Evidence map›Paper›PMID 22279155›Full record

ArticleJournal of the Royal Society, Interface2012

Optimal decision-making in mammals: insights from a robot study of rodent texture discrimination.

Nathan F Lepora, Charles W Fox, Mathew H Evans, Mathew E Diamond, Kevin Gurney, Tony J Prescott

Open access · bronzeAbstract read
In one paragraph

Article in Journal of the Royal Society, Interface, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
4.1field-weighted citation impact, top 5% of its field
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

7 citing papers in PubMed, 40 citations in OpenAlex.

  1. Article
  2. Review
  3. Probing perceptual decisions in rodents.Nature neuroscience · 2013
    Review
  4. Article
  5. Robots in the service of animal behavior.Communicative & integrative biology · 2012
    Article
  6. Article
  7. 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

6 authors at 2 institutions in 2 countries.

Nathan F LeporaDepartment of Psychology, University of Sheffield, Sheffield S10 2TP, UK. n.lepora@sheffield.ac.uk
Charles W Fox
Mathew H Evans
Mathew E Diamond
Kevin Gurney
Tony J Prescott
University of Sheffield · GBScuola Internazionale Superiore di Studi Avanzati · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Texture perception is studied here in a physical model of the rat whisker system consisting of a robot equipped with a biomimetic vibrissal sensor. Investigations of whisker motion in rodents have led to several explanations for texture discrimination, such as resonance or stick-slips. Meanwhile, electrophysiological studies of decision-making in monkeys have suggested a neural mechanism of evidence accumulation to threshold for competing percepts, described by a probabilistic model of Bayesian sequential analysis. For our robot whisker data, we find that variable reaction-time decision-making with sequential analysis performs better than the fixed response-time maximum-likelihood estimation. These probabilistic classifiers also use whatever available features of the whisker signals aid the discrimination, giving improved performance over a single-feature strategy, such as matching the peak power spectra of whisker vibrations. These results cast new light on how the various proposals for texture discrimination in rodents depend on the whisker contact mechanics and suggest the possibility of a common account of decision-making across mammalian species.

Indexed as

Decision MakingModels, BiologicalPattern Recognition, PhysiologicalVibrissaeAnimalsRatsRoboticsSurface Properties

Identifiers

PMID22279155
PMCPMC3367812
OpenAlexW2101511752

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.