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Temporally Unconstrained Decoding Reveals Consistent but Time-Varying Stages of Stimulus Processing.

ABSTRACT: In this article, we propose a method to track trial-specific neural dynamics of stimulus processing and decision making with high temporal precision. By applying this novel method to a perceptual template-matching task, we tracked representational brain states associated with the cascade of neural processing, from early sensory areas to higher order areas that are involved in integration and decision making. We address a major limitation of the traditional decoding approach: that it relies on consistent timing of these processes over trials. Using a TUDA approach, we found that the timing of the cognitive processes involved in perceptual judgments can vary considerably over trials. This revealed that the sequence of processing states was consistent for all subjects and trials, even when the timing of these states varied. Furthermore, we found that the specific timing of states on each trial was related to the quality of performance over trials. Altogether, this work not only highlights the serious pitfalls and misleading interpretations that result from assuming stimulus processing to be synchronous across trials but can also open important avenues to investigate learning and quantify plasticity.

SUBMITTER: Vidaurre D

PROVIDER: S-EPMC6319313 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

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Temporally Unconstrained Decoding Reveals Consistent but Time-Varying Stages of Stimulus Processing.

Vidaurre Diego D Myers Nicholas E NE Stokes Mark M Nobre Anna C AC Woolrich Mark W MW

Cerebral cortex (New York, N.Y. : 1991) 20190201 2

In this article, we propose a method to track trial-specific neural dynamics of stimulus processing and decision making with high temporal precision. By applying this novel method to a perceptual template-matching task, we tracked representational brain states associated with the cascade of neural processing, from early sensory areas to higher order areas that are involved in integration and decision making. We address a major limitation of the traditional decoding approach: that it relies on co ...[more]

PMID: 30535141

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Project description:This research evaluates the performance of areal interpolation coupled with dasymetric refinement to estimate different demographic attributes, namely population sub-groups based on race, age structure and urban residence, within consistent census tract boundaries from 1990 to 2010 in Massachusetts. The creation of such consistent estimates facilitates the study of the nuanced micro-scale evolution of different aspects of population, which is impossible using temporally incompatible small-area census geographies from different points in time. Various unexplored ancillary variables, including the Global Human Settlement Layer (GHSL), the National Land-Cover Database (NLCD), parcels, building footprints and the proprietary ZTRAX® dataset are utilized for dasymetric refinement prior to areal interpolation to examine their effectiveness in improving the accuracy of multi-temporal population estimates. Different areal interpolation methods including Areal Weighting (AW), Target Density Weighting (TDW), Expectation Maximization (EM) and its data-extended approach are coupled with different dasymetric refinement scenarios based on these ancillary variables. The resulting consistent small area estimates of white and black subpopulations, people of age 18-65 and urban population show that dasymetrically refined areal interpolation is particularly effective when the analysis spans a longer time period (1990-2010 instead of 2000-2010) and the enumerated population is sufficiently large (e.g., counts of white vs. black). The results also demonstrate that current census-defined urban areas overestimate the spatial distribution of urban population and dasymetrically refined areal interpolation improves estimates of urban population. Refined TDW using building footprints or the ZTRAX® dataset outperforms all other methods. The implementation of areal interpolation enriched by dasymetric refinement represents a promising strategy to create more reliable multi-temporal and consistent estimates of different population subgroups and thus demographic compositions. This methodological foundation has the potential to advance micro-scale modeling of various subpopulations, particularly urban population to inform studies of urbanization and population change over time as well as future population projections.

Dataset Information

Temporally Unconstrained Decoding Reveals Consistent but Time-Varying Stages of Stimulus Processing.

Publications

Temporally Unconstrained Decoding Reveals Consistent but Time-Varying Stages of Stimulus Processing.

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