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Lenticular nucleus volume predicts performance in real-time strategy game - cross-sectional and training approach using voxel-based morphometry

July 2020

DOI:10.1101/2020.07.20.205864

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CC BY-NC-ND 4.0

Authors:

Natalia Kowalczyk-Grębska

SWPS University of Social Sciences and Humanities

Maciek Skorko

Polish Academy of Sciences

Pawel Dobrowolski

Polish Academy of Sciences

Bartosz Kossowski

Nencki Institute of Experimental Biology

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It is unclear why some people learn faster than others. We performed two independent studies in which we investigated the neural basis of real-time strategy (RTS) gaming and neural predictors of RTS games skill-acquisition. In the first (cross-sectional) study we found that experts in the RTS game StarCraft II (SC2) had a larger lenticular nucleus volume than non-RTS players. We followed a cross validation procedure where we used the volume of regions identified in the first study to predict the quality of learning a new, complex skill (SC2) in a sample of individuals who were naive to RTS games (second training study). Our findings provide new insights into how the volume of lenticular nucleus, which is associated with motor as well as cognitive functions, can be utilized to predict successful skill-learning, and be applied to a much broader context than just video games, e.g. contributing to optimizing cognitive training interventions.

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Title

Lenticular nucleus volume predicts performance in real-time strategy game - cross-

sectional and training approach using voxel-based morphometry

Authors

N. Kowalczyk,1 M. Skorko,2 P. Dobrowolski,2 B. Kossowski,3 M. Myśliwiec,1 N.

Hryniewicz,4 M Gaca,3 A. Marchewka,3 M. Kossut,5 A. Brzezicka1,6*

Affiliations

1. Faculty of Psychology, SWPS University of Social Sciences and Humanities,

Warsaw, Poland

2. Institute of Psychology, Polish Academy of Sciences, Warsaw, Poland

3. Laboratory of Brain Imaging, Neurobiology Center, Nencki Institute of Experimental

Biology of Polish Academy of Sciences, Warsaw, Poland

4. CNS Lab of the Nalecz Institute of Biocybernetics and Biomedical Engineering,

PAS, Warsaw, Poland

5. Laboratory of Neuroplasticity, Department of Molecular and Cellular Neurobiology,

Nencki Institute of Experimental Biology of Polish Academy of Sciences, Warsaw,

Poland

6. Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles,

California, USA

*Correspondence: Natalia Kowalczyk: nkowalczyk@swps.edu.pl, SWPS University of Social Sciences and Humanities,

Chodakowska 19/31 street, 03-815 Warsaw, Poland, phone: (+48 22) 51 79 957; fax: (+48 22) 517 96 25.

.CC-BY-NC-ND 4.0 International license(which was not certified by peer review) is the author/funder. It is made available under a The copyright holder for this preprintthis version posted July 21, 2020. . https://doi.org/10.1101/2020.07.20.205864doi: bioRxiv preprint

Abstract

It is unclear why some people learn faster than others. We performed two independent

studies in which we investigated the neural basis of real-time strategy (RTS) gaming

and neural predictors of RTS games skill-acquisition. In the first (cross-sectional) study

we found that experts in the RTS game StarCraft II (SC2) had a larger lenticular

nucleus volume than non-RTS players. We followed a cross validation procedure

where we used the volume of regions identified in the first study to predict the quality

of learning a new, complex skill (SC2) in a sample of individuals who were naïve to

RTS games (second training study). Our findings provide new insights into how the

volume of lenticular nucleus, which is associated with motor as well as cognitive

functions, can be utilized to predict successful skill-learning, and be applied to a much

broader context than just video games, e.g. contributing to optimizing cognitive training

interventions.

Introduction

Some people learn faster than others. Skill learning - the process that makes people

more accurate, efficient and faster in a given task - depends on several personal

characteristics. From the psychological perspective, there have been theories and

data regarding the prediction of learning based on individual differences in non-

cognitive and cognitive determinants since the 1950s. Specifically, skill learning was

well-predicted by age (1), general ability measures, such as verbal, spatial and

numerical reasoning, as well as working memory capacity (2-4) and fluid intelligence

(5). Additionally, Yesavage et al. (6) showed that individuals with higher mental status

in terms of their scores in the Mini-Mental State Examination (MMSE) (7) were

characterized by better outcomes after memory training. There were also attempts to

verify more basic cognitive abilities like perceptual-speed and psychomotor

characteristics as predictors of skill development in complex paradigms (i.e. air traffic

control simulation task) (8). It needs to be pointed out that because the process of skill

acquisition is dynamic, cognitive and non-cognitive constructs may differentially

determine individual differences in task performance, depending on e.g. the type and

stage of the task’s practice (9). For example, one of the most well documented

associations between individual differences in the non-cognitive domain and skill

acquisition were from the personality domain (e.g. Big Five inventory) (10).

The picture is even more complicated when it comes to the relationship between the

ability to master new skills and its neuroanatomical predictors. Plenty of cross-

sectional imaging studies have demonstrated structural brain differences between

experts in music, sport, video games and non-experts and showed that experts had

more gray matter volume (GMV) in certain brain regions (11-17). However, deliberate

practice is necessary but not sufficient to account for individual differences in experts

and novices (18-20). One of the criticisms of cross-sectional studies as providing the

evidence for practice-dependent brain changes is that preexisting differences in brain

organization could explain some of the differences we observe between experts and

non-experts. For example, the GMV in the hippocampus of London taxi drivers may

be larger because they have regular experience with navigation, or because they have

some brain structure characteristic that predisposed them to become taxi drivers (21).

