Evidence map›Paper›PMID 42396205›Full record

ArticleArXiv2026

Discrete turn strategies emerge in information-limited navigation.

Jose M Betancourt, Matthew P Leighton, Thierry Emonet, Benjamin B Machta, Michael C Abbott

Abstract readPreprint
In one paragraph

Article in ArXiv, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Jose M BetancourtDepartment of Physics and Quantitative Biology Institute, Yale University, New Haven, CT, USA.
Matthew P LeightonDepartment of Physics and Quantitative Biology Institute, Yale University, New Haven, CT, USA.
Thierry EmonetDepartment of Physics and Quantitative Biology Institute, Yale University, New Haven, CT, USA.
Benjamin B MachtaDepartment of Physics and Quantitative Biology Institute, Yale University, New Haven, CT, USA.
Michael C AbbottDepartment of Physics and Quantitative Biology Institute, Yale University, New Haven, CT, USA.

Funding

Physics and Systems Biology of Cellular DomainsR35GM138341 · NIGMS · YALE UNIVERSITY · PI MACHTA, BENJAMIN BROOKS · 2020 to 2024
$2.0M
Variation in chemotactic strategies within and across bacterial speciesR35GM158058 · NIGMS · YALE UNIVERSITY · PI Thierry Emonet · 2025 to 2026
$1.2M
NIGMS NIH HHS R35 GM138341NIGMS NIH HHS R35 GM158058
6 · The paper itself

Abstract

Navigation up a smooth sensory gradient is one of the simplest behavioural tasks, and some organisms solve it by making continuous adjustments to their course. Bacteria instead employ a variety of discrete strategies, including run and tumble motion, direction reversals, and turns by specific angles. Here we ask what drives the choice of these strategies, framing the problem as maximising up-gradient speed with a given amount of sensory information per unit time. We find that, without directional information on which way to turn, behavioural strategies that take discrete actions perform better than gradual steering. As the amount of information is increased, we see a series of transitions between optimal strategies, including a shift from direction reversals to fully re-orienting tumbles. Among more complex re-orientation strategies, we show that discrete turn angles are best, and observe transitions in the number of angles employed by the optimal strategy. More broadly, such emergent simplicity in behaviour is a tractable example of a widespread phenomenon in which biology chooses a discrete solution, despite the underlying physics being continuous.

Identifiers

PMID42396205
PMCPMC13321328

What Socratic 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 Socratic graph.