Using Matching “Smarts” and Interest to Successfully Address Depression Caused by Existential Crisis

This chapter outlines the background, nature, and explanations of existential crises. An unresolved existential crisis commonly causes depression. Crises occur in periods throughout the life cycle. They usually involve careers, relationships, or identity. The resolution often requires a development of a new stage of intellectual functioning, through which people can reflect on their interests and stage. The Existential Crisis Assessment measures severity of an existential crisis. A factor analysis showed the most important items in a person’s existential crisis. My life, life in the universe, and relationships were the most important factors determining the severity of a person’s existential crisis. The first solution is to match a person to a career. Another solution is to match one person to another. Three scales are used to match people to careers and partners: (1) decision-making measures how well a person addresses tasks of increasing difficulty; (2) perspective-taking predicts how well a person understands behavior of self and others; (3) core complexity interest scale identifies the reinforcement value of engaging. A further solution is that of cognitive behavioral therapy that can be used to both treat depression and offer training on social perspective-taking, a key ingredient to resolving one’s crisis

Measuring an Approximate g in Animals and People

A science of comparative cognition ultimately needs a measurement theory, allowing the comparison of performance in different species of animals, including humans. Current theories are often based on human performance only, and may not easily apply to other species. It is proposed that such a theory include a number of indexes: an index of the stage of development based on the order of hierarchical complexity of the tasks the species can perform; an index of horizontal complexity; and measures of g (for general intelligence) and related indexes. This article is an early-stage proposal of ways to conceive of g in animals and people. It responds to Geary’s argument that domain-general mechanisms are essential for evolutionary psychologists. Existing research is used to enumerate domains, such as problem solving behavior in pursuit of food, or behaviors in pursuit of mates and/or reproduction, and itemize identifiable human social domains. How to construct g, across domains and within domains, is described

The stage-value model: Implications for the changing standards of care.

The standard of care is a legal and professional notion against which doctors and other medical personnel are held liable. The standard of care changes as new scientific findings and technological innovations within medicine, pharmacology, nursing and public health are developed and adopted. This study consists of four parts. Part 1 describes the problem and gives concrete examples of its occurrence. The second part discusses the application of the Model of Hierarchical Complexity on the field, giving examples of how standards of care are understood at different behavioral developmental stage. It presents the solution to the problem of standards of care at a Paradigmatic Stage 14. The solution at this stage is a deliberative, communicative process based around why certain norms should or should not apply in each specific case, by the use of "meta-norms". Part 3 proposes a Cross-Paradigmatic Stage 15 view of how the problem of changing standards of care can be solved. The proposed solution is to found the legal procedure in each case on well-established behavioral laws. We maintain that such a behavioristic, scientifically based justice would be much more proficient at effecting restorative legal interventions that create desired behaviors

Adaptive Neural Networks Accounted for by Five Instances of “Respondent-Based” Conditioning

Neural Networks may be made much faster and more efficient by reducing the amount of memory and computation used. In this paper, a new type of neural network called an Adaptive Neural Network is introduced. The proposed neural network is comprised of five unique pairings of events. Each pairing is a module and the modules are connected within a single neural network. The pairings are a simulation of respondent conditioning. The simulations do not necessarily represent conditioning in actual organisms. In the theory presented here, the pairings in respondent conditioning become aggregated together to form a basis for operant conditioning. The specific pairings are as follows. The first pairing is between the reinforcer and the neural stimulus that elicits the behavior. This pairing strengthens and makes salient that eliciting neural stimulus. The second pairing is that of the now salient neural stimulus with the external environmental stimulus that precedes the operant behavior. The third is the pairing of the environmental stimulus event with the reinforcing stimulus. The fourth is the pairing of the stimulus elicited by the drive with the reinforcement event, changing the strength of the reinforcer. The fifth pairing is that after repeated exposure the external environmental stimulus is paired with the drive stimulus. This drive stimulus is generated by an intensifying drive. Within each module, a “0” means no occurrence of a pairing A of Stimuli A and a “1” means an occurrence of a pairing A of Stimuli A. Similarly, a “0” means no occurrence of a pairing Band a “1” means an occurrence of a pairing B, and so on for all 5 pairings. To obtain an output one multiplies the values of pairings through E. In one trial or instance, all 5 pairings will occur. The results of the multiplications are then accumulated and divided by the number of instances. The use of these simple respondent pairings as a basis for neural networks reduces errors. Examples of problems that may be addressable by such networks are included.

Adaptive Neural Networks Accounted for by Five Instances of “Respondent-Based” Conditioning

Neural Networks may be made much faster and more efficient by reducing the amount of memory and computation used. In this paper, a new type of neural network called an Adaptive Neural Network is introduced. The proposed neural network is comprised of five unique pairings of events. Each pairing is a module and the modules are connected within a single neural network. The pairings are a simulation of respondent conditioning. The simulations do not necessarily represent conditioning in actual organisms. In the theory presented here, the pairings in respondent conditioning become aggregated together to form a basis for operant conditioning. The specific pairings are as follows. The first pairing is between the reinforcer and the neural stimulus that elicits the behavior. This pairing strengthens and makes salient that eliciting neural stimulus. The second pairing is that of the now salient neural stimulus with the external environmental stimulus that precedes the operant behavior. The third is the pairing of the environmental stimulus event with the reinforcing stimulus. The fourth is the pairing of the stimulus elicited by the drive with the reinforcement event, changing the strength of the reinforcer. The fifth pairing is that after repeated exposure the external environmental stimulus is paired with the drive stimulus. This drive stimulus is generated by an intensifying drive.  Within each module, a “0” means no occurrence of a pairing  A of Stimuli A and a “1” means an occurrence of a pairing  A of Stimuli A. Similarly, a “0” means no occurrence of a pairing  B and a “1” means an occurrence of a pairing  B , and so on for all 5 pairings. To obtain an output one multiplies the values of pairings through E . In one trial or instance, all 5 pairings will occur. The results of the multiplications are then accumulated and divided by the number of instances. The use of these simple respondent pairings as a basis for neural networks reduces errors. Examples of problems that may be addressable by such networks are included. &nbsp