Another study showed complementary evidence in the domain of specific

predispositions and experience-dependent brain plasticity (22). There are separate

groups of studies that assessed regional brain morphometry characteristics of

subjects who underwent longitudinal assessments using magnetic resonance imaging

(MRI) (23). They showed changes in gray matter during skill acquisition of e.g. juggling

(24), playing video games (25, 26), learning languages (27), playing music (28, 29),

aerobics (30, 31) and established a theory of brain volume expansion in task-relevant

areas as an indicator for neural plasticity (32), especially during initial stage of learning

(33). Given the above described examples, the debate about the predictive neural

markers of learning has thus far been inconclusive and still is one of the most

challenging areas in cognitive neuroscience. Currently we can observe a growing

interest in how individual differences in the structure of the human brain can influence

the ability to learn and master complex skills (34, 35). Particularly, since the brain’s

gray matter characteristics are one of the most adequate biological structures that

could determine cognitive abilities, it is essential to look at it as a variable predicting

training efficacy.

Scientists reported that pre-existing neuroanatomical profiles, including both cortical

thickness and white matter microstructure, predict the outcomes of individuals

following multi-strategic memory training (36). There is also evidence suggesting that

variance in white matter structure correlates with the ability to learn musical skills in

non-musicians, offering an alternative explanation for the structural differences

observed between musicians and non-musicians (37). Only a few studies have

explored pre-existing neural characteristics in the case of learning how to play video

games, as an example of complex skill acquisition. Momi in 2018 (38) identified that

lingual gyrus is involved in the ability to predict the trajectories of moving objects in

action video games. Other researchers have mostly investigated the volumetric

characteristics of the basal ganglia, a group of subcortical nuclei involved in motor and

procedural learning, as well as in reward learning and memory (39-41).

In the study reported here, we examined brain GMV - related differences in the

acquisition of skill in a novel and complex cognitive-motor task - the RTS game. We

chose SC2 game based on evidence suggesting that playing this cognitively

demanding strategic video game requires a host of specialized skills, including

translating mental plans into motor movements, performing actions with precise

timing, bimanual hand coordination, and processing rapid visual information (42).

These skills are trained and become more and more automatic with quantity of practice

(43). What is more, we can use telemetry data from the game (e.g. Perception Action

Cycle (PAC) latency, Actions Per Minute (AMP) or Hotkey Selects (HS) usage) to have

more detailed measures of skill learning during the course of the game and use it for

further investigations. Moreover, a player’s current skill level can be determined by the

their position in one of the six tiers in the game (a detailed description is provided in

the method section) and, what is also important, it classifies players based on Elo

score like rating systems what allows for the objective assessment of changes in

player’s expertise over time. One additional benefit of SC2 is that it belongs to a group

of games that comprise professional electronic sports (eSports). Because competitive

and professional players of eSports titles dedicate a great deal of time to playing

individual games, they are a sample with a more stable source of video game

experience. This makes the analysis of connections between in-game actions and

brain structures more feasible.

In the current study we wanted to test whether it is possible to predict the level of skills

acquired during RTS game training based on specific brain GMVs. Our main

hypothesis tested the possibility of predicting the quality of skill acquisition (SC2 game)

based on the volume of brain regions identified in a group of expert players. In our

attempt to understand the neural predictors of learning success in the SC2

environment, we took a two-step approach. First, we analyzed a cross-sectional

sample of expert RTS players (those placed in the top five SC2 leagues) and non-

players (NVGPs) to investigate whether RTS video game experience is associated

with volumetric differences in gray matter. In the second step, we used information

gathered during the first study to inform the analyses on data gathered during the

second, training study, where naïve volunteers were trained with SC2.

With those steps, we followed a cross validation procedure (44) in which one sample

of subjects is used to identify brain regions (ROIs) that differ two groups (in our case:

RTS experts and NVGPs), and another sample (NVGPs) to predict skill acquisition (in

our case: complex skill learning during SC2 training) from the ROIs identified in the

first step. Details of this procedure are depicted in Fig. 1A and 1B. To our knowledge,

this is the first study where neuroanatomical individual differences between healthy

adults were used as a predictor of learning outcomes in an RTS action video game.

Fig. 1. Overview of the study design. First, the GMV ROIs were identified in the

cross-sectional study A, and then they were used to predict RTS game skill acquisition

in independent training study B. As a next step a longitudinal study was conducted

with the same RTS game as in the first study (SC2) on a new group of non-video game

players.

Abbreviations: GMV - gray matter volume, MRI - magnetic resonance imaging,

NVGPs - non-video game players, ROIs - regions of interest, SC2 - StarCraft II

Results - cross-sectional study

Higher GMV in RTS experts compared to NVGPs

Thirty-one RTS experts (in SC2 game) were compared to thirty-one NVGPs using high

resolution T1-weighted images (Tw1). Differences between players and non-players

in GMV were calculated using whole brain voxel-based morphometry (VBM) analyses.

