On being drawn to different types of arguments: a mouse-tracking study

Figures & data

Table 1. Example arguments from each scheme used in the study, with commentary in italics.

Scheme	Strong (sound)	Weak (violates scheme-specific criteria)
The government should provide compensation for phishing victims’ monetary losses …
Causal	because without help, vulnerable phishing victims would slide into poverty. (the consequence is likely)	because it would reduce poverty. (the consequence is unlikely; compensations to a few would have little effect on poverty in general)
Analogous	given that it already provides compensation for physical injuries suffered by victims of crimes. (both are victims of crimes, comparison is justified)	because even damages caused by storms are covered by the government. (storms and crimes are not clearly comparable sources of problems)
Symptomatic	as the phishing methods being used nowadays are sophisticated, and anyone can fall prey to them. (that anyone call fall prey with little own fault supports the idea that it is right to help victims)	as it is the responsibility of the state to ensure the wellbeing of those who have lost money. (having lost money does not by itself entitle one to receive help from the state)
Authority	because consumer associations consider this to be fair. (consumer associations have relevant expertise in the matter)	because many victims have publicly spoken in support of a governmental compensation system. (phishing victims’ credibility in judging on the matter is compromised because a compensation system would be in their own interests)

Table 2. Agreement with the claims.

Claim	Mean	SD
The death penalty should be reinstated.	2.85	1.73
The government should cover for the monetary losses of phishing victims.	3.36	1.41
Measures to combat climate change can wait.	1.98	1.48

Figure 1. Proportions of correctly rated arguments by topic and argument scheme.

Figure 2. Response times on different types of trials.

Figure 3. Area under curve on different types of trials.

Table 3. Analyses of differences in the area under curve, separately for arguments on each topic and each argument scheme.

	Analysis
	Main effect of conflict (Hypothesis 1c)					Pairwise comparison (Hypothesis 2b)
	df	F	p	ηp^2	Log BF 10	t	p	Cohen’s d	Log BF 10
Topic
Death penalty	221	0.704	.402	0.003	−1.923	0.816	.415	0.08	−2.222
Phishing	221	0.037	.848	<0.001	−2.527	0.904	.367	0.09	−1.603
Climate measures	221	7.085	.008	0.030	−0.289	2.051	.041	0.20	−0.393
Scheme
Causal	221	1.971	.162	<0.001	−2.086	1.784	.076	0.14	−2.383
Analogous	221	0.185	.668	<0.001	−2.507	2.156	.032	0.24	0.591
Symptomatic	221	0.744	.389	<0.001	−2.465	2.226	.027	0.19	0.321
Authority	221	1.134	.288	<0.001	−2.361	0.049	.961	0.00	−2.585

Note. Effects significant at the p < 0.05 level marked in boldface.

Figure 4. Mean trajectories on different types of trials, total, and by topic.

Figure 5. Mean trajectories of arguments representing different argument schemes.