Literature DB >> 33569719

Hearing hooves, thinking zebras: A review of the inverse base-rate effect.

Hilary J Don1,2,3, Darrell A Worthy4, Evan J Livesey5.   

Abstract

People often fail to use base-rate information appropriately in decision-making. This is evident in the inverse base-rate effect, a phenomenon in which people tend to predict a rare outcome for a new and ambiguous combination of cues. While the effect was first reported in 1988, it has recently seen a renewed interest from researchers concerned with learning, attention and decision-making. However, some researchers have raised concerns that the effect arises in specific circumstances and is unlikely to provide insight into general learning and decision-making processes. In this review, we critically evaluate the evidence for and against the main explanations that have been proposed to explain the effect, and identify where this evidence is currently weak. We argue that concerns about the effect are not well supported by the data. Instead, the evidence supports the conclusion that the effect is a result of general mechanisms that provides a useful opportunity to understand the processes involved in learning and decision making. We discuss gaps in our knowledge and some promising avenues for future research, including the relevance of the effect to models of attentional change in learning, an area where the phenomenon promises to contribute new insights.

Entities:  

Keywords:  Attention in learning; Decision making; Human associative learning; Inverse base-rate effect

Year:  2021        PMID: 33569719     DOI: 10.3758/s13423-020-01870-0

Source DB:  PubMed          Journal:  Psychon Bull Rev        ISSN: 1069-9384


  23 in total

1.  The inverse base-rate effect is not explained by eliminative inference.

Authors:  J K Kruschke
Journal:  J Exp Psychol Learn Mem Cogn       Date:  2001-11       Impact factor: 3.051

2.  High-level reasoning and base-rate use: do we need cue-competition to explain the inverse base-rate effect?

Authors:  P Juslin; P Wennerholm; A Winman
Journal:  J Exp Psychol Learn Mem Cogn       Date:  2001-05       Impact factor: 3.051

3.  Learned associability and associative change in human causal learning.

Authors:  M E Le Pelley; I P L McLaren
Journal:  Q J Exp Psychol B       Date:  2003-02

4.  Eye gaze and individual differences consistent with learned attention in associative blocking and highlighting.

Authors:  John K Kruschke; Emily S Kappenman; William P Hetrick
Journal:  J Exp Psychol Learn Mem Cogn       Date:  2005-09       Impact factor: 3.051

5.  The role of attention shifts in the categorization of continuous dimensioned stimuli.

Authors:  M L Kalish; J K Kruschke
Journal:  Psychol Res       Date:  2000

6.  Learning reward frequency over reward probability: A tale of two learning rules.

Authors:  Hilary J Don; A Ross Otto; Astin C Cornwall; Tyler Davis; Darrell A Worthy
Journal:  Cognition       Date:  2019-08-17

7.  Peak shift and rules in human generalization.

Authors:  Jessica C Lee; Brett K Hayes; Peter F Lovibond
Journal:  J Exp Psychol Learn Mem Cogn       Date:  2018-03-19       Impact factor: 3.051

8.  Base rates in category learning.

Authors:  J K Kruschke
Journal:  J Exp Psychol Learn Mem Cogn       Date:  1996-01       Impact factor: 3.051

9.  Physician's use of probabilistic information in a real clinical setting.

Authors:  J J Christensen-Szalanski; J B Bushyhead
Journal:  J Exp Psychol Hum Percept Perform       Date:  1981-08       Impact factor: 3.332

10.  Base-rate expectations modulate the causal illusion.

Authors:  Fernando Blanco; Helena Matute
Journal:  PLoS One       Date:  2019-03-05       Impact factor: 3.240

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