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How biological attention mechanisms improve task performance in a large-scale visual system model.

ABSTRACT: How does attentional modulation of neural activity enhance performance? Here we use a deep convolutional neural network as a large-scale model of the visual system to address this question. We model the feature similarity gain model of attention, in which attentional modulation is applied according to neural stimulus tuning. Using a variety of visual tasks, we show that neural modulations of the kind and magnitude observed experimentally lead to performance changes of the kind and magnitude observed experimentally. We find that, at earlier layers, attention applied according to tuning does not successfully propagate through the network, and has a weaker impact on performance than attention applied according to values computed for optimally modulating higher areas. This raises the question of whether biological attention might be applied at least in part to optimize function rather than strictly according to tuning. We suggest a simple experiment to distinguish these alternatives.

SUBMITTER: Lindsay GW

PROVIDER: S-EPMC6207429 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature

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How biological attention mechanisms improve task performance in a large-scale visual system model.

Lindsay Grace W GW Miller Kenneth D KD

eLife 20181001

How does attentional modulation of neural activity enhance performance? Here we use a deep convolutional neural network as a large-scale model of the visual system to address this question. We model the feature similarity gain model of attention, in which attentional modulation is applied according to neural stimulus tuning. Using a variety of visual tasks, we show that neural modulations of the kind and magnitude observed experimentally lead to performance changes of the kind and magnitude obse ...[more]

PMID: 30272560

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Project description:Older adults show higher dual task performance decrements than younger adults. While this is assumed to be related to attentional capacity reductions, the precise affected functions are not specified. Such specification is, however, possible based on the "theory of visual attention" (TVA) which allows for modeling of distinct attentional capacity parameters. Furthermore, it is unclear whether older adults show qualitatively different attentional effects or whether they show the same effects as younger adults experience under more challenging conditions. By varying the complexity of the secondary task, it is possible to address this question. In our study, participants performed a verbal whole report of briefly presented letter arrays. TVA-based fitting of report performance delivered parameters of visual threshold t0, processing speed C, and visual short-term memory (VSTM) storage capacity K. Furthermore, participants performed a concurrent motor task consisting of continuous tapping of a (simple or complex) sequence. Both TVA and tapping tasks were performed under single and dual task conditions. Two groups of 30 younger adults each performed either the simple or complex tapping, and a group of 30 older adults performed the simple tapping condition. In older participants, VSTM storage capacity declined under dual task conditions. While no such effect was found in younger subjects performing the simple tapping sequence under dual task conditions, the younger group performing the complex tapping task under dual task conditions also showed a significant VSTM capacity reduction. Generally, no significant effect on other TVA parameters or on tapping accuracy was found. Comparable goodness-of-fit measures were obtained for the TVA modeling data in single and dual tasks, indicating that tasks were executed in a qualitatively similar, continuous manner, although quantitatively less efficiently under dual- compared to single-task conditions. Taken together, our results show that the age-specific effects of motor-cognitive dual task interference are reflected by a stronger decline of VSTM storage capacity. They support an interpretation of VSTM as central attentional capacity, which is shared across visual uptake and concurrent motor performance. Capacity limits are reached earlier, and already under lower motor task complexity, in older compared to younger adults.

Project description:The fast development of high throughput genotyping has opened up new possibilities in genetics while at the same time producing considerable data handling issues. TheSNPpit is a database system for managing large amounts of multi panel SNP genotype data from any genotyping platform. With an increasing rate of genotyping in areas like animal and plant breeding as well as human genetics, already now hundreds of thousand of individuals need to be managed. While the common database design with one row per SNP can manage hundreds of samples this approach becomes progressively slower as the size of the data sets increase until it finally fails completely once tens or even hundreds of thousands of individuals need to be managed. TheSNPpit has implemented three ideas to also accomodate such large scale experiments: highly compressed vector storage in a relational database, set based data manipulation, and a very fast export written in C with Perl as the base for the framework and PostgreSQL as the database backend. Its novel subset system allows the creation of named subsets based on the filtering of SNP (based on major allele frequency, no-calls, and chromosomes) and manually applied sample and SNP lists at negligible storage costs, thus avoiding the issue of proliferating file copies. The named subsets are exported for down stream analysis. PLINK ped and map files are processed as in- and outputs. TheSNPpit allows management of different panel sizes in the same population of individuals when higher density panels replace previous lower density versions as it occurs in animal and plant breeding programs. A completely generalized procedure allows storage of phenotypes. TheSNPpit only occupies 2 bits for storing a single SNP implying a capacity of 4 mio SNPs per 1MB of disk storage. To investigate performance scaling, a database with more than 18.5 mio samples has been created with 3.4 trillion SNPs from 12 panels ranging from 1000 through 20 mio SNPs resulting in a database of 850GB. The import and export performance scales linearly with the number of SNPs and is largely independent of panel and database size. Import speed is around 6 mio SNPs/sec, export between 60 and 120 mio SNPs/sec. Being command line based, imports and exports can easily be integrated into pipelines. TheSNPpit is available under the Open Source GNU General Public License (GPL) Version 2.

Dataset Information

How biological attention mechanisms improve task performance in a large-scale visual system model.

Publications

How biological attention mechanisms improve task performance in a large-scale visual system model.

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