Anticipating Cognitive Effort: Roles of Perceived Error-likelihood and Time Demands

Anticipating Cognitive Effort: Roles of Perceived Error-likelihood and Time Demands

Anticipating Cognitive Effort: Roles of Perceived Error-likelihood and Time Demands

Article (PDF Available)inPsychological Research 83(8) · November 2017 with 304 Reads

DOI: 10.1007/s00426-017-0943-x
Why are some actions evaluated as effortful? In the present set of experiments we address this question by examining individuals’ perception of effort when faced with a trade-off between two putative cognitive costs: how much time a task takes vs. how error-prone it is. Specifically, we were interested in whether individuals anticipate engaging in a small amount of hard work (i.e., low time requirement, but high error-likelihood) vs. a large amount of easy work (i.e., high time requirement, but low error-likelihood) as being more effortful. In between-subject designs, Experiments 1 through 3 demonstrated that individuals anticipate options that are high in perceived error-likelihood (yet less time consuming) as more effortful than options that are perceived to be more time consuming (yet low in error-likelihood). Further, when asked to evaluate which of the two tasks was (a) more effortful, (b) more error-prone, and (c) more time consuming, effort-based and error-based choices closely tracked one another, but this was not the case for time-based choices. Utilizing a within-subject design, Experiment 4 demonstrated overall similar pattern of judgments as Experiments 1 through 3. However, both judgments of error-likelihood and time demand similarly predicted effort judgments. Results are discussed within the context of extant accounts of cognitive control, with considerations of how error-likelihood and time demands may independently and conjunctively factor into judgments of cognitive effort.
Dunn, T.L., Inzlicht, M., & Risko, E.F. (2019). Psychological Research, 83, 1033-1056.

References

  1. Ackerman, R., & Thompson, V. A. (2017). Meta-reasoning: Monitoring and control of thinking and reasoning. Trends in Cognitive Sciences, 21(8), 607–617.

    Article PubMed Google Scholar

  2. Akçay, Ç., & Hazeltine, E. (2007). Conflict monitoring and feature overlap: Two sources of sequential modulations. Psychonomic Bulletin and Review, 14(4), 742–748.

    Article PubMed Google Scholar

  3. Alain, C., McNeely, H. E., He, Y., Christensen, B. K., & West, R. (2002). Neurophysiological evidence of error-monitoring deficits in patients with schizophrenia. Cerebral Cortex, 12(8), 840–846.

    Article PubMed Google Scholar

  4. Apps, M. A., Grima, L. L., Manohar, S., & Husain, M. (2015). The role of cognitive effort in subjective reward devaluation and risky decision-making. Scientific Reports, 5, 16880.

    Article PubMed PubMed Central Google Scholar

  5. Ashcraft, M. H., & Faust, M. W. (1994). Mathematics anxiety and mental arithmetic performance: An exploratory investigation. Cognition and Emotion, 8(2), 97–125.

    Article Google Scholar

  6. Baddeley, A. D., & Hitch, G. (1974). Working memory. Psychology of Learning and Motivation, 8, 47–89.

    Article Google Scholar

  7. Bates, A. T., Kiehl, K. A., Laurens, K. R., & Liddle, P. F. (2002). Error-related negativity and correct response negativity in schizophrenia. Clinical Neurophysiology, 113(9), 1454–1463.

    Article PubMed Google Scholar

  8. Behrens, T. E., Woolrich, M. W., Walton, M. E., & Rushworth, M. F. (2007). Learning the value of information in an uncertain world. Nature Neuroscience, 10(9), 1214–1221.

    Article PubMed Google Scholar

  9. Bijleveld, E., Custers, R., & Aarts, H. (2009). The unconscious eye opener: Pupil dilation reveals strategic recruitment of resources upon presentation of subliminal reward cues. Psychological Science, 20(11), 1313–1315.

    Article PubMed Google Scholar

  10. Blain, B., Hollard, G., & Pessiglione, M. (2016). Neural mechanisms underlying the impact of daylong cognitive work on economic decisions. Proceedings of the National Academy of Sciences, 113(25), 6967–6972.

    Article Google Scholar

  11. Boehler, C. N., Hopf, J. M., Krebs, R. M., Stoppel, C. M., Schoenfeld, M. A., Heinze, H. J., & Noesselt, T. (2011). Task-load-dependent activation of dopaminergic midbrain areas in the absence of reward. Journal of Neuroscience, 31(13), 4955–4961.

    Article PubMed Google Scholar

  12. Botvinick, M. M. (2007). Conflict monitoring and decision making: reconciling two perspectives on anterior cingulate function. Cognitive, Affective, & Behavioral Neuroscience, 7(4), 356–366.

    Article Google Scholar

  13. Botvinick, M. M., & Braver, T. S. (2015). Motivation and cognitive control: From behavior to neural mechanism. Annual Review of Psychology, 66, 83–113.

    Article PubMed Google Scholar

  14. Botvinick, M. M., & Cohen, J. D. (2014). The computational and neural basis of cognitive control: charted territory and new frontiers. Cognitive Science, 38(6), 1249–1285.

    Article PubMed Google Scholar

  15. Botvinick, M. M., Huffstetler, S., & McGuire, J. T. (2009). Effort discounting in human nucleus accumbens. Cognitive, Affective, and Behavioral Neuroscience, 9(1), 16–27.

    Article Google Scholar

  16. Botvinick, M. M., & Rosen, Z. B. (2009). Anticipation of cognitive demand during decision-making. Psychological Research PRPF, 73(6), 835–842.

    Article Google Scholar

  17. Boureau, Y. L., Sokol-Hessner, P., & Daw, N. D. (2015). Deciding how to decide: Self control and meta-decision making. Trends in Cognitive Sciences, 19(11), 700–710.

    Article PubMed Google Scholar

  18. Brown, J. W., & Braver, T. S. (2005). Learned predictions of error likelihood in the anterior cingulate cortex. Science, 307(5712), 1118–1121.

    Article PubMed Google Scholar

  19. Brown, J. W., & Braver, T. S. (2007). Risk prediction and aversion by anterior cingulate cortex. Cognitive, Affective, & Behavioral Neuroscience, 7(4), 266–277.

    Article Google Scholar

  20. Bryce, D., & Bratzke, D. (2014). Introspective reports on reaction times in dual-tasks reflect experienced difficulty rather than the timing of cognitive processes. Consciousness and Cognition, 27, 254–267.

    Article PubMed Google Scholar

  21. Buhrmester, M., Kwang, T., & Gosling, S. D. (2011). Amazon’s Mechanical Turk a new source of inexpensive, yet high-quality, data? Perspectives on Psychological Science, 6(1), 3–5.

    Article PubMed Google Scholar

  22. Cacioppo, J. T., & Petty, R. E. (1982). The need for cognition. Journal of Personality and Social Psychology, 42(1), 116–131.

    Article Google Scholar

  23. Cameron, D., Hutcherson, C., Ferguson, A. M., Scheffer, J. A., & Inzlicht, M. (2017). Empathy is hard work: People choose to avoid empathy because of its cognitive costs. http://psyarxiv.com/jkc4n. Accessed 25 Sept 2017.

  24. Chong, T. T. J., Apps, M., Giehl, K., Sillence, A., Grima, L. L., & Husain, M. (2017). Neurocomputational mechanisms underlying subjective valuation of effort costs. PLoS Biology, 15(2), e1002598.

    Article PubMed PubMed Central Google Scholar

  25. Danckert, J. A., & Allman, A. A. A. (2005). Time flies when you’re having fun: Temporal estimation and the experience of boredom. Brain and Cognition, 59(3), 236–245.

    Article PubMed Google Scholar

  26. Davenport, H. J. (1911). Cost and its significance. The American Economic Review, 1(4), 724–752.

    Google Scholar

  27. Dehaene, S., Posner, M. I., & Tucker, D. M. (1994). Localization of a neural system for error detection and compensation. Psychological Science, 5(5), 303–305.

    Article Google Scholar

  28. Desender, K., Buc Calderon, C., Van Opstal, F., & Van den Bussche, E. (2017a). Avoiding the conflict: Metacognitive awareness drives the selection of low-demand contexts. Journal of Experimental Psychology: Human Perception and Performance, 43(7), 1397–1410.

    PubMed Google Scholar

  29. Desender, K., Van Opstal, F., & Van den Bussche, E. (2017b). Subjective experience of difficulty depends on multiple cues. Scientific Reports, 7, 44222. https://doi.org/10.1038/srep44222.

    Article PubMed PubMed Central Google Scholar

  30. Diede, N. T., & Bugg, J. M. (2017). Cognitive effort is modulated outside of the explicit awareness of conflict frequency: Evidence from pupillometry. Journal of Experimental Psychology. Learning, Memory, and Cognition, 43(5), 824–835.

    Article PubMed PubMed Central Google Scholar

  31. Dixon, M. L., & Christoff, K. (2012). The decision to engage cognitive control is driven by expected reward-value: Neural and behavioral evidence. PLoS One, 7(12), e51637.

    Article PubMed PubMed Central Google Scholar

  32. Dreisbach, G., & Fischer, R. (2012). Conflicts as aversive signals. Brain and Cognition, 78(2), 94–98.

    Article PubMed Google Scholar

  33. Dunn, T. L., Koehler, D. J., & Risko, E. F. (2017). Evaluating effort: Influences of evaluation mode on judgments of task-specific efforts. Journal of Behavioral Decision Making, 30(4), 869–888.

    Article Google Scholar

  34. Dunn, T. L., Lutes, D. J. C., & Risko, E. F. (2016). Metacognitive evaluation in the avoidance of demand. Journal of Experimental Psychology: Human Perception and Performance, 42(9), 1372–1387.

    PubMed Google Scholar

  35. Dunn, T. L., & Risko, E. F. (2016a). Toward a metacognitive account of cognitive offloading. Cognitive Science, 40(5), 1080–1127.

    Article PubMed Google Scholar

  36. Dunn, T. L., & Risko, E. F. (2016b). Understanding the Cognitive Miser: Cue-utilization in Effort Avoidance. https://www.researchgate.net/publication/303543690_Understanding_the_Cognitive_Miser_Cue-utilization_in_Effort_Avoidance. Accessed 01 May 2016.

  37. Eriksen, C. W. (1995). The flankers task and response competition: A useful tool for investigating a variety of cognitive problems. Visual Cognition, 2–3, 101–118.

    Article Google Scholar

  38. Evans, J. S. B., & Stanovich, K. E. (2013). Dual-process theories of higher cognition: Advancing the debate. Perspective on Psychological Science, 8(3), 223–241.

    Article Google Scholar

  39. Falkenstein, M., Hoormann, J., Christ, S., & Hohnsbein, J. (2000). ERP components on reaction errors and their functional significance: A tutorial. Biological Psychology, 51(2), 87–107.

    Article PubMed Google Scholar

  40. Feng, S. F., Schwemmer, M., Gershman, S. J., & Cohen, J. D. (2014). Multitasking versus multiplexing: Toward a normative account of limitation in the simultaneous execution of control-demanding behaviors. Cognitive, Affective, and Behavioral Neuroscience, 14(1), 129–146.

    Article Google Scholar

  41. Forster, K. I., & Forster, J. C. (2003). DMDX: A windows display program with millisecond accuracy. Behavior Research Methods, Instruments, and Computers, 35, 116–124.

