000 02264nam a22003017a 4500
003 NUCLARK
005 20241203161544.0
008 241203b ph ||||| |||| 00| 0 eng d
022 _a0033-295X
040 _aNU CLARK
_cNU CLARK
245 _aTime-evolving psychological processes over repeated decisions /
_cDavid Gunawan, Guy E. Hawkins, Robert Kohn, Minh-Ngoc Tran, and Scott D. Brown
260 _aWashington DC :
_bAmerican Psychological Association,
_cc2022
500 _aIncludes appendices (pages 452-456).
504 _aIncludes bibliographical references (pages 449-451).
520 _aMany psychological experiments have subjects repeat a task to gain the statistical precision required to test quantitative theories of psychological performance. In such experiments, time-on-task can have sizable effects on performance, changing the psychological processes under investigation. Most research has either ignored these changes, treating the underlying process as static, or sacrificed some psychological content of the models for statistical simplicity. We use particle Markov chain Monte-Carlo methods to study psychologically plausible time-varying changes in model parameters. Using data from three highly cited experiments, we find strong evidence in favor of a hidden Markov switching process as an explanation of time-varying effects. This embodies the psychological assumption of “regime switching,” with subjects alternating between different cognitive states representing different modes of decision-making. The switching model explains key long- and short-term dynamic effects in the data. The central idea of our approach can be applied quite generally to quantitative psychological theories, beyond the models and datasets that we investigate.
650 _aDYNAMIC
650 _aDECISION MAKING
650 _aREGIME SWICTHING
650 _aHIDDEN MARKOV PROCESS
650 _aPRACTICE
700 _aHawkins, Guy E.
_eauthor
700 _aKohn, Robert
_eauthor
700 _aTran, Minh-Ngoc
_eauthor
700 _aBrown, Scott D.
_eauthor
773 _tPsychological Review
_gVolume 129, Number 3, April 2022, pages 438-456.
856 _uhttps://doi.org/10.1037/rev0000351
_zSupplemental material
942 _2lcc
_cCR
_n0
999 _c3457
_d3457