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

This selective attention allows us to make sense of the vast amount of sensory information that we encounter every day.



#### 17.3b Perception and memory



Memory, like attention, plays a significant role in perception. It is through memory that we are able to recognize objects, recall past experiences, and make sense of our surroundings. Memory and perception are interconnected in a way that they influence each other. The way we perceive information can affect how we encode, store, and retrieve it in our memory. Conversely, our memories can also influence our perception.



The subsequent memory paradigm is a useful tool in understanding the relationship between perception and memory. This paradigm involves presenting participants with a series of items during a study phase and then testing their memory for these items in a subsequent test phase. The neural activity during the study phase is then compared between items that are later remembered and those that are forgotten, resulting in the difference due to memory (Dm) effect (Paller, Kutas & Mayes, 1987).



The Dm effect has been shown to be influenced by the level of processing and rehearsal at encoding. For instance, when participants are instructed to make deeper, semantic judgments about the items (e.g., "Is this item edible?"), they typically have a better representation of the item and a more positive Dm effect compared to when they make shallow judgments based on the physical properties of the item (e.g., "Does this word contain more than two vowels?") (Paller, Kutas & Mayes, 1987; Friedman, Ritter & Snodgrass, 1996).



Moreover, the Dm effect seems to be sensitive to the type of rehearsal strategies a participant performs. Fabiani, Karis, and Donchin (1987) found that P300 modulation at encoding, particularly for "isolates" (stimuli presented in a deviant font relative to all other stimuli), correlated with later memory performance. This suggests that the way we perceive and process information can have a significant impact on how well we remember it.



In addition, Weyerts et al. (1997) found that both recognition memory and the Dm effect were larger for pairs of words that were relationally encoded (e.g., "Are these two words semantically related?") versus non-relationally encoded (e.g., "Can the color white be associated with one of these words?"). This further suggests that the Dm effect may be enhanced when items are encoded on a semantic level, highlighting the importance of perception in memory encoding.



In conclusion, perception and memory are closely intertwined. The way we perceive information can influence how we encode, store, and retrieve it in our memory. Conversely, our memories can also shape our perception. Understanding this interplay can provide valuable insights into the cognitive processes underlying human consciousness.



#### 17.3c Perception and language



Language, like memory, is deeply intertwined with perception. It is through language that we are able to communicate our perceptions, understand the perceptions of others, and even shape our own perceptions. The relationship between language and perception is a complex one, with research suggesting that the language we speak can influence the way we perceive the world (Regier & Kay, 2009).



The Sapir-Whorf hypothesis, also known as linguistic relativity, posits that the structure of a language affects its speakers' cognition or world view (Whorf, 1956). This hypothesis has been the subject of much debate and research, with some studies providing evidence in support of it and others refuting it. For instance, research has shown that speakers of languages with numerous words for different shades of blue are better at distinguishing between these shades than speakers of languages with fewer blue-related terms (Winawer et al., 2007). This suggests that language can shape our perceptual categories.



However, it's important to note that the influence of language on perception is not deterministic. Just as our perception can be influenced by language, our language can also be influenced by our perception. For example, the categories we perceive in the world around us can shape the words and grammatical structures we use to describe that world (Goldstone & Hendrickson, 2010).



In the context of categorical perception, language plays a crucial role. As we've seen, categorical perception can be both evolved and learned. Language is a key factor in the learned aspect of categorical perception. The categories we learn through language can shape our perception, and our perception can in turn shape the categories we use in language.



In conclusion, the relationship between perception and language is a complex and reciprocal one. Understanding this relationship is crucial for a comprehensive understanding of cognitive science. Future research in this area promises to shed more light on the intricate interplay between language and perception, and the implications this has for our understanding of the human mind.



### Conclusion



In this chapter, we have explored the fascinating field of perception in computational cognitive science. We have delved into the complex processes that allow us to perceive and interpret the world around us, and how these processes can be modeled and understood through computational methods. 



We have seen how computational models can help us understand the intricate workings of perception, from the basic sensory processes to the higher-level cognitive processes that interpret and make sense of the sensory information. We have also discussed the challenges and limitations of these models, and the ongoing research aimed at overcoming these challenges.



The field of computational cognitive science is a rapidly evolving one, with new discoveries and advancements being made on a regular basis. As we continue to develop and refine our computational models of perception, we can expect to gain even deeper insights into the workings of the human mind.



