scholarly journals Drift Diffusion modelling reveals decision mechanisms underlying consumers' evaluation of prices.

2020 ◽  
Author(s):  
Jasper Dezwaef ◽  
Silvia Formica ◽  
Emiel Cracco ◽  
Pieter Huycke ◽  
Jelle Demanet ◽  
...  
Keyword(s):  
Reaction Times ◽  
Decision Time ◽  
Diffusion Models ◽  
Added Value ◽  
Behavioral Experiment ◽  
Drift Diffusion ◽  
Fine Grained ◽  
Price Manipulation ◽  
Starting Point ◽  
Decision Mechanisms

Little is known about how price evaluation processes unfold. In the current study we explored if reaction times (RTs) can be used to study price evaluations. Additionally, we explored to what extent drift diffusion models (DDMs) are suitable to decompose the different aspects that underlay this decision processes. In a behavioral experiment, participants were asked to evaluate prices as fast as possible as ‘cheap’ or ‘expensive’. We expected that the time needed to evaluate prices would vary in accordance with a price manipulation that was used, and that RTs therefore could be interpreted a proxy of decision difficulty. Analysis of the behavioral data provided evidence for this hypothesis: very cheap and very expensive prices were evaluated faster compared to ambiguous prices. Then, drift diffusion models (DDMs) were used to decompose the different aspect of this decision process, with the goal to obtain a more fine-grained understanding of how the effect in RT data emerged. Results showed that the drift rate of the model was modulated by the price manipulation. Whereas there was no significant effect of the price manipulation on the non-decision time and the starting point parameter. We then contrasted the findings of the RT analysis with the results of the DDMs and outlined what the added value of DDMs is within this context.

Decision Confidence and Uncertainty in Diffusion Models with Partially Correlated Neuronal Integrators

2010 ◽  
Vol 22 (7) ◽  
pp. 1786-1811 ◽  
Author(s):  
Rubén Moreno-Bote
Keyword(s):  
Decision Making ◽  
Exact Solution ◽  
Reaction Times ◽  
Decision Time ◽  
Diffusion Models ◽  
Time Varying ◽  
Decision Confidence ◽  
Human Decision ◽  
Decision Boundaries ◽  
Natural Description

Diffusion models have become essential for describing the performance and statistics of reaction times in human decision making. Despite their success, it is not known how to evaluate decision confidence from them. I introduce a broader class of models consisting of two partially correlated neuronal integrators with arbitrarily time-varying decision boundaries that allow a natural description of confidence. The dependence of decision confidence on the state of the losing integrator, decision time, time-varying boundaries, and correlations is analytically described. The marginal confidence is computed for the half-anticorrelated case using the exact solution of the diffusion process with constant boundaries and compared to that of the independent and completely anticorrelated cases.

scholarly journals An Overcomplete Approach to Fitting Drift-Diffusion Decision Models to Trial-By-Trial Data

2021 ◽  
Vol 4 ◽  
Author(s):  
Q. Feltgen ◽  
J. Daunizeau
Keyword(s):  
Parameter Estimation ◽  
Data Analysis ◽  
Reaction Times ◽  
Mechanistic Explanation ◽  
Added Value ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Drift Diffusion ◽  
Neural Noise ◽  
Computational Bottleneck

