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Related Concept Videos

Observational Learning01:12

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Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
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Learned Behavior I01:19

Learned Behavior I

Learned Behavior ILearned behaviors are actions that animals develop through experience, observation, or practice rather than being born with them. For example, a dog learning to roll over or a baby bird figuring out how to crack open a seed are both learned behaviors. Unlike instincts, learned behaviors aren’t something you're born knowing. You pick them up through life and experience.Animals, including you, learn in all sorts of ways, such as copying others, solving problems, or remembering...
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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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Learned Behavior II01:20

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Learned Behavior IITeaching a dog to sit or learning how to ride a bike are examples of learned behaviors—actions acquired through experience and practice. These behaviors are not instinctive; instead, they develop over time as individuals interact with their environment. While some behaviors are automatic (like blinking), others are learned over time by watching, practicing, or being trained.Animals learn from their parents, their environment, and sometimes from trial and error. Whether a...
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E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
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Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Proselfs depend more on model-based than model-free learning in a non-social probabilistic state-transition task.

Mineki Oguchi1, Yang Li1,2, Yoshie Matsumoto1,3

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Individual differences in social preferences, whether proself or prosocial, are linked to distinct learning systems. Proself individuals rely more on model-based learning, while prosocial individuals utilize model-free learning.

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Area of Science:

  • Cognitive Neuroscience
  • Social Psychology
  • Behavioral Economics

Background:

  • Social decision-making involves balancing self-interest (proself) and concern for others (prosocial).
  • Individual differences in social value orientation are not fully explained.
  • Dual-process theories propose domain-general learning systems (model-free and model-based) underlie social decision-making.

Purpose of the Study:

  • To test the hypothesis that social preferences stem from the dominance of either model-free or model-based learning systems.
  • To investigate the relationship between individual learning system dominance and social value orientation.

Main Methods:

  • A non-social state transition task was employed to assess the balance between model-free and model-based learning.
  • Participants' social value orientations were measured.
  • Analyses of reward amount and reaction time were conducted.

Main Results:

  • Proself individuals showed a greater dependence on model-based learning.
  • Prosocial individuals demonstrated a stronger reliance on model-free learning.
  • Proself individuals learned the task structure earlier than prosocial individuals.

Conclusions:

  • Findings support the "learning hypothesis" explaining differences in social preferences.
  • Dominant learning systems (model-based vs. model-free) are associated with distinct social value orientations.
  • This research offers insights into the mechanisms underlying prosocial behavior.