4 years ago

Color Image Norms in Mandarin Chinese.

Qi Chen, Dandan Zhou
The present study comprises two parts, an object picture naming task and rating tasks, and reports naming latencies and norms for 435 color images in Mandarin Chinese. These norms include name agreement (%), H-value, concept agreement, familiarity, visual complexity, age of acquisition (AOA) based on adult ratings, object agreement, viewpoint agreement, word frequency, and word length. We examined correlations between the norms and explored the internal structure among these correlative variables by a factor analysis. Four factors were extracted, which accounted for 74.86% of the total variance. These data were analyzed to identify variables with significant contributions to naming latencies using multiple regression analysis, including norms of name agreement (%), familiarity, word frequency, concept agreement, AOA, and object agreement. These variables explained 54.70% of the total variance of naming latencies. This work presents a new set of photo stimuli and a large set of normalized variables. We expect that this study will provide useful materials for further researches.

Publisher URL: http://doi.org/10.3389/fpsyg.2017.01880

DOI: 10.3389/fpsyg.2017.01880

You might also like
Discover & Discuss Important Research

Keeping up-to-date with research can feel impossible, with papers being published faster than you'll ever be able to read them. That's where Researcher comes in: we're simplifying discovery and making important discussions happen. With over 19,000 sources, including peer-reviewed journals, preprints, blogs, universities, podcasts and Live events across 10 research areas, you'll never miss what's important to you. It's like social media, but better. Oh, and we should mention - it's free.

  • Download from Google Play
  • Download from App Store
  • Download from AppInChina

Researcher displays publicly available abstracts and doesn’t host any full article content. If the content is open access, we will direct clicks from the abstracts to the publisher website and display the PDF copy on our platform. Clicks to view the full text will be directed to the publisher website, where only users with subscriptions or access through their institution are able to view the full article.