    Article PubMed Google Scholar

  42. Frank, M. J., Woroch, B. S., & Curran, T. (2005). Error-related negativity predicts reinforcement learning and conflict biases. Neuron, 47(4), 495–501.

    Article PubMed Google Scholar

  43. Gehring, W. J., & Fencsik, D. E. (2001). Functions of the medial frontal cortex in the processing of conflict and errors. Journal of Neuroscience, 21(23), 9430–9437.

    Article PubMed Google Scholar

  44. Gehring, W. J., Goss, B., Coles, M. G., Meyer, D. E., & Donchin, E. (1993). A neural system for error detection and compensation. Psychological Science, 4(6), 385–390.

    Article Google Scholar

  45. Gehring, W. J., Himle, J., & Nisenson, L. G. (2000). Action-monitoring dysfunction in obsessive-compulsive disorder. Psychological Science, 11(1), 1–6.

    Article PubMed Google Scholar

  46. Gershman, S. J., Horvitz, E. J., & Tenenbaum, J. B. (2015). Computational rationality: A converging paradigm for intelligence in brains, minds, and machines. Science, 349(6245), 273–278.

    Article PubMed Google Scholar

  47. Gigerenzer, G. (2008). Why heuristics work. Perspectives on Psychological Science, 3(1), 20–29.

    Article PubMed Google Scholar

  48. Gigerenzer, G., & Goldstein, D. G. (1996). Reasoning the fast and frugal way: Models of bounded rationality. Psychological Review, 103(4), 650–669.

    Article PubMed Google Scholar

  49. Gigerenzer, G., Todd, P. M., & ABC Research Group. (1999). Simple heuristics that makes us smart. New York, NY: Oxford University Press.

    Google Scholar

  50. Gläscher, J., Hampton, A. N., & O’Doherty, J. P. (2009). Determining a role for ventromedial prefrontal cortex in encoding action-based value signals during reward-related decision making. Cerebral Cortex, 19(2), 483–495.

    Article PubMed Google Scholar

  51. Gold, J. M., Kool, W., Botvinick, M. M., Hubzin, L., August, S., & Waltz, J. A. (2015). Cognitive effort avoidance and detection in people with schizophrenia. Cognitive, Affective, & Behavioral Neuroscience, 15(1), 145–154.

    Article Google Scholar

  52. Gray, W. D., Sims, C. R., Fu, W.-T., & Schoelles, M. J. (2006). The soft constraints hypothesis: A rational analysis approach to resource allocation for interactive behavior. Psychological Review, 113(3), 461–482.

    Article PubMed Google Scholar

  53. Griffiths, T. L., Lieder, F., & Goodman, N. D. (2015). Rational use of cognitive resources: Levels of analysis between the computational and the algorithmic. Topics in Cognitive Science, 7(2), 217–229.

    Article PubMed Google Scholar

  54. Hajcak, G., & Foti, D. (2008). Errors are aversive: Defensive motivation and the error related negativity. Psychological Science, 19(2), 103–108.

    Article PubMed Google Scholar

  55. Hajcak, G., McDonald, N., & Simons, R. F. (2003). To err is autonomic: Error-related brain potentials, ANS activity, and post-error compensatory behavior. Psychophysiology40(6), 895–903.

    Article PubMed Google Scholar

  56. Hajcak, G., McDonald, N., & Simons, R. F. (2004). Error-related psychophysiology and negative affect. Brain and Cognition, 56(2), 189–197.

    Article PubMed Google Scholar

  57. Hajcak, G., Moser, J. S., Yeung, N., & Simons, R. F. (2005). On the ERN and the significance of errors. Psychophysiology, 42(2), 151–160.

    Article PubMed Google Scholar

  58. Hernandez-Lallement, J., van Wingerden, M., Marx, C., Srejic, M., & Kalenscher, T. (2014). Rats prefer mutual rewards in a prosocial choice task. Frontiers in Neuroscience, 8, 443.

    PubMed Google Scholar

  59. Hockey, G. R. J. (2011). A motivational control theory of cognitive fatigue. In P. L. Ackerman (Ed.), Cognitive fatigue: Multidisciplinary perspectives on current research and future applications (pp. 167–188). Washington, DC: American Psychological Association.

    Google Scholar

  60. Holroyd, C. B., & Coles, M. G. (2002). The neural basis of human error processing: Reinforcement learning, dopamine, and the error-related negativity. Psychological Review, 109(4), 679–709.

    Article PubMed Google Scholar

  61. Inzlicht, M., Bartholow, B. D., & Hirsh, J. B. (2015). Emotional foundations of cognitive control. Trends in Cognitive Sciences, 19(3), 126–132.

    Article PubMed PubMed Central Google Scholar

  62. Inzlicht, M., Schmeichel, B. J., & Macrae, C. N. (2014). Why self-control seems (but may not be) limited. Trends in Cognitive Sciences, 18(3), 127–133.

    Article PubMed Google Scholar

  63. Jeffreys, H. (1961). Theory of probability (3rd ed.). Oxford, England: Oxford University Press.

    Google Scholar

  64. John, O. P., & Srivastava, S. (1999). The Big-Five trait taxonomy: History, measurement, and theoretical perspectives. In L. A. Pervin & O. P. John (Eds.), Handbook of personality: Theory and research (Vol. 2, pp. 102–138). New York: Guilford Press.

    Google Scholar

  65. Jordan, K., & Huntsman, L. A. (1990). Image rotation of misoriented letter strings: Effects of orientation cuing and repetition. Perception and Psychophysics, 48(4), 363–374.

    Article PubMed Google Scholar

  66. Kahneman, D. (1973). Attention and effort. Englewood Cliffs, NJ: Prentice-Hall.

    Google Scholar

  67. Kahneman, D., & Beatty, J. (1966). Pupil diameter and load on memory. Science, 154(3756), 1583–1585.

    Article PubMed Google Scholar

  68. Kahneman, D., Tursky, B., Shapiro, D., & Crider, A. (1969). Pupillary, heart rate, and skin resistance changes during a mental task. Journal of Experimental Psychology, 79(1, Pt 1), 164–167.