RTS experts had significantly higher regional GMV in the right lenticular nucleus

(putamen and pallidum) compared with non-experts (peak MNI coordinates x = 22; y

= −11; z = 7; t = 5.54; cluster size = 2125 voxels), p = 0.04 corrected for multiple

comparisons with family Wise Error (FWE) correction at cluster-level using cluster

size. The obtained result is in the lenticular nucleus, a structure consisting of the

putamen and the pallidum (also commonly called globus pallidus), which are

separated by white matter tracts called lateral medullary lamina. In the whole brain

analysis, the right lenticular nucleus was the only area showing a significant difference

in RTS experts in comparison to NVGPs (Fig. 2 A). There were no significant

differences in GMV for the reverse contrast (NVGPs vs. RTS experts).

Fig. 2. Differences in GMVs between RTS expert players and non-players. A

Results from VBM y analyses showing RTS experts > NVGPs difference in GMV - part

of the lenticular nucleus (peak MNI coordinate x = 22; y = -11; z = 7; t = 5.54; cluster

size = 2125 voxels), Clusters from the whole brain exploratory analysis using FWE

cluster correction. The lenticular nucleus is a collective name given to the putamen

and pallidum (also commonly called the globus pallidus); both are nuclei in the basal

ganglia. B Presentation of differences in GM between RTS experts and NVGPs.

Cohen’s d presented to show the effect size of the difference (d = 1.06).

Abbreviations: GMV - gray matter volume, NVGPs - non-video game players, ROIs -

regions of interest, L - left, R - right, SC2 - StarCraft II

We calculated Cohen's d together with power (Fig. 2 B). Cohen's d was 1.061 and

using G*Power (45) we had 82 percent power to detect differences between groups.

No significant correlation (Spearman's) was observed between the GMV within the

lenticular nucleus and the index of experience in SC2 (hours spent playing SC2, RTS

expert group only), r = 0.19, p = 0.32.

Results - training study

Regional GMV as a predictor of RTS game skill acquisition

In the next study we used RTS game - SC2 as a tool to study complex skill learning in

a longitudinal setup. We computed the variable indexing the weighted time spent on

every level of SC2 difficulty, which reflects performance in the game. Figure 3 presents

the time (hours) spent on each level for all participants.

Fig. 3. Distribution of the average time (hours) spent on each difficulty level in

SC2 for all participants. Presentation of possible difficulty levels in the SC2 matches:

Very Easy, Easy, Medium, Hard, Harder, Very Hard, Elite. None of the participants

reached the Cheater level, so we included only seven levels. The weighted time spent

on each level of SC2 difficulty (the time spent on the second level was multiplied by

two, the time spent on the third level by three, and so on) was computed for each

participant. The final result is a standardized (group-wise) sum of the time spent on all

difficulty levels, which reflects performance in the game. This indicator was used in

the correlational analyses. Presentation of average time (M) and standard errors (SE)

for number of hours played for each difficulty level in SC2 game.

Abbreviations: SC2 - StarCraft II

To specifically target our hypothesis, we employed the ROI analysis method to

longitudinal data. Our ROIs were defined based on the results from our cross-sectional

study, which showed that SC2 performance was associated with volumes of the

ventral striatum (putamen and pallidum).

We used anatomical ROIs based on GMV differences in areas that were related to

RTS gaming activity in our first, cross-sectional independent study. We found that the

volume of both putamens (left: r = 0.67, p = 0.01 and right: r = 0.57, p = 0.02) (Fig. 4

A) as well as both pallidums (left: r = 0.62, p = 0.01 and right: r = 0.62 , p = 0.01)

correlated positively with RTS game skill acquisition (Fig. 4 B) (Spearman’s

correlation). No correlation metrics survived the false discovery rate (FDR) p<0.05

correction for multiple comparisons so the results presented here are uncorrected for

multiple comparisons. There were no training related changes in the GVM of the

examined brain structures.

Fig. 4. Predefined ROIs (right, left putamen A and pallidum B) and scatter-plots

portraying the relationship between GMV in ROIs and RTS game skill

acquisition. Based on the significant difference in both putamen and pallidum (part of

the lenticular nucleus from Figure 3) in cross-sectional study, we chose those areas

as a ROIs for training study to evaluate the patterns of RTS game skill acquisition.

Panels show areas with a significant (bolded) positive correlation between mean

GMVs in the ROIs with SC2 game performance. The blue color represents the results

for the putamen, and the pink color represents the results for the pallidum. The results

of correlation analyses are uncorrected for multiple comparisons.

Abbreviations: ROIs - regions of interest, L - left, R - right

Regional GMV and Perception Action Cycle latency in RTS skill acquisition

Our next step was to check what type of in-game behavior correlates with VBM

assessments of GMV. Using measures of cognitive-motor abilities extracted from SC2

game replay data from sixteen participants, we constructed three indicators based on

in-game actions performed by trainees: [1] PAC latency - time (in milliseconds) from a

point-of-view change (switch in focus of attention) to the occurrence of the first action

issued by the player (indexing motor reaction). [2] HS usage - expressed as the

average number of hotkey presses per minute in each game, where each such action

represents an automated selection of multiple units or buildings. Hotkeys are used to

aid in the management of dispersed elements of the game. [3] APM - the average

number of actions performed during each minute of the game (measure of cognitive-

motor speed). We defined PAC latency as a cognitive marker of SC2 expertise, APM

and HS usage as a motor markers of SC2 expertise (42). We divided the whole training

time of each trainee into quartiles and computed PAC latency, HS usage and APM for

each quartile (within subject) (42).