### Exercises



#### Exercise 1

Research and write a brief report on a recent advancement in computational models of perception. Discuss how this advancement contributes to our understanding of perception.



#### Exercise 2

Choose a specific sensory process (e.g., vision, hearing, touch) and describe how it is modeled in computational cognitive science. Discuss the strengths and limitations of this model.



#### Exercise 3

Design a simple computational model of a perceptual process. Explain the assumptions and parameters of your model, and discuss how it could be tested and validated.



#### Exercise 4

Discuss the role of perception in decision-making. How can computational models of perception help us understand the decision-making process?



#### Exercise 5

Explore the relationship between perception and memory. How do computational models of perception account for the influence of past experiences on our current perceptions?



## Chapter: Language



### Introduction



Language, as a cognitive function, is a complex system that involves a multitude of processes. It is a unique human ability that allows us to communicate, express our thoughts, and understand others. This chapter delves into the computational cognitive science perspective of language, exploring how computational models can help us understand the cognitive processes involved in language comprehension and production.



The study of language from a computational cognitive science perspective involves the use of computational models to simulate and understand the cognitive processes involved in language. These models can range from symbolic models that represent knowledge as symbols and rules, to connectionist models that simulate the neural networks in the brain, to probabilistic models that represent knowledge as statistical patterns in data.



In this chapter, we will explore the different computational models used in cognitive science to understand language. We will discuss how these models are used to simulate the cognitive processes involved in language comprehension and production, such as word recognition, sentence parsing, and semantic interpretation. We will also discuss how these models can help us understand the cognitive processes involved in language learning, such as the acquisition of vocabulary and grammar.



We will also delve into the challenges and limitations of these computational models. While these models have provided valuable insights into the cognitive processes involved in language, they also have limitations and challenges. For example, how can these models account for the creativity and flexibility of human language use? How can they simulate the social and cultural aspects of language? These are some of the questions that we will explore in this chapter.



In conclusion, this chapter aims to provide a comprehensive overview of the computational cognitive science perspective of language. By exploring the different computational models and their applications in understanding language, we hope to provide a deeper understanding of the cognitive processes involved in language comprehension, production, and learning.



### Section: 18.1 Language acquisition



Language acquisition is a complex process that involves the learning of sounds, words, and grammar rules. It is a critical aspect of cognitive development and is central to our ability to communicate and understand others. In this section, we will explore the process of language acquisition from a computational cognitive science perspective, focusing on how computational models can help us understand this process.



#### 18.1.1 Stages of Language Acquisition



As discussed in the related context, language acquisition typically follows a series of stages, starting from the "one-word stage" and progressing to the "two-word stage" and the "telegraphic stage" (O'Grady & Cho, 2011). These stages reflect the increasing complexity of the child's language use, from single-word utterances to two-word "mini-sentences" and then to more complex sentences.



Computational models can help us understand these stages by simulating the cognitive processes involved in language acquisition. For example, symbolic models can represent the child's growing knowledge of words and grammar rules, while connectionist models can simulate the neural networks in the brain that are involved in language learning. Probabilistic models, on the other hand, can represent the statistical patterns in the child's language use, reflecting the probabilistic nature of language learning.



#### 18.1.2 Computational Models of Language Acquisition



Computational models of language acquisition aim to simulate the cognitive processes involved in learning a language. These models can be broadly classified into three types: symbolic models, connectionist models, and probabilistic models.



Symbolic models represent knowledge as symbols and rules. In the context of language acquisition, these models can represent the child's growing vocabulary and grammar rules as symbols and rules. For example, a symbolic model might represent the word "dog" as a symbol and the rule "noun + verb" as a rule.



Connectionist models, on the other hand, simulate the neural networks in the brain. These models can represent the child's growing language abilities as changes in the connections between neurons. For example, a connectionist model might represent the learning of a new word as the strengthening of connections between neurons associated with that word.



Probabilistic models represent knowledge as statistical patterns in data. In the context of language acquisition, these models can represent the child's language use as statistical patterns in their speech data. For example, a probabilistic model might represent the transition from the "one-word stage" to the "two-word stage" as a change in the statistical patterns of the child's speech.



