A commentary on

Re-starting a neural race: anti-saccade correction



by Noorani, I., and Carpenter, R. H. S. (2014). Eur. J. Neurosci. 39, 159–164. doi: 10.1111/ejn.12396



Decision making is the process of accumulating evidence about the world and the utility of possible outcomes (Cutsuridis, 2010). A paradigm often used by behavioral neuroscientists to investigate decision processes is the antisaccade paradigm (see Figure 1A; Hallett, 1978). In the antisaccade paradigm subjects are required to suppress an erroneous saccade (error prosaccade) toward a peripheral stimulus and instead make an eye movement to a position in the opposite hemifield (antisaccade). The response repertoire of a subject performing the antisaccade task has been reported to be: (1) the subject makes an erroneous response (i.e., looking toward the peripheral stimulus), (2) the subject makes the antisaccade (i.e., looking in the opposite direction of the peripheral stimulus, and (3) the subject makes an erroneous response followed by a corrected antisaccade (Evdokimidis et al., 2002)

Brown

Cutsuridis

Evdokimidis

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Hallett

Munoz

Noorani

Phongphanphanee

Polsky

Takahashi

Vassilis Cutsuridis

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Frontiers in Neuroscience

Neural competition via lateral inhibition between decision processes and not a STOP signal accounts for the antisaccade performance in healthy and schizophrenia subjects

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Neural Competition via Lateral Inhibition between Decision Processes and Not a STOP Signal Accounts for the Antisaccade Performance in Healthy and Schizophrenia Subjects