We found a significant, negative correlation between PAC latency in the first quartile

and GMV in all of our predefined ROIs (left and right putamen [left: r = -0.58, p = 0.02

and right: r = -0.43, p = 0.10 - tendency level; Fig. 5 A), as well as both pallidums (left:

r = -0.57, p = 0.02 and right: r = -0.54 , p = 0.03; Fig. 5 B). No correlation analysis

survived the FDR p<0.05 correction for multiple comparisons so the results presented

here are uncorrected for multiple comparisons. Correlations between PAC latency and

ROIs volume for the second, third and fourth quartiles were not found. We also

conducted correlational analyses for all quartiles for HS usage, APM, and all

predefined ROIs, but there were no significant correlations. All correlation coefficients

and significance levels are provided in Table 1. PAC latency distribution for each

participant is presented in Fig. 6.

Fig. 5. Predefined ROIs (right, left putamen A and pallidum B) and scatter-plots

portraying the relationship between GMVs in ROIs and Perception Action Cycle

latency in the first quartile. The brain area with a significant difference (part of the

lenticular nucleus from Figure 3.) was the datum point to choose the GMVs in the ROIs

(both putamen and pallidum) which were included to evaluate the patterns of

Perception Action Cycle latency in the first quartile (Q1). The panels show areas with

a significant (bolded) negative correlation between the mean GMVs in the ROIs with

PAC latency in Q1. The blue color represents the results for the putamen, and the pink

color represents the results for the pallidum. The results for correlation analyses are

uncorrected for multiple comparisons.

Abbreviations: ROIs - regions of interest, PAC - Perception Action Cycle, Q - quartile

Fig. 6. Perception Action Cycle latency distribution for each participant.

Additionally, the plot represents the PAC latency distribution in the first quartile (Q1)

separately for participants below and above the median: computed from the average

GMV for all the ROIs (both right/left putamen and pallidum).

Abbreviations: ROIs - regions of interest, PAC - Perception Action Cycle, Q - quartile

Discussion

In training-related plasticity studies, interindividual differences in learning performance

have not received much attention. The large number of publications focused on

behavioral improvement and experience-dependent structural changes in the brain.

However, neural factors of predisposing to complex skill learning, such as video

games acquisition, appear to play an important role in optimizing the training

paradigms dedicated to increase the subject’s efficiency and brain plasticity.

In the two studies described here, using VBM, we observed that the volume of the

right lenticular nucleus (part of the basal ganglia) was predictive of success in the

complex RTS game SC2. Experts in SC2 (players from the top five leagues) had larger

basal ganglia compared to people who do not play RTS games. When we took into

consideration the volume of the areas identified in the first study in a completely new,

unrelated sample of individuals who were naïve to RTS games, we were able to predict

the quality of learning in SC2. The regional differences in the volume of the basal

ganglia correlated with the pace at which participants learned to play this complex

video game. In our opinion, the results presented here provide new insights into how

brain volume measurements can be utilized to predict the success of the skill-learning

process, and can be applied to a much broader, than just video games, context.

The first, cross-sectional, study showed more GMV in the right lenticular nucleus

(putamen and pallidum) in RTS experts when compared to NVGPs. In the second,

training study we explored whether the pre-existing volume of the putamen and

pallidum can predict improvement in the RTS skill acquisition in novice players. We,

in fact, confirmed that the GMV of predefined ROIs was correlated with complex skill

acquisition, measured as time spent on more demanding game levels, which is treated

here as a proxy of a complex skill learning. The correlation was found in both the right

and left lenticular nucleus (putamen and pallidum), whereas in the first study we found

differences between experts and non-players in the right lenticular nucleus only. The

lenticular nucleus is a subcortical structure within the basal ganglia, comprised of the

putamen and pallidum and constitutes a relay station, conveying information between

different subcortical areas and the cerebral cortex (mainly primary motor cortex and

the supplementary motor area) (46). Both pallidum and putamen play an important

role variety of motor acts, including sequential motor learning (47) and movements

control (48-50) including the operation of a joystick (51). It is also abundantly clear that

the pallidum and putamen neurons are involved in more than just the organization

and/or execution of movements. They are also actively involved in a variety of

cognitive functions such as visual attention (52), working memory (53, 54) and

cognitive control (55). Additionally, pallidum neurons encode actions such as actual

location of the target on a screen, as well as monitor behavioral goals (spatial or

object), indicating that this region is involved in goal-directed decisions and action

selection (56).

To properly understand the results from the two studies presented here, we need to

take into consideration the dynamics of the learning process and expertise levels.

Playing a demanding RTS game like SC2 requires the engagement of a wide range

of cognitive and motor functions (42, 57, 58). However, the degree to which each of

these functions is engaged is not likely to be equally engaged across all stages of

SC2’s learning. Specifically, early attempts to acquire a novel skill, especially as

complex as learning to play SC2, are characterized by effortful, explicit information

processing which proceeds under executive control functions (especially working

memory). As the practice advances, the skill becomes less effortful and more

proceduralized, with almost complete automaticity attained at the expert levels of

performance (59). Our observations paint a pattern of results suggesting that such a

process is taking place within the basal ganglia structures. The result of more GMV in

the right lenticular nucleus of expert RTS players may stem from effective use of

learned motor sequences (especially automatic movements). On the road to success

in most RTS games - including SC2 - expertise level is commonly multi-staged, and

connected with acquiring a higher and higher degree of automatization of specific

sequences of movements. Such automatization allows expert players (like those in

our first study) to perform actions that were at first (in novice players, like in our second

study) complicated and cognitively demanding, with minimal effort (60). SC2 has an

economic component, which means that players have to spend resources on the

production of military units and structures, and hence many of the player’s decisions /

strategies are related to the balancing of expenses on military and economic strength.

