scholarly journals Real‐world multimodal lifelog dataset for human behavior study

ETRI Journal ◽  
2021 ◽  
Author(s):  
Seungeun Chung ◽  
Chi Yoon Jeong ◽  
Jeong Mook Lim ◽  
Jiyoun Lim ◽  
Kyoung Ju Noh ◽  
...  
Keyword(s):  
Human Behavior ◽  
Real World ◽  
Behavior Study

More obstacles on the road to unification

2007 ◽  
Vol 30 (1) ◽  
pp. 41-41 ◽  
Author(s):  
Eric Alden Smith
Keyword(s):  
Decision Making ◽  
Human Behavior ◽  
Real World ◽  
Institutional Constraints ◽  
Historical Contingency ◽  
Behavioral Sciences ◽  
The Road ◽  
On The Road

The synthesis proposed by Gintis is valuable but insufficient. Greater consideration must be given to epistemological diversity within the behavioral sciences, to incorporating historical contingency and institutional constraints on decision-making, and to vigorously testing deductive models of human behavior in real-world contexts.

Just My Imagination: Beauty premium and the evolved mental model

2017 ◽  
Vol 40 ◽  
Author(s):  
Ryo Oda
Keyword(s):  
Social Environment ◽  
Human Behavior ◽  
Real World ◽  
Mental Model ◽  
Human Mind ◽  
The Real ◽  
Beauty Premium ◽  
Human Capacity

AbstractImagination, an important feature of the human mind, may be at the root of the beauty premium. The evolved human capacity for simulating the real world, developed as an adaptation to a complex social environment, may offer the key to understanding this and many other aspects of human behavior.

Perception of Approaching and Retreating Floor-Projected Shapes in a Large, Immersive, Multimedia Learning Environment

2009 ◽  
Vol 108 (2) ◽  
pp. 623-630 ◽  
Author(s):  
Igor Dolgov ◽  
David A. Birchfield ◽  
Michael K. McBeath ◽  
Harvey Thornburg ◽  
Christopher G. Todd
Keyword(s):  
Learning Environment ◽  
Human Computer Interaction ◽  
Human Behavior ◽  
Real World ◽  
Mixed Reality ◽  
Multimedia Learning ◽  
Psychological Phenomena ◽  
Systematic Exploration ◽  
Multimedia Environment ◽  
Multimedia Learning Environment

Perception of floor-projected moving geometric shapes was examined in the context of the Situated Multimedia Arts Learning Laboratory (SMALLab), an immersive, mixed-reality learning environment. As predicted, the projected destinations of shapes which retreated in depth (proximal origin) were judged significantly less accurately than those that approached (distal origin). Participants maintained similar magnitudes of error throughout the session, and no effect of practice was observed. Shape perception in an immersive multimedia environment is comparable to the real world. One may conclude that systematic exploration of basic psychological phenomena in novel mediated environments is integral to an understanding of human behavior in novel human-computer interaction architectures.

scholarly journals Launching a Revolution, based on Islamic Foundations

2019 ◽  
Vol 32 (2) ◽  
pp. 77-88
Author(s):  
Asad Zaman Asad Zaman
Keyword(s):  
Financial Crisis ◽  
Social Responsibility ◽  
Human Behavior ◽  
Real World ◽  
Global Financial Crisis ◽  
World History ◽  
Homo Economicus ◽  
Wealth Maximization ◽  
The Rich ◽  
The Global Financial Crisis

Conventional economics is deeply and fundamentally flawed, beyond the possibility of reform and repair. Its failings in the real world became obvious to all following the global financial crisis. The root of the problem is the theory of human behavior represented by “homo economicus”. The idea that short-sighted greed is “rational” is sheer folly. The theory is maintained in face of overwhelming empirical evidence to the contrary only because it serves the ideological interests of the rich and powerful capitalist classes. The philosophy of wealth maximization has led to the destruction of families, societies, economies, environment, fauna, and flora, as all are ruthlessly exploited for the creation and maximization of profits. Islamic teachings created a revolution in world history by promoting a society based on cooperation, generosity, and social responsibility. These ideas, completely missing from modern economics, have the same revolutionary potential today. The challenge for the Muslims today is to demonstrate this potential by translating these ideas into reality.

