Dịch đoạn văn sau sang tiếng việt trừ những từ ngữ chuyên ngành như trong kỹ thuật, lập trình, học máy,..., giữ nguyên định dạng và không đề cập thêm gì hết

The number of AD patients will increase rapidly in the coming years, as the majority of the Baby Boomer generation has reached the age of 65 and the population of Americans over the age of 65 is projected to grow to 88 million by 2050[^2^].



African Americans are about twice as likely to have AD as Caucasians[^3^]. This disparity is a topic of ongoing research, with recent studies indicating that there are clear disparities in the disease among racial groups, with higher prevalence and incidence in African Americans than the overall average[^3^]. Pathologies for Alzheimer’s also seem to manifest differently in African Americans, including with neuroinflammation markers, cognitive decline, and biomarkers[^3^].



##### Socioeconomic disparities



There are also socioeconomic disparities—such as education, representation in clinical trials, and cost of care services—between African Americans and other racial groups that are important for the care and research of AD in African Americans[^3^]. These disparities can contribute to the higher prevalence and incidence of AD in African Americans, and addressing them is a critical part of improving care and outcomes for this population.



##### Genetic risk factors



While there are genetic risk factors for Alzheimer’s, these account for few cases in all racial groups[^3^]. The most well-known genetic risk factor for AD is the presence of the E4 allele of the apolipoprotein E (APOE) gene[^4^]. However, the relationship between APOE and AD is complex and not fully understood, and other genetic and environmental factors also play a role in the development of the disease[^4^].



[^1^]: National Institute on Aging. (n.d.). Alzheimer's Disease Fact Sheet. https://www.nia.nih.gov/health/alzheimers-disease-fact-sheet

[^2^]: Alzheimer's Association. (2020). 2020 Alzheimer's disease facts and figures. Alzheimer's & Dementia, 16(3), 391–460. https://doi.org/10.1002/alz.12068

[^3^]: Barnes, L. L., & Bennett, D. A. (2014). Alzheimer's disease in African Americans: risk factors and challenges for the future. Health affairs, 33(4), 580-586.

[^4^]: Corder, E. H., Saunders, A. M., Strittmatter, W. J., Schmechel, D. E., Gaskell, P. C., Small, G. W., ... & Pericak-Vance, M. A. (1993). Gene dose of apolipoprotein E type 4 allele and the risk of Alzheimer's disease in late onset families. Science, 261(5123), 921-923.



#### 16.3c Dementia



Dementia is a broad term that describes a group of symptoms associated with a decline in memory, reasoning, or other thinking skills. It is a chronic or persistent disorder of the mental processes caused by brain disease or injury and marked by memory disorders, personality changes, and impaired reasoning[^4^]. 



##### Types of Dementia



There are several types of dementia, including Alzheimer's disease, vascular dementia, dementia with Lewy bodies, and frontotemporal dementia. Alzheimer's disease is the most common type of dementia, accounting for 60-80% of cases[^4^]. Vascular dementia, which occurs after a stroke, is the second most common type of dementia. Other types of dementia are less common, and each has unique characteristics and symptoms[^4^].



##### Prevalence and Incidence



Dementia affects a significant number of people worldwide. In 2015, an estimated 46.8 million people globally were living with dementia. This number is projected to increase to 131.5 million by 2050[^5^]. The risk of dementia increases with age, and the condition is most common in individuals over the age of 65[^5^].



##### Symptoms and Diagnosis



The symptoms of dementia can vary greatly from person to person. However, at least two of the following core mental functions must be significantly impaired to be considered dementia: memory, communication and language, ability to focus and pay attention, reasoning and judgment, and visual perception[^4^].



Diagnosis of dementia is based on a combination of physical examination, laboratory tests, and the characteristic changes in thinking, day-to-day function, and behavior associated with each type[^4^]. 



##### Treatment and Management



While there is currently no cure for dementia, there are ways to manage symptoms. Medications, non-drug therapies, and support for caregivers can all help to improve the quality of life for people with dementia[^4^].



##### Assistive Technology



Assistive technology can play a significant role in helping people with dementia manage their daily lives. Devices such as clocks, communication aids, GPS location/tracking devices, and medication management tools can all be beneficial. However, more research is needed to determine the most effective types of assistive technology for people with dementia[^6^].



##### Exercise



Regular physical activity is associated with a reduced risk of developing dementia. It is recommended that individuals engage in a balance of strength and balance exercises for approximately 2.5 hours per week to reduce the risk of cognitive decline and other health risks[^7^].



[^4^]: Alzheimer's Association. (2021). What Is Dementia? Retrieved from https://www.alz.org/alzheimers-dementia/what-is-dementia



[^5^]: Prince, M., Wimo, A., Guerchet, M., Ali, G., Wu, Y., & Prina, M. (2015). World Alzheimer Report 2015: The Global Impact of Dementia. Alzheimer's Disease International.