Secondly, the game board, called the map, is much larger than what the player can

see at one time. Thirdly, players do not have to wait for the opponent to play their turn,

so the pace of the game is incomparably faster than in e.g. chess. Players who can

more effectively and quickly implement their strategy have a huge advantage.

Therefore, motor skills, mainly related to handling the keyboard, are an integral part of

the game that leads to victory. And thus, the growth of the subcortical structure is

probably the result of the above-described experience. We know from other studies

that the putamen plays a special role in game related processes, and is also important

for movement preparation, learning, and motor sequence control (50, 61-63). An

additional confirmation that video games strongly stimulate motor skills, especially

those that are highly specialized and automated, was conducted by Borecki et al. (64).

Their study assessed a wide range of hand-movement coordination skills and

demonstrated that FPS players were able to use motor skills more effectively than

control subjects, and the scope of these skills included: improved targeting accuracy,

reduced tremors, more effective eye-hand coordination, and an increased speed of

wrist movements. This range of motor skills has been investigated using the game

Counter Strike, due to its interactive nature. In Counter Strike, players perceive

battlefield-like conditions from the first-person perspective, which forces them to

engage in various military activities requiring immediate response. The biggest

advantage of the top video games players over other opponents is speed, which

develops toward expertise.

It should also be added that all subjects in our first study were right-handed, but their

left hand, for many years, was extensively used during gaming. It can be seen as

intensive training of the left hand, and what we see on the level of VBM are differences

in the right hemisphere. Evidence for lateralization related to specific movement has

already been well described in the literature (65, 66). What is more, the observed result

is consistent with the findings of other researchers, who showed that action video

game players in comparison to non-players are characterized by faster reaction times

in tasks that measure visual and spatial abilities, but only when responses were given

using the non-dominant hand (67). It should be mentioned that we did not observe a

relationship between the size of the lenticular nucleus and experience with RTS

games. Other studies (68-70) have similarly failed to show such correlations,

suggesting that the relationship between anatomical plasticity measured using VBM

methods and behavior may be more complex and may be mediated by other variables

(71). There was also low variability in SC2 experience among our participants, which

may explain the lack of correlation. The lack of correlation can also be interpreted as

an argument for the existence of certain predispositions in complex video game skills

acquisition, which we tested and confirmed in our second, training study. Because of

the correlational nature of the first study, we cannot determine whether the structural

differences between the RTS and NVGP groups were the result of extensive video-

game experience or because RTS players have brain structure characteristics that

predispose them to engage in activities like playing RTS video games. We designed

our longitudinal study, which followed a cross validation procedure (44) and introduced

a training regimen with the same RTS game as in the first study on a group of NVGPs,

to shed some light on this conundrum.

Based on the dominant theory concerning basal ganglia involvement in motor skills

we assumed that the differences seen in our first study (cross-sectional) were driven

mainly by the motor component of the prolonged SC2 usage. To test that, we followed

the methodological approach proposed by Thompson and others (42) and focused on

the game telemetry. We performed an analysis of both more cognitive game

indicators, namely PAC latency, as well as more motor-related game characteristics:

APM and HS usage. We found a negative correlation between the GMV of the left

putamen and both the left and right pallidum, and PAC latency at the beginning of RTS

game skill acquisition. From the cognitive perspective, PACs represent shifts in

attention focus followed by a set of motor actions, as SC2 players have to constantly

relocate a narrow Point-of-View (PoV) window over a large map area to attend to

different locations and execute actions associated with the current state of the game.

Technically, each PAC is a PoV that contains one or more actions. PACs encompass

roughly 87% of player game time (42) and closely resemble the structure of individual

trials in experimental tasks that record the set of a participant’s reactions to presented

stimuli. We found that PAC latency was relevant to game performance at the beginning

of RTS skill acquisition among novice players. This advantage in the early stage of

training can be explained by better attentional filtering of relevant game objects. This

edge diminishes in later stages of learning, as the game has a finite number of visual

elements with meaningful affordances that can be learned over time. For new players,

most of the "work" being done is within an individual PAC. To engage a PAC, players

have to first attend to a cue, assess what they are looking at within that region of

interest, and then start producing actions. PACs latency represents the time it takes

for perceptual abilities to paint a picture of the situation, and for attentional abilities to

pick through the relevant stimuli. As players accumulate experience and game

knowledge, the attentional demands within a PAC should decrease. A larger

pretraining basal ganglia volume could boost attention by focally releasing inhibition

of task-relevant representations (72) at the beginning of learning how to play RTS

games. That demand is constantly being stressed through each cycle, and should

improve up to some biological limits if game knowledge permits. However, in contrast

to our predictions, correlations between HS usage, APM and basal ganglia volume

were not found. The usage of HS speeds execution, and the speed of execution is

represented by APMs. However, speed plays a very crucial role at the top level of

players, but not in novice players and 30 hours of training was likely not enough to

develop automatization.

From the perspective of a novice player, success in most RTS video games is by

design based on tactical planning, which involves the memory functions and attention

of players in many ways. As in almost all strategy games, players devise the most

optimal game opening strategies and counter strategies and commit them to memory.

