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A Low-Level Perceptual Correlate of Behavioral and Clinical Deficits in ADHD Cover

A Low-Level Perceptual Correlate of Behavioral and Clinical Deficits in ADHD

Open Access
|Oct 2018

Figures & Tables

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Figure 1. 

Task design. A) Trial sequence example. A feature dimension cue indicated whether orientation (cross)–depicted here–or color (colored circle) was relevant, while a simultaneous endogenous spatial cue (line segment) indicated which side (left or right) was relevant. Thus, the participant received one of four possible cue screens. We always chose the spatial cue randomly. The participant had to respond whether the orientation of the ellipse on the relevant side was clockwise or counterclockwise with respect to vertical or whether its color was more yellow or more blue, with the associated set of keys (left or right). The color and orientation continua are shown above the stimulus screen, with the dashed line at vertical and respectively mid-level green. To respond, the participant could press any one of eight keys, but only two were task-relevant on a given trial; the other six keys being considered task-irrelevant motor output. The participant received correctness feedback. B) Left: Cue–relevant stimulus–relevant response buttons pairings for the four types of trials as they arise from the four feature and spatial cue combinations (2 × 2). Relevant is marked with pink for visualization only. Pressing any other button would result in task-irrelevant motor output. Right: During Ori and Col blocks, only two types of trials are possible, while during Switch blocks, all four trial types are possible.

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Figure 2. 

Dissociation of perceptual and executive processes. Schematic of the early perceptual encoding and late stimulus–response rule selection (executive) processes that may play a role in this task, and the corresponding task metrics.

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Figure 3. 

ADHD participants had higher task-irrelevant motor output and longer and more variable reaction times. A) Proportion of TIMO across conditions. Here and elsewhere, values represent medians across participants and error bars the bootstrapped 95% confidence intervals. B) Empirical cumulative density functions of reaction times, collapsed across all conditions. Thin lines: individual participants. Thick lines: median for the RT distribution collapsed across all participants in a group. C) Reaction time median by condition and group. Throughout the article, we use RT median because reaction time distributions are not Gaussian. D) Reaction time variability metric, the τ parameter from ex-Gaussian distribution fits, by condition and group.

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Figure 4. 

Fitted psychometric curves and parameters; attention-deficit hyperactivity disorder participants had higher perceptual variability. A) Psychometric curve fits across all conditions. Here and elsewhere, n.u. stands for normalized units. Thin lines: individual participants. Thick lines: medians for each group. For fits overlaid on top of data, see Mihali et al. (2018, Appendix, Figure A8). B) Perceptual variability parameter values, medians, and bootstrapped 95% confidence intervals. Top inset plot: black psychometric curve has low noise, while the gray has higher noise. C) Lapse rate. Top inset plot: black psychometric curve has low lapse, while the gray has higher lapse.

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Figure 5. 

Logistic regression based on task metrics can classify participants into attention-deficit hyperactivity disorder and controls with accuracies larger than 70%. A) Dots: combinations of log TIMO and log perceptual variability (σ) across participants. Dashed lines: logistic regression classifiers trained on log σ only (olive), TIMO only (old rose), and both (black). B) Full receiver operating characteristic curves obtained by varying the diagnosis threshold for the three classifiers in A, as well as for one based on all five behavioral metrics (purple). C) Full ROC curves, this time with stratified 10-fold cross-validation, for the same classifiers as in B.

Table 1. 

Pairwise Spearman correlations across log task metrics (both behavioral and clinical)

TIMOTIMORTRT τ Perceptual variability (σ)Lapse rate (λ)GEC
RT ρ = 0.46      
p = 0.003
RT τ ρ = 0.42 ρ = 0.84     
p = 0.007 p < 0.0001
Perceptual variability (σ) ρ = 0.41 ρ = 0.55 ρ = 0.57    
p = 0.0085 p = 0.0003 p = 0.0002
Lapse rate (λ) ρ = 0.46 ρ = 0.23 ρ = 0.17 ρ = 0.28  
p = 0.003 p = 0.15 p = 0.30 p = 0.08
GEC ρ = 0.53 ρ = 0.25 ρ = 0.34 ρ = 0.50 ρ = 0.30 
p = 0.0005 p = 0.12 p = 0.03 p = 0.0009 p = 0.06
ACDS ρ = 0.40 ρ = 0.31 ρ = 0.45 ρ = 0.51 ρ = 0.18 ρ = 0.80
p = 0.01 p = 0.05 p = 0.004 p = 0.0008 p = 0.26 p < 0.0001

[i] Note. Both TIMO and perceptual variability are significantly correlated with several other variables. Boldface denotes significance after multiple-comparisons correction (α = 0.0089; see Methods).

Language: English
Submitted on: Oct 23, 2017
Accepted on: Jul 10, 2018
Published on: Oct 1, 2018
Published by: MIT Press
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2018 Andra Mihali, Allison G. Young, Lenard A. Adler, Michael M. Halassa, Wei Ji Ma, published by MIT Press
This work is licensed under the Creative Commons Attribution 4.0 License.