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 Brain Areas Consistently Linked to Individual Differences in Perceptual Decision-making in Younger as well as Older Adults before and after Training

2011 ◽  
Vol 23 (9) ◽  
pp. 2147-2158 ◽  
Author(s):  
Simone Kühn ◽  
Florian Schmiedek ◽  
Björn Schott ◽  
Roger Ratcliff ◽  
Hans-Jochen Heinze ◽  
...  
Keyword(s):  
Older Adults ◽  
Decision Making ◽  
Individual Differences ◽  
Drift Rate ◽  
Parietal Lobe ◽  
Perceptual Decision Making ◽  
Inferior Parietal Lobe ◽  
Perceptual Decision ◽  
Boundary Separation ◽  
Before And After

Perceptual decision-making performance depends on several cognitive and neural processes. Here, we fit Ratcliff's diffusion model to accuracy data and reaction-time distributions from one numerical and one verbal two-choice perceptual-decision task to deconstruct these performance measures into the rate of evidence accumulation (i.e., drift rate), response criterion setting (i.e., boundary separation), and peripheral aspects of performance (i.e., nondecision time). These theoretical processes are then related to individual differences in brain activation by means of multiple regression. The sample consisted of 24 younger and 15 older adults performing the task in fMRI before and after 100 daily 1-hr behavioral training sessions in a multitude of cognitive tasks. Results showed that individual differences in boundary separation were related to striatal activity, whereas differences in drift rate were related to activity in the inferior parietal lobe. These associations were not significantly modified by adult age or perceptual expertise. We conclude that the striatum is involved in regulating response thresholds, whereas the inferior parietal lobe might represent decision-making evidence related to letters and numbers.

Age-Related Differences in the Acquisition and Transfer of Perceptual Decision-Making Skills

1997 ◽  
Vol 41 (1) ◽  
pp. 130-133 ◽  
Author(s):  
Victoria A. Spaulding ◽  
Donita A. Phipps
Keyword(s):  
Older Adults ◽  
Decision Making ◽  
Stress Conditions ◽  
Perceptual Task ◽  
Perceptual Decision Making ◽  
Rule Based ◽  
Perceptual Decision ◽  
Time Stress ◽  
Age Related ◽  
Better Than

Younger and older participants were trained to perform a computerized football task. Half of the participants received rule-based training and the remainder received color enhancements in alternating blocks. Both younger and older adults improved RT performance, with the younger participants performing about twice as fast as the older participants. The participants transferred to Novel, Cluttered and Time-Stress conditions. The effect of training type (rules better than enhancements) failed to persist during transfer. Age-related impairments of RT and overall accuracy persisted during transfer, although older participants maintained a higher primary accuracy (except for Time-Stress). Their performance plummeted during the Time-Stress, but improved across the blocks. During the subsequent baseline block, primary accuracy returned to the pre-Cluttered level and RT slightly declined. These results suggest that the older participants changed strategies under time stress, and with more practice, their performance on this complex perceptual task may increase dramatically.

scholarly journals A qualitative difference in decision-making of rats vs. humans explained by quantitative differences in behavioral variability

2020 ◽  
Author(s):  
Quynh Nhu Nguyen ◽  
Pamela Reinagel
Keyword(s):  
Decision Making ◽  
Response Time ◽  
Visual Motion ◽  
Drift Rate ◽  
Species Difference ◽  
Qualitative Difference ◽  
Decision Time ◽  
Model Parameters ◽  
Behavioral Variability ◽  
Starting Point

