German normative data with naming latencies for 283 action pictures and 600 action verbs

Abstract

Timed picture naming is a common psycholinguistic paradigm. In this task, participants are asked to label visually depicted objects or actions. Naming performance can be influenced by several picture and verb characteristics which demands fully characterized normative data. In this study, we provide a first German normative data set of picture and verb characteristics associated with a compilation of 283 freely available action pictures and 600 action verbs including naming latencies from 55 participants. We report standard measures for pictures and verbs such as name agreement indices, visual complexity, word frequency, word length, imageability and age of acquisition. In addition, we include less common parameters, such as orthographic Levenshtein distance, transitivity, reflexivity, morphological complexity, and motor content of the pictures and their associated verbs. We use repeated measures correlations in order to investigate associations between picture and word characteristics and linear mixed effects modeling for the prediction of naming latency. Our analyses reveal comparable results to previous studies in other languages, indicating high construct validity. We found that naming latency varied as a function of entropy of responses, word frequency and motor content of pictures and words. In summary, we provide first German normative data for action pictures and their associated verbs and identify variables influencing naming latency.

Keywords: Action naming; German; Motor content; Name agreement; Normative data; Picture naming; Verbs.

Fig. 1

Setup of Experiment 1. Example stimulus from the IPNP database (Székely et al., 2005). English translation of the motor content rating prompt: “How much movement is needed to perform the depicted action?”

Fig. 2

Distribution of naming latency (RT) and picture characteristics. Binned data are indicated by blue histogram bars. Fitted probability density function is depicted as red overlay. The vertical dashed line indicates the median value. RT = reaction time (i.e., naming latency), H = entropy, NA = name agreement, n_response = number of different answers, MC_pic = motor content of pictures, VC = visual complexity, arb. = arbitrary

Fig. 3

Distribution of verb characteristics. Binned data are indicated by blue histogram bars. Fitted probability density function is depicted as red overlay. The vertical dashed line indicates the median value. AoA = age of acquisition, MC_word = motor content of the word, IM = imageability, FR = frequency per million, as derived from SUBTLEX-DE, LE = length of answers (in letters), OLD20 = mean orthographic Levenshtein distance of the 20 nearest neighbors, arb. = arbitrary

Fig. 4

Correlational analyses between a naming latency and picture and verb characteristics, b verb characteristics, c picture and verb characteristics and d picture characteristics. RT = reaction time (i.e., naming latency), H = entropy, n_response = number of different answers, NA = name agreement, MC_pic = motor content of the picture, VC = visual complexity, MC_word = motor content of the word, AoA = age of acquisition, IM = imageability, LE = length of answers (in letters), FR = frequency per million, as derived from SUBTLEX-DE, OLD20 = mean orthographic Levenshtein distance of the 20 nearest neighbors, TR = transitivity, CO = morphological complexity, R = repeated measures correlation coefficient