#pragmatism — Public Fediverse posts

Emotional Expressions Reconsidered: Challenges to Inferring Emotion From Human Facial Movements

Abstract

It is commonly assumed that a person’s emotional state can be readily inferred from his or her facial movements, typically called emotional expressions or facial expressions. This assumption influences legal judgments, policy decisions, national security protocols, and educational practices; guides the diagnosis and treatment of psychiatric illness, as well as the development of commercial applications; and pervades everyday social interactions as well as research in other scientific fields such as artificial intelligence, neuroscience, and computer vision. In this article, we survey examples of this widespread assumption, which we refer to as the common view, and we then examine the scientific evidence that tests this view, focusing on the six most popular emotion categories used by consumers of emotion research: anger, disgust, fear, happiness, sadness, and surprise. The available scientific evidence suggests that people do sometimes smile when happy, frown when sad, scowl when angry, and so on, as proposed by the common view, more than what would be expected by chance. Yet how people communicate anger, disgust, fear, happiness, sadness, and surprise varies substantially across cultures, situations, and even across people within a single situation. Furthermore, similar configurations of facial movements variably express instances of more than one emotion category. In fact, a given configuration of facial movements, such as a scowl, often communicates something other than an emotional state. Scientists agree that facial movements convey a range of information and are important for social communication, emotional or otherwise. But our review suggests an urgent need for research that examines how people actually move their faces to express emotions and other social information in the variety of contexts that make up everyday life, as well as careful study of the mechanisms by which people perceive instances of emotion in one another. We make specific research recommendations that will yield a more valid picture of how people move their faces to express emotions and how they infer emotional meaning from facial movements in situations of everyday life. This research is crucial to provide consumers of emotion research with the translational information they require.

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Maybe it’s just me, but I have always thought these findings were extremely obvious. What is obvious is not just facial expressions. The same rough analysis applies to tone of voice, word choice, gesture, body language, perceived meaning, pragmatics of speaking and everything else involving interpersonal communication. For these reasons, I developed the FIML technique. FIML reduces the ‘irreducible complexity’1 of human interactions by examining small bits of interpersonal communications in real-time, real-world situations. Perfect communication between two humans (the smallest unit) is not possible. BUT, much better communication IS. FIML is not hard to do if you have a good partner who can understand why it is necessary. The world is a lonely place and good partners are rare mainly because interpersonal human understanding is really bad, frightfully so. If you do FIML, everything will be better for you. ABN

1. See A theory of FIML ↩︎

I've been reading #Horkheimer as my vacation book (real light reading, huh?), and it really strikes me of that all the problems that #fascism exploited, many of them are related to why #AI sucks.

In Horkheimer's view, the Nazis were able come along because society had already abandoned absolute truths and turned into #pragmatism & #positivism defining #philosophy instead of the other way around.

Today, we're basing our future on #probabilisticcomputing .. 🙈

#bookstodon

Beyond the War Budget? Trump Is Bringing Back the American System

https://www.youtube.com/watch?v=Ljx7FT5eDr4

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In this effort, USA has the ironic advantage of coming from behind in many areas. This both forces and allows us to build with the newest and best. ABN

Theme One Program • Motivation 1
• https://inquiryintoinquiry.com/2024/06/03/theme-one-program-motivation-1-b/

The main idea behind the Theme One program is the efficient use of graph‑theoretic data structures for the tasks of “learning” and “reasoning”.

I am thinking of “learning” in the sense of learning about an environment, in essence, gaining information about the nature of an environment and being able to apply the information acquired to a specific purpose.

Under the heading of “reasoning” I am simply lumping together all the ordinary sorts of practical activities which would probably occur to most people under that name.

There is a natural relation between the tasks. Learning the character of an environment leads to the recognition of laws which govern the environment and making full use of that recognition requires the ability to reason logically about those laws in abstract terms.

Resources —

Theme One Program • Overview
• https://oeis.org/wiki/Theme_One_Program_%E2%80%A2_Overview

Theme One Program • Exposition
• https://oeis.org/wiki/Theme_One_Program_%E2%80%A2_Exposition

Theme One Program • User Guide
• https://www.academia.edu/5211369/Theme_One_Program_User_Guide

Survey of Theme One Program
• https://inquiryintoinquiry.com/2024/02/26/survey-of-theme-one-program-6/

#ThemeOneProgram #Learning #Reasoning
#Logic #LogicalGraphs #FormalLanguages
#Algorithm #DataStructure #GraphTheory
#Peirce #PragmaticSemioticInformation
#Empiricism #Rationalism #Pragmatism

#RaymondTallis - What is #Truth?

https://www.youtube.com/watch?v=WLhTG7etzlc&ab_channel=CloserToTruth

#Philosophy #Logic #PhilosophyOfLogic #Assertions #States #StatesOfAffairs #Belief #JTB #JustifiedTrueBelief #Pragmatism #Minimalism #Predicate #Predicates #Relativism #Noumenal #Ontological #Kant #Kantian #DingAnSich #CloserToTruth #RobertKuhn