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GENERAL COMMENTARY
published: 29 January 2015
doi: 10.3389/fnins.2015.00005
Neural competition via lateral inhibition between decision
processes and not a STOP signal accounts for the
antisaccade performance in healthy and schizophrenia
subjects
Vassilis Cutsuridis*
Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology – Hellas, Heraklion, Greece
*Correspondence: vcutsuridis@gmail.com
Edited by:
Tobias H. Donner, University of Amsterdam, Netherlands
Reviewed by:
Florian Ostendorf, Charité - Universitätsmedizin Berlin, Germany
Keywords: decision making, neural network model, eye movement, accumulator, superior colliculus, cortex
A commentary on
Re-starting a neural race: anti-saccade
correction
by Noorani, I., and Carpenter, R. H. S.
(2014). Eur. J. Neurosci. 39, 159–164. doi:
10.1111/ejn.12396
Decision making is the process of accumu-
lating evidence about the world and the
utility of possible outcomes (Cutsuridis,
2010). A paradigm often used by behav-
ioral neuroscientists to investigate decision
processes is the antisaccade paradigm (see
Figure 1A; Hallett, 1978). In the anti-
saccade paradigm subjects are required
to suppress an erroneous saccade (error
prosaccade) toward a peripheral stimu-
lus and instead make an eye movement
to a position in the opposite hemifield
(antisaccade). The response repertoire of
a subject performing the antisaccade task
has been reported to be: (1) the subject
makes an erroneous response (i.e., look-
ing toward the peripheral stimulus), (2)
the subject makes the antisaccade (i.e.,
looking in the opposite direction of the
peripheral stimulus, and (3) the subject
makes an erroneous response followed
by a corrected antisaccade (Evdokimidis
et al., 2002).
Many computer models of decision
making have been advanced. In these
models, decision making involves a grad-
ual accumulation of evidence with a
variable rate r. When this accumulation
process crosses a threshold ST , then a
response is generated. Response time (RT)
is then the time from the onset of the
decision process till when the decision
processes crosses ST .
Recently the LATER (Linear Approach
to Threshold at Ergodic Rate) model has
been extended in the realm of the anti-
saccade task (see Figure 1B): (Noorani
and Carpenter, 2014). The model con-
sisted of three accumulator units rac-
ing to threshold: an “anti” unit, a “pro”
unit, and a “stop” unit. The “stop” unit
prevented the “pro” unit from reaching
threshold, thus allowing the “anti” unit
to reach a different threshold a little later.
The authors hypothesized that the thresh-
old level of the “pro” unit was higher
than the “anti” unit’s threshold, reflect-
ing this way the advice given by the
experimenters to every subject to avoid
errors. How often the “stop” unit can-
celed the “pro” unit depended on its
rate of accumulation (μ) and its vari-
ance (σ2). In the case the “pro” unit
reached the threshold first, it restarted the
“anti” unit allowing it to reach the thresh-
old and generate the antisaccade response.
The model’s performance was contrasted
against the performance of five healthy
subjects performing the antisaccade task.
The model captured most of the response
repertoire observed in the antisaccade
task, namely the antisaccades and error
prosaccades followed by corrected anti-
saccades, but not the error prosaccades,
their corresponding latency distributions
and the error response rate. Despite the
model’s successes, the model had several
shortcomings.
First, the model is unable to pro-
duce just the error prosaccade. This
shortcoming is inherent in the model.
The authors postulated that if the STOP
signal did not prevent the error prosac-
cade response, then the “pro” unit will
always restart the “anti” unit (Noorani and
Carpenter, 2014). This means the error
prosaccades followed by corrected anti-
saccades will always be produced. If the
“stop” unit did prevent the “pro” unit, then
the “anti” unit would not re-start, and an
antisaccade response would be generated
(Noorani and Carpenter, 2014). In either
scenario, just an error prosaccade response
cannot be generated. Psychophysical stud-
ies of the antisaccade task (Evdokimidis
et al., 2002) have reported that subjects
make just erroneous prosaccades, but their
response frequency is low.
Furthermore, the model implies that
the latency of the corrected antisaccade is
the result of the linear sum of latencies
of the error prosaccade and the antisac-
cade minus the latency of the STOP activ-
ity. This shortcoming is also inherent in
the model, because its units are consid-
ered linear encoders of the input informa-
tion. In contrast, all neurons in the brain