For players learning a game like SC2, the most challenging aspect involves

memorizing the visuals of interactive game elements and the complex mechanics

associated with particular units (e.g. What can that building produce? What types of

special actions can this unit make?). Moreover, as the underlying concept of SC2

gameplay is the “counter play” mechanic, it forces players to memorize complex

interactions between units (e.g. Which unit will be most effective against a specific

threat?), and as the game progresses players need to constantly monitor and update

their internal representation of the opponent’s unit composition to react accordingly.

On a higher strategic level, RTS video games require players to be able to memorize

many game states from their past experience, as this allows for more accurate

prediction of the opponents intentions. The putamen and pallidum were shown as

uniquely sensitive brain structures in the above-mentioned situations (73).

It needs to be added that the obtained results of a greater GMV in the RTS group (our

first study) may be interpreted as the effect of a long-term training in the planning and

execution of motor sequences (as it has been discussed in the above section), but the

GMV of the lenticular nucleus as a neural predictor of RTS training outcomes should

be also considered. And the interaction of these two factors seems to be the most

probable, as people who engage in intensive and effective video game playing

probably have some structural brain predispositions to take such actions and be good

at them (which - in turn - motivates them to engage even more). This does not mean

that there is no effect of training, but that the correlational nature of our study does not

allow us to conclude whether people who start playing video games have different

brain structure characteristics in comparison to non-gamers. This unresolved question

about brain predispositions in acquiring new skills motivated us to perform a training

study.

Our results are in line with Erickson and others (39), who showed that putamen

volumes were positively correlated with learning new procedures and developing new

strategies in a non-commercial RTS video game designed by psychologists.

Additionally, Vo and collaborators (41) found that patterns of time-averaged T2*-

weighted signal in the dorsal striatum recorded before the start of extensive training

were predictive of future learning success in the same game. Other regions were

recognized as predictors of RTS skill acquisition in an elderly population, such as the

prefrontal and frontal regions including the frontal gyrus, anterior commissure, central

gyrus, cerebellum, precentral gyrus, and premotor cortex (40).

Additionally, we found no effects of training on brain structures in longitudinal study.

This result does not support the hypothesis that short term RTS video game training

(30h in total) causes alterations in GMV. This does not rule out the possibility that there

were changes in GMV, but they are too small to detect using the VBM method (74).

Using diffusion-weighted MRI to study white matter can provide complementary

information about neuroplastic changes after video game training.

Conclusions and future directions

This paper presents novel findings showing that RTS video game players have a larger

lenticular nucleus than NVGPs. Greater volume of the lenticular nucleus can be

explained as a result of intensive and complex motor sequence learning (especially

automated movements) by our group of RTS experts. However, the contra argument

is supported by the assumption that people with specific brain structure characteristics

(larger lenticular nucleus) have predispositions to become good video game players.

To resolve it, we conducted a second study and checked if there are some neural

predispositions that define the manner in which playing a game is learned. We showed

that regional differences in volumes of brain areas identified in the first study (on expert

RTS players) correlated with the learning pace observed in the second study

conducted on a completely new, unrelated and naive to RTS game participants. The

present study provides new insights into how skill learning success can depend on

brain characteristics. Our results show the importance of individual features of the

brain in the effectiveness of training, and in the case of our study - learning to play a

new video game. The conclusion that comes to mind is that people with a specific

brain structure have a better chance of acquiring new skills. In our study, we showed

this in relation to learning a video game, but there is a good chance that it is a more

general attribute of the human brain. These findings also point to the usefulness of

MRI-brain structure characteristics in predicting relevant intervention outcomes and

greatly improve the practicability and effectiveness of those interventions. On the level

of a more direct application, our results may open the window to identifying the

structural characteristics of successful professional eSports players, much like

physical measurements are used in professional sports.

Materials and Methods - cross-sectional study

Participants

Sixty-four (n = 64) right-handed, male subjects with a mean age of years 24.55 (SD =

3.66) participated in this study. Two subjects (n = 2) were excluded from the analysis

because of bad quality MRI data (image artifacts), so the final sample consisted of

sixty-two (n = 62) participants. All subjects completed an on-line questionnaire about

demographics, education status, and video-game playing experience. In our self-

designed questionnaire (on-line questionnaire on GEX platform) (75), we asked

additional questions to assess how often individuals engage in various game genres.

We broke the games genres down into the following categories: first person shooter

(FPS), RTS, role playing, sports, multiplayer online battle arena, racing, puzzles,

fighting, turn-based strategy, adventure, and platform games. Inclusion criteria for the

RTS experts in our study were as follows: [1] experience with SC2 play, [2] played

RTS games at least 6 h/week for the previous six months, [3] declared playing SC2

for more than 60% of total game play time, [4] be an active player (played matches in

the last two seasons) and be placed in one of five SC2 leagues (Gold, Platinum,

Diamond, Master, Grandmaster). Inclusion criteria for NVGPs were as follows: [1] little

or no previous experience with RTS video-game play, and experience with other types

of video games totaling no more than 8 h/week (most played less than 6 h) over the

past six months. The mean age of the RTS expert group (n = 31) was 24.71 years (SD

= 4.27) and 24.39 years (SD = 3.00) in the NVGP group (n = 31).

Education level was matched between groups (all participants were at an

undergraduate level). The mean years of education of the RTS expert group was 15.55

years (SD = 2.77) and 16.10 years for the NVGP group (SD = 2.95). We controlled for

working memory capacity using the Operation Span Task (OSPAN) (76); the mean

score was 51.77 (SD = 12.74) for RTS experts and 51.71 (SD = 13.19) for NVGPs.