    Article PubMed Google Scholar

  69. Kahneman, D., & Tversky, A. (1996). On the reality of cognitive illusions. Psychological Review, 103(3), 582–591.

    Article PubMed Google Scholar

  70. Kerns, J. G., Cohen, J. D., MacDonald, A. W., Cho, R. Y., Stenger, V. A., & Carter, C. S. (2004). Anterior cingulate conflict monitoring and adjustments in control. Science, 303(5660), 1023–1026.

    Article Google Scholar

  71. Klein-Flügge, M. C., Kennerley, S. W., Friston, K., & Bestmann, S. (2016). Neural signatures of value comparison in human cingulate cortex during decisions requiring an effort-reward trade-off. Journal of Neuroscience, 36(39), 10002–10015.

    Article PubMed Google Scholar

  72. Kolling, N., Behrens, T. E. J., Wittmann, M. K., & Rushworth, M. F. S. (2016). Multiple signals in anterior cingulate cortex. Current Opinion in Neurobiology, 37, 36–43.

    Article PubMed PubMed Central Google Scholar

  73. Kool, W., & Botvinick, M. M. (2014). A labor/leisure tradeoff in cognitive control. Journal of Experimental Psychology: General, 143(1), 131–141.

    Article Google Scholar

  74. Kool, W., McGuire, J. T., Rosen, Z. B., & Botvinick, M. M. (2010). Decision making and the avoidance of cognitive demand. Journal of Experimental Psychology: General, 139(4), 665–682.

    Article Google Scholar

  75. Koriat, A., & Norman, J. (1984). What is rotated in mental rotation? Journal of Experimental Psychology. Learning, Memory, and Cognition, 10(3), 421–434.

    Article PubMed Google Scholar

  76. Kruschke, J. K. (2013). Bayesian estimation supersedes the t test. Journal of Experimental Psychology: General, 142(2), 573–603.

    Article Google Scholar

  77. Kurzban, R. (2016). The sense of effort. Current Opinion in Psychology, 7, 67–70.

    Article Google Scholar

  78. Kurzban, R., Duckworth, A., Kable, J. W., & Myers, J. (2013). An opportunity cost model of subjective effort and task performance. Behavioral and Brain Sciences, 36(6), 661–679.

    Article Google Scholar

  79. Lawrence, M. A. (2015). ez: Easy analysis and visualization of factorial experiments. R package version 4.3. http://CRAN.Rproject.org/package=ez. Accessed 01 Mar 2016.

  80. Lee, M. D., & Wagenmakers, E. J. (2013). Bayesian data analysis for cognitive science: A practical course. New York, NY: Cambridge University Press.

    Google Scholar

  81. Lu, C. H., & Proctor, R. W. (1995). The influence of irrelevant location information on performance: A review of the Simon and spatial Stroop effects. Psychonomic Bulletin and Review, 2(2), 174–207.

    Article PubMed Google Scholar

  82. Luu, P., Collins, P., & Tucker, D. M. (2000). Mood, personality, and self-monitoring: Negative affect and emotionality in relation to frontal lobe mechanisms of error monitoring. Journal of Experimental Psychology: General, 129(1), 43–60.

    Article Google Scholar

  83. Luu, P., Tucker, D. M., Derryberry, D., Reed, M., & Poulsen, C. (2003). Electrophysiological responses to errors and feedback in the process of action regulation. Psychological Science, 14(1), 47–53.

    Article PubMed Google Scholar

  84. Ma, Q., Meng, L., Wang, L., & Shen, Q. (2014). I endeavor to make it: Effort increases valuation of subsequent monetary reward. Behavioural Brain Research, 261, 1–7.

    Article PubMed Google Scholar

  85. MacLeod, C. M. (1991). Half a century of research on the Stroop effect: An integrative review. Psychological Bulletin, 109(2), 163–203.

    Article PubMed Google Scholar

  86. Maier, M. E., Scarpazza, C., Starita, F., Filogamo, R., & Làdavas, E. (2016). Error monitoring is related to processing internal affective states. Cognitive, Affective, and Behavioral Neuroscience, 16(6), 1050–1062.

    Article Google Scholar

  87. Marti, S., Sackur, J., Sigman, M., & Dehaene, S. (2010). Mapping introspection’s blind spot: Reconstruction of dual-task phenomenology using quantified introspection. Cognition, 115(2), 303–313.

    Article PubMed Google Scholar

  88. McGuire, J. T., & Botvinick, M. M. (2010). Prefrontal cortex, cognitive control, and the registration of decision costs. Proceedings of the National Academy of Sciences, 107(17), 7922–7926.

    Article Google Scholar

  89. Miller, J., Vieweg, P., Kruize, N., & McLea, B. (2010). Subjective reports of stimulus, response, and decision times in speeded tasks: How accurate are decision time reports? Consciousness and Cognition, 19(4), 1013–1036.

    Article PubMed Google Scholar

  90. Milyavskaya, M., Inzlicht, M., Johnson, T., & Larson, M. J. (2017). Reward sensitivity following boredom and cognitive effort: A high-powered neurophysiological investigation. Retrieved from http://psyarxiv.com/2czjv. Accessed 16 Aug 2017.

  91. Monsell, S. (2003). Task switching. Trends in Cognitive Sciences, 7(3), 134–140.

    Article PubMed Google Scholar

  92. Montague, P. R., Dayan, P., & Sejnowski, T. J. (1996). A framework for mesencephalic dopamine systems based on predictive Hebbian learning. The Journal of Neuroscience, 16(5), 1936–1947.