#### 18.1.3 Challenges and Limitations



While computational models have provided valuable insights into the process of language acquisition, they also have limitations. For example, it is unclear how these models can account for the creativity and flexibility of human language use. Moreover, these models often struggle to simulate the social and cultural aspects of language, such as the influence of the child's social environment on their language development.



In conclusion, computational cognitive science provides a powerful tool for understanding the process of language acquisition. By simulating the cognitive processes involved in learning a language, computational models can provide insights into the stages of language acquisition, the learning of vocabulary and grammar, and the challenges and limitations of language learning. However, further research is needed to address the limitations of these models and to develop more comprehensive models of language acquisition.



### Section: 18.2 Language and cognition



Language and cognition are intricately linked. Language is not only a tool for communication but also a cognitive process that shapes our perception and understanding of the world. In this section, we will explore the relationship between language and cognition from a computational cognitive science perspective.



#### 18.2.1 Language as a Cognitive Process



As discussed in the related context, language involves a host of different cognitive systems. The knowledge of language, or the knowledge of a/several languages, is not a straightforward concept. It involves both subconscious knowledge of grammar and conscious metalinguistic knowledge, which can be thought about and analyzed (Sharwood Smith, 2020).



From a computational perspective, these two types of knowledge can be represented as different types of data structures. The subconscious knowledge of grammar can be represented as a set of rules or a grammar tree, while the conscious metalinguistic knowledge can be represented as a network of concepts and their relationships.



#### 18.2.2 Language and Perception



Language also plays a crucial role in our perception of the world. The words and concepts we use can shape how we perceive and interpret the world around us. This is known as the linguistic relativity hypothesis, or the Sapir-Whorf hypothesis (Whorf, 1956).



Computational models can help us understand this relationship between language and perception. For example, a connectionist model can simulate how the activation of certain words or concepts can influence our perception of a situation. Similarly, a probabilistic model can represent the likelihood of perceiving a situation in a certain way based on the words or concepts activated.



#### 18.2.3 Language and Thought



Language is also closely tied to our thought processes. It provides a framework for organizing and expressing our thoughts, and it can also influence the way we think. This is known as the linguistic determinism hypothesis (Whorf, 1956).



Computational models can simulate this relationship between language and thought. For example, a symbolic model can represent how the structure of a language can shape the structure of our thoughts. A connectionist model, on the other hand, can simulate how the activation of certain words or concepts can influence our thought processes.



In conclusion, language is a complex cognitive process that involves both subconscious and conscious knowledge, and it plays a crucial role in our perception and thought processes. Computational cognitive science provides a powerful tool for understanding this complex process.



### Section: 18.3 Language disorders



Language disorders are a type of communication disorder where a person has persistent difficulties in understanding or producing speech. These disorders can be developmental, meaning they occur in children from a young age, or they can be acquired, meaning they occur as a result of brain damage, such as from a stroke or traumatic brain injury. In this section, we will explore some of the most common language disorders, their symptoms, and the latest research in their treatment and management.



#### 18.3a Aphasia



Aphasia is a language disorder that results from damage to the parts of the brain that are involved in language production or processing. This damage can be caused by a stroke, traumatic brain injury, brain tumors, or infections. Aphasia can affect a person's ability to speak, write, and understand language, both verbal and written. The severity and scope of the problems depend on the extent and location of the brain damage.



##### 18.3a.1 Symptoms of Aphasia



The symptoms of aphasia can vary widely, depending on the type of aphasia and the specific brain areas affected. Some people may have difficulty speaking, while others may struggle to understand spoken language. Some common symptoms include:



- Difficulty finding the right words

- Using strange or inappropriate words in conversation

- Difficulty understanding speech

- Difficulty with reading or writing

- Problems with numbers or calculations



##### 18.3a.2 Treatment and Research



Treatment for aphasia often involves speech and language therapy, where a therapist works with the individual to improve their language skills and use alternative methods of communication. The effectiveness of therapy can depend on the severity of the aphasia, the cause of the brain damage, the individual's personality, and the individual's general health.



Recent research into aphasia has been exploring the use of functional magnetic resonance imaging (fMRI) to understand how language is processed in aphasic brains compared to normal brains. This research could help to develop more effective treatments for aphasia by revealing how the brain recovers from damage and how different areas of the brain respond to injury.



