This feels like the right choice: how decision aids may facilitate affect-based valuation

Figures & data

Table 1. Means and standard deviations as well as answer proportions for questions in Pretest 1.

Question	M	SD	% yes	% no	Test against scale mean (4)
How realistic is the scenario that somebody flips a coin to make a decision? (1 = not realistic at all, 7 = very realistic)	5.71	1.34			t(94) = 12.37, p < .001, d = 1.27
How realistic is the scenario that somebody experiences an emotional reaction towards the outcome of a coin flip? (1 = not realistic at all, 7 = very realistic)	6.04	1.05			t(94) = 18.94, p < .001, d = 1.94
How realistic is the scenario that somebody then acts upon this emotional reaction (and therefore, e.g. does the opposite of what the coin suggests)? (1 = not realistic at all, 7 = very realistic)	5.87	1.08			t(94) = 16.85, p < .001, d = 1.73
Have you ever experienced a similar situation in which you used a decision-making aid like a coin?			83.2	16.8
Has someone you know ever experienced a similar situation in which they used a decision-making aid like a coin?			80.0	20.0
Do you think flipping a coin can help you find out more about your preferences? (1 = not at all, 7 = very much)	5.09	1.76			t(94) = 6.05, p < .001, d = 0.62
Do you think flipping a coin can help you find out what you really want? (1 = not at all, 7 = very much)	4.96	1.85			t(94) = 5.05, p < .001, d = 0.52
Do you think flipping a coin can help you listen to your gut feeling? (1 = not at all, 7 = very much)	5.28	1.77			t(94) = 7.09, p < .001, d = 0.73

Figure 1. Sequence of events in the standard control, wheel control, and catalyst conditions for Study 1a.

Figure 2. Donation behaviour (in €) as a function of condition and scope (error bars reflect standard errors).

Table 2. Descriptive results for Study 2.

Scope		Lottery wheel		Coin flip
		Catalyst	Control	Catalyst	Control
One panda	n	54	57	43	50
	M	17.43	16.96	18.47	14.20
	SD	13.84	14.39	14.03	10.93
Four pandas	n	43	51	61	53
	M	18.63	15.55	20.49	14.85
	SD	12.89	13.76	14.50	10.37

Table 3. Results of the multiple regression analysis from Study 3.

Predictor	b	SE b	t-value	p-value
Intercept	14.59	0.54	26.88	<.001
Condition	−0.58	1.09	−0.54	.591
Based on feelings	1.87	0.55	3.41	.001
Based on reason	−0.01	0.55	−0.02	.988
Condition × feelings	−0.80	1.10	−0.73	.466
Condition × reason	−1.18	1.10	−1.08	.282