Evidence map›Paper›PMID 30027454›Full record

ArticleMemory & cognition2018

Mental models and omissive causation.

Sangeet Khemlani, Christina Wasylyshyn, Gordon Briggs, Paul Bello

Abstract read
PubMed Publisher
In one paragraph

Article in Memory & cognition, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Human verifications: Computable with truth values outside logic.Proceedings of the National Academy of Sciences of the United States of America · 2023
    Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Sangeet KhemlaniNavy Center for Applied Research in Artificial Intelligence, Naval Research Laboratory, 4555 Overlook Ave SW, Washington, DC, 20375, USA. sunny.khemlani@nrl.navy.mil.
Christina WasylyshynNavy Center for Applied Research in Artificial Intelligence, Naval Research Laboratory, 4555 Overlook Ave SW, Washington, DC, 20375, USA.
Gordon BriggsNavy Center for Applied Research in Artificial Intelligence, Naval Research Laboratory, 4555 Overlook Ave SW, Washington, DC, 20375, USA.
Paul BelloNavy Center for Applied Research in Artificial Intelligence, Naval Research Laboratory, 4555 Overlook Ave SW, Washington, DC, 20375, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Some causal relations refer to causation by commission (e.g., "A gunshot causes death"), and others refer to causation by omission (e.g., "Not breathing causes death"). We describe a theory of the representation of omissive causation based on the assumption that people mentally simulate sets of possibilities-mental models-that represent causes, enabling conditions, and preventions (Goldvarg & Johnson-Laird, 2001). The theory holds that omissive causes, enabling conditions, and preventions each refer to distinct sets of possibilities. For any such causal relation, reasoners typically simulate one initial possibility, but they are able to consider alternative possibilities through deliberation. These alternative possibilities allow them to deliberate over finer-grained distinctions when reasoning about causes and effects. Hence, reasoners should be able to distinguish between omissive causes and omissive enabling conditions. Four experiments corroborated the predictions of the theory. We describe them and contrast the results with the predictions of alternative accounts of causal representation and inference.

Indexed as

LogicAdultFemaleHumansMaleThinkingAbsencesCausal reasoningDouble preventionMental modelsNegative possibilitiesOmissions

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.