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Prediction and classification of performance errors by machine learning: Focusing on inter-trial intervals

The Korean Journal of Cognitive and Biological Psychology / The Korean Journal of Cognitive and Biological Psychology, (P)1226-9654; (E)2733-466X
2016, v.28 no.3, pp.543-562
https://doi.org/10.22172/cogbio.2016.28.3.008


Abstract

It is important to understand causes of human errors and to predict errors in order to prevent various accidents in our daily lives and industrial fields. Although a previous study employing a fixed inter-trial interval (ITI) in a cognitive task successfully predicted errors, it is unlikely to generalize from the previous results to other situations. The current study sought to predict errors by reaction times, task conditions, and ITIs extracted from six consecutive trials preceding error trials, in the context of machine learning. The results showed that various types of errors could be observed and predicted. Especially, presence of repeated patterns or time-pressure chances in the ITI trends might be related to errors. This is interpreted that ITI variation is important to predict errors as well as related to participants’ mental states affecting errors. Therefore, this study suggests that various types of errors in a variety of situations can be predicted, in which those errors would be caused by various factors.

keywords
기계학습, 시행 간 간격, 인간 오류, Machine learning, Inter-trial interval, Human error

Reference

1.

Allain, S., Burle, B., Hasbroucq, T., & Vidal, F. (2009). Sequential adjustments before and after partial errors. Psychonomic Bulletin & Review, 16(2), 356-362.

2.

Bellotti, T., Matousek, R., & Stewart, C. (2011). Are rating agencies’ assignments opaque? Evidence from international banks. Expert Systems with Applications, 38(4), 4206-4214.

3.

Bohm, N., Gladman, B., Brown, I., Schaufler, C., Schiller, J., & Anderson, R. (2008). Banking and bookkeeping. Security Engineering: a guide to building dependable distributed systems, 313-364.

4.

Botvinick, M. M., Braver, T. S., Barch, D. M., Carter, C. S., & Cohen, J. D. (2001). Conflict monitoring and cognitive control. Psychological Review, 108(3), 624-652.

5.

Botvinick, M., Nystrom, L. E., Fissell, K., Carter, C. S., & Cohen, J. D. (1999). Conflict monitoring versus selection-for-action in anterior cingulate cortex. Nature, 402(6758), 179-181.

6.

Brewer, N., & Smith, G. (1984). How normal and retarded individuals monitor and regulate speed and accuracy of responding in serial choice tasks. Journal of Experimental Psychology:General, 113(1), 71-93.

7.

Cheyne, J. A., Solman, G. J., Carriere, J. S., & Smilek, D. (2009). Anatomy of an error: A bidirectional state model of task engagement/disengagement and attention-related errors. Cognition, 111(1), 98-113.

8.

Choi, J., & Kim, C. (2015). Performance error prediction based on reaction times. Journal of Social Science, 26(1), 3-21.

9.

De Jong, R., Berendsen, E., & Cools, R. (1999). Goal neglect and inhibitory limitations:Dissociable causes of interference effects in conflict situations. Acta Psychologica, 101(2), 379-394.

10.

Eichele, T., Debener, S., Calhoun, V. D., Specht, K., Engel, A. K., Hugdahl, K., von Cramon, D. Y., & Ullsperger, M. (2008). Prediction of human errors by maladaptive changes in event-related brain networks. Proceedings of the National Academy of Sciences, 105(16), 6173-6178.

11.

Fedota, J. R., & Parasuraman, R. (2010). Neuroergonomics and human error. Theoretical Issues in Ergonomics Science, 11(5), 402-421.

12.

Gratton, G., Coles, M. G., & Donchin, E. (1992). Optimizing the use of information: strategic control of activation of responses. Journal of Experimental Psychology: General, 121(4), 480-506.

13.

Gratton, G., Coles, M. G., Sirevaag, E. J., Eriksen, C. W., & Donchin, E. (1988). Pre-and poststimulus activation of response channels:a psychophysiological analysis. Journal of Experimental Psychology: Human Perception and Performance, 14(3), 331-344.