[^6^]: Fleming, R., & Sum, S. (2014). Empirical studies on the effectiveness of assistive technology in the care of people with dementia: a systematic review. Journal of Assistive Technologies, 8(1), 14-34.



[^7^]: Norton, S., Matthews, F. E., Barnes, D. E., Yaffe, K., & Brayne, C. (2014). Potential for primary prevention of Alzheimer's disease: an analysis of population-based data. The Lancet Neurology, 13(8), 788-794.



### Conclusion



In this chapter, we have delved into the fascinating world of memory from a computational cognitive science perspective. We have explored the various models and theories that attempt to explain how memory works, and how these models can be implemented computationally. We have seen how memory is not a single, unified system, but rather a complex network of systems that work together to encode, store, and retrieve information. 



We have also discussed the role of memory in cognitive processes such as learning, decision making, and problem solving. We have seen how computational models of memory can help us understand these processes better, and how they can be used to develop more effective learning strategies and decision-making tools. 



Finally, we have looked at the challenges and future directions in the field of computational cognitive science of memory. We have seen how advances in technology and neuroscience are opening up new possibilities for research and application, and how computational cognitive science is playing a crucial role in these developments.



In conclusion, memory is a fundamental aspect of cognition, and understanding it from a computational perspective is essential for advancing our knowledge of the human mind and brain. The field of computational cognitive science of memory is a vibrant and rapidly evolving one, and we look forward to seeing where it will take us in the future.



### Exercises



#### Exercise 1

Explain the difference between declarative and procedural memory. Give examples of each and discuss how they are represented in computational models.



#### Exercise 2

Discuss the role of memory in decision making. How can computational models of memory help us understand this process better?



#### Exercise 3

Describe a computational model of memory that you find particularly interesting or compelling. What are its strengths and weaknesses?



#### Exercise 4

Discuss the challenges in modeling memory computationally. What are some potential solutions to these challenges?



#### Exercise 5

Imagine you are a researcher in the field of computational cognitive science of memory. What would be a research question you would be interested in exploring? How would you go about investigating it?



## Chapter: 17 - Perception



### Introduction



Perception, as a cognitive process, is the means by which we interpret and understand our environment through the sensory information we receive. This chapter, Chapter 17: Perception, delves into the computational cognitive science perspective of this intricate process. 



In the realm of computational cognitive science, perception is not merely a passive reception of information, but an active computational process. It involves complex algorithms and models that our brain uses to interpret sensory data, construct meaningful categories, and make predictions about the world around us. 



This chapter will explore the computational models of perception, discussing how they simulate the way humans perceive and interpret sensory information. We will delve into the various types of perception such as visual, auditory, and tactile, and how computational models can help us understand these processes better. 



We will also discuss the role of perception in higher cognitive functions, such as decision making and problem solving. The chapter will highlight how perception is not an isolated process, but is deeply intertwined with other cognitive functions, and how computational models can help us understand these complex interactions.



In this chapter, we will also touch upon the challenges and limitations of computational models of perception. While these models have greatly advanced our understanding of perception, they are still approximations of the complex processes that occur in the human brain. 



By the end of this chapter, you will have a deeper understanding of the computational perspective of perception, its role in cognitive science, and its implications for fields such as artificial intelligence and machine learning. 



Join us as we embark on this fascinating journey into the computational understanding of perception, a cornerstone of cognitive science.



### Section: 17.1 Visual Perception



Visual perception is a complex process that involves the reception and interpretation of visual stimuli. It is a primary sense for humans, allowing us to navigate and interact with our environment. In this section, we will delve into the computational models that attempt to simulate and understand this intricate process.



#### 17.1.1 The Visual System



The human visual system is a sophisticated network that begins with the eyes and extends into various parts of the brain. Light enters the eye and is focused onto the retina, a dense layer of photosensitive cells that capture information about the intensity, color, and position of incoming light. This information is then processed by the neurons on the retina before being sent to the brain via the optic nerve.



The timing of perception of a visual event, at points along the visual circuit, have been measured. A sudden alteration of light at a spot in the environment first alters photoreceptor cells in the retina, which send a signal to the retina bipolar cell layer which, in turn, can activate a retinal ganglion neuron cell. A retinal ganglion cell is a bridging neuron that connects visual retinal input to the visual processing centers within the central nervous system. Light-altered neuron activation occurs within about 5–20 milliseconds in a rabbit retinal ganglion, although in a mouse retinal ganglion cell the initial spike takes between 40 and 240 milliseconds before the initial activation. The initial activation can be detected by an action potential spike, a sudden spike in neuron membrane electric voltage.



#### 17.1.2 Computational Models of Visual Perception



Computational models of visual perception aim to simulate and understand the processes involved in visual perception. These models use mathematical and computational techniques to represent and simulate the mechanisms of the visual system.



One of the most well-known computational models of visual perception is the Marr's theory of vision. Marr proposed that visual perception occurs in a series of stages, each of which involves a different level of representation and processing. The first stage involves the detection of edges and contours in the visual scene, the second stage involves the construction of a 2D sketch of the scene, and the third stage involves the construction of a 3D model of the scene.



