Dataset Information

Positive Feeling, Negative Meaning: Visualizing the Mental Representations of In-Group and Out-Group Smiles.

ABSTRACT: Even though smiles are seen as universal facial expressions, research shows that there exist various kinds of smiles (i.e., affiliative smiles, dominant smiles). Accordingly, we suggest that there also exist various mental representations of smiles. Which representation is employed in cognition may depend on social factors, such as the smiling person's group membership: Since in-group members are typically seen as more benevolent than out-group members, in-group smiles should be associated with more benevolent social meaning than those conveyed by out-group members. We visualized in-group and out-group smiles with reverse correlation image classification. These visualizations indicated that mental representations of in-group smiles indeed express more benevolent social meaning than those of out-group smiles. The affective meaning of these visualized smiles was not influenced by group membership. Importantly, the effect occurred even though participants were not instructed to attend to the nature of the smile, pointing to an automatic association between group membership and intention.

SUBMITTER: Paulus A

PROVIDER: S-EPMC4786158 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Positive Feeling, Negative Meaning: Visualizing the Mental Representations of In-Group and Out-Group Smiles.

Paulus Andrea A Rohr Michaela M Dotsch Ron R Wentura Dirk D

PloS one 20160310 3

Even though smiles are seen as universal facial expressions, research shows that there exist various kinds of smiles (i.e., affiliative smiles, dominant smiles). Accordingly, we suggest that there also exist various mental representations of smiles. Which representation is employed in cognition may depend on social factors, such as the smiling person's group membership: Since in-group members are typically seen as more benevolent than out-group members, in-group smiles should be associated with ...[more]

PMID: 26963621

Similar Datasets

Project description:The present paper explores nonnative (L2) phonological encoding of lexical entries and dissociates the difficulties associated with L2 phonological and phonolexical encoding by focusing on similarly sounding L2 words that are not differentiated by difficult phonological contrasts. We test two main claims of the fuzzy lexicon hypothesis: (1) L2 fuzzy phonolexical representations are not fully specified and lack details at both phonological and phonolexical levels of representation (Experiment 1); and (2) fuzzy phonolexical representations can lead to establishing incorrect form-to-meaning mappings (Experiment 2). The Russian-English Translation Judgment Task (Experiment 1, TJT) explores how the degree of phonolexical similarity between a word and its lexical competitor affects lexical access of Russian words. Words with smaller phonolexical distance (e.g., parent-parrot) show longer reaction times and lower accuracy compared to words with a larger phonolexical distance (e.g., parent-parchment) in lower-proficiency nonnative speakers, and, to a lesser degree, higher-proficiency speakers. This points to a lack of detail in nonnative phonolexical representations necessary for efficient lexical access. The Russian Pseudo-Semantic Priming task (Experiment 2, PSP) addresses the vulnerability of form-to-meaning mappings as a consequence of fuzzy phonolexical representations in L2. We primed the target with a word semantically related to its phonological competitor, or a potentially confusable word. The findings of Experiment 2 extend the results of Experiment 1 that, unlike native speakers, nonnative speakers do not properly encode phonolexical information. As a result, they are prone to access an incorrect lexical representation of a competitor word, as indicated by a slowdown in the judgments to confusable words. The study provides evidence that fuzzy phonolexical representations result in unfaithful form-to-meaning mappings, which lead to retrieval of incorrect semantic content. The results of the study are in line with existing research in support of less detailed L2 phonolexical representations, and extend the findings to show that the fuzziness of phonolexical representations can arise even when confusable words are not differentiated by difficult phonological contrasts.

Project description:BackgroundNowadays, patients with chronic diseases such as diabetes and hypertension have reached alarming numbers worldwide. These diseases increase the risk of developing acute complications and involve a substantial economic burden and demand for health resources. The widespread adoption of Electronic Health Records (EHRs) is opening great opportunities for supporting decision-making. Nevertheless, data extracted from EHRs are complex (heterogeneous, high-dimensional and usually noisy), hampering the knowledge extraction with conventional approaches.MethodsWe propose the use of the Denoising Autoencoder (DAE), a Machine Learning (ML) technique allowing to transform high-dimensional data into latent representations (LRs), thus addressing the main challenges with clinical data. We explore in this work how the combination of LRs with a visualization method can be used to map the patient data in a two-dimensional space, gaining knowledge about the distribution of patients with different chronic conditions. Furthermore, this representation can be also used to characterize the patient's health status evolution, which is of paramount importance in the clinical setting.ResultsTo obtain clinical LRs, we considered real-world data extracted from EHRs linked to the University Hospital of Fuenlabrada in Spain. Experimental results showed the great potential of DAEs to identify patients with clinical patterns linked to hypertension, diabetes and multimorbidity. The procedure allowed us to find patients with the same main chronic disease but different clinical characteristics. Thus, we identified two kinds of diabetic patients with differences in their drug therapy (insulin and non-insulin dependant), and also a group of women affected by hypertension and gestational diabetes. We also present a proof of concept for mapping the health status evolution of synthetic patients when considering the most significant diagnoses and drugs associated with chronic patients.ConclusionOur results highlighted the value of ML techniques to extract clinical knowledge, supporting the identification of patients with certain chronic conditions. Furthermore, the patient's health status progression on the two-dimensional space might be used as a tool for clinicians aiming to characterize health conditions and identify their more relevant clinical codes.

Dataset Information

Positive Feeling, Negative Meaning: Visualizing the Mental Representations of In-Group and Out-Group Smiles.

Publications

Positive Feeling, Negative Meaning: Visualizing the Mental Representations of In-Group and Out-Group Smiles.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets