Evidence map›Paper›PMID 27018020›Full record

ArticleCognitive processing2016

Lateral specialization in unilateral spatial neglect: a cognitive robotics model.

Daniela Conti, Santo Di Nuovo, Angelo Cangelosi, Alessandro Di Nuovo

Open access · hybridAbstract read
In one paragraph

Article in Cognitive processing, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.5field-weighted citation impact, top 19% 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

4 citing papers in PubMed, 34 citations in OpenAlex.

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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 at 4 institutions in 2 countries.

Daniela ContiDepartment of Education Sciences, University of Catania, Via Biblioteca 4, 95124, Catania, Italy.
Santo Di NuovoPsychology Operative Unit, IRCCS "Maria SS" Oasi di Troina, 73, Conte Ruggero, 94018, Troina, Italy.
Angelo CangelosiCentre for Robotics and Neural Systems, Plymouth University, Drake Circus, Plymouth, PL48AA, UK.
Alessandro Di NuovoSheffield Robotics, Sheffield Hallam University, Howard Street, Sheffield, S11WB, UK. a.dinuovo@shu.ac.uk.ORCID http://orcid.org/0000-0003-2677-2650
Oasi Maria SS · ITUniversità degli Studi di Enna Kore · ITUniversity of Catania · ITUniversity of Plymouth · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this paper, we present the experimental results of an embodied cognitive robotic approach for modelling the human cognitive deficit known as unilateral spatial neglect (USN). To this end, we introduce an artificial neural network architecture designed and trained to control the spatial attentional focus of the iCub robotic platform. Like the human brain, the architecture is divided into two hemispheres and it incorporates bio-inspired plasticity mechanisms, which allow the development of the phenomenon of the specialization of the right hemisphere for spatial attention. In this study, we validate the model by replicating a previous experiment with human patients affected by the USN and numerical results show that the robot mimics the behaviours previously exhibited by humans. We also simulated recovery after the damage to compare the performance of each of the two hemispheres as additional validation of the model. Finally, we highlight some possible advantages of modelling cognitive dysfunctions of the human brain by means of robotic platforms, which can supplement traditional approaches for studying spatial impairments in humans.

Indexed as

Models, PsychologicalRoboticsFunctional LateralityHumansPerceptual DisordersSpace PerceptionCognitive roboticsEmbodied cognitionHemisphere specializationNeuropsychologyUnilateral spatial neglect

Identifiers

PMID27018020
PMCPMC4933727
OpenAlexW2320634934

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.