Araştırma Makalesi
Yıl 2020,
Cilt: 3 Sayı: 2, 121 - 130, 28.08.2020
Öz
Kaynakça
- S. Finger, Origins of neuroscience: a history of explorations into brain function. Oxford University Press, USA, 2001.
- J. Ward, The student's guide to cognitive neuroscience. Psychology Press, 2015.
- P. Dayan and F. A. Laurence,” Theoretical neuroscience: computational and mathematical modeling of neural systems”, Journal of Cognitive Neuroscience, vol.15, no.1, pp.154-155.
- T. C. Napier, J. C. Corvol, A. A. Grace, J.D. Roitman, J. Rowe, J., V. Voon and A. P. Strafella,” Linking neuroscience with modern concepts of impulse control disorders in Parkinson's disease”, Movement Disorders, vol.30, pp.141-149, 2015.
- M. Filippi, fMRI techniques and protocols. Humana press, 2016.
- A. Poldrack Russell, “The role of fMRI in cognitive neuroscience: where do we stand?”, Current opinion in neurobiology, 18223-227, 2008.
- U. Goswami, “Neuroscience, education and special education”, British Journal of Special Education, vol.31, pp.175-183, 2004.
- Y. Immordino, M. Helen , A. Damasio, "We feel, therefore we learn: The relevance of affective and social neuroscience to education", Mind, brain, and education, vol.1, pp. 3-10, 2007.
- B. Aberšek, Cognitive science in education and alternative teaching strategies. Cambridge Scholars Publishing, 2017.
- B. Sung, N. J. Wilson, J. H. Yun, and E. J. LEE, “What can neuroscience offer marketing research?”, Asia Pacific Journal of Marketing and Logistics, pp. 4-23, 2019.
- S. Ullman, “Using neuroscience to develop artificial intelligence”, Science, vol. 363, no. 6428, pp. 692-693, 2019.
- G. Deco, E. T. Rolls, Computational neuroscience of vision. Oxford university press, 2007.
- D. Dunning,” The Dunning–Kruger effect: On being ignorant of one's own ignorance”, In Advances in experimental social psychology, vol.44, pp. 247-296, 2011.
- G. Pennycook, R. M. Ross, D. J. Koehler and J. A., Fugelsang,” Dunning–Kruger effects in reasoning: Theoretical implications of the failure to recognize incompetence”, Psychonomic Bulletin & Review, vol.24, no.6, pp. 1774-1784, 2017.
- N. Altman, M. Krzywinski, ”The curse (s) of dimensionality”, Nat Methods, vol.15, pp. 399-400, 2018.
- V. Zelenyuk, “Aggregation of inputs and outputs prior to Data Envelopment Analysis under big data”, European Journal of Operational Research, vol.282, no.1, pp. 172-187, 2020.
- M. Ramscar, P. Hendrix, C. Shaoul, P. Milin, H. Baayen, “The myth of cognitive decline: Non‐linear dynamics of lifelong learning”, Topics in cognitive science, vol.6, no.1, pp.5-42, 2014.
- Y. Wang, and V. Chiew, “On the cognitive process of human problem solving”, Cognitive systems research, vol.11, no.1, pp.81-92, 2010.
- H. Kuai, X. Zhang, Y. Yang, J.Chen, B. Shi, N. Zhong, “THINKING-LOOP: The Semantic Vector Driven Closed-Loop Model for Brain Computing”, IEEE Access, vol.8, pp.4273-4288, 2020.
- Y. Wang et al., “Brain-Inspired Systems: A Transdisciplinary Exploration on Cognitive Cybernetics, Humanity, and Systems Science Toward Autonomous Artificial Intelligence”, IEEE Systems, Man, and Cybernetics Magazine, vol. 6, no. 1, pp. 6-13, Jan. 2020
- R. Shivhare, A. K.Cherukuri, and J. Li, ,”Establishment of cognitive relations based on cognitive informatics”, Cognitive Computation, vol.9, no.5, pp.721-729, 2017.
