An Analysis of Operant Conditioning and its Relationship with Video Game Addiction

A report published by the Entertainment Software Association revealed that in 2015, 155 million Americans play video games with an average of two gamers in each game-playing household (Entertainment Software Association, “Essential Facts about the Computer and Video Game Industry”). With this massive popularity that has sprung alongside video games, the question must be asked: how are video games affecting today\u27s people? With the current way some video games are structured, the video game rewards players for achieving certain accomplishments. For example, competitive video games reward players who achieve victories by giving them a higher ranking or other games display the player\u27s score so that other players can see their score. With this in mind, some video game players may place more emphasis on their gaming achievements rather than their happiness or success in their own real lives. Once this emphasis has been placed, video game players have a chance to become addicted to their respective game; however, a distinction must be set between video game addiction and operant conditioning. Opereant conditioning is a video game design that many of today\u27s video games utilize. The use of operant conditioning towards a gamer can be one of the factors contributing towards video game addiction; operant conditioning is the strategy while video game addiction can be the byproduct of operant conditioning

Cooperation in the iterated prisoner's dilemma is learned by operant conditioning mechanisms

The prisoner's dilemma (PD) is the leading metaphor for the evolution of cooperative behavior in populations of selfish agents. Although cooperation in the iterated prisoner's dilemma (IPD) has been studied for over twenty years, most of this research has been focused on strategies that involve nonlearned behavior. Another approach is to suppose that players' selection of the preferred reply might he enforced in the same way as feeding animals track the best way to feed in changing nonstationary environments. Learning mechanisms such as operant conditioning enable animals to acquire relevant characteristics of their environment in order to get reinforcements and to avoid punishments. In this study, the role of operant conditioning in the learning of cooperation was evaluated in the PD. We found that operant mechanisms allow the learning of IPD play against other strategies. When random moves are allowed in the game, the operant learning model showed low sensitivity. On the basis of this evidence, it is suggested that operant learning might be involved in reciprocal altruism.Fil: Gutnisky, D. A.. Universidad de Buenos Aires. Facultad de Ingenieria. Instituto de Ingeniería Biomédica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Biología y Medicina Experimental. Fundación de Instituto de Biología y Medicina Experimental. Instituto de Biología y Medicina Experimental; ArgentinaFil: Zanutto, Bonifacio Silvano. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Biología y Medicina Experimental. Fundación de Instituto de Biología y Medicina Experimental. Instituto de Biología y Medicina Experimental; Argentina. Universidad de Buenos Aires. Facultad de Ingenieria. Instituto de Ingeniería Biomédica; Argentin

Operant evaluative conditioning

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Comparisons of Animal “Smarts” Using the First Four Stages of the Model of Hierarchical Complexity

The Model of Hierarchical Complexity is a behavioral model of development and evolution of the complexity of behavior. It is based on task analysis. Tasks are ordered in terms of their hierarchical complexity, which is an ordinal scale that measures difficulty. The hierarchical difficulty of tasks is categorized as the order of hierarchical complexity. Successful performance on a task is called the behavioral stage. This model can be applied to non-human animals, and humans. Using data from some of the simplest animals and also somewhat more complex ones, this analysis describes the four lowest behavioral stages and illustrate them using the behaviors of a range of simple organisms. For example, Stage 1 tasks, and performance on them, are addressed with automatic unconditioned responses. Behavior at this Stage includes sensing, tropisms, habituation and, other automatic behaviors. Single cell organisms operate at this Stage. Stage 2 tasks include these earlier behaviors, but also include respondent conditioning but not operant conditioning. Animals such as some simple invertebrates have shown respondent conditioning, but not operant conditioning. Stage 3 tasks coordinate three instances of these earlier tasks to make possible operant conditioning. These stage 3 performances are similar to those of some invertebrates and also insects. Stage 4 tasks organisms coordinate 2 or more circular sensory-motor task actions into a superordinate “concept”. This explanation of the early stages of the Model of Hierarchical Complexity may help future research in animal behavior, and comparative psychology.