(units in the model) non-linearly trans-
form the sum of the dendritic outputs
before they generate a neuronal response.
Dendritic subunits are perhaps the only
linear encoders of incoming information
(Polsky et al., 2004).
Moreover, the model postulates the
existence of a STOP signal, which occa-
sionally stops the error prosaccade
www.frontiersin.org January 2015 | Volume 9 | Article 5 | 1
Cutsuridis Antisaccade performance via neural competition
FIGURE 1 | (A) Mirror antisaccade task. (B) Noorani and Carpenter (2014)
model for antisaccades (reprinted with permission from Noorani and
Carpenter, 2014). (C) Cutsuridis et al. (2014) neural network model of the
superior colliculus (SC) for antisaccades in healthy and schizophrenia
subjects (reprinted with permission from Cutsuridis et al., 2014). Neurons
are represented as nodes. Short-range lateral excitation and long distance
lateral inhibition was assumed between all nodes in the network. The left
half of the network represented the left SC, whereas the right half
represented the right SC. The left SC was activated by a reactive input Ir
(error prosaccade decision signal), whereas the right SC was activated by a
planned input Ip (antisaccade decision signal). The strengths of the inputs
were not equal.
response and indirectly allows just the
antisaccade response to be expressed
(Noorani and Carpenter, 2014). Many
past experimental studies have specu-
lated the origins of such a signal (basal
ganglia, FEF, DLPFC, etc.) (Munoz and
Everling, 2004), but recent experimen-
tal evidence has challenged the existence
of such a signal (Everling and Johnston,
2013). On the other hand, computational
studies have suggested that such a STOP
signal might operate as a top-down exci-
tatory signal which activates the local
inhibitory neurons in a distant area, which
in turn inhibit their neighboring excitatory
neurons (Brown et al., 2004).
Attractive alternatives of the Noorani
and Carpenter (2013, 2014) models
of the antisaccade performance are
the models of Cutsuridis et al. (2007,
2014) (see Figure 1C). Cutsuridis et al.
(2007) proposed that competition via
lateral inhibition (Takahashi et al.,
2005; Phongphanphanee et al., 2014)
between neurons encoding the voli-
tional antisaccade and neurons encoding
the erroneous prosaccade is sufficient
to accurately reproduce the error rate
and antisaccade, error prosaccade and
corrected antisaccade latency distributions
of antisaccade data from a large cohort of
healthy subjects (Evdokimidis et al., 2002).
The model’s neurons were non-linear
accumulators of incoming information
and represented the build-up neurons
experimentally recorded in the superior
colliculus (SC) (Munoz and Wurtz, 1995).
Recently, Cutsuridis et al. (2014)
extended their model in the realm of
schizophrenia. Their model showed in
a quantitative way why the antisaccade
performance of schizophrenia patients
is so poor. It predicted that this per-
formance is not due to a deficit in
the top-down inhibitory control of the
erroneous response as many specu-
lated, but instead it is a product of the
competition between the neuronal rep-
resentations of the erroneous prosaccade
and antisaccade responses in the superior
colliculus. The model was successful at
capturing the response repertoire (error
rates, the median antisaccade, median
error prosaccade and median corrected
antisaccade latencies as well as the anti-
saccade, error prosaccade and corrected
antisaccade distributions) of both healthy
and schizophrenia subjects [see Figures
1, 3 and 4 in Cutsuridis et al. (2014)
study].
Overall, competition via lateral inhi-
bition between non-linear accumulator
neurons seems to be a better mechanism
than the “stop-and-restart” mechanism of
Noorani and Carpenter (2014), because it
captures the full antisaccade performance
(latencies of error prosaccades, antisac-
cades and corrected antisaccades and error
rates) of healthy and diseased subjects
performing the antisaccade task. Other
parameters that may affect the antisaccade
performance are the differential strengths
of the erroneous prosaccade and the voli-
tional antisaccade signals, or different
baseline and/or threshold levels. Although
recent experimental evidence has just
demonstrated that lateral interactions
within SC intermediate segment are
more suitable for faithfully accumulating
subthreshold signals for saccadic decision-
making (Phongphanphanee et al., 2014),
a lot more work needs to be done to con-
clusively show that a STOP-and-restart
mechanism is unnecessary in the deci-
sion making process. An experimental
study in that direction was recently pub-
lished by Everling and colleagues and
challenges the (prevailing) idea of a sup-