14.

Hart, E., Dumas, E., Reijntjes, R., van der Hiele, K., van den Bogaard, S., Middelkoop, H., Roos, R., & van Dijk, J. (2012). Deficient sustained attention to response task and P300characteristics in early Huntington’s disease. Journal of Neurology, 259(6), 1191-1198.

15.

Herrmann, M. J., Saathoff, C., Schreppel, T. J., Ehlis, A.-C., Scheuerpflug, P., Pauli, P., & Fallgatter, A. J. (2009). The effect of ADHD symptoms on performance monitoring in a non-clinical population. Psychiatry Research, 169(2), 144-148.

16.

Jentzsch, I., & Leuthold, H. (2006). Control over speeded actions: A common processing locus for micro-and macro-trade-offs?. The Quarterly Journal of Experimental Psychology, 59(8), 1329-1337.

17.

Keerthi, S. S., Shevade, S. K., Bhattacharyya, C., & Murthy, K. R. K. (2001). Improvements to Platt’s SMO Algorithm for SVM Classifier Design. Neural Computation, 13(3), 637-649.

18.

Kim, C., Johnson, N. F., & Gold, B. T. (2014). Conflict adaptation in prefrontal cortex: Now you see it, now you don’t. Cortex, 50, 76-85.

19.

Kohavi, R. (1995, August). A study of crossvalidation and bootstrap for accuracy estimation and model selection. International Joint Conference on Artificial Intelligence. Paper presented at the Proceedings of the 14th international joint conference on Artificial intelligence, Montreal, Canada.

20.

Krueger, L. E., & Shapiro, R. G. (1981). Intertrial effects of same-different judgements. The Quarterly Journal of Experimental Psychology, 33(3), 241-265.

21.

Laming, D. (1979). Choice reaction performance following an error. Acta Psychologica, 43(3), 199-224.

22.

Larson, M. J., Good, D. A., & Fair, J. E. (2010). The relationship between performance monitoring, satisfaction with life, and positive personality traits. Biological Psychology, 83(3), 222-228.

23.

Lee, S., Byoun, S., Chang, M., & Kwak, H. (2015). Characteristics of post-error behavior in adult ADHD tendency. The Korean Journal of Cognitive and Biological Psychology, 27(3), 519-542.

24.

MacDonald, A. W., 3rd, Cohen, J. D., Stenger, V. A., & Carter, C. S. (2000). Dissociating the role of the dorsolateral prefrontal and anterior cingulate cortex in cognitive control. Science, 288(5472), 1835-1838.

25.

Mayr, U., Awh, E., & Laurey, P. (2003). Conflict adaptation effects in the absence of executive control. Nature Neuroscience, 6(5), 450-452.

26.

Nieuwenhuis, S., Ridderinkhof, K. R., Blom, J., Band, G. P., & Kok, A. (2001). Errorrelated brain potentials are differentially related to awareness of response errors: Evidence from an antisaccade task. Psychophysiology, 38(5), 752-760.

27.

O’Hara, J. M., Higgins, J. C., & Brown, W. S. (2008). Human factors considerations with respect to emerging technology in nuclear power plants. New York: Brookhaven National Laboratory.

28.

Platt, J. C. (1999). Fast training of support vector machines using sequential minimal optimization. Advances in Kernel Methods, 185-208.

29.

Rabbitt, P. (1966). Errors and error correction in choice-response tasks. Journal of Experimental Psychology, 71(2), 264-272.

30.

Rabbitt, P. (1980). The effects of RS interval duration on serial choice reaction time:Preparation time or response monitoring time?. Ergonomics, 23(1), 65-77.

31.

Rabbitt, P. M., & Phillips, S. (1967). Errordetection and correction latencies as a function of S-R compatibility. Quarterly Journal of Experimental Psychology, 19(1), 37-42.

32.

Rabbitt, P., & Rodgers, B. (1977). What does a man do after he makes an error? An analysis of response programming. The Quarterly Journal of Experimental Psychology, 29 (4), 727-743.

33.