    Article PubMed Google Scholar

  93. Morey, R. D., & Rouder, J. N. (2015). BayesFactor: Computation of Bayes factors for common designs. R package version 0.9.11-1. http://CRAN.Rproject.org/package=BayesFactor. Accessed 01 Mar 2016

  94. Naccache, L., Dehaene, S., Cohen, L., Habert, M. O., Guichart-Gomez, E., Galanaud, D., & Willer, J. C. (2005). Effortless control: Executive attention and conscious feeling of mental effort are dissociable. Neuropsychologia, 43(9), 1318–1328.

    Article PubMed Google Scholar

  95. Navon, D. (1984). Resources—A theoretical soup stone? Psychological review, 91(2), 216.

    Article Google Scholar

  96. Navon, D., & Gopher, D. (1979). On the economy of the human-processing system. Psychological Review, 86(3), 214–255.

    Article Google Scholar

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

    Article PubMed Google Scholar

  98. Nishiyama, R. (2014). Response effort discounts the subjective value of rewards. Behavioural Processes, 107, 175–177.

    Article PubMed Google Scholar

  99. Nishiyama, R. (2016). Physical, emotional, and cognitive effort discounting in gain and loss situations. Behavioural Processes, 125, 72–75.

    Article PubMed Google Scholar

  100. Niv, Y., Daw, N. D., Joel, D., & Dayan, P. (2007). Tonic dopamine: Opportunity costs and the control of response vigor. Psychopharmacology (Berl), 191(3), 507–520.

    Article Google Scholar

  101. O’Reilly, J. X., Schüffelgen, U., Cuell, S. F., Behrens, T. E., Mars, R. B., & Rushworth, M. F. (2013). Dissociable effects of surprise and model update in parietal and anterior cingulate cortex. Proceedings of the National Academy of Sciences, 110(38), E3660–E3669.

    Article Google Scholar

  102. Pailing, P. E., & Segalowitz, S. J. (2004). The error-related negativity as a state and trait measure: Motivation, personality, and ERPs in response to errors. Psychophysiology, 41(1), 84–95.

    Article PubMed Google Scholar

  103. Payne, J. W., Bettman, J. R., & Johnson, E. J. (1993). The adaptive decision maker. New York City, NY: Cambridge University Press.

    Google Scholar

  104. Phillips, P. E., Walton, M. E., & Jhou, T. C. (2007). Calculating utility: Preclinical evidence for cost–benefit analysis by mesolimbic dopamine. Psychopharmacology (Berl), 191(3), 483–495.

    Article Google Scholar

  105. Protopapas, A. (2007). CheckVocal: A program to facilitate checking the accuracy and response time of vocal responses from DMDX. Behavior Research Methods, 39, 859–862.

    Article PubMed Google Scholar

  106. R Core Team (2014). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org. Accessed 01 Mar 2016.

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

    Article PubMed Google Scholar

  108. Reber, R., Winkielman, P., & Schwarz, N. (1998). Effects of perceptual fluency on affective judgments. Psychological Science, 9(1), 45–48.

    Article Google Scholar

  109. Rouder, J. N. (2014). Optional stopping: No problem for Bayesians. Psychonomics Bulletin and Review, 21(2), 301–308.

    Article Google Scholar

  110. Schönbrodt, F. D., Wagenmakers, E. J., Zehetleitner, M., & Perugini, M. (2017). Sequential hypothesis testing with Bayes factors: Efficiently testing mean differences. Psychological Methods, 22(2), 322.

    Article PubMed Google Scholar

  111. Schraw, G., & Dennison, R. S. (1994). Assessing metacognitive awareness. Contemporary Educational Psychology, 19(4), 460–475.

    Article Google Scholar

  112. Schultz, W., Dayan, P., & Montague, P. R. (1997). A neural substrate of prediction and reward. Science, 275(5306), 1593–1599.

    Article Google Scholar

  113. Shah, A. K., & Oppenheimer, D. M. (2008). Heuristics made easy: An effort-reduction framework. Psychological Bulletin, 134(2), 207–222.

    Article PubMed Google Scholar

  114. Shenhav, A., Botvinick, M. M., & Cohen, J. D. (2013). The expected value of control: An integrative theory of anterior cingulate cortex function. Neuron, 79(2), 217–240.

    Article PubMed PubMed Central Google Scholar

  115. Shenhav, A., Cohen, J. D., & Botvinick, M. M. (2016). Dorsal anterior cingulate cortex and the value of control. Nature Neuroscience, 19(10), 1286–1291.

    Article PubMed Google Scholar

  116. Shenhav, A., Musslick, S., Lieder, F., Kool, W., Griffiths, T. L., Cohen, J. D., & Botvinick, M. M. (2017). Toward a rational and mechanistic account of cognitive effort. Annual Review of Neuroscience, 40, 99–124.

    Article Google Scholar

  117. Shiffrin, R. M., & Schneider, W. (1977). Controlled and automatic human information processing: II. Perceptual learning, automatic attending and a general theory. Psychological Review, 84(2), 127–190.

    Article Google Scholar

  118. Siegler, R. S., & Lemaire, P. (1997). Older and younger adults’ strategy choices in multiplication: Testing predictions of ASCM using the choice/no-choice method. Journal of Experimental Psychology: General, 126(1), 71–92.

    Article Google Scholar

  119. Simmons, J. P., Nelson, L. D., & Simonsohn, U. (2012). A 21 Word Solution. Dialogue, The Official Newsletter of the Society for Personality and Social Psychology, 26(2), 4–7.

    Google Scholar

  120. Simon, H. A. (1982). Models of bounded rationality (Vol. 3): Empirically grounded economic reason. Cambridge, MA: MIT Press.

