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

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.

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

10.31234/osf.io/jwsmb ◽

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.

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Assessing model-based inferences in decision making with single-trial response time decomposition

10.31234/osf.io/kc26f ◽

2020 ◽

Cited By ~ 2

Author(s):

Gabriel Weindel ◽

Royce anders ◽

F.-Xavier Alario ◽

Boris BURLE

Keyword(s):

Decision Making ◽

Decision Processes ◽

Emg Activity ◽

Parameter Estimates ◽

Model Parameters ◽

Response Force ◽

Model Based ◽

Wide Range ◽

Response Execution ◽

Speed Accuracy

Decision-making models based on evidence accumulation processes (the most prolific one being the drift-diffusion model – DDM) are widely used to draw inferences about latent psychological processes from chronometric data. While the observed goodness of fit in a wide range of tasks supports the model’s validity, the derived interpretations have yet to be sufficiently cross-validated with other measures that also reflect cognitive processing. To do so, we recorded electromyographic (EMG) activity along with response times (RT), and used it to decompose every RT into two components: a pre-motor (PMT) and motor time (MT). These measures were mapped to the DDM's parameters, thus allowing a test, beyond quality of fit, of the validity of the model’s assumptions and their usual interpretation. In two perceptual decision tasks, performed within a canonical task setting, we manipulated stimulus contrast, speed-accuracy trade-off, and response force, and assessed their effects on PMT, MT, and RT. Contrary to common assumptions, these three factors consistently affected MT. DDM parameter estimates of non-decision processes are thought to include motor execution processes, and they were globally linked to the recorded response execution MT. However, when the assumption of independence between decision and non-decision processes was not met, in the fastest trials, the link was weaker. Overall, the results show a fair concordance between model-based and EMG-based decompositions of RTs, but also establish some limits on the interpretability of decision model parameters linked to response execution.

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

Nature Human Behaviour ◽

10.1038/s41562-018-0465-6 ◽

2018 ◽

Vol 2 (12) ◽

pp. 955-966 ◽

Cited By ~ 7

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

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Causal evidence for a domain-specific role of left superior frontal sulcus in human perceptual decision-making

10.1101/2021.10.04.462977 ◽

2021 ◽

Author(s):

Miguel Barretto Garcia ◽

Marcus Grueschow ◽

Marius Moisa ◽

Rafael Polania ◽

Christian Carl Ruff

Keyword(s):

Decision Making ◽

Sequential Sampling ◽

Computational Modelling ◽

Perceptual Decision Making ◽

Domain Specific ◽

Neural Processes ◽

Sequential Sampling Model ◽

Evidence Integration ◽

Superior Frontal Sulcus

Humans and animals can flexibly choose their actions based on different information, ranging from objective states of the environment (e.g., apples are bigger than cherries) to subjective preferences (e.g., cherries are tastier than apples). Whether the brain instantiates these different choices by recruiting either specialized or shared neural circuitry remains debated. Specifically, domain-general theories of prefrontal cortex (PFC) function propose that prefrontal areas flexibly process either perceptual or value-based evidence depending on what is required for the present choice, whereas domain-specific theories posit that PFC sub- areas, such as the left superior frontal sulcus (SFS), selectively integrate evidence relevant for perceptual decisions. Here we comprehensively test the functional role of the left SFS for choices based on perceptual and value-based evidence, by combining fMRI with a behavioural paradigm, computational modelling, and transcranial magnetic stimulation. Confirming predictions by a sequential sampling model, we show that TMS-induced excitability reduction of the left SFS selectively changes the processing of decision-relevant perceptual information and associated neural processes. In contrast, value-based decision making and associated neural processes remain unaffected. This specificity of SFS function is evident at all levels of analysis (behavioural, computational, and neural, including functional connectivity), demonstrating that the left SFS causally contributes to evidence integration for perceptual but not value-based decisions.

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Age-related decreases in relapses among adults with relapsing-onset multiple sclerosis

Multiple Sclerosis Journal ◽

10.1177/1352458519866613 ◽

2019 ◽

Vol 26 (12) ◽

pp. 1510-1518 ◽

Cited By ~ 2

Author(s):

Natalie A Schwehr ◽

Karen M Kuntz ◽

Mary Butler ◽

Eva A Enns ◽

Nathan D Shippee ◽

...

Keyword(s):

Multiple Sclerosis ◽

Simulation Modeling ◽

Scale Model ◽

Model Parameters ◽

Specific Component ◽

Dynamic Relationship ◽

Age Related ◽

Wide Range ◽

Disability Status ◽

Relapse Rates

Background: Relapsing-onset multiple sclerosis (MS) typically starts in early- to mid-adulthood, yet the trajectory of disease activity over the subsequent lifetime remains poorly defined. Previous studies have not quantified the age-specific portion of decreases in annualized relapse rates (ARR). Objective: The aim of this article is to determine, under a range of disease-related assumptions, the age-specific component of decreases in ARR over time among adults with relapsing-onset MS. Methods: We used a simulation modeling approach to examine a range of assumptions about changes in ARR due to age versus disability status. Scenarios included variations in initial ARR and rate of worsening on the Expanded Disability Status Scale. Model parameters were developed through analysis of MS patients in British Columbia, Canada, and literature review. Results: We found a substantial age-specific decrease in ARR in all simulated scenarios, independent of disability worsening. Under a range of clinically plausible assumptions, 88%–97% of the decrease was attributed to age and 3%–13% to disability. The age-specific decrease ranged from 22% to 37% per 5 years for a wide range of initial ARR (0.33–1.0). Conclusion: Decreases in ARR were due mostly to age rather than disability status. To facilitate informed decision making in MS, it is important to quantify the dynamic relationship between relapses and age.

