A New Spin on Spatial Cognition in ADHD: A Diffusion Model Decomposition of Mental Rotation

2020 ◽  
pp. 1-12
Author(s):  
Jason S. Feldman ◽  
Cynthia Huang-Pollock
Keyword(s):  
Mental Rotation ◽  
Diffusion Model ◽  
Cognitive Processing ◽  
Drift Rate ◽  
Mental Rotation Task ◽  
Model Decomposition ◽  
Children With Adhd ◽  
Boundary Separation ◽  
Slow Drift ◽  
Nondecision Time

Abstract Objectives: Multiple studies have found evidence of task non-specific slow drift rate in ADHD, and slow drift rate has rapidly become one of the most visible cognitive hallmarks of the disorder. In this study, we use the diffusion model to determine whether atypicalities in visuospatial cognitive processing exist independently of slow drift rate. Methods: Eight- to twelve-year-old children with (n = 207) and without ADHD (n = 99) completed a 144-trial mental rotation task. Results: Performance of children with ADHD was less accurate and more variable than non-ADHD controls, but there were no group differences in mean response time. Drift rate was slower, but nondecision time was faster for children with ADHD. A Rotation × ADHD interaction for boundary separation was also found in which children with ADHD did not strategically adjust their response thresholds to the same degree as non-ADHD controls. However, the Rotation × ADHD interaction was not significant for nondecision time, which would have been the primary indicator of a specific deficit in mental rotation per se. Conclusions: Poorer performance on the mental rotation task was due to slow rate of evidence accumulation, as well as relative inflexibility in adjusting boundary separation, but not to impaired visuospatial processing specifically. We discuss the implications of these findings for future cognitive research in ADHD.

scholarly journals An investigation of age related slowing in cognitive processing speed in a mental rotation task.

1998 ◽  
Vol 69 (5) ◽  
pp. 393-400 ◽  
Author(s):  
Yasuyuki Gondo ◽  
Osamu Ishihara ◽  
Katsuharu Nakazato ◽  
Yoshiko Shimonaka ◽  
Leonard W. Poon
Keyword(s):  
Mental Rotation ◽  
Cognitive Processing ◽  
Processing Speed ◽  
Mental Rotation Task ◽  
Age Related ◽  
Cognitive Processing Speed

Task-Related Versus Stimulus-Specific Practice

2011 ◽  
Vol 58 (6) ◽  
pp. 434-442 ◽  
Author(s):  
Gilles Dutilh ◽  
Angelos-Miltiadis Krypotos ◽  
Eric-Jan Wagenmakers
Keyword(s):  
Information Processing ◽  
Diffusion Model ◽  
Response Bias ◽  
Response Times ◽  
Cognitive Task ◽  
Transfer Experiment ◽  
Model Decomposition ◽  
Speed Of Information Processing ◽  
Nondecision Time ◽  
Specific Practice

When people repeatedly practice the same cognitive task, their response times (RT) invariably decrease. Dutilh, Vandekerckhove, Tuerlinckx, and Wagenmakers (2009) argued that the traditional focus on how mean RT decreases with practice offers limited insight; their diffusion model analysis showed that the effect of practice is multifaceted, involving an increase in rate of information processing, a decrease in response caution, adjusted response bias, and, unexpectedly, a strong decrease in nondecision time. In this study, we aim to further disentangle these effects into stimulus-specific and task-related components. The data of a transfer experiment, in which repeatedly presented sets and new sets of stimuli were alternated, show that the practice effects on both speed of information processing and time needed for peripheral processing are partly task-related and partly stimulus-specific. The effects on response caution and response bias appear to be task-related. This diffusion model decomposition provides a perspective on practice that is more detailed and more informative than the traditional analysis of mean RT.

Does the Name-Race Implicit Association Test Measure Racial Prejudice?

2011 ◽  
Vol 58 (4) ◽  
pp. 271-277 ◽  
Author(s):  
Don van Ravenzwaaij ◽  
Han L. J. van der Maas ◽  
Eric-Jan Wagenmakers
Keyword(s):  
Diffusion Model ◽  
Implicit Association Test ◽  
Alternative Explanation ◽  
Group Membership ◽  
Racial Prejudice ◽  
Decision Time ◽  
Association Test ◽  
Implicit Association ◽  
Model Decomposition ◽  
Test Measure

Research using the Implicit Association Test (IAT) has shown that names labeled as Caucasian elicit more positive associations than names labeled as non-Caucasian. One interpretation of this result is that the IAT measures latent racial prejudice. An alternative explanation is that the result is due to differences in in-group/out-group membership. In this study, we conducted three different IATs: one with same-race Dutch names versus racially charged Moroccan names; one with same-race Dutch names versus racially neutral Finnish names; and one with Moroccan names versus Finnish names. Results showed equivalent effects for the Dutch-Moroccan and Dutch-Finnish IATs, but no effect for the Finnish-Moroccan IAT. This suggests that the name-race IAT-effect is not due to racial prejudice. A diffusion model decomposition indicated that the IAT-effects were caused by changes in speed of information accumulation, response conservativeness, and non-decision time.

