Prediction English word concreteness ratios by their multidimensional Perceptual and action strength norms

Many dataset resulted by participant rating for word norms also concreteness ratio areavailable. However infrequent word and none words concreteness rank is rare. Here we usedLancaster sensory motor words’ norms , to predict word concreteness ratios of Brysbaerdataset. After removing missing values and collinear variables, we employed a SW-MLR forchoosing optimum number of norms to make a prediction MLR model. Finally we validate ourmodel using 10-fold cross-validation. The final model could predict concreteness by RMSE0.5123 and R-square 0.7262.

Turkish Emotional Word Norms for Arousal, Valence, and Discrete Emotion Categories

The present study combined dimensional and categorical approaches to emotion to develop normative ratings for a large set of Turkish words on two major dimensions of emotion: arousal and valence, as well as on five basic emotion categories of happiness, sadness, anger, fear, and disgust. A set of 2031 Turkish words obtained by translating Affective Norms for English Words to Turkish and pooling from the Turkish Word Norms were rated by a large sample of 1527 participants. This is the first comprehensive and standardized word set in Turkish offering discrete emotional ratings in addition to dimensional ratings along with concreteness judgments. Consistent with Affective Norms for English Words and word databases in several other languages, arousal increased as valence became more positive or more negative. As expected, negative emotions (anger, sadness, fear, and disgust) were positively correlated with each other, whereas the positive emotion, happiness, was negatively correlated with the negative emotion categories. Data further showed that the valence dimension was strongly correlated with happiness, and the arousal dimension was mostly correlated with fear. These findings show highly similar and consistent patterns with word sets provided in other languages in terms of the relationships between arousal and valence dimensions, relationships between dimensions and specific emotion categories, relationships among specific emotions, and further support the stability of the relationship between basic discrete emotions at the word level across different cultures.

Turkish Emotional Word Norms for Arousal, Valence, and Discrete Emotion Categories

The present study combined dimensional and categorical approaches to emotion to develop normative ratings for a large set of Turkish words on two major dimensions of emotion: arousal and valence, as well as on five basic emotion categories of happiness, sadness, anger, fear, and disgust. A set of 2031 Turkish words obtained by translating ANEW (Bradley & Lang, 1999) words to Turkish and pooling from the Turkish Word Norms (Tekcan & Göz, 2005) were rated by a large sample of 1685 participants. This is the first comprehensive and standardized word set in Turkish offering discrete emotional ratings in addition to dimensional ratings along with concreteness judgments. Consistent with ANEW and word databases in several other languages, arousal increased as valence became more positive or more negative. As expected, negative emotions (anger, sadness, fear, and disgust) were positively correlated with each other; whereas the positive emotion, happiness, was negatively correlated with the negative emotion categories. Data further showed that the valence dimension was strongly correlated with happiness and the arousal dimension was mostly correlated with fear. These findings show highly similar and consistent patterns with word sets provided in other languages in terms of the relationships between arousal and valence dimensions, relationships between dimensions and specific emotion categories, relationships among specific emotions, and further support the stability of the relationship between basic discrete emotions at the word level across different cultures.

Modeling Memory: Exploring the Relationship Between Word Overlap and Single Word Norms when Predicting Relatedness Judgments and Retrieval

This study examined the interactive relationship between semantic, thematic, and associative word pair strength in the prediction of item relatedness judgments and cued-recall performance. Previously, we found significant three-way interactions between associative, semantic, thematic word overlap when predicting participant judgment strength and recall performance (Maxwell & Buchanan, 2018), expanding upon previous work by Maki (2007). In this study, we first seek to replicate findings from the original study using a novel stimuli set. Second, this study will further explore the nature of the structure of memory, by investigating the effects of single concept information (i.e., word frequency, concreteness, etc.) on relatedness judgments and recall accuracy. We hypothesize that associative, semantic, and thematic memory networks are interactive in their relationship to judgments and recall, even after controlling for base rates of single concept information, implying a set of interdependent memory systems used for both cognitive processes.