- H. Faris, I. Aljarah and S. Mirjalili, Evolving radial basis function networks using moth–flame optimizer. In Handbook of neural computation, Academic Press, pp. 537-550, 2017.
- C. Zhang, H. Wei, L. Xie, Y. Shen, and K. Zhang, “Direct interval forecasting of wind speed using radial basis function neural networks in a multi-objective optimization framework”, Neurocomputing, vol. 205, pp.53-63, 2016.
- I.T. Jolliffe and J. Cadima, “Principal component analysis: a review and recent developments”, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol.374, no.2065, 20150202, 2016.
- G. R. Naik, Advances in Principal Component Analysis: Research and Development. Springer, 2017.
- I. Craw, The aberdeen set of Psychological Image Collection at Stirling (PICS) database (PICS), URL: http://pics.psych.stir.ac.uk/zips/Aberdeen.zip [Accessed: 19-July-2020].
- N. Kriegeskorte,”Deep neural networks: a new framework for modeling biological vision and brain information processing”, Annual review of vision science, vol.1, pp.417-446, 2015.
- J. C. Hong, , M. Y. Hwang, , K. H. Tai and C. R. Tsai, ”An exploration of students’ science learning interest related to their cognitive anxiety, cognitive load, self-confidence and learning progress using inquiry-based learning with an iPad”, Research in Science Education, vol.47, no.6, pp.1193-1212, 2017.
Yıl 2020,
Cilt: 3 Sayı: 2, 121 - 130, 28.08.2020
Öz
This study addresses a novel perspective for analyzing the source of confidence in human behavior. The concept of confidence was examined via the relationship between two phenomena in the area of machine learning and psychology, namely the Dunning-Kruger effect and the curse of dimensionality. A relationship was established between these two phenomena which were investigated in the light of neuroscience. This study claims that confidence is highly related with the total time it takes to reach specific information and this relationship is inversely proportional. Image gender classification algorithm was used to analyze this relationship for this study and the curves which were obtained as a result of this analysis was compared with the curve of Dunning-Kruger effect and curse of dimensionality. This relationship has been explained by the knowledge of human's problem-solving ability and mathematical models of memory.
Anahtar Kelimeler
Neuroscience Artificial Intelligence Dunning-Kruger Information Theory Entropy
Kaynakça
- S. Finger, Origins of neuroscience: a history of explorations into brain function. Oxford University Press, USA, 2001.
- J. Ward, The student's guide to cognitive neuroscience. Psychology Press, 2015.
- P. Dayan and F. A. Laurence,” Theoretical neuroscience: computational and mathematical modeling of neural systems”, Journal of Cognitive Neuroscience, vol.15, no.1, pp.154-155.
- T. C. Napier, J. C. Corvol, A. A. Grace, J.D. Roitman, J. Rowe, J., V. Voon and A. P. Strafella,” Linking neuroscience with modern concepts of impulse control disorders in Parkinson's disease”, Movement Disorders, vol.30, pp.141-149, 2015.
- M. Filippi, fMRI techniques and protocols. Humana press, 2016.
- A. Poldrack Russell, “The role of fMRI in cognitive neuroscience: where do we stand?”, Current opinion in neurobiology, 18223-227, 2008.
- U. Goswami, “Neuroscience, education and special education”, British Journal of Special Education, vol.31, pp.175-183, 2004.
- Y. Immordino, M. Helen , A. Damasio, "We feel, therefore we learn: The relevance of affective and social neuroscience to education", Mind, brain, and education, vol.1, pp. 3-10, 2007.
- B. Aberšek, Cognitive science in education and alternative teaching strategies. Cambridge Scholars Publishing, 2017.
- B. Sung, N. J. Wilson, J. H. Yun, and E. J. LEE, “What can neuroscience offer marketing research?”, Asia Pacific Journal of Marketing and Logistics, pp. 4-23, 2019.
- S. Ullman, “Using neuroscience to develop artificial intelligence”, Science, vol. 363, no. 6428, pp. 692-693, 2019.
- G. Deco, E. T. Rolls, Computational neuroscience of vision. Oxford university press, 2007.