Individual Differences in Mental Rotation

2013 ◽  
Vol 60 (3) ◽  
pp. 164-171 ◽  
Author(s):  
Peter Khooshabeh ◽  
Mary Hegarty ◽  
Thomas F. Shipley
Keyword(s):  
Individual Differences ◽  
Mental Rotation ◽  
Response Times ◽  
Task Demands ◽  
Mental Rotation Task ◽  
Imagery Ability ◽  
Alternative Strategies

Two experiments tested the hypothesis that imagery ability and figural complexity interact to affect the choice of mental rotation strategies. Participants performed the Shepard and Metzler (1971) mental rotation task. On half of the trials, the 3-D figures were manipulated to create “fragmented” figures, with some cubes missing. Good imagers were less accurate and had longer response times on fragmented figures than on complete figures. Poor imagers performed similarly on fragmented and complete figures. These results suggest that good imagers use holistic mental rotation strategies by default, but switch to alternative strategies depending on task demands, whereas poor imagers are less flexible and use piecemeal strategies regardless of the task demands.

Neuroimaging correlates of insight in obsessive compulsive disorder: A fMRI study

2017 ◽  
Vol 41 (S1) ◽  
pp. S409-S409
Author(s):  
A. Gadad ◽  
D.Y.C.J. Reddy ◽  
D.G. Venkatasubramanian ◽  
D.J. C.N
Keyword(s):  
Mental Rotation ◽  
Negative Correlation ◽  
Mental Rotation Task ◽  
Healthy Controls ◽  
Inferior Parietal Lobule ◽  
Good Insight ◽  
Compulsive Disorder ◽  
Accuracy Ratio ◽  
Parietal Lobule ◽  
Medial Frontal Gyrus

Aim of the studyTo study the neural substrates of insight in OCD by comparing patients with good insight, patients with poor insight and matched healthy controls using functional MRI.MethodologySubjects were recruited from among patients attending OCD clinic, adult psychiatry services and psychiatry ward inpatients of National Institute of Mental Health And Neurosciences (NIMHANS), Bangalore. They were further divided into ‘good insight’ (n = 30) and ‘poor insight’ (n = 14) using Brown's assessment of belief's scale. Control subjects (n = 30) were recruited from consenting volunteers. 3 T MRI was used mental rotation task was paradigm used for fMRI and analysis was done by SPM 8.ResultsPoor insight patients and good insight patients comparison revealed differential activation in left superior/medial frontal gyrus (corresponding to the DLPFC). A negative correlation between BABS score and activation of right inferior parietal lobule. Mental rotation task behavioural data results: OCD patients as a group had significantly lower accuracy compared to healthy controls. Poor insight group had significantly decreased accuracy ratio compared to good insight group and healthy controls. A negative correlation was noted between BABS score and accuracy ratio, indicating that poorer the insight, greater the errors during the active task.ConclusionInsight has been important prognostic factor in OCD. Poor insight patients had specific deficits in left medial frontal gyrus and right inferior parietal lobule as compared to good insight patients and healthy controls. Together, these indicate that insight has a strong neurobiological underpinning in OCD.Disclosure of interestThe authors have not supplied their declaration of competing interest.

scholarly journals Mental Rotation Meets the Motion Aftereffect: The Role of hV5/MT+ in Visual Mental Imagery

2011 ◽  
Vol 23 (6) ◽  
pp. 1395-1404 ◽  
Author(s):  
Ruth Seurinck ◽  
Floris P. de Lange ◽  
Erik Achten ◽  
Guy Vingerhoets
Keyword(s):  
Mental Rotation ◽  
Mental Imagery ◽  
Parietal Cortex ◽  
Neural Activity ◽  
Posterior Parietal Cortex ◽  
Visual Motion ◽  
Motion Aftereffect ◽  
Mental Rotation Task ◽  
Behavioral Performance ◽  
Visual Mental Imagery

A growing number of studies show that visual mental imagery recruits the same brain areas as visual perception. Although the necessity of hV5/MT+ for motion perception has been revealed by means of TMS, its relevance for motion imagery remains unclear. We induced a direction-selective adaptation in hV5/MT+ by means of an MAE while subjects performed a mental rotation task that elicits imagined motion. We concurrently measured behavioral performance and neural activity with fMRI, enabling us to directly assess the effect of a perturbation of hV5/MT+ on other cortical areas involved in the mental rotation task. The activity in hV5/MT+ increased as more mental rotation was required, and the perturbation of hV5/MT+ affected behavioral performance as well as the neural activity in this area. Moreover, several regions in the posterior parietal cortex were also affected by this perturbation. Our results show that hV5/MT+ is required for imagined visual motion and engages in an interaction with parietal cortex during this cognitive process.

scholarly journals Acoustic Neurofeedback Increases Beta ERD During Mental Rotation Task

2018 ◽  
Vol 44 (2) ◽  
pp. 103-115 ◽  
Author(s):  
Wioletta Karina Ozga ◽  
Dariusz Zapała ◽  
Piotr Wierzgała ◽  
Paweł Augustynowicz ◽  
Robert Porzak ◽  
...  
Keyword(s):  
Mental Rotation ◽  
Mental Rotation Task

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 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.

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