Counterfactual thinking and recency effects in causal judgment

Abstract

People tend to judge more recent events, relative to earlier ones, as the cause of some particular outcome.For instance, people are more inclined to judge that the last basket, rather than the first, caused the team to win the basketball game. This recency effect, however, reverses in cases of overdetermination: people judge that earlier events, rather than more recent ones, caused the outcome when the event is individually sufficient but not individually necessary for the outcome. In five experiments (N = 5507), we find evidence for the recency effect and the primacy effect for causal judgment. Traditionally, these effects have been a problem for counterfactual views of causal judgment. However, we argue that an extension of a recent counterfactual model of causal judgment explains both the recency and the primacy effect. In line with the predictions of our extended counterfactual model, we also find that, regardless of causal structure, people tend to imagine the counterfactual alternative to the more recent event rather than to the earlier one. Moreover, manipulating this tendency affects causal judgments in the ways predicted by this extended model: asking participants to imagine the counterfactual alternative to the earlier event weakens the interaction between recency and causal structure, and asking participants to imagine the counterfactual alternative to the more recent event strengthens the interaction between recency and causal structure. We discuss these results in relation to work on counterfactual thinking, causal modeling, and late-preemption.

Modeling Confidence in Causal Judgments

“…Among the many possibilities, counterfactual sampling models have had particular success [10,11,12,13,14]. These models account for known effects of probability [2,13,15,16], the presence of alternative causes [17,18], temporal recency [12,19,20], and foreseeability [21] on causal judgments, among other phenomena. Counterfactual sampling models have even been shown to predict eye movements during causal judgment [22,23] and judgments of omissive causation [24,25].…”

Confidence and gradation in causal judgment

“…For example, people should give higher causal judgments of the lightning than of the lack of rain because they think that the lightning strike is a stronger cause of the fire than the lack of rain. As mentioned earlier, people could think that the lightning strike is a stronger cause of the fire for a number of different reasons, including that the lightning strike is statistically abnormal, temporally recent, and connected through a physical process to the fire (Henne et al, 2021a;Icard et al, 2017;Kahneman & Miller, 1986;Wolff, 2007). But causes can also differ in the extent to which they contribute or could have contributed to an outcome, as in voting cases where some votes are weighed more heavily than others (Bernstein, 2017;Kaiserman, 2016;Kaiserman, 2018;Quillien, 2021).…”

“…A popular explanation is that lightning is statistically abnormal, which allows it to stand out from more normal events like the lack of rain (Gerstenberg & Icard, 2020;Hart & Honoré, 1985;Henne et al, 2017;Henne et al, 2021b;Hilton & Slugoski, 1986;Icard et al, 2017;Kahneman & Miller, 1986;Knobe & Fraser, 2008;McGrath, 2005). In addition to normality, research also indicates that people are more likely to judge events as causal when they are temporally recent (Henne et al, 2021a;Lagnado & Channon, 2008;Spellman, 1997), necessary or sufficient (Icard et al, 2017;Pearl, 2009), robust to a range of background circumstances (Gerstenberg et al, 2021;Grinfeld et al, 2020;Hitchcock, 2012;Lombrozo, 2010;Quillien, 2020;Vasilyeva et al, 2018;Woodward, 2006), intentional or agentive (Alicke et al, 2011;Kirfel & Lagnado, 2021;Lagnado & Channon, 2008), connected through a physical process (Wolff, 2007;Wolff et al, 2010), and when there are few alternate causes (Lagnado et al, 2013).…”

Changing Minds ― Epistemic Interventions in Causal Reasoning

“…Counterfactual models of causation are able to capture various structural aspects that influence people's causal judgments about a cause Gerstenberg, Halpern, & Tenenbaum, 2015), such as causal structure, number of causes, temporal order, probabilities etc. (Gerstenberg, Goodman, Lagnado, & Tenenbaum, 2015;Gerstenberg, Halpern, & Tenenbaum, 2015;Gerstenberg & Icard, 2020;Henne, Kulesza, Perez, & Houcek, 2021;Icard, Kominsky, & Knobe, 2017;Woodward, 2011), both for inanimate causal factors as well as causal agents (Gerstenberg, Halpern, & Tenenbaum, 2015;Gerstenberg & Icard, 2020;Icard et al, 2017). However, a crucial factor that has not yet been considered in detail is the influence of epistemic states on causal judgments about agents (Hilton et al, 2016).…”