Fitting the ratcliff diffusion model to experimental data

Vandekerckhove, Joachim; Tuerlinckx, Francis

doi:10.3758/bf03193087

Cited by 196 publications

(208 citation statements)

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“…Further details about these methods can be found in Vandekerckhove & Tuerlinckx (2007). A total of 36 participants (1.6%) had more than 50% of missing/outlier responses and were excluded from the analysis.…”

Section: Deriving Indices Of Bipmentioning

confidence: 99%

Mechanisms underpinning inattention and hyperactivity: neurocognitive support for ADHD dimensionality

et al. 2014

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Background. Taxometric and behavioral genetic studies suggest that attention deficit hyperactivity disorder (ADHD) is best modeled as a dimension rather than a category. We extended these analyses by testing for the existence of putative ADHD-related deficits in basic information processing (BIP) and inhibitory-based executive function (IB-EF) in individuals in the subclinical and full clinical ranges. Consistent with the dimensional model, we predicted that ADHD-related deficits would be expressed across the full spectrum, with the degree of deficit linearly related to the severity of the clinical presentation.Method. A total of 1547 children (aged 6-12 years) participated in the study. The Development and Well-Being Assessment (DAWBA) was used to classify children into groups according to levels of inattention and hyperactivity independently: (1) asymptomatic, (2) subthreshold minimal, (3) subthreshold moderate and (4) clinical ADHD. Neurocognitive performance was evaluated using a two-choice reaction time task (2C-RT) and a conflict control task (CCT). BIP and IB-EF measures were derived using a diffusion model (DM) for decomposition of reaction time (RT) and error data.Results. Deficient BIP was found in subjects with minimal, moderate and full ADHD defined in terms of inattention (in both tasks) and hyperactivity/impulsivity dimensions (in the 2C-RT). The size of the deficit increased in a linear manner across increasingly severe presentations of ADHD. IB-EF was unrelated to ADHD.Conclusions. Deficits in BIP operate at subclinical and clinical levels of ADHD. The linear nature of this relationship provides support for a dimensional model of ADHD in which diagnostic thresholds are defined in terms of clinical and societal burden rather than representing discrete pathophysiological states.

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Section: Deriving Indices Of Bipmentioning

confidence: 99%

Mechanisms underpinning inattention and hyperactivity: neurocognitive support for ADHD dimensionality

et al. 2014

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“…Here, we employed a similar approach and computed separate DDMs for each subject, with reaction times and type of responses being the dependent variables of the models. For computing the DDMs we used the DMA toolbox (VandeKerckhove and Tuerlinckx, 2007).…”

Section: Behavioral Modelsmentioning

confidence: 99%

“…This comparison was based on the Bayesian Information Criterion (BIC; (VandeKerckhove and Tuerlinckx, 2007). For every subject, the model where the drift rates vary across difficulty levels provided lower values of BIC than the model where parameters were considered constant across difficulty.…”

Section: Behavioral Modelsmentioning

confidence: 99%

“…The mean boundary separation across subjects was 0.23 ± 0.02, the mean starting point of the diffusion (zo) was 0.12 ± 0.01 and the total non-decision time (Ter) was 559 ± 4 ms. This last parameter reflects the time needed for processes that do not contribute in the decision itself, such as the encoding of visual stimuli, or the motor response (VandeKerckhove and Tuerlinckx, 2007).…”

Section: Behaviormentioning

confidence: 99%

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Quantifying the time for accurate EEG decoding of single value-based decisions

