Developments In Cognitive Psychology And Phrenologists In Ni

Developments In Cognitive Psychologyphrenologists In Nineteenth Centur

Developments In Cognitive Psychologyphrenologists In Nineteenth Centur

DEVELOPMENTS IN COGNITIVE PSYCHOLOGY Phrenologists in nineteenth-century Victorian England believed that aspects of a person—such as weight, temperament, and organ function—represented themselves as bumps on the head. By fingering the ridges on the skulls of individuals, practitioners believed that they could make determinations about any number of somatic and psychological qualities. Humans have long sought to understand how the brain functions to create the self—how it “knows.” This search has evolved into today’s study of cognition. Long dismissed by the scientific community, the pseudoscience of phrenology gave way to a twentieth-century understanding of neurological function as a general mystery that science had yet to solve.

This belief persisted into the 1990s, until advances in technology and theory development brought about an exponential increase in neurologists’ understanding of cognitive processes. With the advent of imaging technology, neurologists, psychologists, and even laypersons have access to “pictures of the mind.” When bolstered by theory and research, these images expand our awareness of the ways in which the brain helps us to think, feel, and act (Cacioppo, Berntson, & Nusbaum, 2008). For this Discussion, consider your definition of cognitive psychology. Think about developments in the field, and contributions that they have made. Post your personal definition of cognitive psychology.

Then describe two important developments in the field of cognitive psychology beyond the use of neuroimaging. Finally, explain how the developments contribute to the field of psychology. Support your response using the Learning Resources and current research. Be sure to support your postings and responses with specific references to the Learning Resources. References Cacioppo, J. T., Berntson, C. G., & Nusbaum, H. C. (2008). Neuroimaging as a new tool in the toolbox of psychological science. Current Directions in Psychological Science, 17 (2), 62–67. Kanwisher, N. (2010). Functional specificity in the human brain: A window into the functional architecture of the mind. Proceedings of the National Academy of Sciences USA, 11163–11170.

Paper For Above instruction

Cognitive psychology is the scientific study of mental processes such as perception, memory, reasoning, problem-solving, and language. It aims to understand how people acquire, process, and store information, ultimately shaping their behavior and experiences. Over time, the field has evolved significantly from its early concepts rooted in pseudosciences like phrenology to a sophisticated discipline supported by empirical evidence and advanced technology. This progression has contributed to a deeper understanding of the human mind and its intricate functions.

Among the pivotal developments in cognitive psychology, two stand out beyond neuroimaging techniques: the advent of cognitive models and the development of computational neuroscience. These innovations have profoundly enhanced our understanding of mental processes and contributed to the field's maturation.

Development 1: Cognitive Modeling

Cognitive modeling involves creating computational representations of mental processes to simulate how the mind works. Early models, such as Allan Newell and Herb Simon's Information Processing Model, conceptualized cognition as a sequence of stages involving perception, memory, decision-making, and action. These models provided a framework for understanding complex mental functions by breaking them down into discrete components and operations. For example, the development of the Atkinson-Shiffrin model of memory explained how information transitions between sensory registers, short-term, and long-term memory (Atkinson & Shiffrin, 1968).

This approach allowed scientists to formulate hypotheses, design experiments, and interpret behavioral data within a structured conceptual framework. It enabled a more systematic investigation of cognitive processes and fostered the development of theories that describe mental functions with precision. The utility of computational models has also extended to understanding disorders like dyslexia and Alzheimer's disease, where discrepancies between model predictions and actual performance illuminate underlying deficits (Bracy et al., 2014).

Development 2: Computational Neuroscience

Computational neuroscience bridges the gap between neural activity and cognitive functions by simulating neural systems with algorithms and mathematical models. It utilizes computational tools to understand how networks of neurons produce complex behaviors and mental states. For example, models of neural connectivity elucidate how distributed brain circuits give rise to consciousness, decision-making, and language processing (Dayan & Abbott, 2001).

This development has been crucial in deciphering the neural basis of cognition, allowing researchers to explore how specific patterns of neural activity correlate with psychological phenomena. It also facilitates the testing of hypotheses about brain function in ways that are not possible through experimentation alone. For instance, neural network models have been instrumental in understanding how the brain learns and adapts through mechanisms like synaptic plasticity, which underpins learning and memory (Seung, 2003).

Contribution to the Field of Psychology

These advancements significantly contribute to the broader field of psychology by providing mechanistic explanations of mental functions and fostering interdisciplinary collaboration. Cognitive modeling bridges theoretical insights from psychology with computational techniques borrowed from computer science, leading to more robust and testable theories. This integration has informed the development of artificial intelligence systems that emulate human cognition, further expanding our understanding of intelligence and learning.

Meanwhile, computational neuroscience enriches the biological basis of psychological theories. It helps translate neural data into psychological constructs, thus bridging the gap between neurobiology and behavior. This synergy enhances clinical applications, allowing for more targeted interventions in neuropsychological disorders. Moreover, these models have implications for education, where understanding cognitive load and information processing guides curriculum development and instructional methods.

Conclusion

In conclusion, the evolution of cognitive psychology has been marked by groundbreaking developments such as cognitive modeling and computational neuroscience. These innovations have advanced our understanding of mental processes by providing structured frameworks and biological insights. They have facilitated the integration of psychology with computer science and neuroscience, fostering a comprehensive approach to understanding the human mind. As technology continues to evolve, these foundational developments will likely remain central to future discoveries in cognitive science.

References

• Atkinson, R.C., & Shiffrin, R.M. (1968). Human memory: A proposed system and its control processes. In K.W. Spence & J.T. Spence (Eds.), The psychology of learning and motivation: Advances in research and theory (Vol. 2, pp. 47–89). Academic Press.
• Bracy, B., Rowe, S., & Tallal, P. (2014). Cognitive modeling of developmental dyslexia. Journal of Learning Disabilities, 47(6), 547–560.
• Dayan, P., & Abbott, L. F. (2001). Theoretical neuroscience: Computational and mathematical modeling of neural systems. MIT Press.
• Seung, H. S. (2003). Learning in spiking neurons by reinforcement of stochastic synaptic transmission. Neuron, 40(6), 1063–1073.
• Kanwisher, N. (2010). Functional specificity in the human brain: A window into the functional architecture of the mind. Proceedings of the National Academy of Sciences USA, 107(25), 11163–11170.
• Cacioppo, J. T., Berntson, C. G., & Nusbaum, H. C. (2008). Neuroimaging as a new tool in the toolbox of psychological science. Current Directions in Psychological Science, 17(2), 62–67.