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

Scientific Reports ◽

10.1038/s41598-019-55890-5 ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 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.

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Parameter Estimation in a Crossbridge Muscle Model

Journal of Biomechanical Engineering ◽

10.1115/1.1537262 ◽

2003 ◽

Vol 125 (1) ◽

pp. 132-140 ◽

Cited By ~ 7

Author(s):

David C. Lin ◽

T. Richard Nichols

Keyword(s):

Parameter Estimates ◽

Model Parameters ◽

Single Muscle ◽

Rate Parameter ◽

Attachment Rate ◽

Detachment Rate ◽

Wide Range ◽

Distribution Moment ◽

Force Velocity ◽

Physiological Variations

Models of muscle crossbridge dynamics have great potential for understanding muscle contraction and having a wide range of application. However, the estimation of many model parameters, most of which are difficult to measure, limits their applicability. This study developed a method of estimating parameters in the Distribution Moment crossbridge model from measurements of force-length and force-velocity relationships in cat soleus single muscle fibers. Analysis of the parameter estimates showed that the detachment rate parameters had more uncertainty than the attachment rate parameter, which could reflect physiological variations in the contractile protein content and in the response of muscle to lengthenings.

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Leave-One-Trial-Out (LOTO): A general approach to link single-trial parameters of cognitive models to neural data

10.1101/403766 ◽

2018 ◽

Author(s):

Sebastian Gluth ◽

Nachshon Meiran

Keyword(s):

Cognitive Model ◽

Parameter Estimates ◽

Model Parameters ◽

Single Trial ◽

Neural Data ◽

Brain Signals ◽

Complete Dataset ◽

Advance Research ◽

The Difference ◽

Parameter Values

AbstractIt has become a key goal of model-based neuroscience to estimate trial-by-trial fluctuations of cognitive model parameters for linking these fluctuations to brain signals. However, previously developed methods were limited by being difficulty to implement, time-consuming, or model-specific. Here, we propose an easy, efficient and general approach to estimating trial-wise changes in parameters: Leave-One-Trial-Out (LOTO). The rationale behind LOTO is that the difference between the parameter estimates for the complete dataset and for the dataset with one omitted trial reflects the parameter value in the omitted trial. We show that LOTO is superior to estimating parameter values from single trials and compare it to previously proposed approaches. Furthermore, the method allows distinguishing true variability in a parameter from noise and from variability in other parameters. In our view, the practicability and generality of LOTO will advance research on tracking fluctuations in latent cognitive variables and linking them to neural data.

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

Proceedings of the Human Factors and Ergonomics Society Annual Meeting ◽

10.1177/107118139704100130 ◽

1997 ◽

Vol 41 (1) ◽

pp. 130-133 ◽

Cited By ~ 1

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.

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Effect of Macroporosity on Physical Property Estimates for Peat Soils

10.5194/egusphere-egu21-5238 ◽

2021 ◽

Author(s):

Miaorun Wang ◽

Haojie Liu ◽

Bernd Lennartz

Keyword(s):

Hydraulic Conductivity ◽

Soil Properties ◽

Total Porosity ◽

Pedotransfer Functions ◽

Parameter Estimates ◽

Model Parameters ◽

Soil Water Retention ◽

Peat Soils ◽

Undisturbed Soil ◽

Wide Range

Hydrophysical soil properties play an important role in regulating the water balance of peatlands and are known to be a function of the status of peat degradation. The objective of this study was to revise multiple regression models (pedotransfer functions, PTFs) for the assessment of hydrophysical properties from readily available soil properties. We selected three study sites, each representing a different state of peat degradation (natural, degraded and extremely degraded). At each site, 72 undisturbed soil cores were collected. The saturated hydraulic conductivity (Ks), soil water retention curves, total porosity, macroporosity, bulk density (BD) and soil organic matter (SOM) content were determined for all sampling locations. The van Genuchten (VG) model parameters (&#952;s,&#160;&#945;,&#160;n) were optimized using the RETC software package. Macroporosity and the&#160;Ks&#160;were found to be highly correlated, but the obtained functions differ for differently degraded peatlands. The introduction of macroporosity into existing PTFs substantially improved the derivation of hydrophysical parameter values as compared to functions based on BD and SOM content alone. The obtained PTFs can be applied to a wide range of natural and degraded peat soils. We assume that the incorporation of macroposity helps to overcome effects possibly resulting from soil management. Our results suggest that the extra effort required to determine macroporosity is worth it, considering the quality of parameter estimates for hydraulic conductivity as well as the soil hydraulic VG model.

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