Please wait a minute...
Journal of Integrative Neuroscience  2018, Vol. 17 Issue (3): 297-306    DOI: 10.31083/JIN-170056
Case study Previous articles |
QEEG-based neural correlates of decision making in a well-trained eight year-old chess player
Abolfazl Alipour1, 2, Sahar Seifzadeh3, Hadi Aligholi2, 4, Mohammad Nami2, 4, 5, *()
1 Department of Psychological and Brain Sciences, Indiana University, Bloomington, Indiana, USA
2 Neuroscience Laboratory-NSL (Brain, Cognition and Behavior), Department of Neuroscience, School of Advanced Medical Sciences and Technologies, Shiraz University of Medical Sciences, Shiraz, Iran;
3 Young Researchers and Elite Club, Qazvin Branch, Islamic Azad University, Qazvin, Iran
4 Department of Neuroscience, School of Advanced Medical Sciences and Technologies, Shiraz University of Medical Sciences, Shiraz, Iran
5 Clinical Neurology Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
Download:  PDF(2762KB)  ( 556 ) Full text   ( 33 )
Export:  BibTeX | EndNote (RIS)      

The neurocognitive substrates of decision making in the context of chess has appealed to the interest of investigators for decades. Expert and beginner chess players are hypothesized to employ different functional brain networks when involved in episodes of critical decision making while playing chess. Cognitive capacities including, but not restricted to, pattern recognition, visuospatial search, reasoning, planning, and decision making are perhaps the key determinants of the reward and judgment decisions made during chess games. Meanwhile, the precise neural correlates of decision making in this context has largely remained elusive. Quantitative electroencephalography is an investigative tool possessing an appropriate temporal resolution for the study of the neural correlates of cognitive tasks at a cortical level. A 22-channel electroencephalography setup and digital polygraphy were employed in the investigation of a well-trained eight-year old boy while engaged in playing chess against a computer. Quantitative analyses mapped and source-localized electroencephalography signals. Analyses indicated a lower power spectral density for higher frequency bands in the right hemisphere during decision making related epochs. Moreover, in the given subject, the information flow of decision making blocks tended to move from posterior towards anterior brain regions.

Key words:  Chess      decision making      QEEG      electroencephalography      power spectra      functional connectivity     
Submitted:  09 September 2017      Accepted:  13 October 2017      Published:  15 August 2018     
*Corresponding Author(s):  Mohammad Nami     E-mail:

Cite this article: 

Abolfazl Alipour, Sahar Seifzadeh, Hadi Aligholi, Mohammad Nami. QEEG-based neural correlates of decision making in a well-trained eight year-old chess player. Journal of Integrative Neuroscience, 2018, 17(3): 297-306.

URL:     OR

Fig. 1.  Data acquisition setup. Subject engaged in chess against a computer. The subject reported six episodes of critical decision making during which a real-time EEG recording was marked and subsequently grand averaged in brain mapping. The galvanic skin conductance (GSC) and heart rate variability (HRV) were simultaneously monitored during the procedure with special markings indicating decision making episodes.

Fig. 2.  Spiderweb chart plot of absolute power values for the theta frequency band upon decision making (orange) and resting state (blue) QEEG data. The analysis revealed higher theta power upon decision making task in F8-T3, F4-C3, T4-P3, C4-P3 and T6-O1 dipoles compared to resting state. Values are presented in $ \mu $V$ ^2 $. # p < 0.05, ## p < 0.01 and ### p < 0.001. FP: frontopolar, C: central, F: frontal, P: parietal, T: temporal and O: occipital.

Fig. 3.  Spiderweb chart plot of absolute power values for the alpha frequency band upon decision making (orange) and during resting state (blue) QEEG data. Analysis revealed higher alpha power during decision making task at F4-C3 dipoles compared with the resting state, ## p < 0.01. The F8-T3 dipole, however, showed the alpha power to dominate the resting state when compared to task-positive epochs, * p < 0.05. Values are presented as $ \mu $V$^2$. FP: frontopolar, C: central, F: frontal, P: parietal, T: temporal and O: occipital.

Fig. 4.  Spiderweb chart plot of absolute power values for the beta frequency band upon decision making (orange) and during resting state (blue) QEEG data. Analysis revealed higher beta power during the resting state when compared with DM epochs at F8-T3, F8-F4, F4-C4, C4-P4, and FP2-F3. * p < 0.05, ** p < 0.01, ## p < 0.01. Values are presented in $ \mu $V$^2$. FP: frontopolar, C: central, F: frontal, P: parietal, T: temporal and O: occipital.

