Decision research has experienced a shift from simple algebraic theories of choice to an appreciation of mental processes underlying choice. A variety of process-tracing methods has helped researchers test these process explanations. Here, we provide a survey of these methods, including specific examples for subject reports, movement-based measures, peripheral psychophysiology, and neural techniques. We show how these methods can inform phenomena as varied as attention, emotion, strategy use, and understanding neural correlates. Two important future developments are identified: broadening the number of explicit tests of proposed processes through formal modeling and determining standards and best practices for data collection.
AshbyN. J.JohnsonJ. G.KrajbichI.WedelM. (2016). Applications and innovations of eye-movement research in judgment and decision making. Journal of Behavioral Decision Making, 29, 96–102. Lead article in a special issue on eye-tracking research in judgement and decision making providing many examples from theory to application.
2.
Schulte-MecklenbeckM.KühbergerA.JohnsonJ. G. (2018). A handbook of process tracing methods. New York, NY: Taylor & Francis. Comprehensive overview of 13 process-tracing techniques used in judgment and decision making.
3.
StewartN.HermensF.MatthewsW. J. (2016). Eye movements in risky choice. Journal of Behavioral Decision Making, 29, 116–136. Bridging the gap between the recording attention and models like decision field theory, decision by sampling, or parallel constraints satisfaction.
4.
BeattyJ. (1982). Task-evoked pupillary responses, processing load, and the structure of processing resources. Psychological Bulletin, 91, 276.
5.
BecharaA.DamasioA. R. (2005). The somatic marker hypothesis: A neural theory of economic decision. Games and Economic Behavior, 52, 336–372.
6.
BernoulliD. (1738). Specimen theoriae novae de mensura sortis. Econometrica, 22, 23–36.
BurkD.IngramJ. N.FranklinD. W.ShadlenM. N.WolpertD. M. (2014). Motor effort alters changes of mind in sensorimotor decision making. PLoS ONE, 9(3), e92681.
9.
BusemeyerJ. R.JohnsonJ. G. (2004). Computational models of decision making. In KoehlerD. J.HarveyN. (Eds.), Blackwell handbook of judgment and decision making (pp. 133–154). Oxford, UK: Blackwell Publishing.
10.
CerfM.ThiruvengadamN.MormannF.KraskovA.QuirogaR. Q.KochC.FriedI. (2010). On-line, voluntary control of human temporal lobe neurons. Nature, 467, 1104–1108.
11.
Costa-GomesM. A.CrawfordV. P.BrosetaB. (2001). Cognition and behavior in normal-formal games: An experimental study. Econometrica, 69, 1193–1235.
12.
CroneE. A.SomsenR. J.BeekB. V.Van Der MolenM. W. (2004). Heart rate and skin conductance analysis of antecendents and consequences of decision making. Psychophysiology, 41, 531–540.
13.
CsikszentmihalyiM.LarsonR. (2014). Validity and reliability of the experience-sampling method. In CsikszentmihalyiM.LarsonR. (Eds.), Flow and the foundations of positive psychology (pp. 35–54). Dordrecht, The Netherlands: Springer.
14.
EricssonK. A.SimonH. A. (1992). Protocol analysis. Cambridge, MA: The MIT Press.
15.
FiedlerS.Schulte-MecklenbeckM.RenkewitzF.OrquinJ. (2017). Reporting eye-tracking results effectively: The i-guidelines. Manuscript in preparation.
16.
FignerB.KnochD.JohnsonE. J.KroschA. R.LisanbyS. H.FehrE.WeberE. U. (2010). Lateral prefrontal cortex and self-control in intertemporal choice. Nature Neuroscience, 13, 538–539.
17.
ForstmannB. U.RatcliffR.WagenmakersE. J. (2016). Sequential sampling models in cognitive neuroscience: Advantages, applications, and extensions. Annual Review of Psychology, 67, 641–666.
18.
FreemanJ. B.AmbadyN. (2010). MouseTracker: Software for studying real-time mental processing using a computer mouse-tracking method. Behavior Research Methods, 42, 226–241.
19.
GiorgettaC.GrecucciA.BoniniN.CoricelliG.DemarchiG.BraunC.SanfeyA. G. (2013). Waves of regret: A meg study of emotion and decision-making. Neuropsychologia, 51, 38–51.
20.
GoldsteinD. G.SuriS.McAfeeR. P.Ekstrand-AbuegM.DiazF. (2014). The economic and cognitive costs of annoying display advertisements. Journal of Marketing Research, 51, 742–752.
21.
HolmqvistK.NyströmM.AnderssonR.DewhurstR.JarodzkaH.VandeWeijerJ. (2011). Eye tracking: A comprehensive guide to methods and measures. Oxford, UK: Oxford University Press.
22.
JohnsonE. J.RatcliffR. (2014). Computational and process models of decision making in psychology and behavioral economic. In GlimcherP. W.FehrE. (Eds.), Neuroeconomics: Decision making and the brain (2nd ed., pp. 35–47). New York, NY: Academic Press.
23.
JohnsonE. J.Schulte-MecklenbeckM.WillemsenM. C. (2008). Process models deserve process data: Comment on Brandstätter, Gigerenzer, and Hertwig (2006). Psychological Review, 115, 263–272.
24.
JustM. A.CarpenterP. A. (1980). A theory of reading: From eye fixations to comprehension. Psychological Review, 87, 329–354.
25.