  121. Simon, H. A. (1990). Invariants of human behavior. Annual Review of Psychology, 41(1), 1–20.

    Article PubMed Google Scholar

  122. Taylor, S. F., Stern, E. R., & Gehring, W. J. (2007). Neural systems for error monitoring: Recent finding and theoretical perspectives. The Neuroscientist, 13(2), 160–172.

    Article PubMed Google Scholar

  123. Van Steenbergen, H., & Band, G. P. H. (2013). Pupil dilation in the Simon task as a marker of conflict processing. Frontiers in Human Neuroscience, 7, 215. https://doi.org/10.3389/fnhum.2013.00215.

    Article PubMed PubMed Central Google Scholar

  124. Vassena, E., Holroyd, C. B., & Alexander, W. H. (2017). Computational models of anterior cingulate cortex: At the crossroads between prediction and effort. Frontiers in Neuroscience, 11, 1–9.

    Article Google Scholar

  125. Vassena, E., Silvetti, M., Boehler, C. N., Achten, E., Fias, W., & Verguts, T. (2014). Overlapping neural systems represent cognitive effort and reward anticipation. PLoS One, 9(3), e91008.

    Article PubMed PubMed Central Google Scholar

  126. Verguts, T., Vassena, E., & Silvetti, M. (2015). Adaptive effort investment in cognitive and physical tasks: A neurocomputational model. Frontiers in Behavioral Neuroscience, 9, 57. https://doi.org/10.3389/fnbeh.2015.00057.

    Article PubMed PubMed Central Google Scholar

  127. Walsh, M. M., & Anderson, J. R. (2009). The strategic nature of changing your mind. Cognitive Psychology, 58(3), 416–440.

    Article PubMed Google Scholar

  128. Wang, L., Zheng, J., & Meng, L. (2017). Effort provides its own reward: Endeavors reinforce subjective expectation and evaluation of task performance. Experimental Brain Research, 235(4), 1107–1118.

    Article PubMed Google Scholar

  129. Westbrook, A., & Braver, T. S. (2015). Cognitive effort: A neuroeconomic approach. Cognitive, Affective, & Behavioral Neuroscience, 15(2), 395–415.

    Article Google Scholar

  130. Westbrook, A., & Braver, T. S. (2016). Dopamine does double duty in motivating cognitive effort. Neuron, 89(4), 695–710.

    Article PubMed PubMed Central Google Scholar

  131. Westbrook, A., Kester, D., & Braver, T. S. (2013). What is the subjective cost of cognitive effort? Load, trait, and aging effects revealed by economic preference. PLoS One, 8(7), e68210.

    Article PubMed PubMed Central Google Scholar

  132. Wickens, C. D. (2002). Multiple resources and performance prediction. Theoretical Issues in Ergonomics Science, 3(2), 159–177.

    Article Google Scholar

  133. Winkielman, P., Schwarz, N., Fazendeiro, T., & Reber, R. (2003). The hedonic marking of processing fluency: Implications for evaluative judgment. In J. Musch & K. C. Klauer (Eds.), The psychology of evaluation: Affective processes in cognition and emotion (pp. 189–217). Mahwah, NJ: Erlbuam.

    Google Scholar

  134. 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), 931–959.

    Article PubMed Google Scholar

  135. Zipf, G. K. (1949). Human behavior and the principle of least effort. Cambridge, MA: Addison-Wesley.

    Google Scholar

Download references

Author information

Affiliations

 

Additional information

All data and corresponding code are freely available via the Open Science Framework at http://osf.io/2szy3.

Inner Work Life: understanding the subtext of business performance

Inner Work Life: understanding the subtext of business performance

Inner work life: understanding the subtext of business performance
  • PMID: 17494252

Abstract

Anyone in management knows that employees have their good days and their bad days–and that, for the most part, the reasons for their ups and downs are unknown. Most managers simply shrug their shoulders at this fact of work life. But does it matter, in terms of performance, if people have more good days than bad days? Teresa Amabile and Steven Kramer’s new stream of research, based on more than 12,000 diary entries logged by knowledge workers over three years, reveals the dramatic impact of employees’ inner work lives–their perceptions, emotions, and motivation levels–on several dimensions of performance. People perform better when their workday experiences include more positive emotions, stronger intrinsic motivation (passion for the work), and more favorable perceptions of their work, their team, their leaders, and their organization. What the authors also found was that managers’ behavior dramatically affects the tenor of employees’ inner work lives. So what makes a difference to inner work life? When the authors compared the study participants’ best days to their worst days, they found that the single most important differentiator was their sense of being able to make progress in their work. The authors also observed interpersonal events working in tandem with progress events. Praise without real work progress, or at least solid efforts toward progress, had little positive impact on people’s inner work lives and could even arouse cynicism. On the other hand, good work progress without any recognition–or, worse, with criticism about trivial issues–could engender anger and sadness. Far and away, the best boosts to inner work life were episodes in which people knew they had done good work and their managers appropriately recognized that work.

Similar articles

Article Excerpt:

if your organization demands knowledge work from its people, then you undoubtedly appreciate the importance of sheer brainpower. You probably recruit high-intellect people and ensure they have access to good information. You probably also respect the power of incentives and use formal compensation systems to channel that intellectual energy down one path or another. But you might be overlooking another crucial driver of a knowledge worker’s performance—that person’s inner work life. People experience a constant stream of emotions, perceptions, and motivations as they react to and make sense of the events of the workday. As people arrive at their workplaces they don’t check their hearts and minds at the door. Unfortunately, because inner work life is seldom openly expressed in modern organizations, it’s all too easy for managers to pretend that private thoughts and feelings don’t matter.

As psychologists, we became fascinated a decade ago with day-to-day work life. But our research into inner work life goes well beyond intellectual curiosity about the complex operations of emotions, perceptions, and motivations. It addresses the very pragmatic managerial question of how these dynamics affect work performance. To examine this question, we constructed a research project that would give us a window into the inner work lives of a broad population of knowledge workers. Specifically, we recruited 238 professionals from 26 project teams and had them complete daily diary entries, in a standard format, for the duration of their projects. Nearly 12,000 diary entries later, we have discovered the dynamics of inner work life and the significant effect it can have on the performance of your people—and, by implication, your entire organization.

It may stun you, if you are a manager, to learn what power you hold. Your behavior as a manager dramatically shapes your employees’ inner work lives. But the key levers in your hands for driving motivation and performance may not be the ones you’d suspect…

Manage with a human touch.

None of this emphasis on the managerial behaviors that influence progress diminishes the importance of the interpersonal managerial events that we mentioned earlier—events in which people are or are not treated decently as human beings. Although such events weren’t quite as important in distinguishing the best days from the worst days, they were a close second. We frequently observed interpersonal events working in tandem with progress events. Praise without real work progress, or at least solid efforts toward progress, had little positive impact on people’s inner work lives and could even arouse cynicism. On the other hand, good work progress without any recognition—or, worse, with criticism about trivial issues—could engender anger and sadness. Far and away, the best boosts to inner work life were episodes in which people knew they had done good work and managers appropriately recognized that work.

Peter Drucker once wrote, “So much of what we call management consists of making it difficult for people to do work.” The truth of this has struck us as our ongoing analyses reveal more of the negative managerial behaviors that affect inner work life. But we have also been struck by the wealth of managerial opportunities for improving inner work life. Managers’ day-to-day (and moment-to-moment) behaviors matter not just because they directly facilitate or impede the work of the organization. They’re also important because they affect people’s inner work lives, creating ripple effects on organizational performance. When people are blocked from doing good, constructive work day by day, for instance, they form negative impressions of the organization, their coworkers, their managers, their work, and themselves; they feel frustrated and unhappy; and they become demotivated in their work. Performance suffers in the short run, and in the longer run, too. But when managers facilitate progress, every aspect of people’s inner work lives are enhanced, which leads to even greater progress. This positive spiral benefits the individual workers—and the entire organization. Because every employee’s inner work life system is constantly operating, its effects are inescapable.

Because every employee’s inner work life system is constantly operating, its effects are inescapable.

Discovering how inner work life affects organizational performance is clearly valuable. But as researchers we hope we have also made progress on another front. Inner work lives matter deeply to the people living them. Studies of the modern workweek show that knowledge workers today, as compared with workers of past eras, spend more time in the office and more time focused on work issues while outside the office. As the proportion of time that is claimed by work rises, inner work life becomes a bigger component of life itself. People deserve happiness. They deserve dignity and respect. When we act on that realization, it is not only good for business. It affirms our value as human beings.

A version of this article appeared in the May 2007 issue of Harvard Business Review.
Managing Motivation A Manager’s Guide to Diagnosing and Improving Motivation

Managing Motivation A Manager’s Guide to Diagnosing and Improving Motivation

Managing Motivation
A Manager’s Guide to Diagnosing and Improving Motivation

Book Description

This slim motivation guidebook was written to bridge the gap between the academic research on motivation and to present it in a form that is useful to the practicing manager. In essence, the book presents a theory of motivation and how to use it without ever mentioning the word “theory”. The goal of the book is to give managers a kind of mental model to use in thinking about motivation and to show them how to use this mental model for practical management actions to diagnose and improve motivation of subordinates. The book is written in three sections: Understanding Motivation, Diagnosing Motivation and Improving Motivation. The book incorporates case studies and many examples of how to successfully manage motivation.

Table of Contents

Preface. 1. Motivation and Management. 2. Understanding Needs and Energy. 3. Understanding Motivation. 4. Dynamics of the Motivation Model. 5. Planning a Motivation Improvement Project. 6. Diagnosing Action-to-Results Connections. 7. Diagnosing Results-to-Evaluation Connections. 8. Diagnosing Evaluation-to-Outcome Connections. 9. Diagnosing Outcome-to-Need Satisfaction Connections. 10. Making Improvements. 11. Predicting the Effects of Change. Concluding Comments. References and Bibliography. Appendix 1: Our Approach to Assessing Motivation. Appendix 2: Drawing Connection Graphs. Index.

Author(s)

Biography

Robert D. Pritchard is currently Professor of Psychology and Management at the University of Central Florida. His PhD is from the University of Minnesota in Industrial /Organizational Psychology. He recently won the Distinguished Scientific Contribution Award at the SIOP meeting (2002) and is a Fellow of APS and APA .He has been the series editor for the Society for Organizational Psychology Frontiers Book Series since 2003. He is currently a board member of the following journals:

  • Organizational Behavior and Human Performance
  • Motivation and Emotion
  • Journal of Applied Psychology

Elissa L . Ashwood is currently Director , Organizational Development and Training for AIG Retirement Services, Los Angeles. Formerly she was Vice president, Finance for Citibank in New York.

She has an MBA from William E Simon Graduate School of Business Administration, University of Rochester and is currently studying for a Certificate in Organization Design from U of Southern California, Marshall School of Business.