Drift-diffusion models or DDMs are becoming a standard in the field of computational neuroscience. They extend models from signal detection theory by proposing a simple mechanistic explanation for the observed relationship between decision outcomes and reaction times (RT). In brief, they assume that decisions are triggered once the accumulated evidence in favor of a particular alternative option has reached a predefined threshold. Fitting a DDM to empirical data then allows one to interpret observed group or condition differences in terms of a change in the underlying model parameters. However, current approaches only yield reliable parameter estimates in specific situations (c.f. fixed drift rates vs drift rates varying over trials). In addition, they become computationally unfeasible when more general DDM variants are considered (e.g., with collapsing bounds). In this note, we propose a fast and efficient approach to parameter estimation that relies on fitting a “self-consistency” equation that RT fulfill under the DDM. This effectively bypasses the computational bottleneck of standard DDM parameter estimation approaches, at the cost of estimating the trial-specific neural noise variables that perturb the underlying evidence accumulation process. For the purpose of behavioral data analysis, these act as nuisance variables and render the model “overcomplete,” which is finessed using a variational Bayesian system identification scheme. However, for the purpose of neural data analysis, estimates of neural noise perturbation terms are a desirable (and unique) feature of the approach. Using numerical simulations, we show that this “overcomplete” approach matches the performance of current parameter estimation approaches for simple DDM variants, and outperforms them for more complex DDM variants. Finally, we demonstrate the added-value of the approach, when applied to a recent value-based decision making experiment.

scholarly journals Fitting drift-diffusion decision models to trial-by-trial data

2020 ◽  
Author(s):  
Q. Feltgen ◽  
J. Daunizeau
Keyword(s):  
Empirical Distribution ◽  
Reaction Times ◽  
Mechanistic Explanation ◽  
Relevant Information ◽  
Added Value ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Drift Diffusion ◽  
Evidence Strength ◽  
Reliable Parameter

AbstractDrift-diffusion models or DDMs are becoming a standard in the field of computational neuroscience. They extend models from signal detection theory by proposing a simple mechanistic explanation for the observed relationship between decision outcomes and reaction times (RT). In brief, they assume that decisions are triggered once the accumulated evidence in favor of a particular alternative option has reached a predefined threshold. Fitting a DDM to empirical data then allows one to interpret observed group or condition differences in terms of a change in the underlying model parameters. However, current approaches do not provide reliable parameter estimates when, e.g., evidence strength is varied over trials. In this note, we propose a fast and efficient approach that is based on fitting a self-consistency equation that the DDM fulfills. Using numerical simulations, we show that this approach enables one to extract relevant information from trial-by-trial variations of RT data that would typically be buried in the empirical distribution. Finally, we demonstrate the added-value of the approach, when applied to a recent value-based decision making experiment.

scholarly journals The European Bioinformatics Institute in 2020: building a global infrastructure of interconnected data resources for the life sciences

2019 ◽  
Vol 48 (D1) ◽  
pp. D17-D23 ◽  
Author(s):  
Charles E Cook ◽  
Oana Stroe ◽  
Guy Cochrane ◽  
Ewan Birney ◽  
Rolf Apweiler
Keyword(s):  
Data Exchange ◽  
Life Sciences ◽  
Image Data ◽  
European Bioinformatics Institute ◽  
Added Value ◽  
Value Analysis ◽  
Starting Point ◽  
Core Mission ◽  
Training Team ◽  
Exchange Data

Abstract Data resources at the European Bioinformatics Institute (EMBL-EBI, https://www.ebi.ac.uk/) archive, organize and provide added-value analysis of research data produced around the world. This year's update for EMBL-EBI focuses on data exchanges among resources, both within the institute and with a wider global infrastructure. Within EMBL-EBI, data resources exchange data through a rich network of data flows mediated by automated systems. This network ensures that users are served with as much information as possible from any search and any starting point within EMBL-EBI’s websites. EMBL-EBI data resources also exchange data with hundreds of other data resources worldwide and collectively are a key component of a global infrastructure of interconnected life sciences data resources. We also describe the BioImage Archive, a deposition database for raw images derived from primary research that will supply data for future knowledgebases that will add value through curation of primary image data. We also report a new release of the PRIDE database with an improved technical infrastructure, a new API, a new webpage, and improved data exchange with UniProt and Expression Atlas. Training is a core mission of EMBL-EBI and in 2018 our training team served more users, both in-person and through web-based programmes, than ever before.

