Strategy switching in a sequence of decisions: Evidence from the Iowa Gambling Task

doi:10.3724/SP.J.1041.2023.01793

Abstract

Abstract:

Much previous research has investigated strategies for various decision tasks with a sequence of trials. By assuming each individual adopted a single decision strategy across all trials and comparing corresponding computational cognitive models in terms of their performances in fitting empirical data, such research has revealed multiple decision strategies for various decision tasks. A common drawback of such research, however, was a neglect of the possibility that individuals switched their strategies during the relevant tasks. In Study 1, we developed a computational cognitive model for the Iowa Gambling Task (IGT) allowing for one switch between a reinforcement learning strategy and a heuristic strategy (i.e., strategy-switch-once or SSO model). The results of model comparison between the SSO model and single-strategy models provided clear evidence that individuals might change their strategies along the sequence of decisions in the IGT. Study 2 showed further that a higher proportion of individual data from a 200-trial IGT were fitted best by the SSO model than was the case among individual data from the standard, 100-trial IGT. These findings underscored the importance of considering potential strategy switching in a sequence of decision trials for a more proper understanding of decision strategies in various tasks, especially for a long sequence of decisions. For a deeper understanding of psychological mechanisms underlying sequences of decisions, future research might further investigate various forms of strategy switching such as gradual versus abrupt switches and task and individual factors that trigger such switches.

Key words: decision task with a sequence of trials, the Iowa Gambling Task, strategy switching, computational cognitive modeling, reinforcement-learning and heuristic strategies

HU Xinyun, SHEN Yue, DAI Junyi. (2023). Strategy switching in a sequence of decisions: Evidence from the Iowa Gambling Task. Acta Psychologica Sinica, 55(11), 1793-1805.

Figures/Tables 9

References 40

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Deck	A	B	C	D
Gain on every trial	100	100	50	50
Number of losses in every 10 selections	5	1	5	1
Potential amounts of loss	-150	-1250	-25	-250
	-200		-50
	-250		-75
	-300
	-350
Total return after 10 selections	-250	-250	250	250

Deck	A	B	C	D
Gain on every trial	100	100	50	50
Number of losses in every 10 selections	5	1	5	1
Potential amounts of loss	-150	-1250	-25	-250
	-200		-50
	-250		-75
	-300
	-350
Total return after 10 selections	-250	-250	250	250

Model	AIC_C as index		BIC as index
Model	Mean (std)	Number (proportion) of participants whose data were best fitted	Mean (std)	Number (proportion) of participants whose data were best fitted
WSLS	236.27 (72.65)	42 (6.81%)	241.46 (72.76)	190 (30.79%)
PVL2	225.25 (72.28)	114 (18.48%)	235.48 (72.49)	209 (33.87%)
VPP	220.37 (70.25)	194 (31.44%)	240.13 (70.71)	101 (16.37%)
SSO	219.60 (71.06)	267 (43.27%)	239.36 (71.48)	117 (18.96%)

Model	AIC_C as index		BIC as index
Model	Mean (std)	Number (proportion) of participants whose data were best fitted	Mean (std)	Number (proportion) of participants whose data were best fitted
WSLS	236.27 (72.65)	42 (6.81%)	241.46 (72.76)	190 (30.79%)
PVL2	225.25 (72.28)	114 (18.48%)	235.48 (72.49)	209 (33.87%)
VPP	220.37 (70.25)	194 (31.44%)	240.13 (70.71)	101 (16.37%)
SSO	219.60 (71.06)	267 (43.27%)	239.36 (71.48)	117 (18.96%)

Model for simulation	Model for fitting
Model for simulation	WSLS	PVL2	VPP	SSO
WSLS	88.60%	3.67%	0.92%	6.81%
PVL2	33.55%	46.14%	10.97%	9.35%
VPP	14.37%	16.69%	59.97%	8.97%
SSO	13.99%	7.73%	2.76%	75.53%