The average hours per week of video games played in different genres from the last

six months in RTS experts was 22.74 (11.79) and 2.39 (2.28) for NVGPs. The data

from these participants are also a part of our other study (77), but the GMV analyses

are unpublished in the case of all of the included participants. Table 2 shows the

overall video game playing characteristics and average weekly playtime in each video

game genre. None of the participants had a history of neurological illness, and they

did not declare the use of any psychoactive substances. We also had access to

information about each players’ overall performance in the game (wins and losses

from the last two seasons) and number of games played.

All subjects participated in additional MRI and cognitive measurement sessions in

order to obtain DTI measurements and assess several cognitive functions, which were

not related to the project described in this article.

All subjects gave their informed consent to participate in the study, in accordance with

the SWPS University Ethical Committee. All participants were male because of

difficulties in recruiting female participants with sufficient video game experience. They

were paid (approx. 52 USD) for participating in the study.

MRI - image acquisition

High-resolution whole brain images were acquired on a 3-Tesla MRI scanner

(Siemens Magnetom Trio TIM, Erlangen, German) equipped with a 32-channel

phased array head coil. T1w images were acquired with the following specification:

repetition time, TR = 2530 ms, echo time, TE = 3.32 ms, flip angle, FA = 7°, field of

view; FOV = 256 mm, inversion time; TI = 1100 m; voxel size = 1x 1x 1 mm3., 176

axial slices. Foam padding was used around the head to minimize head motion during

scanning. During these sequences, subjects were asked to relax and try not to fall

asleep or move.

The study was a part of a larger project where participants underwent three more MRI

sessions (two functional magnetic resonance imaging (fMRI) tasks, diffusion tensor

imaging (DTI) session) and a cognitive session on other days.

Data preprocessing

The same approach was used for both studies. For data preprocessing and statistical

analyses, we used Statistical Parametric Mapping (SPM8, Wellcome Trust Center for

Neuroimaging, London, UK) running on MATLAB R2015 (The Mathworks, Inc., Natick,

MA, USA). We applied standard processing steps as proposed by Ashburner and

Friston (2009) (78): [1] Checking for anatomical abnormalities and scanner artifacts

for each participant, [2] Setting the image origin to the anterior commissure (AC), [3]

Manual reorientation to canonical T1 (canonical\avg152T1.nii), [4] Segmentation of

tissue classes, [5] Normalization using DARTEL, [6] Modulation of different tissue

segments, and [7] Smoothing. A segment algorithm was used in order to obtain basic

tissue classes: white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF)

(78). Next, a study specific template was computed from all participants using

Diffeomorphic Anatomical Registration through the Exponentiated Lie Algebra

(DARTEL) toolbox (79) to determine the nonlinear deformations for warping all the

gray and white matter images so that they match each other. This step was followed

by affine registration of the gray matter maps to the Montreal Neurological Institute

(MNI) space. Modulation (Jacobian determinant) of different tissue segments by

nonlinear normalization parameters was applied to correct for individual differences in

brain sizes. Finally, data were smoothed with an 8-mm isotropic Gaussian kernel. A

group-wise brain mask was computed for statistical analysis to decrease false

positives occurring outside the brain. Coordinates of significant effects are reported in

MNI space. XjView was used to identify the structures showing effects

(http://www.alivelearn.net/xjview). The results were visualized using BrainNet Viewer

software (80) (http://www.nitrc.org/projects/bnv/).

Statistical analysis

Whole brain GMV comparison between RTS experts and NVGPs

Differences in GMV between RTS experts and NVGPs were calculated using two-

sample t-tests. The two-group difference was adjusted for participant age. Given that

the total intracranial volume (TIV) could affect the relationships between regional brain

volume and measures of skill acquisition, we included TIV in our analyses. An explicit

mask was employed (group brain mask with no threshold) to exclude false positives.

The group mask was computed by summing GM, WM and CSF for each individual

and then computing an average mask for the whole group. The masking was

performed using MaskingToolbox (81). The model was computed without an absolute

threshold since clusters which include voxels with smaller intensity are excluded from

the statistical analysis (81).

Clusters from the whole brain exploratory analysis (the statistical threshold was set at

p < 0.001) were corrected to p < 0.05 for multiple comparisons using FWE correction

at the cluster-level, using a cluster size of 2125 voxels. Next, the average GMV signal

from a significant cluster was extracted using the MarsBaR toolbox (82). Then, the

GMVs (both right and left putamens and pallidums) were fed to the correlation

analysis.

Materials and Methods - training study

Participants

Twenty subjects (n = 20) participated in the study, but four were excluded from

analysis because of low MRI data quality (image artifacts) (n = 2) as well as training

dropout (n = 2). The final sample consisted of sixteen (n = 16) right-handed

participants with a mean age of 22.94 years (SD = 2.11): five males (22.20 years, SD

= 2.39) and eleven females (23.27 years, SD = 2.01). The mean years of education

was 15.10 years (SD = 1.93). All subjects completed the same on-line questionnaire

as described above (cross-sectional study). Their mean OSPAN score was 52.31 (SD

= 18.15). We also asked about their video-game playing experience, and the number

of mean weekly hours spent playing video games over the last six months was 0.97

hours (SD = 1.16), with no experience in any action video game genres. None of the

participants had a history of neurological illness, and they did not report using

psychoactive substances.