Quantum-corrected drift-diffusion models for transport in semiconductor devices

2005 ◽  
Vol 204 (2) ◽  
pp. 533-561 ◽  
Author(s):  
Carlo de Falco ◽  
Emilio Gatti ◽  
Andrea L. Lacaita ◽  
Riccardo Sacco
Keyword(s):  
Semiconductor Devices ◽  
Diffusion Models ◽  
Drift Diffusion

scholarly journals Calculating Value Added of Prostitution with Multiple Data: A New Approach for Belgium

2017 ◽  
Vol 47 (1) ◽  
pp. 58-86 ◽  
Author(s):  
Stef Adriaenssens ◽  
Jef Hendrickx
Keyword(s):  
Sex Workers ◽  
Relative Size ◽  
Value Added ◽  
Added Value ◽  
Standard Errors ◽  
New Approach ◽  
Economic Output ◽  
Multiple Data ◽  
Innovative Methodology ◽  
Starting Point

Economic output implies that underground sectors such as prostitution are taken into account. This article presents an innovative methodology to measure turnover and added value in prostitution based on a combination of observational and Internet data. The method is applied to Belgium. Turnover is broken down in transactions and price per segment. The starting point is an observation-based measure of turnover in one locational and visible segment of the market: window prostitution. Fundamental differences between segments make linear generalizations from one segment invalid. Therefore, we estimate the relative size of transactions in other segments (such as brothels or escort) with Internet data. In combination with measures of average price per transaction, a consolidated estimate of turnover in prostitution in Belgium is measured. Estimates of nonresident production are based on data on sex workers’ country of origin. Several bootstrap replications allow for robustness checks of the delta-based standard errors.

scholarly journals Speed, Accuracy, and the Optimal Timing of Choices

2018 ◽  
Vol 108 (12) ◽  
pp. 3651-3684 ◽  
Author(s):  
Drew Fudenberg ◽  
Philipp Strack ◽  
Tomasz Strzalecki
Keyword(s):  
Sequential Sampling ◽  
Decision Time ◽  
Optimal Timing ◽  
Drift Diffusion Model ◽  
Drift Diffusion ◽  
Prior Beliefs ◽  
Constant Cost ◽  
Speed Accuracy ◽  
Decision Times ◽  
Choice Probabilities

We model the joint distribution of choice probabilities and decision times in binary decisions as the solution to a problem of optimal sequential sampling, where the agent is uncertain of the utility of each action and pays a constant cost per unit time for gathering information. We show that choices are more likely to be correct when the agent chooses to decide quickly, provided the agent’s prior beliefs are correct. This better matches the observed correlation between decision time and choice probability than does the classical drift-diffusion model (DDM), where the agent knows the utility difference between the choices. (JEL C41, D11, D12, D83)

scholarly journals The structural characteristics of world trade and the merchandise exports of Serbia

2009 ◽  
Vol 54 (181) ◽  
pp. 55-91
Author(s):  
Radovan Kovacevic
Keyword(s):  
Developing Countries ◽  
World Trade ◽  
New Technologies ◽  
Structural Characteristics ◽  
Developed Countries ◽  
Added Value ◽  
Production Networks ◽  
International Production ◽  
Starting Point ◽  
Merchandise Trade

This paper analyses the world merchandise trade structure and the structure of Serbian merchandise exports. The analysis shows that the prominent characteristic of post-World War II world trade is more dynamic growth in the volume of manufactured goods as compared to agricultural goods. Due to the lessening share of agricultural products world merchandise trade has decreased and rapid industrialization has been fostered in developing countries. An increased share for developing countries followed the developed countries' decreasing share in world manufacturing trade. The developing countries' increased share was strongest in telecom and office equipment exports. These sectors are characterized by production fragmentation, which is being realized by transnational companies. Serbia, like the other South East European countries, has not yet managed to significantly integrate into international production networks. Serbia's most important exports are manufactured products with a low level of added value . In addition, Serbia still has a high share of primary products in its exports. A higher share of exports of goods and services in the gross domestic products (GDP) cannot be achieved without increasing imports of new technologies and equipment, i.e. without a higher investment share of the GDP. The main conclusion of this article is that the creation of a favorable investment climate and an increase in Serbia's international credit rating are the preconditions for stronger foreign direct investment (FDI), which would be the main channel for restructuring in the real sector. Creation of new small and medium enterprises (SMEs) through greenfield investment and their integration into the international production networks is the starting point for the restructuring of Serbian industrial production and merchandise export, i.e. the way of increasing the share of merchandise exports in the GDP.

