お知らせ

博士後期課程の白木優馬が博士号取得

2018年3月26日に名古屋大学の卒業式が行われ、五十嵐研究室では博士後期課程の白木優馬が博士（心理学）を取得しました。

白木さんの今後のご活躍をお祈りしています。

五十嵐研究室2017年度の業績一覧を公開しました

2017年度の五十嵐研究室の業績は以下の通りです。

論文（査読有）

学会発表（国際学会）

学会発表（国内学会）

賞罰

• 平成29年度名古屋大学学術奨励賞　人文社会系（白木優馬） テーマ：感謝感情に基づく｢恩送り｣の社会的伝播：社会心理学的アプローチによる実験的検討 2017年6月19日

SPSP2018で発表を行います

The 19th Annual Meeting of the Society for Personality and Social Psychology (SPSP2018) (Atlanta) で以下の発表を行います。

“Asian Journal of Social Psychology”への論文掲載が決定しました

"Asian Journal of Social Psychology"に、以下の論文が掲載されます。感謝感情の喚起がペイ・フォワードや恩送りといった第三者への利他行動を促進するプロセスに、関係性欲求の充足が介在することを明らかにしました。

論文PDF

People who receive kindness tend to feel gratitude and act in a prosocial manner toward third persons (i.e., “paying-it-forward”). Combining the separate evidence that (1) gratitude leads to the formation of strong psychological bonds from a beneficiary to a benefactor; and (2) people become more prosocial toward strangers when the Need for Relatedness (NFR) is satisfied, two online experiments were conducted to examine if NFR satisfaction mediates the association between gratitude and prosocial behavior toward third persons. After evoking gratitude by recalling past experiences (Study 1) or writing a letter to someone (Study 2), participants were asked to make a donation from their remuneration for the experiment to a charity organization. As predicted, emotion manipulation promoted donation via feelings of gratitude and satisfied NFR. Implications of the current model to integrate previous findings are discussed.

ASNAC2017で発表を行います

2017年11月28日～29日にかけて開催されるThe 2nd Australian Social Network Analysis Conference (ASNAC 2017) (Sydney, Australia) で以下の発表を行います。

Oral Presentation

Igarashi, T, Kato, J, Shiraki, Y, Hirashima, T, & Tamai, R (2017). Chasing stars and confirming alliance: Two effective strategies for learning social network structure.

This study reports a novel technique that unveils the process for learning social network structure. In an online experiment, participants were asked to remember and recall the structure of a directed social network (i.e. a set of influence relationships among employees in a company). Participants were shown a list of names of nodes and asked to remember information about who (source) influences whom (target) in the network with a time limit. The information appeared on the display only when participants hovered the mouse pointer over a source node; in other words, participants needed to collect source-target information across all nodes in an efficient way. A social network stimulus was generated at each experimental session based on a random graph varying in three properties: size (5 to 20), density (.05 to .20), and the degree of reciprocity (0 to 1). Analysis of patterns of mouse tracking/fixation across nodes in a generalized linear mixed modeling (GLMM) framework revealed two fundamental strategies that significantly increased a recall rate of whole network structure. The first was a “chasing stars” strategy, or spending more time to remember active (more influential) nodes than inactive (less influential) odes. The second was a “confirming alliance” strategy, or searching pairs of nodes forming reciprocal relationships, and was notably efficient to improve the performance in the learning task of large social network structure. The current findings suggest that the exploration of information-searching processes for social network learning has potential to understand the nature of network schema/bias.