AbstractWhen observers make rapid, difficult sensory decisions, their response time is highly variable from trial to trial. We previously compared humans and rats performing the same visual motion discrimination task. Their response time distributions were similar, but for humans accuracy was negatively correlated with response time, whereas for rats it was positively correlated. This is of interest because different mathematical theories of decision-making differ in their predictions regarding the correlation of accuracy with response time. On the premise that sensory decision-making mechanisms are likely to be conserved in mammals, our objective is to reconcile these results within a common theoretical framework. A bounded drift diffusion model (DDM) with stochastic parameters is a strong candidate, because it is known to be able to produce either late errors like humans, or early errors like rats. We consider here such a model with seven free parameters: the evidence accumulator’s starting point z, drift rate v, non-decision-time t, threshold separation a, and three noise terms σz, σv and σt. We fit the model parameters to data from both rats and humans. Trial data simulated by the model recapitulate quantitative details of the relationship between accuracy and response time in both species. On this model, the species difference can be explained by greater variability in the starting point of the diffusion process (σz) in rats, and greater variability in the drift rate (σv) in humans.

scholarly journals Reconciling age-related changes in behavioural and neural indices of human perceptual decision making

2017 ◽  
Author(s):  
David P. McGovern ◽  
Aoife Hayes ◽  
Simon P. Kelly ◽  
Redmond O’Connell
Keyword(s):  
Decision Making ◽  
Change Detection ◽  
Computational Modelling ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Perceptual Decision Making ◽  
Neural Data ◽  
Motion Discrimination ◽  
Age Related ◽  
Wide Range

Ageing impacts on decision making behaviour across a wide range of cognitive tasks and scenarios. Computational modeling has proven highly valuable in providing mechanistic interpretations of these age-related differences; however, the extent to which model parameter differences accurately reflect changes to the underlying neural computations has yet to be tested. Here, we measured neural signatures of decision formation as younger and older participants performed motion discrimination and contrast-change detection tasks, and compared the dynamics of these signals to key parameter estimates from fits of a prominent accumulation-to-bound model (drift diffusion) to behavioural data. Our results indicate marked discrepancies between the age-related effects observed in the model output and the neural data. Most notably, while the model predicted a higher decision boundary in older age for both tasks, the neural data indicated no such differences. To reconcile the model and neural findings, we used our neurophysiological observations as a guide to constrain and adapt the model parameters. In addition to providing better fits to behaviour on both tasks, the resultant neurally-informed models furnished novel predictions regarding other features of the neural data which were empirically validated. These included a slower mean rate of evidence accumulation amongst older adults during motion discrimination and a beneficial reduction in between-trial variability in accumulation rates on the contrast-change detection task, which was linked to more consistent attentional engagement. Our findings serve to highlight how combining human brain signal measurements with computational modelling can yield unique insights into group differences in neural mechanisms for decision making.

scholarly journals Reconciling age-related changes in behavioural and neural indices of human perceptual decision-making

2018 ◽  
Vol 2 (12) ◽  
pp. 955-966 ◽  
Author(s):  
David P. McGovern ◽  
Aoife Hayes ◽  
Simon P. Kelly ◽  
Redmond G. O’Connell
Keyword(s):  
Decision Making ◽  
Perceptual Decision Making ◽  
Perceptual Decision ◽  
Age Related ◽  
Age Related Changes

scholarly journals Same model, different conclusions: An identifiability issue in the linear ballistic accumulator model of decision-making

2020 ◽  
Author(s):  
Nathan J. Evans
Keyword(s):  
Decision Making ◽  
Diffusion Model ◽  
Large Scale ◽  
Drift Rate ◽  
Decision Time ◽  
Measurement Properties ◽  
Decision Threshold ◽  
Experimental Conditions ◽  
Model Application ◽  
Linear Ballistic Accumulator

Evidence accumulation models (EAMs) – the dominant modelling framework for speeded decision-making – have become an important tool for model application. Model application involves using specific model to estimate parameter values that relate to different components of the cognitive process, and how these values differ over experimental conditions and/or between groups of participants. In this context, researchers are often agnostic to the specific theoretical assumptions made by different EAM variants, and simply desire a model that will provide them with an accurate measurement of the parameters that they are interested in. However, recent research has suggested that the two most commonly applied EAMs – the diffusion model and the linear ballistic accumulator (LBA) – come to fundamentally different conclusions when applied to the same empirical data. The current study provides an in-depth assessment of the measurement properties of the two models, as well as the mapping between, using two large scale simulation studies and a reanalysis of Evans (2020a). Importantly, the findings indicate that there is a major identifiability issue within the standard LBA, where differences in decision threshold between conditions are practically unidentifiable, which appears to be caused by a tradeoff between the threshold parameter and the overall drift rate across the different accumulators. While this issue can be remedied by placing some constraint on the overall drift rate across the different accumulators – such as constraining the average drift rate or the drift rate of one accumulator to have the same value in each condition – these constraints can qualitatively change the conclusions of the LBA regarding other constructs, such as non-decision time. Furthermore, all LBA variants considered in the current study still provide qualitatively different conclusions to the diffusion model. Importantly, the current findings suggest that researchers should not use the unconstrained version of the LBA for model application, and bring into question the conclusions of previous studies using the unconstrained LBA.

