFC) paradigms, coupled with the unified analysis of accuracy and response times within specific decision theoretic frameworks, have provided a wealth of information regarding decision-making processes. One problem of associated experimental tasks is that they are typically not engaging and do not contain stimuli or task representations familiar to participants, resulting in contaminants in the data due to boredom and distraction. Furthermore, when investigating decision strategies, use of noisy stimulus attributes result in undesired variance in the perceptual process complicating the analysis and interpretation of results. To address these fundamental issues, we developed a 2AFC soccer game in which participants’ task is to score goals by making leftward or rightward shots after observing the trajectory of the goalkeeper within a trial. The goalkeeper’s location is repeatedly sampled from a normal distribution with a constant variance and a mean either to the left or right of the midpoint. We tested participants on three difficulty levels parameterized by the distance between the two means and expected the rate of evidence integration to decrease with increasing difficulty and after errors as characteristic of standard 2AFC tasks. Drift- diffusion model provided good fits to data, and their outputs confirmed our predictions outlined above. Furthermore, we found the evidence integration rates to be negatively correlated with individual differences in maladaptive perfectionism, but not in anxiety or obsessive-compulsive traits.

scholarly journals Benefits of Neuroeconomic Modeling: New Policy Interventions and Predictors of Preference

2014 ◽  
Vol 104 (5) ◽  
pp. 501-506 ◽  
Author(s):  
Ian Krajbich ◽  
Bastiaan Oud ◽  
Ernst Fehr
Keyword(s):  
Diffusion Model ◽  
Choice Behavior ◽  
Response Times ◽  
Time Limit ◽  
Drift Diffusion Model ◽  
Drift Diffusion ◽  
Policy Interventions ◽  
Indifference Points

Neuroeconomics strives to use knowledge from neuroscience to improve models of decisionmaking. Here we introduce a biologically plausible, drift-diffusion model that is able to jointly predict choice behavior and response times across different choice environments. The model has both normative and positive implications for economics. First, we consistently observe that decisionmakers inefficiently allocate their time to choices for which they are close to indifference. We demonstrate that we can improve subjects' welfare using a simple intervention that puts a time limit on their choices. Second, response times can be used to predict indifference points and the strength of preferences.

scholarly journals The critical role of interference control in metaphor comprehension evidenced by the drift–diffusion model

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hee-Dong Yoon ◽  
Minho Shin ◽  
Hyeon-Ae Jeon
Keyword(s):  
Executive Functions ◽  
Diffusion Model ◽  
Word Association ◽  
Response Times ◽  
Critical Role ◽  
Interference Control ◽  
Drift Diffusion Model ◽  
Drift Diffusion ◽  
Metaphor Comprehension ◽  
Fine Grained

AbstractWe address the question of, among several executive functions, which one has a strong influence on metaphor comprehension. To this end, participants took part in a metaphor comprehension task where metaphors had varying levels of familiarity (familiar vs. novel metaphors) with different conditions of context (supporting vs. opposing contexts). We scrutinized each participant’s detailed executive functions using seven neuropsychological tests. More interestingly, we modelled their responses in metaphor comprehension using the drift–diffusion model, in an attempt to provide more systematic accounts of the processes underlying metaphor comprehension. Results showed that there were significant negative correlations between response times in metaphor comprehension and scores of the Controlled Oral Word Association Test (COWAT)-Semantic, suggesting that better performances in comprehending metaphors were strongly associated with better interference control. Using the drift–diffusion model, we found that the familiarity, compared to context, had greater leverage in the decision process for metaphor comprehension. Moreover, individuals with better performance in the COWAT-Semantic test demonstrated higher drift rates. In conclusion, with more fine-grained analysis of the decisions involved in metaphor comprehension using the drift–diffusion model, we argue that interference control plays an important role in processing metaphors.

scholarly journals A rational model of people's inferences about others' preferences based on response times

2021 ◽  
Author(s):  
Vael Gates ◽  
Frederick Callaway ◽  
Mark K Ho ◽  
Tom Griffiths
Keyword(s):  
Bayesian Inference ◽  
Decision Maker ◽  
Mental State ◽  
Response Times ◽  
Rational Model ◽  
Drift Diffusion Model ◽  
Drift Diffusion ◽  
Previous Response ◽  
Model Predictions ◽  
Relative Preferences

There's a difference between someone instantaneously saying "Yes!" when you ask them on a date compared to "...yes." Psychologists and economists have long studied how people can infer preferences from others' choices. However, these models have tended to focus on what people choose and not how long it takes them to make a choice. We present a rational model for inferring preferences from response times, using a Drift Diffusion Model to characterize how preferences influence response time and Bayesian inference to invert this relationship. We test our model's predictions for three experimental questions. Matching model predictions, participants inferred that a decision-maker preferred a chosen item more if the decision-maker spent longer deliberating (Experiment 1), participants predicted a decision-maker's choice in a novel comparison based on inferring the decision-maker's relative preferences from previous response times and choices (Experiment 2), and participants could incorporate information about a decision-maker's mental state of cautious or careless (Experiments 3, 4A, and 4B).

scholarly journals Least Likely Observations in Regression Models for Categorical Outcomes

2002 ◽  
Vol 2 (3) ◽  
pp. 296-300 ◽  
Author(s):  
Jeremy Freese
Keyword(s):  
Logistic Regression ◽  
Regression Models ◽  
Binary Logistic Regression ◽  
Positive Outcome ◽  
Ordinary Least Squares ◽  
Parameter Estimates ◽  
Least Squares Regression ◽  
Binary Logistic Regression Model ◽  
Ordered Logistic Regression ◽  
Negative Outcome

This article presents a method and program for identifying poorly fitting observations for maximum-likelihood regression models for categorical dependent variables. After estimating a model, the program leastlikely will list the observations that have the lowest predicted probabilities of observing the value of the outcome category that was actually observed. For example, when run after estimating a binary logistic regression model, leastlikely will list the observations with a positive outcome that had the lowest predicted probabilities of a positive outcome and the observations with a negative outcome that had the lowest predicted probabilities of a negative outcome. These can be considered the observations in which the outcome is most surprising given the values of the independent variables and the parameter estimates and, like observations with large residuals in ordinary least squares regression, may warrant individual inspection. Use of the program is illustrated with examples using binary and ordered logistic regression.

Sign in / Sign up

Export Citation Format

Share Document