Advances in modern mental chronometry

Medina, José M.; Wong, Willy; Díaz, José A.; Colonius, Hans

doi:10.3389/fnhum.2015.00256

Cited by 14 publications

(7 citation statements)

References 25 publications

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“…The research tradition focusing on the timing of sub-processes is termed mental chronometry ( Posner, 1978 ). Using thoughtful experimental designs, researchers were able to disentangle many of the sub-processes giving rise to performance in cognitive tasks ( Meyer et al, 1988 ; Medina et al, 2015 ). Another, complementary, approach is to estimate the timing of cognitive processes via the timing of their (probable) neuronal correlates ( Meyer et al, 1988 ; Coles, 1989 ; for recent examples, see Hyun et al, 2009 ; Töllner et al, 2012 ; Fortier-Gauthier et al, 2013 ; Ruhnau et al, 2013 ; Liesefeld et al, 2014 ; Dell’Acqua et al, 2015 ; Drisdelle et al, 2016 ; Grubert and Eimer, 2016 ; Liesefeld et al, 2017 ; Ruhnau et al, 2017 ; Xie and Zhang, 2018 ).…”

Section: Estimating the Timing Of Cognitive Operations With Meg/eeg Lmentioning

confidence: 99%

Estimating the Timing of Cognitive Operations With MEG/EEG Latency Measures: A Primer, a Brief Tutorial, and an Implementation of Various Methods

Liesefeld

2018

Front. Neurosci.

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The major advantage of MEG/EEG over other neuroimaging methods is its high temporal resolution. Examining the latency of well-studied components can provide a window into the dynamics of cognitive operations beyond traditional response-time (RT) measurements. While RTs reflect the cumulative duration of all time-consuming cognitive operations involved in a task, component latencies can partition this time into cognitively meaningful sub-steps. Surprisingly, most MEG/EEG studies neglect this advantage and restrict analyses to component amplitudes without considering latencies. The major reasons for this neglect might be that, first, the most easily accessible latency measure (peak latency) is often unreliable and that, second, more complex measures are difficult to conceive, implement, and parametrize. The present article illustrates the key advantages and disadvantages of the three main types of latency-measures (peak latency, onset latency, and percent-area latency), introduces a MATLAB function that extracts all these measures and is compatible with common analysis tools, discusses the most important parameter choices for different research questions and components of interest, and demonstrates its use by various group analyses on one planar gradiometer pair of the publicly available Wakeman and Henson (2015) data. The introduced function can extract from group data not only single-subject latencies, but also grand-average and jackknife latencies. Furthermore, it gives the choice between different approaches to automatically set baselines and anchor points for latency estimation, approaches that were partly developed by me and that capitalize on the informational richness of MEG/EEG data. Although the function comes with a wide range of customization parameters, the default parameters are set so that even beginners get reasonable results. Graphical depictions of latency estimates, baselines, and anchor points overlaid on individual averages further support learning, understanding and trouble-shooting. Once extracted, latency estimates can be submitted to any analysis also available for (averaged) RTs, including tests for mean differences, correlational approaches and cognitive modeling.

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Section: Estimating the Timing Of Cognitive Operations With Meg/eeg Lmentioning

confidence: 99%

Estimating the Timing of Cognitive Operations With MEG/EEG Latency Measures: A Primer, a Brief Tutorial, and an Implementation of Various Methods

Liesefeld

2018

Front. Neurosci.

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“…Behavioral reaction time (RT) has been a widely used measure of perceptual performance (Meyer et al, 1988 ; Grondin, 2010 ; Medina et al, 2015 ). Cook and Maunsell ( 2002 ) found that the neuronal activity in MT and ventral intra-parietal (VIP) areas are correlated with the monkey’s detection RT on a trial-by-trial basis.…”

Section: Introductionmentioning

confidence: 99%

Neural Activity Predicts Reaction in Primates Long Before a Behavioral Response

Dezfouli

Khamechian

Treue

et al. 2018

Front. Behav. Neurosci.

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How neural activity is linked to behavior is a critical question in neural engineering and cognitive neurosciences. It is crucial to predict behavior as early as possible, to plan a machine response in real-time brain computer interactions. However, previous studies have studied the neural readout of behavior only within a short time before the action is performed. This leaves unclear, if the neural activity long before a decision could predict the upcoming behavior. By recording extracellular neural activities from the visual cortex of behaving rhesus monkeys, we show that: (1) both, local field potentials (LFPs) and the rate of neural spikes long before (>2 s) a monkey responds to a change, foretell its behavioral performance in a spatially selective manner; (2) LFPs, the more accessible component of extracellular activity, are a stronger predictor of behavior; and (3) LFP amplitude is positively correlated while spiking activity is negatively correlated with behavioral reaction time (RT). These results suggest that field potentials could be used to predict behavior way before it is performed, an observation that could potentially be useful for brain computer interface applications, and that they contribute to the sensory neural circuit’s speed in information processing.

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“…The time it takes for the brain to execute this process is typically referred to as perceptionresponse time (PRT). This type of model has its basis in a long and rich research tradition in cognitive psychology known as mental chronometry, which refers to the scientific study of reaction time as the basis for inferring cognitive mechanisms (e.g., Medina, Wong, Díaz and Colonius (2015); Posner (1978); Welford (1952)).…”

Section: Traditional Notions Of Perception-response Time and Their Li...mentioning

confidence: 99%

Modeling road user response timing in naturalistic settings: a surprise-based framework

Engström¹,

Liu²,

Dinparastdjadid³

et al. 2022

Preprint

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There is currently no established method for evaluating human response timing across a range of naturalistic traffic conflict types. Traditional notions derived from controlled experiments, such as perception-response time, fail to account for the situation-dependency of human responses and offer no clear way to define the stimulus in many common traffic conflict scenarios. As a result, they are not well suited for application in naturalistic settings. Our main contribution is the development of a novel framework for measuring and modeling response times in naturalistic traffic conflicts applicable to automated driving systems as well as other traffic safety domains. The framework suggests that response timing must be understood relative to the subject's current (prior) belief and is always embedded in, and dependent on, the dynamically evolving situation. The response process is modeled as a belief update process driven by perceived violations to this prior belief, that is, by surprising stimuli. The framework resolves two key limitations with traditional notions of response time when applied in naturalistic scenarios: (1) The strong situation-dependence of response timing and (2) how to unambiguously define the stimulus. Resolving these issues is a challenge that must be addressed by any response timing model intended to be applied in naturalistic traffic conflicts. We show how the framework can be implemented by means of a relatively simple heuristic model fit to naturalistic human response data from real crashes and near crashes from the SHRP2 dataset and discuss how it is, in principle, generalizable to any traffic conflict scenario. We also discuss how the response timing framework can be implemented computationally based on evidence accumulation enhanced by machine learning-based generative models and the information-theoretic concept of surprise.

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Advances in modern mental chronometry

Cited by 14 publications

References 25 publications

Estimating the Timing of Cognitive Operations With MEG/EEG Latency Measures: A Primer, a Brief Tutorial, and an Implementation of Various Methods

Estimating the Timing of Cognitive Operations With MEG/EEG Latency Measures: A Primer, a Brief Tutorial, and an Implementation of Various Methods

Neural Activity Predicts Reaction in Primates Long Before a Behavioral Response

Modeling road user response timing in naturalistic settings: a surprise-based framework

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