Rabbitt, P., & Vyas, S. (1981). Processing a display even after you make a response to it. How perceptual errors can be corrected. The Quarterly Journal of Experimental Psychology, 33(3), 223-239.

34.

Rasmussen, J., & Pedersen, O. (1984). Human factors in probabilistic risk analysis and risk management. Operational Safety of Nuclear Power Plants, 1, 181-194.

35.

Reason, J. (1990). Human error: Models and management. British Medical Journal, 320(7237), 768-770.

36.

Ridderinkhof, K. R., Nieuwenhuis, S., & Bashore, T. R. (2003). Errors are foreshadowed in brain potentials associated with action monitoring in cingulate cortex in humans. Neuroscience letters, 348(1), 1-4.

37.

Robertson, I. H., Manly, T., Andrade, J., Baddeley, B. T., & Yiend, J. (1997). Oops!':performance correlates of everyday attentional failures in traumatic brain injured and normal subjects. Neuropsychologia, 35(6), 747-758.

38.

Sakai, H., Uchiyama, Y., Shin, D., Hayashi, M. J., & Sadato, N. (2013). Neural activity changes associated with impulsive responding in the sustained attention to response task. PLoS One, 8(6), e67391-e67391.

39.

Scheffers, M. K., Humphrey, D. G., Stanny, R. R., Kramer, A. F., & Coles, M. G. (1999). Error-related processing during a period of extended wakefulness. Psychophysiology, 36(2), 149-157.

40.

Senders, J. W., & Moray, N. (1991). Human error:Cause, prediction, and reduction. Hillsdale, New Jersey: Lawrence Erlbaum Associates.

41.

Shalgi, S., O’Connell, R. G., Deouell, L. Y., & Robertson, I. H. (2007). Absent minded but accurate: delaying responses increases accuracy but decreases error awareness. Experimental Brain Research, 182(1), 119-124.

42.

Shappell, S., Detwiler, C., Holcomb, K., Hackworth, C., Boquet, A., & Wiegmann, D. A. (2007). Human Error and Commercial Aviation Accidents: An Analysis Using the Human Factors Analysis and Classification System. Human Factors-The Journal of the Human Factors and Ergonomics Society, 49(2), 227-242.

43.

Stroop, J. R. (1935). Studies of interference in serial verbal reactions. Journal of Experimental Psychology, 18(6), 643.

44.

Su, C. T., Chen, L. S., & Yih, Y. (2006). Knowledge acquisition through information granulation for imbalanced data. Expert Systems with Applications, 31(3), 531-541.

45.

Su, C. T., & Hsiao, Y. H. (2007). An evaluation of the robustness of MTS for imbalanced data. IEEE Transactions on Knowledge and Data Engineering, 19(10), 1321-1332.

46.

Thimbleby, H., & Cairns, P. (2010). Reducing number entry errors: solving a widespread, serious problem. Journal of the Royal Society Interface, 7(51), 1429-1439.

47.

Van Merrienboer, J. J., & Sweller, J. (2005). Cognitive load theory and complex learning:Recent developments and future directions. Educational Psychology Review, 17(2), 147-177.

48.

Vapnik, V. (1995). The Nature of Statistical Learning Theory. New York: Springer Science &Business Media.

49.

Wang, S., & Yao, X. (2012). Multiclass Imbalance Problems: Analysis and Potential Solutions. IEEE Transactions on System Man and Cybernetics Part B-Cybernetics, 42(2), 1119-1130.

50.

Weissman, D., Roberts, K., Visscher, K., & Woldorff, M. (2006). The neural bases of momentary lapses in attention. Nature Neuroscience, 9(7), 971-978.

51.

Yeung, N., Botvinick, M. M., & Cohen, J. D. (2004). The neural basis of error detection:conflict monitoring and the error-related negativity. Psychological Review, 111(4), 931.

52.

Zhang, J.-Y., Kuai, S.-G., Xiao, L.-Q., Klein, S. A., Levi, D. M., & Yu, C. (2008). Stimulus coding rules for perceptual learning. PLoS Biology, 6(8), e197-e197.

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