Fig. 5.  QEEG topographical spectral brain maps. Analysis demonstrated absolute power values across spectra during resting state (section (A), upper panel) and decision making QEEG data (panel (B), upper panel). Resting state QEEG centroparietal activity is compatible with default mode network activity. Grand average for the six decision making blocks (section (B)) suggest lower than expected beta power in the anterior brain region rather than in the occipital and centroparietal areas. The lower panels in sections (A) and (B) give resting state and decision making beta coherence maps indicating a frontal beta hypocoherence during task-positive rather than resting states.

Fig. 6.  Autonomic response during the decision making task. Panels (A)-(C) give heart rate variability (HRV), pNN50 (number of pairs of successive NN intervals that differ by more than 50 ms), and GSR (in $ \mu $Siemens). Yellow color indicates the decision making (DM) blocks from which EEG data were extracted and analyzed. Despite no significant difference in GSR between DM and non-DM blocks, there was an apparent increase in HRV and PNN50 in the DM blocks. This suggests involvement of an autonomic component when the subject was engaged in the DM task.

Fig. 7.  Information flow between major sources and sinks during critical decision making. (A) (left to right): Position of information sources in sagittal, coronal, and transverse sections. (B): Color matrix for information flow between all signal sources. Each square represents the information flow difference between resting and DM states for a specific pair of signal sources. For example, the red elements (four, three) indicate increased information flow and causal effect of signal source three on signal source four during DM epochs. (C): Position of information sources in 3D space. (D) (left to right): Position of "information sinks’’ in sagittal, coronal, and transverse sections. (E): Reciprocal color matrix (versus (B)) for information flow between all signal sources. (F): Position of information sinks in 3D space. Sources and sinks were identified by thresholding the magnitude of their causal effect. Sources of information flow during DM blocks were localized at components 3, 4, 13, 14, 15 and 16 (anatomically linked to the left vmPFC, right occipital and right medial temporal cortices); the information flow sinks were localized at components 9, 11, 19 and 22 (anatomically linked to the left OFC, left PPC and right IPS).

