QUASI-POTENTIAL FIELD: THE SIMULATION OF MOTIVATION IN COMPLEX
PHYSICAL STRUCTURES The Generation of quasi-potential field with high adaptability for complex structure.
Abstract
As a basic element of organisms, motivation shapes the action pattern. There are abundant biological and
social systems that originate from motivations, which have attracted widespread attention. To pave the
foundation of relevant researches, this paper presents an effective model to simulate the spatial distribution
of motivation within complex environments, which is named as the quasi-potential field. Our modelling
process includes three steps. First, we suggest an original algorithm for structure reduction, which can
preserve all the topological properties of a given environment. Second, we define the quasi-potential value
on the reduced structure, which works as the root of the whole field. Third, we extend the field from the root
to all available space. Thus, the motivation distribution in the real environment can be finally generated. In
our paper, several simulation experiments are carried out to verify the effectiveness of our model, whose
results are proved to be ideal.
Keywords: Motivation, Quasi-potential Field, Motivated System.
Introduction
Motivation is the object-dependent tendency of (1) agents, such as human (Atkinson 1964) and other kinds
of selected intelligence (Sendhoff, Körner, Sporns, Ritter, and Doya 2009), and (2) unconscious creatures,
such as neurons (Lidov, Byers, Watkins, and Kunkel 1990) and leukocytes (Rambeaud, Almeida, Pighetti,
and Oliver 2003). For subjects, it describes the trend to approach specific targets during the interaction
with environments. As an ubiquitous element in psychology, biophysics and socio-physics, motivation has
become the basis of many theories.
On a macro scale, motivation is one of the core parameters for describing the dynamics of biological communities.
Previous researches mainly focused on modelling the dynamics induced by specific motivations,
which indicated the explicit effects of them. For instance, with the motivation of avoiding predators, preyflocks
show complex variation in the compression and expansion processing of the prey-flock size, which
could be simulated by the molecular dynamics in a two-dimensional continuum model (Lee, Pak, and Chon
2006). Another example showed that using a phenomenological model, it could be found that there exists
a highly concerted, collective motion of birds when they want to land (Daruka 2008). However, while
analysing the mechanisms resulted from motivations clearly, existing studies didn’t take the strict, quantitative
definitions of motivations into consideration.
On an individual scale, motivation shapes the survival, reproduction and decision-making process. In cognition
science, the role of internal motivation in the decision-making process has been explored at both the
behavioural level (Kuhl 1986, Finn 2002) and the neutral level (Satoh, Nakai, Sato, and Kimura 2003). It
was suggested that lateral intra-parietal neurons are sensitive to the motivational salience of cues, which
paves the basis for motivation formation and maintain of monkeys (Leathers and Olson 2012). As for human,
a lot of researches found that attention and motivations have been confounded and eventually results in
distributed signals relevant with them in human brains (Maunsell 2004), which, in return, also indicated that
the human-level intelligence is equipped with the ability to response to targets in real time. The researches
in this field went deeply into the biological foundation of motivation, but the labile and complex pattern
behind the motivation limited the mathematical modelling.
On the micro scale, motivation defines the formation, migration and patterning of cells and other soft matters.
Regulated by different chemicals, cells or molecules are encoded with different functions. Those chemicals
works as motivations and shape the pattern of how those cells or molecules form the whole system. Based on
neuroscience, it has been confirmed that with different concentrations of signal molecules, there will be patterning
in the dorsal ectoderm of chordates, which finally determines all neural tissues (Kandel, Schwartz,
Jessell, of Biochemistry, Jessell, Siegelbaum, and Hudspeth 2000). In detail, the BPMs, a family of motivations,
have prominent roles in the assignment of neuronal identity within the central nervous system (Lee,
Mendelsohn, and Jessell 1998). To simulate those properties, a growing number of computational models
have been established from the perspective of network dynamics versus topology (Massobrio and Martinoia
2008), statistical fluctuation (Ishii, Ishikawa, Fujita, and Nakazawa 2004) and so on. Using them, we can
analysis the dynamics induced by motivations phenomenologically.
To sum up, we can draw a conclusion that on different scales, previous researches have explored the mechanisms behind motivations and the dynamical phenomenons shaped by them. However, it still reminds controversial how to simulate motivations directly. To address this valuable question, we start a new research.
The main ideas, which are also the main contributions, of our research are as following:
- Simulate motivations based on their spatial distributions within physical spaces. The quantitative variation of the objection-dependent tendency can help to define the corresponding field of it, which is called quasi-potential field;
- Treat the physical structure of environment as a basic element of the definition of quasi-potential field, which makes it possible to simulate motivations in real structures, not just in the ideal plane;
- Analyse the interaction between different motivations by doing weighting summation of different quasi-potential fields, which paves the basis of multi-motivations system simulation