pressive/inhibitory influence (STOP signal
in the Noorani and Carpenter model)
Frontiers in Neuroscience | Decision Neuroscience January 2015 | Volume 9 | Article 5 | 2
Cutsuridis Antisaccade performance via neural competition
of prefrontal cortical areas on reflexive,
erroneous prosaccade generation in this
paradigm (Everling and Johnston, 2013
for a recent review).
REFERENCES
Brown, J. W., Bullock, D., and Grossberg, S.
(2004). How laminar frontal cortex and
basal ganglia circuits interact to control
planned and reactive saccades. Neural Netw.
17, 471–510. doi: 10.1016/j.neunet.2003.
08.006
Cutsuridis, V. (2010). “Neural accumulator models
of decision making,” in Brain Inspired Cognitive
Systems 2008, Advances in Experimental Medicine
and Biology 657, eds A. Hussain, I. Aleksander, L.
Smith, R. Chrisley, A. K. Barros, and V. Cutsuridis
(New York, NY: Springer Science+Business Media,
LLC), 61–72.
Cutsuridis, V., Kumari, V., and Ettinger, U. (2014).
Antisaccade performance in schizophrenia:
a neural model of decision making in the
superior colliculus. Front. Neurosci. 8:13. doi:
10.3389/fnins.2014.00013
Cutsuridis, V., Smyrnis, N., Evdokimidis, I., and
Perantonis, S. (2007). A neural network model
of decision making in an antisaccade task by the
superior colliculus. Neural Netw. 20, 690–704. doi:
10.1016/j.neunet.2007.01.004
Evdokimidis, I., Smyrnis, N., Constantinidis, T. S.,
Stefanis, N. C., Avramopoulos, D., Paximadis, C.,
et al. (2002). The antisaccade task in a sam-
ple of 2006 young men I. Normal population
characteristics. Exp. Brain Res. 147, 45–52. doi:
10.1007/s00221-002-1208-4
Everling, S., and Johnston, K. (2013). Control
of the superior colliculus by the lateral pre-
frontal cortex. Philos. Trans. R. Soc. Lond.
B Biol. Sci. 368:20130068. doi: 10.1098/rstb.
2013.0068
Hallett, P. E. (1978). Primary and secondary sac-
cades to goals defined by instructions. Vis.
Res. 18, 1279–1296. doi: 10.1016/0042-6989(78)
90218-3
Munoz, D. P., and Everling, S. (2004). Look away:
the anti-saccade task and the voluntary control of
eye movement.Nat. Rev. Neurosci. 5, 218–228. doi:
10.1038/nrn1345
Munoz, D., and Wurtz, R. (1995). Saccade related
activity in monkey superior colliculus. I.
Characteristics of burst and buildup cells.
J. Neurophys. 73, 2313–2333.
Noorani, I., and Carpenter, R. H. S. (2013).
Antisaccades as decisions: LATER model predicts
latency distributions and error responses. Eur. J.
Neurosci. 37, 330–338. doi: 10.1111/ejn.12025
Noorani, I., and Carpenter, R. H. S. (2014). Re-
starting a neural-race: anti-saccade correction. Eur.
J. Neurosci. 39, 159–164. doi: 10.1111/ejn.12396
Phongphanphanee, P., Marino, R. A., Kaneda, K.,
Yanagawa, Y., Munoz, D. P., and Isa, T. (2014).
Distinct local circuit properties of the superficial
and intermediate layers of the rodent superior
colliculus. Eur. J. Neurosci. 40, 2329–2343. doi:
10.1111/ejn.12579
Polsky, A., Mel, B. W., and Schiller, J. (2004).
Computational subunits in thin dendrites of
pyramidal cells. Nat. Neurosci. 7, 621–627. doi:
10.1038/nn1253
Takahashi, M., Sugiuchi, Y., Izawa, Y., and
Shinoda, Y. (2005). Commissural excitation
and inhibition by the superior colliculus in
tectoreticular neurons projecting to omnipause
neuron and inhibitory burst neuron regions.
J. Neurophysiol. 94, 1707–1726. doi: 10.1152/jn.
00347.2005
Conflict of Interest Statement: The author declares
that the research was conducted in the absence of any
commercial or financial relationships that could be
construed as a potential conflict of interest.
Received: 03 October 2014; accepted: 07 January 2015;
published online: 29 January 2015.
Citation: Cutsuridis V (2015) Neural competition via
lateral inhibition between decision processes and not a
STOP signal accounts for the antisaccade performance
in healthy and schizophrenia subjects. Front. Neurosci.
9:5. doi: 10.3389/fnins.2015.00005
This article was submitted to Decision Neuroscience, a
section of the journal Frontiers in Neuroscience.
Copyright © 2015 Cutsuridis. This is an open-access
article distributed under the terms of the Creative
Commons Attribution License (CC BY). The use, dis-
tribution or reproduction in other forums is permit-
ted, provided the original author(s) or licensor are
credited and that the original publication in this
journal is cited, in accordance with accepted aca-
demic practice. No use, distribution or reproduc-
tion is permitted which does not comply with these
terms.
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Neural competition via lateral inhibition between decision processes and not a STOP signal accounts for the antisaccade performance in healthy and schizophrenia subjects

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