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

Overview of attention for article published in Frontiers in Computational Neuroscience, June 2016
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Title
Causal Inference for Cross-Modal Action Selection: A Computational Study in a Decision Making Framework
Published in
Frontiers in Computational Neuroscience, June 2016
DOI 10.3389/fncom.2016.00062
Pubmed ID
Authors

Mehdi Daemi, Laurence R. Harris, J. Douglas Crawford

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.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 35 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 35 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 14%
Student > Doctoral Student 5 14%
Professor > Associate Professor 4 11%
Student > Bachelor 3 9%
Student > Master 3 9%
Other 11 31%
Unknown 4 11%
Readers by discipline Count As %
Neuroscience 8 23%
Psychology 7 20%
Engineering 3 9%
Agricultural and Biological Sciences 3 9%
Biochemistry, Genetics and Molecular Biology 1 3%
Other 7 20%
Unknown 6 17%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 25 June 2016.
All research outputs
#14,284,696
of 23,339,727 outputs
Outputs from Frontiers in Computational Neuroscience
#640
of 1,372 outputs
Outputs of similar age
#198,136
of 354,515 outputs
Outputs of similar age from Frontiers in Computational Neuroscience
#19
of 39 outputs
Altmetric has tracked 23,339,727 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,372 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.2. This one is in the 49th percentile – i.e., 49% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 354,515 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 42nd percentile – i.e., 42% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 39 others from the same source and published within six weeks on either side of this one. This one is in the 46th percentile – i.e., 46% of its contemporaries scored the same or lower than it.