All subjects provided written informed consent to participate in the experiment, and

the study protocol was approved by the SWPS University Ethical Committee. They

were paid (approx. 180 USD) for participating in the study.

Experimental task

Sixteen participants carried out 30 hours of SC2 gaming in a control laboratory setting.

The training lasted from 3 to 4 weeks (a minimum of 6 hours per week, maximum of

10 h per week), with a prohibition of gaming elsewhere (outside the laboratory). Before

participants started the training, they had an introduction session with the SC2 trainer.

The training was carried out using dedicated desktop PC running Windows 7

(professional edition, 64-bit operating system) equipped with a dedicated graphic card

(NVIDIA GeForce GTX 770), 8GB or RAM and a 24” LED display, allowing to play at

high graphic quality (1920*1080 pixels resolution, 60Hz). Participants played the game

using a mouse/keyboard/headset setup.

In SC2 game players need to build an economy (gathering resources and building

bases) and develop the military resources (training units) in order to beat their

opponents (destroying their base and army). Cognitive and motor challenges of SC2

game are described in the introduction part of this paper. The participants played using

only one Race (Terrans) against AI (artificial intelligence).

There were eight possible difficulty levels in the SC2 matches: Very Easy, Easy,

Medium, Hard, Harder, Very Hard, Elite and Cheater. For each victory the player

received 1 point. If the player lost, they lost 1 point (-1). The scoring intervals for each

level of difficulty were determined as follows: Very Easy - from 0 to 4 points, Easy -

from 5 to 8 points, Medium - from 9 to 12 points, Hard - from 13 to 16 points, Harder -

from 16 to 20 points, Very Hard - from 21 to 24 points, Elite - from 25 to 28 points, and

Cheater - up from 29 points. None of the participants reached the Cheater level, so

we included seven levels in the analysis.

We computed the variable indexing the weighted time spent on every level of SC2

difficulty (the time spent on the second level was multiplied by two, the time spent on

the third level by three, and so on) for each participant. The final result is a

standardized (group-wise) sum of the time spent on all difficulty levels, which reflects

performance in the game.

RTS game skill acquisition indicator = (hrs*1)+(hrs*2)+(hrs*3)+(hrs*4)+(hrs*5)+(hrs*6)+(hrs*7)

MRI - image acquisition

High-resolution T1w images were collected using the IR-FSPGR sequence performed

using a 3T MRI GE Discovery MR750w scanner before the RTS training. The MRI

scanner was equipped with an 8-channel phased array head coil. T1-weighted (T1w)

images were acquired with the following specification: repetition time, TR = 7 ms, echo

time, TE = 3 ms, flip angle, FA = 11 °, field of view; FOV= 256 mm, inversion time; TI

= 400 ms; voxel size = 1 x 1 x 1 mm3, 200 axial slices. Foam padding was used around

the head to minimize head motion during scanning. Subjects were asked to try and

relax, but not to fall asleep or move. All participants performed a structural MRI and

cognitive assessment consisting of several cognitive tasks at two time points: before

(T0) and after 30 hours of video game practice (T1). In the current study, we focused

on pretraining (T0) MRI scans.

Data preprocessing

The same approach was used for both studies. Details of data processing are

described in Materials and Methods - training study section.

Statistical analysis

Assessing the ROIs for prediction RTS game skill acquisition

Putamen and pallidum were defined using the AAL-116 (83) atlas and based on the

results from cross-sectional study. Due to the fact that it was a group of novices (non-

video game players), we decided to check both putamen and pallidum (bilaterally),

and not only within the result obtained from the cross-sectional study where expert

video game players were recruited. We aimed to explore this data more due to

different skill levels between our groups in the cross-sectional and training study. Each

ROI was extracted using the MarsBaR Toolbox (82). Then, the GMV was fed to the

correlation analysis. Because our data did not meet the assumptions for regression

models, all correlational analyses were conducted using Spearman’s correlation

coefficient. Correction for multiple comparisons (FDR) for correlation analysis was

applied.

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Acknowledgments

General: We are grateful to all participants who agreed to be involved in this study.

We would like to thank Weronika Debowska for her support during data collection

and Michal Chylinski for his help with SC2 participant’s trainings. In Fig. 1. icons

were made by Freepik, DinosoftLabs from www.flaticon.com. Funding: This study

was supported by the Polish National Science Centre NCN grants

2016/23/B/HS6/03843, 2013/10/E/HS6/00186, 2013/11/N/HS6/01335. NK was

supported by the Foundation of Polish Science (FNP) and Kosciuszko Foundation.

Author contributions:

NK: designed the study and methods, collected the structural imaging data, collected

the behavioral data, analyzed and interpreted data, wrote the manuscript, obtained

funding

MS: designed the experiment, SC2 training performed

PD: designed the experiment, corrected the manuscript

BK: prepared the MRI sequence and contributed to the interpretation

MM: involved in structural imaging data collection

NH: involved in structural imaging data collection

MG: involved in behavioral data analysis

AM: involved in structural imaging data analysis, involved in manuscript revising

MK: contributed to the interpretation

AB: designed the study and methods, interpreted data, wrote the manuscript, obtained

funding

Competing interests: The authors report no conflicts of interest with respect to the

content of this manuscript. Data and materials availability: All data needed to

evaluate the conclusions in the paper are presented in the paper. Additional data

related to this paper and custom analysis scripts are available upon reasonable

request.

ResearchGate has not been able to resolve any citations for this publication.

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