5 Paradigms Of Human Interaction

[Crystle Rike]

Allinteractions between people constantly include some sort of negotiation, big or small: Where are we going to dinner? What movie are we going to watch? How much will you sell your product for? How much will you buy it for? How do you reach a resolution? There are six paradigms of human interaction.

The six paradigms of human interaction are win/win, win/lose, lose/win, lose/lose, win, and win/win or no deal. These were identified by Stephen Covey in his book The 7 Habits of Highly Effective People.

There are six paradigms of human interaction, some of which are much more beneficial and effective than others. As you read about the six paradigms of human interaction, keep in mind that a Win/Win paradigm is most effective and should be strived for, although it may not be right for all situations.

People with a Lose/Win mindset lose not only in their interactions, but also in their own well-being: They tend to suppress a lot of feelings, which can fester and bubble up in anger, resentment, cynicism, and psychosomatic illnesses that can especially affect the respiratory, nervous, and circulatory systems.

Ugly divorce battles are often examples of Lose/Lose in the six paradigms of human interaction. In one case, a judge orders a man to sell assets and give half the money to his ex-wife. The man sells his car, worth more than $10,000, for a meager $50 and hands $25 to his ex-wife. He does the same with the rest of the assets, selling them for far less than their true value just so that his wife also gets less money.

In business, the Win/Win or No Deal paradigm is most effective at the start of a relationship or enterprise, because No Deal may no longer be an option in an ongoing business relationship. If implemented from the start, this framework can preserve the core relationship, especially in family businesses or businesses started among friends; if they reach a disagreement somewhere along the lines, they can both agree to walk away (or have a buy/sell or other agreement) without hard feelings.

No single one of the six paradigms of human interaction is best for every situation; there will be times when different frameworks are appropriate. The challenge is to have an accurate enough perspective of a situation to determine which paradigm fits best, without just defaulting to what your scripting has ingrained in you.

The six paradigms of human interaction require careful balance and negotiation. Each one of the paradigms demonstrate that we must take care in relationships and work together to ensure everyone benefits.

The computers are social actors framework (CASA), derived from the media equation, explains how people communicate with media and machines demonstrating social potential. Many studies have challenged CASA, yet it has not been revised. We argue that CASA needs to be expanded because people have changed, technologies have changed, and the way people interact with technologies has changed. We discuss the implications of these changes and propose an extension of CASA. Whereas CASA suggests humans mindlessly apply human-human social scripts to interactions with media agents, we argue that humans may develop and apply human-media social scripts to these interactions. Our extension explains previous dissonant findings and expands scholarship regarding human-machine communication, human-computer interaction, human-robot interaction, human-agent interaction, artificial intelligence, and computer-mediated communication.

Communication Technology and New Media Commons, Interpersonal and Small Group Communication Commons, Other Communication Commons, Robotics Commons, Social Influence and Political Communication Commons, Social Media Commons, Social Psychology Commons, Theory and Philosophy Commons

Copyright: 2018 Liron et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

To approach such a systematic set of concepts, we consider an arena that has developed powerful approaches to re-create social interaction: the practice of theater (including cinema and other modes of performance). Theater often aims to create specific portrayals of human interaction. Accumulated experience shows that instructions for actors based on individual psychological factors such as emotion, motivation and narrative are not enough to generate the desired performance[31]. Theater directors and actors rely on an additional layer which is thought to be essential for creating believable interaction. This facet of behavior is called dramatic action[32]. Here we aim to operationalize and test dramatic action as a potentially useful concept for research on social interaction, and in particular on influence tactics.