[1] Jollans L, Whelan R, Venables L, Turnbull OH, Cella M, Dymond S ( 2016) Computational EEG modelling of decision making under ambiguity reveals spatio-temporal dynamics of outcome evaluation. Behavioural Brain Research 321, 28-35.
doi: 10.1016/j.bbr.2016.12.033 pmid: 28034803
[2] Forbes DP, Milliken FJ ( 1999) Cognition and Corporate Governance: Understanding Boards of Directors as Strategic Decision-Making Groups. Academy of Management Review 24( 3), 489-505.
doi: 10.2307/259138
[3] Mahmoud M, Liu Y, Hartmann H, Stewart S, Wagener T, Semmens D, Stewart R, Gupta H, Dominguez D, Dominguez F ( 2009) A formal framework for scenario development in support of environmental decision-making. Environmental Modelling & Software 24( 7), 798-808.
doi: 10.1016/j.envsoft.2008.11.010
[4] Connors MH, Burns BD, Campitelli G ( 2011) Expertise in complex decision making: the role of search in chess 70 years after de Groot. Cognitive Science 35( 8), 1567-1579.
doi: 10.1111/j.1551-6709.2011.01196.x pmid: 21981829
[5] Eisele P ( 2004) Judgment and decision-making: experts’ and novices’ evaluation of chess positions. Perceptual & Motor Skills 98( 1), 237-248.
doi: 10.2466/PMS.98.1.237-248 pmid: 15058886
[6] Gustafson DH, Bosworth K, Hawkins RP, Boberg EW, Bricker E ( 1992) CHESS: a computer-based system for providing information, referrals, decision support and social support to people facing medical and other health-related crises. In, Proceedings of the Annual Symposium on Computer Application in Medical Care (p. 161-165). American Medical Informatics Association.
pmid: 2248029
[7] Sigman M, Etchemendy P, Slezak DF, Cecchi GA ( 2010) Response time distributions in rapid chess: a large-scale decision making experiment. Frontiers in Neuroscience 4, 60.
doi: 10.3389/fnins.2010.00060 pmid: 2965049
[8] Stanovich KE, West RF ( 2000) Individual differences in reasoning: implications for the rationality debate? Behavioral & Brain Sciences 23( 5), 665-726.
doi: 10.1017/S0140525X00003435 pmid: 11301544
[9] Kørnøv L, Thissen WA ( 2000) Rationality in decision-and policymaking: implications for strategic environmental assessment. Impact Assessment and Project Appraisal 18( 3), 191-200.
doi: 10.3152/147154600781767402
[10] Sternberg, Robert J ( 2000) The ability is not general, and neither are the conclusions. Behavioral & Brain Sciences 23( 5), 697-698.
[11] Burns BD ( 2004) The effects of speed on skilled chess performance. Psychological Science 15( 7), 442-447.
doi: 10.1111/j.0956-7976.2004.00699.x pmid: 15200627
[12] Hl VDM, Wagenmakers EJ ( 2005) A psychometric analysis of chess expertise. American Journal of Psychology 118( 1), 29-60.
pmid: 15822609
[13] Ashjazadeh N, Boostani R, Ekhtiari H, Emamghoreishi M, Farrokhi M, Ghanizadeh A, Hatam G, Hadianfard H, Lotfi M, Mortazavi SMJ ( 2014) Operationalizing Cognitive Science and Technologies’ Research and Development; the “Brain and Cognition Study Group (BCSG)” Initiative from Shiraz, Iran. Basic & Clinical Neuroscience 5( 2), 104.
pmid: 4202589
[14] Nazaraghaie F, Torkamani F, Kiani B, Torabi-Nami M ( 2015) EEGguided meditative training through geometrical approach: an interim analysis. Avicenna Journal of Phytomedicine 5, 146.
[15] Benabdelkader C, Yacoob Y ( 2010) Statistical estimation of human anthropometry from a single uncalibrated image. Computational Forensics, 200-220.
[16] Arnaud D, Tim M, Christian K, Zeynep AA, Nima BS, Andrey V, Scott M ( 2011) EEGLAB, SIFT, NFT, BCILAB, and ERICA: New Tools for Advanced EEG Processing. Computational Intelligence & Neuroscience 2011,130714.
doi: 10.1155/2011/130714 pmid: 3114412
[17] Mullen TR ( 2014) The dynamic brain: Modeling neural dynamics and interactions from human electrophysiological recordings. University of California, San Diego, ProQuest Dissertations Publishing.
[18] Korzeniewska A, Manczak M, Kaminski M, Blinowska KJ, Kasicki S ( 2003) Determination of information flow direction among brain structures by a modified directed transfer function (dDTF) method. Journal of Neuroscience Methods 125( 1,2), 195-207.
doi: 10.1016/S0165-0270(03)00052-9 pmid: 12763246
[19] Thielscher A, Pessoa L ( 2007) Neural correlates of perceptual choice and decision making during fear-disgust discrimination. Journal of Neuroscience 27( 11), 2908-2917.
doi: 10.1523/JNEUROSCI.3024-06.2007 pmid: 17360913
[20] Cieslik CE, Zilles K, Caspers S, Roski C, Kellermann TS, Jakobs O, Langner R, Laird AR, Fox PT, Eickhoff SB ( 2013) Is There “One” DLPFC in Cognitive Action Control? Evidence for Heterogeneity From Co-Activation-Based Parcellation. Cerebral Cortex 23( 11), 2677-2689.
doi: 10.1093/cercor/bhs256 pmid: 3792742
