Can the negative footprint illusion be eliminated by summative priming?

Figures & data

Table 1. A summary of studies that have investigated the existence of a negative footprint illusion, indicating that the illusion is robust across most judgment tasks, dependent measures and study designs.

Judgment items	Holmgren et al., 2018a	Holmgren et al., 2018b	Holmgren et al., 2019	Gorissen & Weijters, 2016	Kim & Schuldt, 2018	Kusch & Fiebelkorn, 2019	MacCutcheon et al., 2020
Buildings	×	×					×
Food products				×		×
Cars					×
Atmospheric CO2 concentration			×
Dependent measures
Carbon footprint	×			×		×	×
Environmental impact					×
Contribution in parts per million			×
Trees to compensate for emissions		×
Design
Within participants		×	×	×*
Between participants	×			×	×	×	×

Note: *This within-participants experiment did not find a negative footprint illusion.

Figure 1. Carbon footprint estimates given by the participants in the control condition and experimental condition. Error bars represent the standard error of the mean.

Figure 2. Carbon dioxide emission estimates given by the participants in the control condition and experimental condition. Error bars represent the standard error of the mean.

Table 2. Mean age in years (and SD) of the participants in each condition in Experiment 2, also showing the percentage of female participants across conditions.

Priming condition	N	Mean age (SD)	% Female
Cars summation	63	34.02 (11.45)	61.9%
Houses summation	59	33.81 (12.44)	71.2%
Cars averaging	79	37.05 (11.95)	68.4%
Houses averaging	64	32.61 (10.82)	67.2%

Figure 3. The graphic used to depict a single petrol car. The number of cars displayed to participants corresponded with the information described in each method section.

Figure 4. The graphic used to depict a single electric car. The number of cars displayed to participants corresponded with the information described in each method section.

Table 3. The number of petrol cars and electric cars in the six priming tasks across conditions.

Number of cars in the six priming tasks
Petrol cars	Electric cars
3	1
5	3
8	6
10	5
18	7
20	10

Figure 5. The graphic used to depict a single existing house in the residential area. The number of houses displayed to participants corresponded with the information described in each method section.

Figure 6. The graphic used to depict a single new additional house in the residential area. The number of houses displayed to participants corresponded with the information described in each method section.

Table 4. The number of existing houses and additional new houses in the six priming tasks across conditions.

Number of houses in the six priming tasks
Existing houses	New houses
3	1
5	3
8	6
10	5
18	7
20	10

Table 5. Manipulation check for each priming condition in Experiment 2, showing the mean response (and SE) when fuel cost is the dependent measure.

Priming condition	Mean fuel cost estimate (SE)	t	Cohen’s d
Cars summation	1.72 (.12)	13.99**	1.98
Houses summation	3.10 (.25)	12.49**	2.09
Cars averaging	−3.90 (.25)	15.63**	1.77
Houses averaging	−2.81 (.20)	13.71**	1.88

Note: ** = p < .001.

The t values derive from one-sample t-tests comparing estimates against zero (effect sizes measured by Cohen’s d).

Figure 7. Carbon footprint estimates for all priming conditions in Experiment 2. Error bars represent the standard error of the mean.

Table 6. Mean age in years (including SD) of the participants in each condition in Experiment 3, also showing the percentage of female participants across conditions.

Priming condition	N	Mean age (SD)	% Female
Cars summation	55	35.20 (12.57)	69.1%
Houses summation	96	32.84 (12.54)	68.8%
Cars averaging	109	36.55 (12.23)	66.1%
Houses averaging	59	35.37 (12.07)	66.1%

Table 7. Manipulation check for each priming condition in Experiment 3, showing the mean response (and SE) when income is the dependent measure.

Priming condition	Mean income estimate (SE)	t	Cohen’s d
Cars summation	3.63 (.22)	16.53**	2.34
Houses summation	2.71 (.21)	12.67**	1.79
Cars averaging	−2.75 (.23)	12.01**	1.67
Houses averaging	−3.16 (.24)	13.43**	1.92

Note: ** = p < .001.

The t values derive from one-sample t-tests comparing estimates against zero (effect sizes measured by Cohen’s d)

Figure 8. Carbon footprint estimates for all priming conditions in Experiment 3. Error bars represent the standard error of the mean.

Table 8. Mean age in years (including SD) of the participants in the two condition in Experiment 4, also showing the percentage of female participants across conditions.

	N	Mean age (SD)	% Female
Neutral cars condition	52	30.15 (11.22)	67%
Neutral houses condition	50	33.06 (10.77)	56%

Table 9. Percentages of participants susceptible to the negative footprint illusion and the zero footprint illusion and percentage of participants that was not susceptible to either illusion (i.e. responding correctly).

Experiments and conditions	Negative footprint illusion	Zero footprint illusion	No illusion
Experiment 2
Cars summation	20%	20%	60%
Houses summation	44%	16%	40%
Cars averaging	79%	7%	14%
Houses averaging	66%	4%	30%
Experiment 3
Cars summation	40%	14%	46%
Houses summation	43%	18%	39%
Cars averaging	77%	6%	17%
Houses averaging	64%	18%	18%
Experiment 4
Neutral cars	60%	10%	30%
Neutral houses	46%	14%	40%

Note that Experiment 2 used a priming task in which fuel cost was the dependent measure, whereas Experiment 3 used a priming task in which income was the dependent measure.

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