.png){kind=icon}
.png){kind=icon}
Return
What's next? Disentangling availability from representativeness using binary decision tasks
.png){kind=icon}
PRE
AI
DOI：10.1016/j.jesp.2018.03.006.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
People's intuitive predictions under uncertainty may rely on the representativeness or on the availability heuristics (Tversky & Kahneman, 1974). However, the distinction between these two heuristics has never been clear, and both have been proposed to underlie the same judgment tasks. For instance, when judging what outcome is likely to be next in a coin flip after a streak, representativeness leads to predicting an alternation in the outcome, ending the streak (gambler's fallacy), whereas availability leads to predicting the streak's continuation. We propose that availability (direct use of accessibility) is computed earlier than representativeness (comparing to an abstract representation of the expected outcome). In five studies, we pit one heuristic against the other in binary prediction tasks, both in coin flip and athletes performance contexts. We find that, although the streak outcome is cognitively more available, judgments are usually based on representativeness, leading more often to a prediction of an alternation after a streak. However, under time-pressure conditions, representativeness processes are constrained and participants are more prone to base their predictions on the most salient and cognitively available outcomes.
Keywords:
Decision heuristics
Availability
Representativeness
Binary decision
Gambler's fallacy
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.1
Papers:
3.7K
Citations:
1.5W
View more organizations