[21] Zhang D, Gu R, Broster LS, Jiang Y, Luo W, Zhang J, Luo Y ( 2014) Linking brain electrical signals elicited by current outcomes with future risk decision-making. Frontiers in Behavioral Neuroscience 8, 84.
doi: 10.3389/fnbeh.2014.00084 pmid: 24672447
[22] Jacobs J, Hwang G, Curran T, Kahana MJ ( 2006) EEG oscillations and recognition memory: Theta correlates of memory retrieval and decision making. Neuroimage 32( 2), 978-987.
doi: 10.1016/j.neuroimage.2006.02.018 pmid: 16843012
[23] Grinband J, Hirsch J, Ferrera, P. V ( 2006) A Neural Representation of Categorization Uncertainty in the Human Brain. Neuron 49( 5), 757-763.
doi: 10.1016/j.neuron.2006.01.032 pmid: 16504950
[24] Heekeren HR, Marrett S, Bandettini PA, Ungerleider LG ( 2004) A general mechanism for perceptual decision-making in the human brain. Nature 431( 7010), 859-862.
[25] Paulus MP, Hozack N, Frank L, Brown GG ( 2002) Error Rate and Outcome Predictability Affect Neural Activation in Prefrontal Cortex and Anterior Cingulate during Decision-Making. Neuroimage 15( 4), 836-846.
doi: 10.1006/nimg.2001.1031 pmid: 11906224
[26] Shadlen MN, Newsome WT ( 2001) Neural basis of a perceptual decision in the parietal cortex (Area LIP) of the rhesus monkey. Journal of Neurophysiology 86( 4), 1916.
doi: 10.3410/f.1001494.23207 pmid: 11600651
[27] Alipour A, Mojdehfarahbakhsh A, Tavakolian A, Morshedzadeh T, Asadi M, Mehdizadeh A, Nami M ( 2016) Neural communication through theta-gamma cross-frequency coupling in a bistable motion perception task. Journal of Integrative Neuroscience 15( 4), 539-551.
doi: 10.1142/S0219635216500291 pmid: 27931147
[28] Kyathanahally S, Franco-Watkins A, Zhang X, Calhoun V, Deshpande G ( 2016) A realistic framework for investigating decision-making in the brain with high spatio-temporal resolution using simultaneous EEG/fMRI and joint ICA. Journal of Biomedical & Health Informatics 21( 3), 814-825.
doi: 10.1109/JBHI.2016.2590434 pmid: 27416610
[29] Larsen T, O’Doherty JP ( 2014) Uncovering the spatio-temporal dynamics of value-based decision-making in the human brain: a combined fMRI-EEG study. Philosophical Transactions of the Royal Society of London 369( 1655), 315-318.
doi: 10.1098/rstb.2013.0473 pmid: 25267816
[30] Volke HJ, Dettmar P, Richter P ( 2002) On-coupling and off-coupling of neocortical areas in chess experts and novices as revealed by evoked EEG coherence measures and factor-based topological analysis-a pilot study. Journal of Psychophysiology 16( 1), 23-36.
doi: 10.1027//0269-8803.16.1.23
[31] Amitay S, Guiraud J, Sohoglu E, Zobay O, Edmonds BA, Zhang YX, Moore DR ( 2013) Human decision making based on variations in internal noise: an EEG study. Plos One 8( 7), e68928.
doi: 10.1371/journal.pone.0068928 pmid: 3698081
[1] Taryn Chalmers, Shamona Maharaj, Ty Lees, CT Lin, Phillip Newton, Roderick Clifton-Bligh, Craig S McLachlan, Sylvia M Gustin, Sara Lal. Impact of acute stress on cortical electrical activity and cardiac autonomic coupling[J]. Journal of Integrative Neuroscience, 2020, 19(2): 239-248.
[2] Fatemeh Zareayan Jahromy, Atena Bajoulvand, Mohammad Reza Daliri. Statistical algorithms for emotion classification via functional connectivity[J]. Journal of Integrative Neuroscience, 2019, 18(3): 293-297.
[3] Zahra Kheradmand Saadi, Mahboobeh Saadat, Ali-Mohammad Kamali, Seyedeh-Saeedeh Yahyavi, Mohammad Nami. Electrophysiological modulation and cognitive-verbal enhancement by multi-session Broca's stimulation: a quantitative EEG transcranial direct current stimulation based investigation[J]. Journal of Integrative Neuroscience, 2019, 18(2): 107-115.
[4] Andrey F. Iznak, Ekaterina V. Iznak, Tatiana P. Klyushnik, Georgy M. Kobel'kov, Elena V. Damjanovich, Igor V. Oleichik, Lilia I. Abramova. Neurobiological parameters in quantitative prediction of treatment outcome in schizophrenic patients[J]. Journal of Integrative Neuroscience, 2018, 17(3): 221-228.
[5] Elham Askari, Seyed Kamaledin Setarehdan, Ali Sheikhani, Mohammad Reza Mohammadi, Mohammad Teshnehlab. Computational model for detection of abnormal brain connections in children with autism[J]. Journal of Integrative Neuroscience, 2018, 17(3): 237-248.
[6] M. Thilaga, Vijayalakshmi Ramasamy, R. Nadarajan, D. Nandagopal. Shortest path based network analysis to characterize cognitive load states of human brain using EEG based functional brain networks[J]. Journal of Integrative Neuroscience, 2018, 17(2): 133-148.
[7] Zhendong Mu, Jinhai Yin, Jianfeng Hu. Application of a brain-computer interface for person authentication using EEG responses to photo stimuli[J]. Journal of Integrative Neuroscience, 2018, 17(1): 53-60.
No Suggested Reading articles found!