scholarly journals Age-dependency in binocular rivalry is reflected by exclusive percepts, not mixed percepts

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Elahe Arani ◽  
Raymond van Ee ◽  
Richard van Wezel
Keyword(s):  
Decision Making ◽  
Binocular Rivalry ◽  
Age Groups ◽  
The Other ◽  
Perceptual Decision Making ◽  
Age Related ◽  
Determining Factors ◽  
Decision Making Processes ◽  
Neural Factors ◽  
Inhibitory Interactions

AbstractSome aspects of decision-making are known to decline with normal aging. One of the known perceptual decision-making processes which is vastly studied is binocular rivalry. It is well-established that the older the person, the slower the perceptual dynamics. However, the underlying neurobiological cause is unknown. So, to understand how age affects visual decision-making, we investigated age-related changes in perception during binocular rivalry. In binocular rivalry, the image presented to one eye competes for perceptual dominance with the image presented to the other eye. Perception during binocular rivalry consists of alternations between exclusive percepts. However, frequently, mixed percepts with combinations of the two monocular images occur. The mixed percepts reflect a transition from the percept of one eye to the other but frequently the transitions do not complete the full cycle and the previous exclusive percept becomes dominant again. The transitional idiosyncrasy of mixed percepts has not been studied systematically in different age groups. Previously, we have found evidence for adaptation and noise, and not inhibition, as underlying neural factors that are related to age-dependent perceptual decisions. Based on those conclusions, we predict that mixed percepts/inhibitory interactions should not change with aging. Therefore, in an old and a young age group, we studied binocular rivalry dynamics considering both exclusive and mixed percepts by using two paradigms: percept-choice and percept-switch. We found a decrease in perceptual alternation Probability for older adults, although the rate of mixed percepts did not differ significantly compared to younger adults. Interestingly, the mixed percepts play a very similar transitional idiosyncrasy in our different age groups. Further analyses suggest that differences in synaptic depression, gain modulation at the input level, and/or slower execution of motor commands are not the determining factors to explain these findings. We then argue that changes in perceptual decisions at an older age are the result of changes in neural adaptation and noise.

Payoff Changes Sensitivity by Modulating the Processing Style

Perception ◽  
10.1068/p7124 ◽  
2012 ◽  
Vol 41 (5) ◽  
pp. 623-625
Author(s):  
Magdalena Krol ◽  
Wael El-Deredy
Keyword(s):  
Decision Making ◽  
Signal Detection ◽  
Sensory Input ◽  
Perceptual Task ◽  
Perceptual Decision Making ◽  
Processing Style ◽  
Detection Analysis ◽  
Signal Detection Analysis ◽  
Picture Signal ◽  
Higher Sensitivity

In a perceptual decision-making task, we compared a neutral payoff and two asymmetric payoffs: a liberal one, favouring positive responses, and a conservative one, favouring negative responses. Participants were presented with ambiguous images composed of superimposed target and non-target photographs, and asked to decide whether the target dominated in the picture. Signal-detection analysis demonstrated that the liberal payoff yielded significantly higher sensitivity than other payoffs. We argue that the liberal payoff encourages confirming the target's domination, hence making it easier to ignore non-target elements of the picture. We conclude that payoff can influence perceptual decisions by changing the approach to the perceptual task, and how attention is allocated between different elements of the sensory input.

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.

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