Marr's theory has been influential in the field of computational vision, and many subsequent models have built upon his ideas. However, it is important to note that while these models provide valuable insights into the processes involved in visual perception, they are still approximations of the complex processes that occur in the human brain.



#### 17.1.3 Challenges and Future Directions



While computational models have greatly advanced our understanding of visual perception, there are still many challenges to be addressed. One of the main challenges is the complexity of the visual system itself. The human visual system is highly complex and involves many different types of cells and connections, which makes it difficult to accurately model.



Another challenge is the lack of a comprehensive understanding of how the different parts of the visual system interact with each other and with other parts of the brain. Future research in this area will likely involve the development of more sophisticated models that can simulate these interactions.



Despite these challenges, the field of computational visual perception is a rapidly evolving field with many exciting opportunities for future research. By continuing to develop and refine computational models, we can gain a deeper understanding of how we perceive the world around us, and how this perception influences our thoughts, decisions, and actions.



### Section: 17.2 Auditory Perception



Auditory perception is the process by which the brain interprets and makes sense of the sounds that we hear. It is a complex process that involves the reception and interpretation of auditory stimuli. In this section, we will delve into the computational models that attempt to simulate and understand this intricate process.



#### 17.2.1 The Auditory System



The human auditory system is a sophisticated network that begins with the ears and extends into various parts of the brain. Sound waves enter the ear and are converted into electrical signals by the hair cells in the cochlea. These signals are then sent to the brain via the auditory nerve.



The timing of perception of an auditory event, at points along the auditory circuit, have been measured. A sudden alteration of sound at a spot in the environment first alters hair cells in the cochlea, which send a signal to the auditory nerve. The auditory nerve then transmits this signal to the auditory cortex in the brain. The initial activation can be detected by an action potential spike, a sudden spike in neuron membrane electric voltage.



#### 17.2.2 Computational Models of Auditory Perception



Computational models of auditory perception aim to simulate and understand the processes involved in auditory perception. These models use mathematical and computational techniques to represent and simulate the mechanisms of the auditory system.



One of the most well-known computational models of auditory perception is the "two-channel model" proposed by Deutsch. This model was used to explain the "octave illusion", a phenomenon where a sequence of two alternating tones (one high and one low) are presented to each ear, but the listener perceives the high tone in one ear and the low tone in the other, regardless of the actual physical location of the tones.



#### 17.2.3 The Octave Illusion and the Two-Channel Model



In a series of experiments, Deutsch and colleagues explored the two-channel model in further detail. They found that the perception of the octave illusion was influenced by factors such as handedness and familial handedness background. For example, right-handers were more likely to hear the high tone on the right (and the low tone on the left) than were mixed-handers, and mixed-handers were more likely to do so than left-handers.



In another experiment, Deutsch and Roll played a repeating pattern of tones pitched at 400 Hz and 800 Hz to 44 right-handed subjects. The results were consistent with the initial experiment, further supporting the two-channel model.



These experiments provide valuable insights into the mechanisms of auditory perception and highlight the complexity of the processes involved. They also underscore the importance of computational models in advancing our understanding of cognitive science.



### Section: 17.3 Perception and cognition



Perception and cognition are two fundamental aspects of human consciousness. Perception refers to the process of receiving, processing, and interpreting sensory information from the environment. Cognition, on the other hand, involves higher mental processes such as thinking, understanding, learning, and remembering. These two processes are closely intertwined, with perception serving as the gateway to cognition.



#### 17.3a Perception and attention



Attention is a cognitive process that allows us to focus on specific stimuli while ignoring others. It plays a crucial role in perception, as it determines which sensory information is processed and which is ignored. 



In the context of visual perception, attention can be directed towards specific parts of the visual field. This is often studied using paradigms that involve presenting visual stimuli in different visual hemifields (Jeffreys and Axford, 1965). In these paradigms, participants are asked to fixate on a cross at the center of the screen while stimuli are presented in different parts of the visual field. The C1 component, an early visual evoked potential, is often used to measure the effects of attention on visual perception.



The P1 component, another visual evoked potential, has also been used to study the effects of attention on perception. Early research on the P1 component focused on identifying the components present when visual stimuli are viewed. This was done using paradigms that involved presenting geometric shapes, colors, or flashes of light for a short time and recording the resulting ERPs from sites above occipital regions (Spehlmann, 1965; Hillyard & Munte, 1984; Cobb & Dawson, 1960).



Later research started to investigate the P1 effect with regards to selective attention. In these studies, participants were asked to attend to a specific part of the visual field while looking for a target in their entire visual field. The important comparison was between the P1 for targets that were presented in the space where a participant was attending versus targets that appeared in other parts of the visual field (Van Voorhis and Hillyard, 1977).



These studies highlight the crucial role of attention in perception. By directing our attention towards specific stimuli, we can filter out irrelevant information and focus on what is important