Evidence map›Paper›PMID 35943820›Full record

ArticlePerspectives on psychological science : a journal of the Association for Psychological Science2023

Computational Scientific Discovery in Psychology.

Laura K Bartlett, Angelo Pirrone, Noman Javed, Fernand Gobet

Abstract read
In one paragraph

Article in Perspectives on psychological science : a journal of the Association for Psychological Science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Observational
  2. Article
  3. Data-driven equation discovery reveals nonlinear reinforcement learning in humans.Proceedings of the National Academy of Sciences of the United States of America · 2025
    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.

Laura K BartlettCentre for Philosophy of Natural and Social Science, London School of Economics and Political Science.ORCID 0000-0001-5202-4504
Angelo PirroneCentre for Philosophy of Natural and Social Science, London School of Economics and Political Science.
Noman JavedCentre for Philosophy of Natural and Social Science, London School of Economics and Political Science.
Fernand GobetCentre for Philosophy of Natural and Social Science, London School of Economics and Political Science.ORCID 0000-0002-9317-6886

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Scientific discovery is a driving force for progress involving creative problem-solving processes to further our understanding of the world. The process of scientific discovery has historically been intensive and time-consuming; however, advances in computational power and algorithms have provided an efficient route to make new discoveries. Complex tools using artificial intelligence (AI) can efficiently analyze data as well as generate new hypotheses and theories. Along with AI becoming increasingly prevalent in our daily lives and the services we access, its application to different scientific domains is becoming more widespread. For example, AI has been used for the early detection of medical conditions, identifying treatments and vaccines (e.g., against COVID-19), and predicting protein structure. The application of AI in psychological science has started to become popular. AI can assist in new discoveries both as a tool that allows more freedom to scientists to generate new theories and by making creative discoveries autonomously. Conversely, psychological concepts such as heuristics have refined and improved artificial systems. With such powerful systems, however, there are key ethical and practical issues to consider. This article addresses the current and future directions of computational scientific discovery generally and its applications in psychological science more specifically.

Indexed as

Artificial IntelligenceCOVID-19AlgorithmsHeuristicsHumansProblem SolvingAIcomputational scientific discoverycreativityphilosophy of sciencepsychology

Identifiers

PMID35943820
PMCPMC9902966

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.