Intrinsic Motivation - Psychology

Published: 7 May 2024| Version 1 | DOI: 10.17632/f99fb68rz4.1
Sunil Maria Benedict


Introduction: The pursuit of understanding intrinsic motivation has long been a subject of interest in psychology and education. Mathematical models offer a structured approach to studying this phenomenon, providing insights into how intrinsic motivation evolves over time. In this essay, we will analyze the potential graphs generated by the code provided, which illustrates six mathematical models of intrinsic motivation. Linear Model: The linear model portrays intrinsic motivation as a linear function of time. It suggests a constant rate of change in motivation, indicating a steady increase or decrease over time. This model is simplistic but provides a straightforward representation of how intrinsic motivation may change linearly with time. Exponential Model: The exponential model depicts intrinsic motivation as an exponential growth or decay function. This model suggests that motivation experiences rapid growth or decline initially, followed by a gradual leveling off as time progresses. It implies that intrinsic motivation may exhibit rapid changes in the early stages, tapering off to reach a stable state later. Power Law Model: The power law model describes intrinsic motivation as a power function of time. This model suggests that motivation follows a nonlinear trajectory, with the rate of change influenced by a power term. It implies that intrinsic motivation may exhibit nonlinear growth or decay patterns, characterized by periods of rapid change followed by slower transitions. Logistic Growth Model: The logistic growth model presents intrinsic motivation as a sigmoidal function of time. This model suggests that motivation initially experiences rapid growth, followed by a period of gradual saturation as it approaches an upper limit. It implies that intrinsic motivation may exhibit an "S-shaped" curve, with an initial phase of acceleration followed by stabilization. Sigmoidal Model: The sigmoidal model also depicts intrinsic motivation as a sigmoidal function of time, similar to the logistic growth model. However, it introduces an additional parameter that influences the baseline level of motivation. This model suggests that intrinsic motivation may vary based on an individual's inherent characteristics, such as personality traits or external influences. Piecewise Linear Model: The piecewise linear model divides the time interval into two segments, each characterized by a linear function of time. This model suggests that intrinsic motivation may undergo distinct phases, with different rates of change in each phase. It implies that motivation may exhibit abrupt transitions or shifts in direction over time.


Steps to reproduce

import numpy as np import matplotlib.pyplot as plt # Define time range t = np.linspace(0, 10, 100) # Linear Model Parameters a_linear = 0.5 b_linear = 1 linear_model = a_linear * t + b_linear # Exponential Model Parameters a_exp = 1 b_exp = 0.1 exp_model = a_exp * np.exp(b_exp * t) # Power Law Model Parameters a_power = 1 b_power = 0.5 power_model = a_power * np.power(t, b_power) # Logistic Growth Model Parameters L_logistic = 1 k_logistic = 0.5 t0_logistic = 5 logistic_model = L_logistic / (1 + np.exp(-k_logistic * (t - t0_logistic))) # Sigmoidal Model Parameters L_sigmoidal = 1 k_sigmoidal = 0.5 t0_sigmoidal = 5 c_sigmoidal = 0.5 sigmoidal_model = L_sigmoidal / (1 + np.exp(-k_sigmoidal * (t - t0_sigmoidal))) + c_sigmoidal # Piecewise Linear Model Parameters t0_piecewise = 5 m1_piecewise = 0.2 b1_piecewise = 0.5 m2_piecewise = 0.1 b2_piecewise = 2 piecewise_model = np.piecewise(t, [t < t0_piecewise, t >= t0_piecewise], [lambda t: m1_piecewise * t + b1_piecewise, lambda t: m2_piecewise * t + b2_piecewise]) # Plotting plt.figure(figsize=(12, 8)) # Linear Model Plot plt.subplot(2, 3, 1) plt.plot(t, linear_model, label='Linear Model') plt.title('Linear Model') plt.xlabel('Time') plt.ylabel('Intrinsic Motivation') # Exponential Model Plot plt.subplot(2, 3, 2) plt.plot(t, exp_model, label='Exponential Model') plt.title('Exponential Model') plt.xlabel('Time') plt.ylabel('Intrinsic Motivation') # Power Law Model Plot plt.subplot(2, 3, 3) plt.plot(t, power_model, label='Power Law Model') plt.title('Power Law Model') plt.xlabel('Time') plt.ylabel('Intrinsic Motivation') # Logistic Growth Model Plot plt.subplot(2, 3, 4) plt.plot(t, logistic_model, label='Logistic Growth Model') plt.title('Logistic Growth Model') plt.xlabel('Time') plt.ylabel('Intrinsic Motivation') # Sigmoidal Model Plot plt.subplot(2, 3, 5) plt.plot(t, sigmoidal_model, label='Sigmoidal Model') plt.title('Sigmoidal Model') plt.xlabel('Time') plt.ylabel('Intrinsic Motivation') # Piecewise Linear Model Plot plt.subplot(2, 3, 6) plt.plot(t, piecewise_model, label='Piecewise Linear Model') plt.title('Piecewise Linear Model') plt.xlabel('Time') plt.ylabel('Intrinsic Motivation') plt.tight_layout()


European International University


Mathematical Modeling, Motivation, Student Motivation, Theory of Motivation