scholarly journals Sinking, fast and slow: Bifurcating beta in financial and behavioral space

2015 ◽  
Vol 11 (2015) ◽  
pp. 124-201 ◽  
Author(s):  
James Ming Chen
Keyword(s):  
Behavioral Space

Problems in Representing Behavioral Space-Time

1981 ◽  
pp. 121-129
Author(s):  
D. Quiatt ◽  
J. Everett ◽  
S. Luerssen ◽  
G. Murdock
Keyword(s):  
Space Time ◽  
Behavioral Space

scholarly journals Mapping the structure of drosophilid behavior

2014 ◽  
Author(s):  
Gordon J Berman ◽  
Daniel M Choi ◽  
William Bialek ◽  
Joshua W Shaevitz
Keyword(s):  
Related Species ◽  
Behavioral Science ◽  
Underlying Structure ◽  
Behavioral Differences ◽  
Closely Related Species ◽  
Movement Data ◽  
Stereotyped Behaviors ◽  
Small Set ◽  
Postural Movement ◽  
Behavioral Space

Most animals possess the ability to actuate a vast diversity of movements, ostensibly constrained only by morphology and physics. In practice, however, a frequent assumption in behavioral science is that most of an animal’s activities can be described in terms of a small set of stereotyped motifs. Here we introduce a method for mapping the behavioral space of organisms, relying only upon the underlying structure of postural movement data to organize and classify behaviors. We find that six different drosophilid species each perform a mix of non-stereotyped actions and over one hundred hierarchically-organized, stereotyped behaviors. Moreover, we use this approach to compare these species’ behavioral spaces, systematically identifying subtle behavioral differences between closely-related species.

scholarly journals The Behavioral Space of Zebrafish Locomotion and Its Neural Network Analog

PLoS ONE ◽  
2015 ◽  
Vol 10 (7) ◽  
pp. e0128668 ◽  
Author(s):  
Kiran Girdhar ◽  
Martin Gruebele ◽  
Yann R. Chemla
Keyword(s):  
Neural Network ◽  
Behavioral Space

scholarly journals Speciation in Behavioral Space for Evolutionary Robotics

2011 ◽  
Vol 64 (3-4) ◽  
pp. 323-351 ◽  
Author(s):  
Leonardo Trujillo ◽  
Gustavo Olague ◽  
Evelyne Lutton ◽  
Francisco Fernández de Vega ◽  
León Dozal ◽  
...  
Keyword(s):  
Evolutionary Robotics ◽  
Behavioral Space

Maximization theory in behavioral psychology

1981 ◽  
Vol 4 (3) ◽  
pp. 371-388 ◽  
Author(s):  
Howard Rachlin ◽  
Ray Battalio ◽  
John Kagel ◽  
Leonard Green
Keyword(s):  
Economic Behavior ◽  
Self Control ◽  
Reinforcement Theory ◽  
State Behavior ◽  
Assigned Value ◽  
Behavioral Psychology ◽  
The One ◽  
And Behavior ◽  
Insight Into ◽  
Behavioral Space

AbstractMaximization theory, which is borrowed from economics, provides techniques for predicing the behavior of animals - including humans. A theoretical behavioral space is constructed in which each point represents a given combination of various behavioral alternatives. With two alternatives - behavior A and behavior B - each point within the space represents a certain amount of time spent performing behavior A and a certain amount of time spent performing behavior B. A particular environmental situation can be described as a constraint on available points (a circumscribed area) within the space. Maximization theory assumes that animals always choose the available point with the highest numerical value. The task of maximization theory is to assign to points in the behavioral space values that remain constant across various environmental situations; as those situations change, the point actually chosen is always the one with the highest assigned value.Maximization theory is an alternative to reinforcement theory as a description of steady-state behavior. Situations to which reinforcement theory has been directly applied (such as reinforcement of rats pressing levers and pigeons pecking keys in Skinner boxes) and situations to which reinforcement theory has occasionally been extended (such as human economic behavior and human self-control) can be described by maximization theory. This approach views behavior as a quantitative outcome of the interaction of the putative instrumental response, the reinforcer, and the other activities available in the situation. It provides new insight into these situations and, because it takes context into account, has greater predictive power than reinforcement theory.

scholarly journals ON THE FAMILIARITY OF BEHAVIORAL SPACE

1969 ◽  
Vol 1 (1) ◽  
pp. 39-47,63
Author(s):  
Tomoaki Miyazaki
Keyword(s):  
Behavioral Space
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