- D. Dunning,” The Dunning–Kruger effect: On being ignorant of one's own ignorance”, In Advances in experimental social psychology, vol.44, pp. 247-296, 2011.
- G. Pennycook, R. M. Ross, D. J. Koehler and J. A., Fugelsang,” Dunning–Kruger effects in reasoning: Theoretical implications of the failure to recognize incompetence”, Psychonomic Bulletin & Review, vol.24, no.6, pp. 1774-1784, 2017.
- N. Altman, M. Krzywinski, ”The curse (s) of dimensionality”, Nat Methods, vol.15, pp. 399-400, 2018.
- V. Zelenyuk, “Aggregation of inputs and outputs prior to Data Envelopment Analysis under big data”, European Journal of Operational Research, vol.282, no.1, pp. 172-187, 2020.
- M. Ramscar, P. Hendrix, C. Shaoul, P. Milin, H. Baayen, “The myth of cognitive decline: Non‐linear dynamics of lifelong learning”, Topics in cognitive science, vol.6, no.1, pp.5-42, 2014.
- Y. Wang, and V. Chiew, “On the cognitive process of human problem solving”, Cognitive systems research, vol.11, no.1, pp.81-92, 2010.
- H. Kuai, X. Zhang, Y. Yang, J.Chen, B. Shi, N. Zhong, “THINKING-LOOP: The Semantic Vector Driven Closed-Loop Model for Brain Computing”, IEEE Access, vol.8, pp.4273-4288, 2020.
- Y. Wang et al., “Brain-Inspired Systems: A Transdisciplinary Exploration on Cognitive Cybernetics, Humanity, and Systems Science Toward Autonomous Artificial Intelligence”, IEEE Systems, Man, and Cybernetics Magazine, vol. 6, no. 1, pp. 6-13, Jan. 2020
- R. Shivhare, A. K.Cherukuri, and J. Li, ,”Establishment of cognitive relations based on cognitive informatics”, Cognitive Computation, vol.9, no.5, pp.721-729, 2017.
- H. Faris, I. Aljarah and S. Mirjalili, Evolving radial basis function networks using moth–flame optimizer. In Handbook of neural computation, Academic Press, pp. 537-550, 2017.
- C. Zhang, H. Wei, L. Xie, Y. Shen, and K. Zhang, “Direct interval forecasting of wind speed using radial basis function neural networks in a multi-objective optimization framework”, Neurocomputing, vol. 205, pp.53-63, 2016.
- I.T. Jolliffe and J. Cadima, “Principal component analysis: a review and recent developments”, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol.374, no.2065, 20150202, 2016.
- G. R. Naik, Advances in Principal Component Analysis: Research and Development. Springer, 2017.
- I. Craw, The aberdeen set of Psychological Image Collection at Stirling (PICS) database (PICS), URL: http://pics.psych.stir.ac.uk/zips/Aberdeen.zip [Accessed: 19-July-2020].
- N. Kriegeskorte,”Deep neural networks: a new framework for modeling biological vision and brain information processing”, Annual review of vision science, vol.1, pp.417-446, 2015.
- J. C. Hong, , M. Y. Hwang, , K. H. Tai and C. R. Tsai, ”An exploration of students’ science learning interest related to their cognitive anxiety, cognitive load, self-confidence and learning progress using inquiry-based learning with an iPad”, Research in Science Education, vol.47, no.6, pp.1193-1212, 2017.
Toplam 28 adet kaynakça vardır.
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Konular | Yapay Zeka |
| Bölüm | Makaleler |
| Yazarlar | |
| Yayımlanma Tarihi | 28 Ağustos 2020 |
| Gönderilme Tarihi | 26 Nisan 2020 |
| Kabul Tarihi | 24 Temmuz 2020 |
| Yayımlandığı Sayı | Yıl 2020Cilt: 3 Sayı: 2 |
Cited By
Market Awareness Of The "Dunning Kruger Effect" Theory
ODÜ Sosyal Bilimler Araştırmaları Dergisi (ODÜSOBİAD)
https://doi.org/10.48146/odusobiad.1106103
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