Mushroom-bodies mediate hierarchical interactions between fact- and skill-learning in _Drosophila_

Different brain circuits mediate the acquisition of skills and habits (via operant/instrumental learning) and the acquisition of facts (via classical/Pavlovian learning). Realistic learning situations always comprise interactions of skill- and fact-learning components (composite learning). So far, these interactions have escaped thorough scrutiny. Fixed flying _Drosophila melanogaster_ at the torque meter provide one of the very few systems where the relationship of operant and classical predictors in composite learning can be studied with sufficient rigor. Experiments with wildtype, mutant and transgenic flies show that there is an interaction between predictive stimuli (classical component) and goal-directed actions (operant component) which makes composite conditioning more effective than the operant and classical components alone. _Rutabaga_ (_rut_) mutants are impaired in learning about the (classical) stimuli, but show improved (operant) behavior learning. This is the first evidence that operant and classical conditioning differ not only at the circuit, but also at the molecular level. The interaction between operant and classical components is reciprocal and hierarchical, such that an impaired classical component (in _rut_ flies) suppresses retrieval and an intact classical component suppresses acquisition of the operant component. Experiments with transgenic flies demonstrate that this suppression of operant acquisition is mediated by the mushroom-bodies and serves to ensure that the classical memories can be generalized for access by other behaviors. Extended training can overcome this suppression and transforms goal-directed actions into habitual responses. In conclusion, composite conditioning consists of two components with reciprocal, hierarchical interactions. Acquisition of the _rut_-dependent classical component suppresses acquisition of the _rut_-independent operant component via the mushroom-bodies. The operant component facilitates acquisition of the classical component via unknown, non-mushroom-body pathways. This interaction leads to efficient learning, enables generalization and prevents premature habit-formation. Habit formation after extended training reveals the gate-keeping role of the mushroom-bodies, allowing only well-rehearsed behaviors to consolidate into habits

Quantum Probability and Operant Conditioning: Behavioral Uncertainty in Reinforcement Learning

An implicit assumption in the study of operant conditioning and reinforcement learning is that behavior is stochastic, in that it depends on the probability that an outcome follows a response and on how the presence or absence of the output affects the frequency of the response. In this paper we argue that classical probability is not the right tool to represent uncertainty operant conditioning and propose an interpretation of behavioral states in terms of quantum probability instead

Volitional enhancement of firing synchrony and oscillation by neuronal operant conditioning: interaction with neurorehabilitation and brain-machine interface.

In this review, we focus on neuronal operant conditioning in which increments in neuronal activities are directly rewarded without behaviors. We discuss the potential of this approach to elucidate neuronal plasticity for enhancing specific brain functions and its interaction with the progress in neurorehabilitation and brain-machine interfaces. The key to-be-conditioned activities that this paper emphasizes are synchronous and oscillatory firings of multiple neurons that reflect activities of cell assemblies. First, we introduce certain well-known studies on neuronal operant conditioning in which conditioned enhancements of neuronal firing were reported in animals and humans. These studies demonstrated the feasibility of volitional control over neuronal activity. Second, we refer to the recent studies on operant conditioning of synchrony and oscillation of neuronal activities. In particular, we introduce a recent study showing volitional enhancement of oscillatory activity in monkey motor cortex and our study showing selective enhancement of firing synchrony of neighboring neurons in rat hippocampus. Third, we discuss the reasons for emphasizing firing synchrony and oscillation in neuronal operant conditioning, the main reason being that they reflect the activities of cell assemblies, which have been suggested to be basic neuronal codes representing information in the brain. Finally, we discuss the interaction of neuronal operant conditioning with neurorehabilitation and brain-machine interface (BMI). We argue that synchrony and oscillation of neuronal firing are the key activities required for developing both reliable neurorehabilitation and high-performance BMI. Further, we conclude that research of neuronal operant conditioning, neurorehabilitation, BMI, and system neuroscience will produce findings applicable to these interrelated fields, and neuronal synchrony and oscillation can be a common important bridge among all of them

Dorsal-CA1 hippocampal neuronal ensembles encode nicotine-reward contextual associations

Natural and drug rewards increase the motivational valence of stimuli in the environment that, through Pavlovian learning mechanisms, become conditioned stimuli that directly motivate behavior in the absence of the original unconditioned stimulus. While the hippocampus has received extensive attention for its role in learning and memory processes, less is known regarding its role in drug-reward associations. We used in vivo Ca2+ imaging in freely moving mice during the formation of nicotine preference behavior to examine the role of the dorsal-CA1 region of the hippocampus in encoding contextual reward-seeking behavior. We show the development of specific neuronal ensembles whose activity encodes nicotine-reward contextual memories and that are necessary for the expression of place preference. Our findings increase our understanding of CA1 hippocampal function in general and as it relates to reward processing by identifying a critical role for CA1 neuronal ensembles in nicotine place preference