Advances in modern mental chronometry

Modeling road user response timing in naturalistic traffic conflicts: A surprise-based framework

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. We present 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.

Comparing attentional disengagement between Prolific and MTurk samples

Attention often disengages from primary tasks in favor of secondary tasks (i.e., multitasking) and task-unrelated thoughts (i.e., mind wandering). We assessed whether attentional disengagement, in the context of a cognitive task, can substantially differ between samples from commonly used online participant recruitment platforms, Prolific and Mechanical Turk (MTurk). Initially, eighty participants were recruited through Prolific to perform an attention task in which the risk of losing points for errors was varied (high risk = 80% chance of loss, low risk = 20% chance of loss). Attentional disengagement was measured via task performance along with self-reported mind wandering and multitasking. On Prolific, we observed surprisingly low levels of disengagement. We then conducted the same experiment on MTurk. Strikingly, MTurk participants exhibited more disengagement than Prolific participants. There was also an interaction between risk and platform, with the high-risk group exhibiting less disengagement, in terms of better task performance, than the low-risk group, but only on MTurk. Platform differences in individual traits related to disengagement and relations among study variables were also observed. Platform differences persisted, but were smaller, after increasing MTurk reputation criteria and remuneration in a second experiment. Therefore, recruitment platform and recruitment criteria could impact results related to attentional disengagement.

A desire for distraction: uncovering the rates of media multitasking during online research studies

Interpretations of task performance in many cognitive studies rest on the assumption that participants are fully attentive to the tasks they agree to complete. However, with research studies being increasingly conducted online where monitoring participant engagement is difficult, this assumption may be inaccurate. If participants were found to be engaging in off-task behaviours while participating in these studies, the interpretation of study results might be called into question. To investigate this issue, we conducted a secondary data analysis across nearly 3000 participants in various online studies to examine the prevalence of one form of off-task behaviour: media multitasking. Rates of media multitasking were found to be high, averaging 38% and ranging from 9 to 85% across studies. Our findings broadly raise questions about the interpretability of results from online studies and urge researchers to consider the likelihood that participants are simultaneously engaging in off-task behaviours while completing online research tasks.

Reaction Time Decomposition as a Tool to Study Subcortical Ischemic Vascular Cognitive Impairment

Background:

The study of reaction time (RT) and its intraindividual variability (IIV) in aging, cognitive impairment, and dementia typically fails to investigate the processing stages that contribute to an overall response. Applying “mental chronometry” techniques makes it possible to separately assess the role of processing components during environmental interaction.

Objective:

To determine whether RT and IIV-decomposition techniques can shed light on the nature of underlying deficits in subcortical ischemic vascular cognitive impairment (VCI). Using a novel iPad task, we examined whether VCI deficits occur during both initiation and movement phases of a response, and whether they are equally reflected in both RT and IIV.

Methods:

Touch cancellation RT and its IIV were measured in a group of younger adults (n = 22), cognitively healthy older adults (n = 21), and patients with VCI (n = 21) using an iPad task.

Results:

Whereas cognitively healthy aging affected the speed (RT) of response initiation and movement but not its variability (IIV), VCI resulted in both slowed RT and increased IIV for both response phases. Furthermore, there were group differences with respect to response phase.

Conclusion:

These results indicate that IIV can be more sensitive than absolute RT in separating VCI from normal aging. Furthermore, compared to cognitively healthy aging, VCI was characterized by significant deficits in planning/initiating action as well as performing movements. Such deficits have important implications for real life actions such as driving safety, employment, and falls risk.

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

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.

Neural Activity Predicts Reaction in Primates Long Before a Behavioral Response

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.

Transfer of time-based task expectancy across different timing environments

Recent research on time-based expectancy has shown that humans base their expectancies for responses on representations of temporal relations (e.g., shorter vs. longer duration), rather than on representations of absolute durations (e.g., 500 vs. 1000 ms). In the present study, we investigated whether this holds also true for time-based expectancy of tasks instead of responses. Using a combination of the time-event correlation paradigm and the standard task-switching paradigm, participants learned to associate two different time intervals with two different tasks in a learning phase. In a test phase, the two intervals were either globally prolonged (Experiment 1), or shortened (Experiment 2), and they were no longer predictive for the upcoming task. In both experiments, performance in the test phase was better when expectancy had been defined in relative terms and worse when expectancy had been defined in absolute terms. We conclude that time-based task expectancy employs a relative, rather than an absolute, representation of time. Humans seem to be able to flexibly transfer their time-based task expectancies between different global timing regimes. This finding is of importance not only for our basic understanding of cognitive mechanisms underlying time-based task expectancy. For human-machine applications, these results mean that adaptation to predictive delay structures in interfaces survives globally speeding up or slowing down of delays due to different transmission rates.