T o w ards a Conceptual F ramew ork for
Cognitiv e Probing
Laurens R. Krol 1 ,  and Thorsten O. Zander 1
1 Biological Psyc hology and Neuro ergonomics,
T ec hnisc he Univ ersit¨ at Berlin, Berlin, German y
[email protected]
Abstract. Cognitiv e probing com bines the abilit y of computers to in-
terpret ongoing measures of arbitrary brain activit y , with the abilit y
of those same computers to activ ely elicit cognitiv e resp onses from their
users. Purp osefully elicited resp onses can b e in terpreted in order to learn
ab out the user, enable sym biotic and implicit in teraction, and supp ort
neuroadaptiv e tec hnology . W e prop ose a w orking definition of cognitiv e
probing that allo ws it to b e generalised across differen t applications and
disciplines.
Keyw ords: cognitive probing · brain-computer in terface · neuroadap-
tiv e tec hnology · implicit in teraction · h uman-computer interaction
1 In tro duction
A brain-computer in terface (BCI) is a system that allows an output c hannel to
b e established directly b et w een a user’s brain and a tec hnological system—an
output c hannel “that is neither neuromuscular nor hormonal” [9]. This allo ws
for example completely paralysed or lo c k ed-in patients to comm unicate with the
outside w orld using mental sp ellers [1] or brain-activ ated prostheses [7]. Through
BCI systems, p eople can con trol suc h devices using only their brain activit y .
A p assive brain-computer in terface (pBCI) [14] uses similar hard- and soft-
w are in order to interpret brain activit y that w as not mean t to con trol a device.
Instead, it detects and in terprets “natural” [5] brain activit y that reflects the
user’s cognitiv e and men tal state, and uses this as implicit input to supp ort
ongoing h uman-computer in teraction [12].
The automatic correction of user resp onse errors is an early example of what
is no w kno wn as pBCI. F or example in a sp eeded reaction task, whenev er an error
negativit y [3] was detected, the response would be undone [8]. This approach
w as later extended to machine errors: whenev er the user observ ed the mac hine
committing an error, it could b e corrected if the appropriate brain signal w as
detected [13].
Note that these are indeed passiv e BCI applications, since the p erception of
suc h an error itself elicits the relev an t brain activit y , and the user exp ends no
additional effort to inform the computer of the fact that an error o ccurred.
This is the author's final preprint version of the following publication: Krol, L. R., & Zander, T. O. (2018). Towards a conceptual
framework for cognitive probing. In J. Ham, A. Spagnolli, B. Blankertz, L. Gamberini, & G. Jacucci (Eds.), Symbiotic interaction
(pp. 74–78). Cham: Springer International Publishing. doi: 10.1007/978-3-319-91593-7_8
The final publication is © Springer International Publishing AG, part of Springer Nature 2018

This conference contribution represents work in progress. See the following paper for updated work on cognitive probing:
Krol, L. R., Haselager, P., & Zander, T. O. (2020). Cognitive and affective probing: a tutorial and review of active learning for
neuroadaptive technology. Journal of Neural Engineering, 17(1), 012001. doi: 10.1088/1741-2552/ab5bb5