scholarly journals Perceptual decision-making in children: Age-related differences and EEG correlates

2020 ◽  
Author(s):  
Catherine Manning ◽  
Eric-Jan Wagenmakers ◽  
Anthony Norcia ◽  
Gaia Scerif ◽  
Udo Boehm
Keyword(s):  
Decision Making ◽  
Drift Rate ◽  
Decision Time ◽  
Diffusion Models ◽  
Perceptual Task ◽  
Model Parameters ◽  
Older Children ◽  
Perceptual Decision Making ◽  
Boundary Separation ◽  
Age Related

Children make faster and more accurate decisions about perceptual information as they get older, but it is unclear how different aspects of the decision-making process change with age. Here, we used hierarchical Bayesian diffusion models to decompose performance in a perceptual task into separate processing components, testing age-related differences in model parameters and links to neural data. We collected behavioural and EEG data from 96 six- to twelve-year-olds and 20 adults completing a motion discrimination task. We used a component decomposition technique to identify two response-locked EEG components with ramping activity preceding the response in children and adults: one with activity that was maximal over centro-parietal electrodes and one that was maximal over occipital electrodes. Younger children had lower drift rates (reduced sensitivity), wider boundary separation (increased response caution) and longer non-decision times than older children and adults. Yet model comparisons suggested that the best model of children’s data included age effects only on drift rate and boundary separation (not non-decision time). Next we extracted the slope of ramping activity in our EEG components and covaried these with drift rate. The slopes of both EEG components related positively to drift rate, but the best model with EEG covariates included only the centro-parietal component. By decomposing performance into distinct components and relating them to neural markers, diffusion models have the potential to identify the reasons why children with developmental conditions perform differently to typically developing children - and to uncover processing differences inapparent in the response time and accuracy data alone.

A focus on pressure-driven membrane technology in olive mill wastewater reclamation: state of the art

2012 ◽  
Vol 66 (12) ◽  
pp. 2505-2516 ◽  
Author(s):  
J. M. Ochando-Pulido ◽  
A. Martinez-Ferez
Keyword(s):  
Olive Oil ◽  
Large Scale ◽  
Wastewater Treatment Plants ◽  
Domestic Wastewater ◽  
Olive Mill Wastewater ◽  
Operating Conditions ◽  
Added Value ◽  
Wastewater Reclamation ◽  
Starting Point ◽  
Olive Mill

Direct disposal of the heavily polluted effluent from olive oil industry (olive mill wastewater, OMW) to the environment or to domestic wastewater treatment plants is actually prohibited in most countries, and conventional treatments are ineffective. Membranes are currently one of the most versatile technologies for environmental quality control. Notwithstanding, studies on OMW reclamation by membranes are still scarce, and fouling inhibition and prediction to improve large-scale membrane performance still remain unresolved. Consequently, adequately targeted pretreatment for the specific binomium membrane-feed, as well as optimized operating conditions for the proper membranes, is today's challenge to ensure threshold flux values. Several membrane materials, configurations and pore sizes have been elucidated, and also different pretreatments including sedimentation, centrifugation, biosorption, sieving, filtration and microfiltration, various types of flocculation as well as advance oxidation processes have been applied so far. Recovery of potential-value compounds, such as a variety of polyphenols highlighting oleuropein and hydroxytyrosol, has been attempted too. All this research should constitute the starting point to proceed with OMW purification beyond recycling for irrigation or depuration for sewer discharge, with the aim of complying with standards to reuse the effluent in the olive oil production process, together with cost-effective recovery of added-value compounds.

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