Cognitive and affective probing: a tutorial and review of active learning for neuroadaptive technology

Abstract

Objective: The interpretation of neurophysiological measurements has a decadeslong history, culminating in current real-time brain-computer interfacing (BCI) applications for both patient and healthy populations. Over the course of this history, one focus has been on the investigation of cortical responses to specific stimuli. Such responses can be informative with respect to the human user's mental state at the time of presentation. An ability to decode neurophysiological responses to stimuli in real time becomes particularly powerful when combined with a simultaneous ability to autonomously produce such stimuli. This allows a computer to gather stimulus-response samples and iteratively produce new stimuli based on the information gathered from previous samples, thus acquiring more, and more specific, information. This information can even be obtained without the explicit, voluntary involvement of the user.Approach: We define cognitive and affective probing, referring to an application of active learning where repeated sampling is done by eliciting implicit brain responses. In this tutorial, we provide a definition of this method that unifies different past and current implementations based on common aspects. We then discuss a number of aspects that differentiate various possible implementations of cognitive probing.Main results: We argue that a key element is the user model, which serves as both information storage and basis for subsequent probes. Cognitive probing can be used to continuously and autonomously update this model, refining the probes, and obtaining increasingly detailed or accurate information from the resulting brain activity. In contrast to a number of potential advantages of the method, cognitive probing may also pose a threat to informed consent, our privacy of thought, and our ability to assign responsibility to actions mediated by the system.Significance: This tutorial provides guidelines to both implement, and critically discuss potential ethical implications of, novel cognitive probing applications and research endeavours.

A Neuroergonomics Approach to Mental Workload, Engagement and Human Performance

“…A crucial step is to design cognitive countermeasures to prevent the occurrence of these phenomena. The formal framework that we proposed (see Table 1) paves the way to design neuro-adaptive technology for augmented cognition and enhanced human-machine teaming (Peysakhovich et al, 2018;Krol et al, 2019;Stamp et al, 2019). The implementation of such neuro-adaptive technology relies on a pipeline that consists of a signal acquisition step, a preprocessing step to improve the signal-to-noise ratio, a feature extraction step, a classification step to diagnose the current mental states, and lastly an adaptation step (Zander and Kothe, 2011;Roy and Frey, 2016).…”

Towards flexible personalized learning and the future educational system in the fourth industrial revolution in the wake of Covid-19

“…Fernando (2018) considers a variety of aspects, especially with relevance to developing countries, and the role of information and communications technology (ICT) to educate the next generation by way of active online learning for schools in Sri Lanka. Active learning has been shown to be effective even within the VES (for example, Beetham, 2020;Healey, Pawson, & Solem, 2013;Krol, Haselager, & Zander, 2020) and lectures can be active and involved (Whalley, 2016). Out of classroom education, such as in fieldwork, has traditionally involved active learning (France et al, 2015).…”

Mapping the Dimensions of Agency

“…have not yet been endorsed for execution by higher cognitive processing, but become appropriated by the agent as intended action after having been executed via the BCI (Krol et al 2020). Thus the use of a BCI device can lead to a kind of agential confabulation.…”