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 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.

scholarly journals Fixation patterns in simple choice reflect optimal information sampling

2019 ◽  
Author(s):  
Frederick Callaway ◽  
Antonio Rangel ◽  
Tom Griffiths
Keyword(s):  
Decision Making ◽  
Decision Maker ◽  
Sequential Sampling ◽  
Empirical Work ◽  
Reaction Times ◽  
Optimal Solution ◽  
Cognitive Resources ◽  
Human Decision ◽  
Information Sampling ◽  
Optimal Information

When faced with a decision between several options, people rarely fully consider every alternative. Instead, we direct our attention to the most promising candidates, focusing our limited cognitive resources on evaluating the options that we are most likely to choose. A growing body of empirical work has shown that attention plays an important role in human decision making, but it is still unclear how people choose with option to attend to at each moment in the decision making process. In this paper, we present an analysis of how a rational decision maker should allocate her attention. We cast attention allocation in decision making as a sequential sampling problem, in which the decision maker iteratively selects from which distribution to sample in order to update her beliefs about the values of the available alternatives. By approximating the optimal solution to this problem, we derive a model in which both the selection and integration of evidence are rational. This model predicts choices and reaction times, as well as sequences of visual fixations. Applying the model to a ternary-choice dataset, we find that its predictions align well with human data.

scholarly journals Accumulation of continuously time-varying sensory evidence constrains neural and behavioral responses in human collision threat detection

2021 ◽  
Vol 17 (7) ◽  
pp. e1009096
Author(s):  
Gustav Markkula ◽  
Zeynep Uludağ ◽  
Richard McGilchrist Wilkie ◽  
Jac Billington
Keyword(s):  
Decision Making ◽  
Behavioral Responses ◽  
Brain Regions ◽  
Threat Detection ◽  
Time Varying ◽  
Evidence Accumulation ◽  
Human Decision ◽  
Detection Response ◽  
Sensory Evidence ◽  
Successful Behavior

Evidence accumulation models provide a dominant account of human decision-making, and have been particularly successful at explaining behavioral and neural data in laboratory paradigms using abstract, stationary stimuli. It has been proposed, but with limited in-depth investigation so far, that similar decision-making mechanisms are involved in tasks of a more embodied nature, such as movement and locomotion, by directly accumulating externally measurable sensory quantities of which the precise, typically continuously time-varying, magnitudes are important for successful behavior. Here, we leverage collision threat detection as a task which is ecologically relevant in this sense, but which can also be rigorously observed and modelled in a laboratory setting. Conventionally, it is assumed that humans are limited in this task by a perceptual threshold on the optical expansion rate–the visual looming–of the obstacle. Using concurrent recordings of EEG and behavioral responses, we disprove this conventional assumption, and instead provide strong evidence that humans detect collision threats by accumulating the continuously time-varying visual looming signal. Generalizing existing accumulator model assumptions from stationary to time-varying sensory evidence, we show that our model accounts for previously unexplained empirical observations and full distributions of detection response. We replicate a pre-response centroparietal positivity (CPP) in scalp potentials, which has previously been found to correlate with accumulated decision evidence. In contrast with these existing findings, we show that our model is capable of predicting the onset of the CPP signature rather than its buildup, suggesting that neural evidence accumulation is implemented differently, possibly in distinct brain regions, in collision detection compared to previously studied paradigms.

scholarly journals Time-varying boundaries for diffusion models of decision making and response time

2014 ◽  
Vol 5 ◽  
Author(s):  
Shunan Zhang ◽  
Michael D. Lee ◽  
Joachim Vandekerckhove ◽  
Gunter Maris ◽  
Eric-Jan Wagenmakers
Keyword(s):  
Decision Making ◽  
Response Time ◽  
Diffusion Models ◽  
Time Varying

The Effect of Part Proliferation on Assembly Line Operators' Decision Making Capabilities

1979 ◽  
Vol 23 (1) ◽  
pp. 169-173
Author(s):  
Susanne M. Gatchell
Keyword(s):  
Decision Making ◽  
Reaction Time ◽  
Choice Reaction Time ◽  
Assembly Line ◽  
Research Study ◽  
Reaction Times ◽  
Decision Time ◽  
Error Rates

In order to quantify the effects of part proliferation on assembly line operators' decision making capabilities, a research study was conducted. Using a Choice Reaction Time technique, 16 operators were tested to determine their reaction times and error rates when selecting parts. These operators were from four training levels (trained, relief, untrained/job and untrained/plant) and had to decide between 4, 7 or 10 major parts. Results show that operators with 10 parts made 46% more errors and needed 13% more decision time than operators with 4 parts. Furthermore, the relief and untrained/job operators made three times more errors than the trained operators. The untrained/plant operators had over five times more errors than the trained operators. These results indicate that all operators could make a selection when working with 10 major parts. However, their reaction times and error rates increased as the number or parts increased from 4 to 10.

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.

Evaluating Human Decision-Making Theories With Monkey Data

2013 ◽  
Author(s):  
Scott D. Brown ◽  
Pete Cassey ◽  
Andrew Heathcote ◽  
Roger Ratcliff
Keyword(s):  
Decision Making ◽  
Human Decision

History among the Junipers: Hosea 14:2–10 as Metahistoriography

2019 ◽  
Vol 63 (1) ◽  
pp. 105-116
Author(s):  
Mark W. Hamilton
Keyword(s):  
Decision Making ◽  
Moral Formation ◽  
Divine Agency ◽  
Human Decision

Abstract The dual endings of Hosea promoted reflection on Israel’s history as the movement from destruction to restoration based on Yhwh’s gracious decision for Israel. It thus clarifies the endings of the prior sections of the book (chs. 3 and 11) by locating Israel’s future in the realm of Yhwh’s activities. The final ending (14:10) balances the theme of divine agency in 14:2–9 with the recognition of human decision-making and moral formation as aspects of history as well. The endings of Hosea thus offer a good example of metahistoriography, a text that uses non-historiographic techniques to speak of the movements of history.

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