The Heritage of the Phineas Gage Study: Cognitive Science
Cognitive science strives to determine the thinking process as structures in mind and sets of algorithms. Before realizing the brain’s role, people used to attribute mental qualities to a variety of organs. The case of Phineas Gage, who survived after a rod perforated his head, greatly influenced nineteen-century discussions about the brain: cerebral localization, the brain’s role in determining personality, and mental changes associated with brain damage.
The “American Crowbar Case” contributed to studies of the brain’s functions – the mapping. Primarily, Harlow compared the pre-accident Gage’s behavior with the changes and concluded that “Gage’s memory and general intelligence seemed unimpaired,” but his personality underwent dramatical modifications (Phineas Gage, 2021). Harlow interpreted these changes due to pivotal cerebral function. Although there was an opposing view of Bigelow, who saw the behavioral changes in Gage as insignificant, the case of Phineas Gage became strongly associated with an incredible transformation into a completely different person.
Investigating the injuries and their consequences, researchers pay special attention to the place of damage. In discussing different brain structure roles, the case gave the grounds for Harlow to declare that the brain’s front injuries are less risky than the damages to the rear part (Phineas Gage, 2021). David Ferrier, who created a map of the cortical processes, used that example to prove his theory of brain localization (Voinea, 2021). The evolution of neuronal mapping studies began with an understanding of the correlation between certain trauma and their localization.
The most striking fact is that Gage’s serious mental changes were impermanent. For neurology, a successful case of Gage’s recovery provided evidence that the damaged brain can re-establish lost connections or build others. Macmillan compared the results of Gage’s recovery to those of patients doing something structured to rebuild their lost skills, hence, came up with a theory of recovery (Phineas Gage, 2021). He described Gage’s stagecoach work as clear sequences of tasks requiring daily exercises in planning – such methods were used by the Soviet neuropsychologist Luria in recovering soldiers with frontal lobe injuries (Phineas Gage, 2021). Thus, the case study of Gage led to discoveries in rehabilitation.
The story of modern neuroscience started with Phineas Gage’s frontal cortex injury. The “American Crowbar Case” stimulated the development of functional cerebral mapping that focuses on the localization of various mental functions. As an example of a changed personality, it provided the promotion for neuropsychiatry. It contributed to the discovery of neuroplasticity: a wonderful gift of the human brain. Hence, Phineas Gage became a hero of all cognitive scientists and an inspirational figure for global medicine.
The Impact of Cognitive Science Research in the Advancement of Artificial Intelligence
The term “artificial intelligence” (AI) is related to computer simulation of human-like behavior and critical thinking. Like a human brain, a neural network consists of data processing elements called neurons. The input layer collects the data from the external environment and sends it further, and the output layer neurons get the results of the procession to compare them with the desired outcome (Ahmed, 2019). The methods of cognitive science are also applied in AI studies.
There is a so-called ‘black box’ problem – understanding the actual organization and operation of AI. The information is distributed across their neurons in neural networks, and researchers do not know where it is stored (Lillian, Meyes, and Meisen, 2018). Although biological neural networks need more investigation, two crucial characteristics of their organization could clarify that of AI neural networks. First, the brain can function even if large areas of neurons are destroyed (Lillian, Meyes, and Meisen, 2018). The brain’s resilience was shown in the previously discussed case of Phineas Gage. The second fact is that the brain tends to specialize in the performance of different operations: in human bodies, every part is mapped to a certain brain area. These findings are utilized both in neuroscience and AI studies. For example, neuroscientists used ablation to understand neuron specialization (Lillian, Meyes, and Meisen, 2018). Likewise, these studies are applied to neural networks to analyze their characteristics.
The ability of neural networks to work with incomplete information is used for business predictions and statistics. According to Ahmed, the possible applications are noise reduction, classification, pattern recognition, and estimates (2019). Nowadays, AI learns to diagnose patients, building associations from accessible databases. The flowchart-based approach involves supplying a large amount of data on symptoms into networks (Amisha, Pathania, and Rathaur, 2019). The database approach is based on the principle of deep learning: it includes learning to recognize specific patterns with the application of repetitive algorithms (Amisha, Pathania, and Rathaur, 2019). For example, with the help of modern neuroimaging techniques, Van Horn et al. estimated the damage to Gage’s white matter and found out that it was more corresponding to his mental changes than gray matter damage (2012). They used an open database to compare a standardized model of a right-handed 25–36-aged male to the model of Gage’s brain with the trajectory of the rod (2012). The implications of their study could be valuable in deep learning of AI to measure the possible scope of damage and prognosis of the clinical outcome in patients with brain trauma.
Neural networks are models of biological processes that occur in the human brain. We owe them the appearance of impressive results in speech and image recognition, medical diagnoses, etc. Further development of AI depends on neuroscience achievements: the structure of modern neural networks is still less complicated than a rat’s brain. That development could be intensive – with the introduction of more sophisticated systems, or extensive, with the growing number of applications based on existing studies and statistics.
Ahmed, M.K. (2019) ‘Neural networks in business applications.’ Journal of Engineering and Applied Sciences, 14(13), pp.4491-4500. Web.
Amisha, P.M., Pathania, M. and Rathaur, V.K. (2019) ‘Overview of artificial intelligence in medicine.’ Journal of Family Medicine and Primary Care, 8(7), pp. 2328–2331.
Lillian, P.E., Meyes, R. and Meisen, T. (2018) ‘Ablation of a robot’s brain: neural networks under a knife.’ Web.
Phineas Gage. (2021) Web.
Van Horn, J.D. et al. (2012) ‘Mapping connectivity damage in the case of Phineas Gage’, PloS one, 7(5). Web.
Voinea, A.I. et al. (2021). ‘A tale of two frontal lobes. Clinical perspectives of cortical interconnectivity’. BRAIN. Broad Research in Artificial Intelligence and Neuroscience, 11(3), pp.127-136. Web.