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A brain network that supports consensus-seeking and conflict-resolving of college couples' shopping interaction.

ABSTRACT: One of the typical campus scenes is the social interaction between college couples, and the lesson couples must keep learning is to adapt to each other. This fMRI study investigated the shopping interactions of 30 college couples, one lying inside and the other outside the scanner, beholding the same item from two connected PCs, making preference ratings and subsequent buy/not-buy decisions. The behavioral results showed the clear modulation of significant others' preferences onto one's own decisions, and the contrast of the "shop-together vs. shop-alone", and the "congruent (both liked or disliked the item, 68%) vs. incongruent (one liked but the other disliked, and vice versa)" together trials, both revealed bilateral temporal parietal junction (TPJ) among other reward-related regions, likely reflecting mentalizing during preference harmony. Moreover, when contrasting "own-high/other-low vs. own-low/other-high" incongruent trials, left anterior inferior parietal lobule (l-aIPL) was parametrically mapped, and the "yield (e.g., own-high/not-buy) vs. insist (e.g., own-low/not-buy)" modulation further revealed left lateral-IPL (l-lIPL), together with left TPJ forming a local social decision network that was further constrained by the mediation analysis among left TPJ-lIPL-aIPL. In sum, these results exemplify, via the two-person fMRI, the neural substrate of shopping interactions between couples.

SUBMITTER: Jo H

PROVIDER: S-EPMC7573624 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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A brain network that supports consensus-seeking and conflict-resolving of college couples' shopping interaction.

Jo HanShin H Chen Chiu-Yueh CY Chen Der-Yow DY Weng Ming-Hung MH Kung Chun-Chia CC

Scientific reports 20201019 1

One of the typical campus scenes is the social interaction between college couples, and the lesson couples must keep learning is to adapt to each other. This fMRI study investigated the shopping interactions of 30 college couples, one lying inside and the other outside the scanner, beholding the same item from two connected PCs, making preference ratings and subsequent buy/not-buy decisions. The behavioral results showed the clear modulation of significant others' preferences onto one's own deci ...[more]

PMID: 33077801

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Project description:Motivation:Protein coding genes can be studied using long-read next generation sequencing. However, high rates of indel sequencing errors are problematic, corrupting the reading frame. Even the consensus of multiple independent sequence reads retains indel errors. To solve this problem, we introduce Reference-Informed Frame-Resolving multiple-Alignment Free template inference algorithm (RIFRAF), a sequence consensus algorithm that takes a set of error-prone reads and a reference sequence and infers an accurate in-frame consensus. RIFRAF uses a novel structure, analogous to a two-layer hidden Markov model: the consensus is optimized to maximize alignment scores with both the set of noisy reads and with a reference. The template-to-reads component of the model encodes the preponderance of indels, and is sensitive to the per-base quality scores, giving greater weight to more accurate bases. The reference-to-template component of the model penalizes frame-destroying indels. A local search algorithm proceeds in stages to find the best consensus sequence for both objectives. Results:Using Pacific Biosciences SMRT sequences from an HIV-1 env clone, NL4-3, we compare our approach to other consensus and frame correction methods. RIFRAF consistently finds a consensus sequence that is more accurate and in-frame, especially with small numbers of reads. It was able to perfectly reconstruct over 80% of consensus sequences from as few as three reads, whereas the best alternative required twice as many. RIFRAF is able to achieve these results and keep the consensus in-frame even with a distantly related reference sequence. Moreover, unlike other frame correction methods, RIFRAF can detect and keep true indels while removing erroneous ones. Availability and implementation:RIFRAF is implemented in Julia, and source code is publicly available at https://github.com/MurrellGroup/Rifraf.jl. Supplementary information:Supplementary data are available at Bioinformatics online.

Dataset Information

A brain network that supports consensus-seeking and conflict-resolving of college couples' shopping interaction.

Publications

A brain network that supports consensus-seeking and conflict-resolving of college couples' shopping interaction.

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