Here, we attempt to operationalize the concept of DA, in order to test these subjective notions and provide a basis for quantitative research on DA. We categorize major groups of DAs and developed a preliminary set of stimuli to test whether people reliably agree on identifying these DAs. We then demonstrate how several commonly-used interaction coding systems can be interpreted in terms of DAs. For this purpose, we use methods from research on emotion categorization[3] and elicitation[38]. We hypothesize that

A total of 231 subjects participated in two experiments (experiment 1: 150 subjects, 63 women; experiment 2: 115 subjects, 57 women; 34 participated in both experiments). Experiments were performed on the Amazon Mechanical Turk (MTurk) platform. The online surveys were restricted to US residents, with a record of at least 1000 previously approved MTurk HITs (human intelligence tasks). All participants passed a short test for English comprehension. Participants were paid 6 US cents per HIT, up to a maximum of 90 HITs in experiment 1 and 60 HITs in experiment 2. Ethics approval was obtained specifically for the surveys in this study by the IRB of the Weizmann Institute of Science, Rehovot, Israel. Consent was not obtained since the surveys were answered anonymously online. All the data was analyzed anonymously.

We used a set of 30 images (Images were purchased from Shutterstock.com, see S1 Text for license information). The images were in minimalistic styles (cartoons, silhouette, contour drawing), and were balanced for gender (26 women out of 60 characters). Images had white background with the person to the right performing the DA, and were sized to fit in a 400x400 pixel box. In experiment 2, a black arrow was added next to one of the characters. 30 images were collected and used for the surveys (Figure B in S1 File). 3 images were removed from the analysis since they were horizontally flipped due to a coding error, for a final set of 27 images.

We tested the consistency of the responses using the approach of Ref [42]. We divided the responses for each of the 594 image-word combination into two equal groups (averaging 21.3 workers per group) and compared median responses across groups. Correlation was 0.91, and the difference was not significant (n = 594, P = 0.9, two-tailed two-sample Kolmogorov-Smirnov test), showing that the procedure yields consistent responses.

We clustered the responses (Fig 2) using the clustergram function of MATLAB v2015b with correlation distance (RowPDist, ColumnPDist = 'correlation'). For principal component analysis (PCA) we used the pca function of MATLAB v2015b with default settings. A second PCA analysis was done after splitting the words into two distinct groups, defined by the sign of the first PC (positive and negative valence). The analysis method of experiments 1 and 2 was identical (Figures H-I in S1 File).

Shown is the median score from 60 replies for each pair of images and DA verbs that exceed a statistical threshold (blue marks pairs below threshold). Images and verbs were ordered according to clustering, such that images that are close to each other have similar DA verbs, and DA verbs that are close to each other have similar images. The lower left block describes negative valence DAs, and the top right block represents positive valence DAs. Cartoons reprinted from Shutterstock.com under a CC BY license, with permission from Shutterstock.

To define a list of dramatic actions, we used the lexical hypothesis[39], that language should contain common words that describe important characteristics of human behavior. To adapt the lexical hypothesis to dramatic action, we note that DA is the effort to change the psychological state (e.g. emotion, status, and energy level) of another. Words for DAs are therefore transitive verbs, verbs in which an agent acts on someone else.

We next asked whether people can identify and agree on DAs in defined stimuli. For this purpose, we used the list of 22 DA verbs to choose evocative images from online image databases. The images show two people with one performing a clear DA on the other. In order to reduce the potential for extraneous information, we chose minimalistic cartoons with no text. We selected several styles of cartoons including silhouettes, contour drawings and cliparts, aiming to avoid biases of age and gender. The set of 27 visual stimuli is shown in Fig 3.

(A) Distribution of all answers to high-score-agreement questions of survey 1. (B) The distribution of high-score-agreement DA words per image. For example, two images had 7 high-score-agreement DA words.

(A) Cartoon stimuli in the space of the first two principal components (PCs) show a V-shape. Each cartoon was described as a vector of responses in a 22-dimensional space of the DA words and projected on the PC1-PC2 plane. (B) Valence of the DA and the valence of the emotion of the actor are not identical. Cartoon stimuli organized by PC1 of DA and PC1 of emotions. Cartoons that didn't receive significant agreement in either DA labels or emotion labels were not included. There is a moderate correlation between the PCs (r = 0.49, p = 0.01). However, the cases where valence of the two PCs is opposite are not outliers, but instead are valid sub groups of the stimuli set. Reprinted from Shutterstock.com under a CC BY license, with permission from Shutterstock.

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