KahnemanD.TverskyA. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47, 263–291.
26.
KrajbichI.ArmelC.RangelA. (2010). Visual fixations and the computation and comparison of value in simple choice. Nature Neuroscience, 13, 1292–1298.
27.
KrieglmeyerR.DeutschR.De HouwerJ.De RaedtR. (2010). Being moved valence activates approach-avoidance behavior independently of evaluation and approach-avoidance intentions. Psychological Science, 21, 607–613.
28.
LejarragaT.Schulte-MecklenbeckM.SmedemaD. (2016). The pyeTribe: Simultaneous eyetracking for economic games. Behavior Research Methods, 1–11. doi:10.3758/s13428-016-0819-9
29.
LinL. C.YuW. Y. (2015). A think-aloud study of strategy use by EFL college readers reading Chinese and English texts. Journal of Research in Reading, 38, 286–306.
30.
LiuZ.MaoJ.WangC.AiQ.LiuY.NieJ. Y. (2017). Enhancing click models with mouse movement information. Information Retrieval Journal, 20, 53–80.
31.
LundbergU.DohnsI. E.MelinB.SandsjöL.PalmerudG.KadeforsR.. . . ParrD. (1999). Psychophysiological stress responses, muscle tension, and neck and shoulder pain among supermarket cashiers. Journal of Occupational Health Psychology, 4, 245–255.
32.
MorgensternO.von NeumannJ. (1944). Theory of games and economic behavior. Princeton, NJ: Princeton University Press.
33.
PetersJ.BüchelC. (2011). The neural mechanisms of inter-temporal decision-making: Understanding variability. Trends in Cognitive Sciences, 15, 227–239.
34.
PolonioL.Di GuidaS.CoricelliG. (2015). Strategic sophistication and attention in games: An eye-tracking study. Games and Economic Behavior, 94, 80–96.
35.
PorterS.ten BrinkeL.BakerA.WallaceB. (2011). Would I lie to you? “Leakage” in deceptive facial expressions relates to psychopathy and emotional intelligence. Personality and Individual Differences, 51, 133–137.
36.
RatcliffR. (1978). A theory of memory retrieval. Psychological Review, 85, 59–108.
37.
RanyardR.SvensonO. (2011). Verbal data and decision process analysis. In Schulte-MecklenbeckM.KühbergerA.RanyardR. (Eds.), A handbook of process tracing methods for decision research (pp. 115–138). Oxford, UK: Psychology Press.
38.
RubinsteinA. (2003). Economics and psychology? The case of hyperbolic discounting. International Economic Review, 44, 1207–1216.
39.
RussoJ. E. (2011). Eye fixations as a process trace. In Schulte-MecklenbeckM.KühbergerA.RanyardR. (Eds.), A handbook of process tracing methods for decision research (pp. 43–64). Oxford, UK: Psychology Press.
40.
SchullerB.RigollG.LangM. (2003, April). Hidden Markov model-based speech emotion recognition. In 2003 IEEE International Conference on Acoustics, speech, and signal processing (ICASSP’03) (Vol. 2, pp. II–1). Baltimore, MD: IEEE.
41.
SimonH. A.NewellA. (1972). Human problem solving: The state of the theory in 1970. American Psychologist, 26(2), 145–159.
42.
SpiveyM. J.DaleR. (2006). Continuous dynamics in real-time cognition. Current Directions in Psychological Science, 15, 207–211.
43.
StewartN.HermensF.MatthewsW. J. (2016). Eye movements in risky choice. Journal of Behavioral Decision Making, 29, 116–136.
44.
StöckliS.Schulte-MecklenbeckM.BorerS.SamsonA. C. (2017). Validation of iMotions emotional facial expression analysis with the AFFDEX and FACET module. Behavior Research Methods. Manuscript submitted for publication.
45.
SwanM. (2009). Emerging patient-driven health care models: An examination of health social networks, consumer personalized medicine and quantified self-tracking. International Journal of Environmental Research and Public Health, 6, 492–525.
46.
UtzK. S.DimovaV.OppenländerK.KerkhoffG. (2010). Electrified minds: Transcranial direct current stimulation (tDCS) and galvanic vestibular stimulation (GVS) as methods of non-invasive brain stimulation in neuropsychology—A review of current data and future implications. Neuropsychologia, 48, 2789–2810.
47.
van VugtM. K.SimenP.NystromL. E.HolmesP.CohenJ. D. (2014). EEG oscillations reveal neural correlates of evidence accumulation. Frontiers in Neuroscience, 5, Article 106, 108–120.
48.
WeberE. U.JohnsonE. J.MilchK. F.ChangH.BrodschollJ. C.GoldsteinD. G. (2007). Asymmetric discounting in intertemporal choice a query-theory account. Psychological Science, 18, 516–523.
49.
WillemsenM. C.JohnsonE. J. (2011). Visiting the decision factory: Observing cognition with mouselabweb and other information acquisition methods. In Schulte-MecklenbeckM.KühbergerA.RanyardR. (Eds.), A handbook of process tracing methods for decision research (pp. 21–42). Oxford, UK: Psychology Press.
50.
XuP.EhingerK. A.ZhangY.FinkelsteinA.KulkarniS. R.XiaoJ. (2015). Turkergaze: Crowdsourcing saliency with webcam based eye tracking. arXiv preprint arXiv:1504.06755.
51.
YarbusA. L. (1967). Eye movements and vision. New York, NY: Plenum Press.
