Evidence map›Paper›PMID 27445780›Full record

ArticleFrontiers in computational neuroscience2016

Causal Inference for Cross-Modal Action Selection: A Computational Study in a Decision Making Framework.

Mehdi Daemi, Laurence R Harris, J Douglas Crawford

Abstract read
In one paragraph

Article in Frontiers in computational neuroscience, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

3 authors.

Mehdi DaemiDepartment of Biology and Neuroscience Graduate Diploma, York UniversityToronto, ON, Canada; Centre for Vision Research, York UniversityToronto, ON, Canada; Canadian Action and Perception NetworkToronto, ON, Canada; Department of Psychology, York UniversityToronto, ON, Canada.
Laurence R HarrisDepartment of Biology and Neuroscience Graduate Diploma, York UniversityToronto, ON, Canada; Centre for Vision Research, York UniversityToronto, ON, Canada; Department of Psychology, York UniversityToronto, ON, Canada; School of Kinesiology and Health Sciences, York UniversityToronto, ON, Canada.
J Douglas CrawfordDepartment of Biology and Neuroscience Graduate Diploma, York UniversityToronto, ON, Canada; Centre for Vision Research, York UniversityToronto, ON, Canada; Canadian Action and Perception NetworkToronto, ON, Canada; Department of Psychology, York UniversityToronto, ON, Canada; School of Kinesiology and Health Sciences, York UniversityToronto, ON, Canada; NSERC Brain and Action Program, York UniversityToronto, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Animals try to make sense of sensory information from multiple modalities by categorizing them into perceptions of individual or multiple external objects or internal concepts. For example, the brain constructs sensory, spatial representations of the locations of visual and auditory stimuli in the visual and auditory cortices based on retinal and cochlear stimulations. Currently, it is not known how the brain compares the temporal and spatial features of these sensory representations to decide whether they originate from the same or separate sources in space. Here, we propose a computational model of how the brain might solve such a task. We reduce the visual and auditory information to time-varying, finite-dimensional signals. We introduce controlled, leaky integrators as working memory that retains the sensory information for the limited time-course of task implementation. We propose our model within an evidence-based, decision-making framework, where the alternative plan units are saliency maps of space. A spatiotemporal similarity measure, computed directly from the unimodal signals, is suggested as the criterion to infer common or separate causes. We provide simulations that (1) validate our model against behavioral, experimental results in tasks where the participants were asked to report common or separate causes for cross-modal stimuli presented with arbitrary spatial and temporal disparities. (2) Predict the behavior in novel experiments where stimuli have different combinations of spatial, temporal, and reliability features. (3) Illustrate the dynamics of the proposed internal system. These results confirm our spatiotemporal similarity measure as a viable criterion for causal inference, and our decision-making framework as a viable mechanism for target selection, which may be used by the brain in cross-modal situations. Further, we suggest that a similar approach can be extended to other cognitive problems where working memory is a limiting factor, such as target selection among higher numbers of stimuli and selections among other modality combinations.

Indexed as

causal inferencedecision-makingmultisensory integrationreport of samenesssaliency map of spacespatiotemporal similarityworking memory

Identifiers

PMID27445780
PMCPMC4917558

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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.