1

That mac hine errors elicit suc h a detectable resp onse, confirmed also b y
other exp erimen ts [2], is a fact that can b e activ ely exploited by the system. F or
example, the system can ten tativ ely p erform an y n um b er of random acts, and
can then assess, using pBCI, whether or not these w ere p erceiv ed as erroneous
or not b y the user. Any action that w as not p erceiv ed to b e in error can then b e
definitiv ely committed. As such, the user w ould ha v e implicitly comm unicated
to the system what they w an ted it to do, without ha ving giv en an y explicit
commands or instructions [11].
Suc h a scenario was recen tly demonstrated to b e p ossible using a form of
implicit cursor con trol [11, 15], and separately b y another group using a rob otic
arm [4]. In this pap er, w e take the former as an example. P articipan ts w ere
observing the initially random mo vemen ts of a cursor on a grid, on whic h one
target lo cation w as indicated. F or each mo v emen t, the computer could assess
from ongoing measuremen ts of brain activit y whether or not that mov emen t
w as p erceiv ed as either “acceptable” or “not acceptable”. Using this information,
the system learned o v er time whic h mov emen ts w ere apparen tly desired b y the
user, and adapted the cursor’s b eha viour in order to steer it to wards the target
lo cation.
W e b elieve that this approac h, where the computer purp osefully elicits re-
sp onses in order to obtain information not explicitly comm unicated by the user,
can b e form ulated more generally . The approach is not unique to the ab o v e-
men tioned example: armed with a general formulation w e can see, in retrosp ect,
that other, older applications ha v e used this metho d as w ell. A standard defi-
nition of this approac h should mak e it more easily recognisable as such, high-
ligh ting it as a worth while metho d of its o wn, and making it more accessible to
other researc hers across disciplines. W e prop ose to name this approac h c o gnitive
pr obing .
2 Cognitiv e Probing
Cognitiv e probing refers to the general use of this approach. A stricter defi-
nition fo cuses on the defining b eha viour of the system at hand: suc h a system
utilises c o gnitive pr ob es . W e prop ose the follo wing definition of a cognitiv e prob e:
A cognitiv e prob e is a single autogenous system adaptation that is
initiated or co-opted b y that system in order to learn from the user’s
con textual, cognitiv e brain resp onse to it.
This definition consists of a n um b er of terms that ma y warran t further dis-
cussion.
First of all, w e use the term system adaptation to refer to an y state c hange
of the tec hnological system [5, 6], b e they the p erceptible presentation of stim uli
or feedbac k, or more subtle c hanges to the state or b eha viour of the system.
This is the author's final preprint version of the following publication: Krol, L. R., & Zander, T. O. (2018). Towards a conceptual
framework for cognitive probing. In J. Ham, A. Spagnolli, B. Blankertz, L. Gamberini, & G. Jacucci (Eds.), Symbiotic interaction
(pp. 74–78). Cham: Springer International Publishing. doi: 10.1007/978-3-319-91593-7_8
The final publication is © Springer International Publishing AG, part of Springer Nature 2018

This conference contribution represents work in progress. See the following paper for updated work on cognitive probing:
Krol, L. R., Haselager, P., & Zander, T. O. (2020). Cognitive and affective probing: a tutorial and review of active learning for
neuroadaptive technology. Journal of Neural Engineering, 17(1), 012001. doi: 10.1088/1741-2552/ab5bb5

2

One suc h state change is auto genous when it is initiated by the system. This
separates cognitiv e prob es from adaptations whose sp ecific form w as decided b y
the h uman user, for example through explicit commands.
Suc h an autogenous state change can be either intended to be a prob e—i.e.
initiate d primarily for that purp ose—or, it can b e a state c hange that o ccurs
primarily for other reasons, for example, the presen tation of feedback to inform
the user. Suc h a latter adaptation may ho w ev er still elicit a detectable brain
resp onse, can th us still b e used for the same purp ose—i.e., it can b e c o-opte d to
serv e as a prob e.
W e are fo cusing in this definition on the user’s c o gnitive br ain r esp onse to
these adaptations, as inferred from measures of their brain activit y . The prob es
m ust thus in one w a y or another elicit cognition-related brain activit y , or a
c hange in ongoing brain activit y .
Ultimately , the goal of cognitive probing is to obtain information from the
user’s brain resp onse to the prob es, either ab out the user, ab out a sp ecific adap-
tation, or ab out the system as a whole. In short, the prob es serv e to le arn .
F or the gathered information to b e meaningfully used as a basis for learning,
further information is required. Not only the resp onse itself m ust b e known, but
also, what elicited that resp onse. That is the minim um p ossible c ontext of the
brain resp onse. Ho w ever, this con text can b e extended further to include other
relev an t con textual asp ects: for example, the resp onse ma y b e dep enden t of the
time of da y , the physical location, or any other n um b er of situational asp ects.
3 Discussion
The example men tioned in the in tro duction fits the prop osed definition of cog-
nitiv e probing in the follo wing w a y . Each single cursor mo v emen t was initiated
b y the com puter itself, with the goal of eliciting a sp ecific brain resp onse. This
resp onse w as then recorded in a user mo del that describ ed the inferred user pref-
erences in relation to the differen t p ossible mov emen t directions. The system th us
learned the user’s preferred cursor b eha viour. The observ ed impro v ement in the
cursor’s p erformance [15] demonstrates the effectiv eness of this approac h. In par-
ticular, the approac h implemented here mak es use of a sequence of prob es. Where
traditional BCI applications often use direct (op en-lo op or closed-lo op [5]) adap-
tations based directly on single-trial brain resp onses, this example sho ws how
m ultiple prob es from a kno wn context, com bined with their (implicit) brain re-
sp onses, can lead to inferences of higher asp ects of cognition, in this case the
desired cursor b eha viour.
Note that the system could ha ve learned this information ev en if the cursor
con tinued to mo v e randomly . Ho wev er, the cursor used the obtained information
in real time in order to reac h its goal more quic kly . This neuroadaptiv e b eha viour
of the cursor is not a necessit y according to the prop osed definition, although it
illustrates ho w this approach can be used to increase the interactivit y of h uman-
computer in teraction, based en tirely on implicitly comm unicated information
This is the author's final preprint version of the following publication: Krol, L. R., & Zander, T. O. (2018). Towards a conceptual
framework for cognitive probing. In J. Ham, A. Spagnolli, B. Blankertz, L. Gamberini, & G. Jacucci (Eds.), Symbiotic interaction
(pp. 74–78). Cham: Springer International Publishing. doi: 10.1007/978-3-319-91593-7_8
The final publication is © Springer International Publishing AG, part of Springer Nature 2018