Measuring Trial-wise Choice Difficulty in Multi-feature Reinforcement Learning

2019 ◽  
Author(s):  
Peter Hitchcock ◽  
Yael Niv ◽  
Angela Radulescu ◽  
Nina Jill Rothstein ◽  
Chris R. Sims
Keyword(s):  
Reinforcement Learning ◽  
Individual Differences ◽  
Standard Deviation ◽  
Reaction Time ◽  
Human Behavior ◽  
Significant Influence ◽  
Real World ◽  
Multiple Features ◽  
Wise Choice ◽  
The Difference

Real world reinforcement learning (RL) requires learning about stimuli composed of multiple features, only some of which are relevant to reinforcement. We investigated RL in a multi-feature task known as the Dimensions Task. Past work developed a computational model of this task, where the expected value of a stimulus comprises weights assigned to the stimulus’s features, hence the weights estimate the importance of each feature. We studies these weights and how they relate to human behavior. We found a sparse subset of features accrued much weight, and just 2 of 9 features exerted a significant influence on reaction time (RT), suggesting this pair of features mostly influences choice. These findings clarify that the Dimensions Task requires selectively attending to just a sparse subset of features while ignoring numerous irrelevant features, emphasizing its distinction from other recent multi-feature RL tasks that either require attending to all features or learning to treat feature conjunctions as objects. We next examined whether we could use the feature weights to develop a trial-wise marker of choice difficulty that related to individual differences. We found that high (vs. low) performing participants were better able to calibrate their responses based on variation in the standard deviation (SD) of the 2 features influencing RT. This suggests better-performing participants may be more responsive to the difference between the features. We discuss how this measure of trial-wise choice difficulty could be applied in experimental and translational research.

scholarly journals Survey data and human computation for improved flu tracking

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Stefan Wojcik ◽  
Avleen S. Bijral ◽  
Richard Johnston ◽  
Juan M. Lavista Ferres ◽  
Gary King ◽  
...  
Keyword(s):  
Real Time ◽  
Survey Data ◽  
Human Behavior ◽  
Real World ◽  
Data Streams ◽  
Behavioral Model ◽  
Human Computation ◽  
Digital Data ◽  
Search Data ◽  
Tracking Model

AbstractWhile digital trace data from sources like search engines hold enormous potential for tracking and understanding human behavior, these streams of data lack information about the actual experiences of those individuals generating the data. Moreover, most current methods ignore or under-utilize human processing capabilities that allow humans to solve problems not yet solvable by computers (human computation). We demonstrate how behavioral research, linking digital and real-world behavior, along with human computation, can be utilized to improve the performance of studies using digital data streams. This study looks at the use of search data to track prevalence of Influenza-Like Illness (ILI). We build a behavioral model of flu search based on survey data linked to users’ online browsing data. We then utilize human computation for classifying search strings. Leveraging these resources, we construct a tracking model of ILI prevalence that outperforms strong historical benchmarks using only a limited stream of search data and lends itself to tracking ILI in smaller geographic units. While this paper only addresses searches related to ILI, the method we describe has potential for tracking a broad set of phenomena in near real-time.

scholarly journals ECONOMICS AND CULTURE

2019 ◽  
Vol 22 (1) ◽  
pp. 282-302 ◽  
Author(s):  
Iwan Jaya Aziz
Keyword(s):  
Social Learning ◽  
Transaction Costs ◽  
Human Behavior ◽  
Economic Performance ◽  
Real World ◽  
Critical Role ◽  
Economic Prosperity ◽  
Central Concept ◽  
Mainstream Economics

The notion that something that cannot be measured does not exist seems to apply to the absence of consideration of culture in economics, where the role of institutions is at the center of the link between the two. Yet, economic prosperity, crisis, and deprivation result from human behavior, reflecting the outcome of social learning—a central concept of culture. Institutions and culture interact and evolve in complementary ways. Each can affect the process of exchange and transaction costs, which in turn determine economic performance. Although more work has been done to better understand the interrelation between economics and culture, most falls on deaf ears among mainstream economists, despite the fact that real-world cases show the critical role of this interrelation. This paper demonstrates a deficiency of mainstream economics in its disregard of the role of culture and institutions.

scholarly journals Geometry of neural computation unifies working memory and planning

2021 ◽  
Author(s):  
Daniel B. Ehrlich ◽  
John D. Murray
Keyword(s):  
Neural Networks ◽  
Decision Making ◽  
Working Memory ◽  
Prefrontal Cortex ◽  
Human Behavior ◽  
Real World ◽  
Recurrent Neural Networks ◽  
Neural Computation ◽  
Neural Data ◽  
The Brain

Real-world tasks require coordination of working memory, decision making, and planning, yet these cognitive functions have disproportionately been studied as independent modular processes in the brain. Here we propose that contingency representations, defined as mappings for how future behaviors depend on upcoming events, can unify working memory and planning computations. We designed a task capable of disambiguating distinct types of representations. Our experiments revealed that human behavior is consistent with contingency representations, and not with traditional sensory models of working memory. In task-optimized recurrent neural networks we investigated possible circuit mechanisms for contingency representations and found that these representations can explain neurophysiological observations from prefrontal cortex during working memory tasks. Finally, we generated falsifiable predictions for neural data to identify contingency representations in neural data and to dissociate different models of working memory. Our findings characterize a neural representational strategy that can unify working memory, planning, and context-dependent decision making.

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