 

Reviews

“The authors have done an excellent job translating the massive scientific literature on motivation into a more concise practical guidebook describing how to identify and address motivation challenges. The literature review is quite current. It is easy to follow and understand, with many examples.” – Rob Ployhart, University of South Carolina

“The proposed book would be appropriate for a lower level college readership and possibly a management development course on work motivation. The principles described are well grounded in scientific research[,] but the book does not read like an advanced text. It is well written, free of jargon, with clear examples, brief overviews of concepts, and helpful charts.” –Craig C. Pinder, Distinguished Professor of Organizational Behavior, University of Victoria, Canada

“Finally, a no nonsense book on motivation that is based on solid scientific principles that HRM can give to their line managers.” –Gary Latham, Secretary of State Professor of Organizational Effectiveness Rotman School of Management University of Toronto

“When it comes to managing motivation, all too often managers rely on fads and half-truths to make critical decisions that can impact the entire organization. This book presents a logical framework for understanding motivation within organizations – one based on years of research and that will stand the test of time. Leaders who want to increase alignment, persistence and intensity will find that they will make better decisions using the insights Pritchard and Ashwood have described.” –Pete Ramstad, Vice President, The Toro Company

“This slim volume provides a literal roadmap for managers to follow, beginning with a lucid discussion of what exactly is meant by motivation. The book then takes managers through a step by step process of how to identify behaviors that need to change, and then how to go about changing those behaviors. The steps are clearly laid out and a continuing case helps make the discussion even more concrete. The suggestions and recommendations are based on years of theoretical development and subsequent research, yet Pritchard and Ashwood discuss conepts clearly and systematically, in terms that any manager can understand and follow. I would recommend this book to any manager who has ever faced a problem trying to motivate employees,or any student who wanted a quick review of the practical side of theories of motivation” –Angelo DeNisi, Dean, A. B. Freeman School of Business, Tulane University

“Bob Pritchard and Elissa Ashwood have done a terrific job in capturing the fundamental truths of what we know about motivating people. Bob Pritchard is a well known expert on motivation in organizations. They provide a very useful roadmap to diagnosing and addressing motivation issues at work. Managers will learn a practical and straightforward approach to motivating people. This book should be included in any course or training program that discusses employee motivation.”       -Rob Silzer, Managing Director, HR Assessment and Development, Inc.

“This excellent book should help first-line supervisors and managers to use concepts in motivation to help their employees and organizations to succeed. The theory and conceptual treatment in the book are sound, but what’s different here is the academic foundation gets nicely translated into highly practical and actionable suggestions.” –Wally Borman, CEO, Personnel Decisions Research Institutes, Professor, University of South Florida

Cyberslacking and the Procrastination Superhighway

Cyberslacking and the Procrastination Superhighway

Cyberslacking and the Procrastination Superhighway

This study was designed to explore the extent to which time spent online was related to self reports of procrastination. A sample of 308 participants (Mean age = 29.4 years, SD = 12.0, 198 females) from various regions of North America completed a survey posted to the World Wide Web. Data collected included demographic information, attitudes toward the Internet, amount of time spent online (at home, work, and school), trait procrastination, and measures of positive and negative emotion. Results demonstrated that 50.7% of the respondents reported frequent Internet procrastination, and respondents spent 47% of online time procrastinating. Internet procrastination was positively correlated with perceiving the Internet as entertaining, a relief from stress, and paradoxically, as an important tool. Internet procrastination was also positively correlated with trait procrastination and negative emotions. Implications regarding Internet procrastination are discussed in relation to procrastination theory and research as well as Neil Postman’s critique of technology.

First Published November 1, 2001 Research Article

The Distracted Mind – Ancient Brains in a High-Tech World

The Distracted Mind – Ancient Brains in a High-Tech World

The Distracted Mind

Ancient Brains in a High-Tech World

By Adam Gazzaley and Larry D. Rosen

Why our brains aren’t built for media multitasking, and how we can learn to live with technology in a more balanced way.
Winner, 2017 PROSE Awards, Biomedicine and Neuroscience category
distracted mind psychology

Summary

Why our brains aren’t built for media multitasking, and how we can learn to live with technology in a more balanced way.

“Brilliant and practical, just what we need in these techno-human times.”—Jack Kornfield, author of The Wise Heart

Most of us will freely admit that we are obsessed with our devices. We pride ourselves on our ability to multitask—read work email, reply to a text, check Facebook, watch a video clip. Talk on the phone, send a text, drive a car. Enjoy family dinner with a glowing smartphone next to our plates. We can do it all, 24/7! Never mind the errors in the email, the near-miss on the road, and the unheard conversation at the table. In The Distracted Mind, Adam Gazzaley and Larry Rosen—a neuroscientist and a psychologist—explain why our brains aren’t built for multitasking, and suggest better ways to live in a high-tech world without giving up our modern technology.

The authors explain that our brains are limited in their ability to pay attention. We don’t really multitask but rather switch rapidly between tasks. Distractions and interruptions, often technology-related—referred to by the authors as “interference”—collide with our goal-setting abilities. We want to finish this paper/spreadsheet/sentence, but our phone signals an incoming message and we drop everything. Even without an alert, we decide that we “must” check in on social media immediately.

Gazzaley and Rosen offer practical strategies, backed by science, to fight distraction. We can change our brains with meditation, video games, and physical exercise; we can change our behavior by planning our accessibility and recognizing our anxiety about being out of touch even briefly. They don’t suggest that we give up our devices, but that we use them in a more balanced way.

AUTHORS

Adam Gazzaley

Adam Gazzaley is Professor in the Departments of Neurology, Physiology, and Psychiatry at the University of Calfornia, San Francisco, where he is also Founding Director of the Neuroscience Imaging Center, Neuroscape Lab, and the Gazzaley Lab. He is cofounder and Chief Science Advisor of Akili Interactive, a company developing therapeutic video games and cofounder and Chief Scientist of JAZZ Venture Partners, a venture capital firm investing in experiential technology to improve human performance. Recipient of the 2015 Society for Neuroscience Science Educator Award, he wrote and hosted the nationally televised PBS special “The Distracted Mind with Dr. Adam Gazzaley.”

Larry D. Rosen

Larry D. Rosen is Professor Emeritus of Psychology at California State University, Dominguez Hills. He is a blogger for Psychology Today and the author of iDisorder: Understanding Our Obsession with Technology and Overcoming Its Hold on Us and six other books.

Reader Resources