This conference contribution represents work in progress. See the following paper for updated work on cognitive probing:
Krol, L. R., Haselager, P., & Zander, T. O. (2020). Cognitive and affective probing: a tutorial and review of active learning for
neuroadaptive technology. Journal of Neural Engineering, 17(1), 012001. doi: 10.1088/1741-2552/ab5bb5

3

[11, 5]. Suc h closed-lo op in teractions based on implicit input can pro vide the
basis for a close, sym biotic relationship b et w een humans and tec hnology .
In the real w orld, w e must deal not only with noisy en vironmen ts, but also
with a ric h, uncon trollable context. Imp ortan tly , b ecause the learning is based
on implicit information and can b e extended o v er longer p erio ds of time, the
single-trial accuracy of the system is not as critical as it w ould b e for an y sort of
direct con trol application. Thus, this approac h can b e effectiv e ev en with sub-
p erfect acquisition tec hnology , such as more user-friendly dry electrodes [10].
F urthermore, even in v arying en vironmen ts and con texts, the primary con text
of in terest is alw a ys kno wn—it is the prob e itself. An y con textual information
that is added ma y b e helpful, but is not required.
Because of this, w e b eliev e cognitive probing to be a promising strategy to
b e used for next-generation neuroadaptiv e technology , pro vided that it is used
with due consideration and resp ect for the user’s priv acy of though t.
A cknow le dgements P art of this work w as supp orted b y the Deutsc he F orsch ungs-
gemeinsc haft (ZA 821/3-1).
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framework for cognitive probing. In J. Ham, A. Spagnolli, B. Blankertz, L. Gamberini, & G. Jacucci (Eds.), Symbiotic interaction
(pp. 74–78). Cham: Springer International Publishing. doi: 10.1007/978-3-319-91593-7_8
The final publication is © Springer International Publishing AG, part of Springer Nature 2018

This conference contribution represents work in progress. See the following paper for updated work on cognitive probing:
Krol, L. R., Haselager, P., & Zander, T. O. (2020). Cognitive and affective probing: a tutorial and review of active learning for
neuroadaptive technology. Journal of Neural Engineering, 17(1), 012001. doi: 10.1088/1741-2552/ab5bb5

4

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This is the author's final preprint version of the following publication: Krol, L. R., & Zander, T. O. (2018). Towards a conceptual
framework for cognitive probing. In J. Ham, A. Spagnolli, B. Blankertz, L. Gamberini, & G. Jacucci (Eds.), Symbiotic interaction
(pp. 74–78). Cham: Springer International Publishing. doi: 10.1007/978-3-319-91593-7_8
The final publication is © Springer International Publishing AG, part of Springer Nature 2018

This conference contribution represents work in progress. See the following paper for updated work on cognitive probing:
Krol, L. R., Haselager, P., & Zander, T. O. (2020). Cognitive and affective probing: a tutorial and review of active learning for
neuroadaptive technology. Journal of Neural Engineering, 17(1), 012001. doi: 10.1088/1741-2552/ab5bb5

5

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