Tzovara

Chavarriaga

Lucia

2015

Journal of Neuroscience Methods

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h i g h l i g h t s• We outline a novel method for assessing time-unlocked topographic EEG activity.• We apply this method for accurately predicting accept vs. reject decisions.• The time when decisions have been made is modulated by task difficulty.• It occurs well before subjects' button press (∼340 ms).• This time is compatible with a diffusion process, evaluated at behavioral level. a r t i c l e i n f o b s t r a c tBackground: Recent neuroimaging studies suggest that value-based decision-making may rely on mechanisms of evidence accumulation. However no studies have explicitly investigated the time when single decisions are taken based on such an accumulation process. New method: Here, we outline a novel electroencephalography (EEG) decoding technique which is based on accumulating the probability of appearance of prototypical voltage topographies and can be used for predicting subjects' decisions. We use this approach for studying the time-course of single decisions, during a task where subjects were asked to compare reward vs. loss points for accepting or rejecting offers. Results: We show that based on this new method, we can accurately decode decisions for the majority of the subjects. The typical time-period for accurate decoding was modulated by task difficulty on a trial-bytrial basis. Typical latencies of when decisions are made were detected at ∼500 ms for 'Easy' vs. ∼700 ms for 'Hard' decisions, well before subjects' response (∼340 ms). Importantly, this decision time correlated with the drift rates of a diffusion model, evaluated independently at the behavioral level. Comparison with existing method(s):We compare the performance of our algorithm with logistic regression and support vector machine and show that we obtain significant results for a higher number of subjects than with these two approaches. We also carry out analyses at the average event-related potential level, for comparison with previous studies on decision-making. Conclusions: We present a novel approach for studying the timing of value-based decision-making, by accumulating patterns of topographic EEG activity at single-trial level.

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“…The drift-diffusion model (DDM) of decision-making is a widely employed theoretical model in behavioral neurobiology ( [34][35][36][37]. DDM accurately reproduces (i.e., simulates) the decision-time histograms (see Experimental in Figure 1l), which was in turn reproduced by PDE almost exactly.…”

Section: Decision-time Histogrammentioning

confidence: 99%

Waves as the Symmetry Principle Underlying Cosmic, Cell, and Human Languages

2017

Information

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Abstract:In 1997, the author concluded that living cells use a molecular language (cellese) that is isomorphic with the human language (humanese) based on his finding that the former shared 10 out of the 13 design features of the latter. In 2012, the author postulated that cellese and humanese derived from a third language called the cosmic language (or cosmese) and that what was common among these three kinds of languages was waves-i.e., sound waves for humanese, concentration waves for cellese, and quantum waves for cosmese. These waves were suggested to be the symmetry principle underlying cosmese, cellese, and humanese. We can recognize at least five varieties of waves-(i) electromagnetic; (ii) mechanical; (iii) chemical concentration; (iv) gravitational; and (v) probability waves, the last being non-material, in contrast to the first four, which are all material. The study of waves is called "cymatics" and the invention of CymaScope by J. S. Reid of the United Kingdom in 2002 is expected to accelerate the study of waves in general. CymaScope has been used to visualize not only human sounds (i.e., humanese) but also sounds made by individual cells (cellese) in conjunction with Atomic Force Microscopy (AFM) (unpublished observations of J. Gimzewski of UCLA and J. Reid). It can be predicted that the gravitational waves recently detected by the Interferometer Gravitational-Wave Observatory (LIGO) will be visualized with CymaScope one day, thereby transforming gravitational waves into CymaGlyphs. Since cellese in part depends on RNA concentration waves (or RNA glyphs) and humanese includes hieroglyphs that were decoded by Champollion in 1822, it seems reasonable to use cymaglyphs, RNA glyphs, and hieroglyphs as symbols of cosmese, cellese, and humanese, respectively, all based on the principle of waves as the medium of communication.

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Fitting the ratcliff diffusion model to experimental data

Cited by 196 publications

References 39 publications

Mechanisms underpinning inattention and hyperactivity: neurocognitive support for ADHD dimensionality

Mechanisms underpinning inattention and hyperactivity: neurocognitive support for ADHD dimensionality

Quantifying the time for accurate EEG decoding of single value-based decisions

Waves as the Symmetry Principle Underlying Cosmic, Cell, and Human Languages

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