Evidence map›Paper›PMID 25349912›Full record

ReviewNature neuroscience2014

Big behavioral data: psychology, ethology and the foundations of neuroscience.

Alex Gomez-Marin, Joseph J Paton, Adam R Kampff, Rui M Costa, Zachary F Mainen

Abstract readReview
PubMed Publisher
In one paragraph

Review in Nature neuroscience, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 139 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
139citing papers in PubMed, 1 pooled it
–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

139 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  14. FERAL: A Video-Understanding System for Direct Video-to-Behavior Mapping.bioRxiv : the preprint server for biology · 2025
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79 more citing papers are in PubMed but not listed here.

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

5 authors.

Alex Gomez-MarinChampalimaud Neuroscience Programme, Champalimaud Centre for the Unknown, Lisbon, Portugal.
Joseph J PatonChampalimaud Neuroscience Programme, Champalimaud Centre for the Unknown, Lisbon, Portugal.
Adam R KampffChampalimaud Neuroscience Programme, Champalimaud Centre for the Unknown, Lisbon, Portugal.
Rui M CostaChampalimaud Neuroscience Programme, Champalimaud Centre for the Unknown, Lisbon, Portugal.
Zachary F MainenChampalimaud Neuroscience Programme, Champalimaud Centre for the Unknown, Lisbon, Portugal.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Behavior is a unifying organismal process where genes, neural function, anatomy and environment converge and interrelate. Here we review the current state and discuss the future effect of accelerating advances in technology for behavioral studies, focusing on rodents as an example. We frame our perspective in three dimensions: the degree of experimental constraint, dimensionality of data and level of description. We argue that 'big behavioral data' presents challenges proportionate to its promise and describe how these challenges might be met through opportunities afforded by the two rival conceptual legacies of twentieth century behavioral science, ethology and psychology. We conclude that, although 'more is not necessarily better', copious, quantitative and open behavioral data has the potential to transform and unify these two disciplines and to solidify the foundations of others, including neuroscience, but only if the development of new theoretical frameworks and improved experimental designs matches the technological progress.

Indexed as

BehaviorData CollectionEthologyNeurosciencesStatistics as TopicAnimalsHumansPsychology

Identifiers

PMID25349912

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.