Another promising area of research is drug therapy. Scientists are investigating whether certain drugs could be used alongside speech-language therapy to enhance recovery of language function. The idea is that a combination of drug treatment and therapy could be more effective than either treatment alone.



Brain stimulation is also being explored as a potential treatment for aphasia. Transcranial Magnetic Stimulation (TMS), a technique that alters brain activity in the area it stimulates, is being studied for its potential to help people re-learn languages.



While research into aphasia is still in its early stages, these approaches offer hope for more effective treatments in the future.



#### 18.3b Dyslexia



Dyslexia is a common language disorder that primarily affects reading abilities. It is often characterized by difficulties with accurate and/or fluent word recognition, poor spelling, and decoding abilities. Dyslexia is a lifelong condition that is neurobiological in origin, and it is often inherited. Despite these challenges, individuals with dyslexia often have normal or above-average intelligence.



##### 18.3b.1 Symptoms of Dyslexia



The symptoms of dyslexia can vary widely, depending on the individual and the severity of the condition. Some common symptoms include:



- Difficulty reading, often reading at a level well below the expected level for the age of the individual

- Problems processing and understanding what is heard

- Difficulty finding the right word or forming answers to questions

- Problems remembering the sequence of things

- Difficulty seeing (and occasionally hearing) similarities and differences in letters and words

- Difficulty spelling



##### 18.3b.2 Deep Dyslexia



Deep dyslexia is a type of dyslexia characterized by the inability to read non-words and a reliance on semantic information for reading. This disorder is often associated with extensive left hemisphere damage and is typically seen in adults following brain injury. 



The "Glosser and Friedman (continuum) model" suggests that deep dyslexia and phonological dyslexia are opposite endpoints on a "continuum" of reading disability. Deep dyslexia appears to be a more severe form of phonological dyslexia, but symptoms in patients can change over time, suggesting recovery is possible along the semantic pathway.



##### 18.3b.3 Treatment and Research



There is no cure for dyslexia, but early intervention can help those with dyslexia learn to read and write more effectively. Treatment often involves a multi-sensory approach to learning, where the individual uses their senses of sight, hearing, movement, and touch to improve reading skills. 



Recent research into dyslexia has been exploring the use of assistive technology, such as text-to-speech software and digital text formats, to help individuals with dyslexia improve their reading skills. Additionally, studies are ongoing to better understand the genetic and neurobiological basis of dyslexia, with the aim of developing more effective interventions and treatments.



#### 18.3c Language Delay



Language delay is a type of communication disorder where a child does not develop language skills at the expected age-appropriate milestones. It is one of the most common developmental issues in children, affecting approximately 5-8% of preschool children. Language delay can be receptive (difficulty understanding language), expressive (difficulty using language), or a combination of both.



##### 18.3c.1 Symptoms of Language Delay



The symptoms of language delay can vary widely, depending on the individual and the severity of the condition. Some common symptoms include:



- Delayed babbling or cooing in infants

- Limited vocabulary for their age

- Difficulty forming sentences

- Problems understanding simple instructions or questions

- Difficulty expressing thoughts or needs

- Problems with articulation or pronunciation



##### 18.3c.2 Causes of Language Delay



Language delay can be caused by a variety of factors, including hearing loss, intellectual disability, autism spectrum disorder, or simply a slower pace of development. In some cases, the cause of the language delay may not be identifiable. 



##### 18.3c.3 Treatment and Research



Early intervention is crucial for children with language delay. Speech-language therapy is the most common form of treatment, where a speech-language pathologist works with the child on language skills. This can include exercises to improve vocabulary, sentence structure, and conversation skills. 



In addition to therapy, parents and caregivers can support language development by reading to the child, encouraging conversation, and providing a language-rich environment. 



Recent research into language delay has focused on early detection and intervention, as well as understanding the underlying causes of the condition. Studies have shown that early intervention can significantly improve language outcomes for children with language delay. 



In conclusion, language delay is a common developmental issue that can have significant impacts on a child's academic and social skills. However, with early detection and intervention, many children with language delay can catch up to their peers and lead successful lives.



### Conclusion



In this chapter, we have delved into the fascinating world of language from a computational cognitive science perspective. We have explored how computational models can help us understand the complex processes involved in language comprehension, production, and acquisition. We have seen how these models can simulate the cognitive processes involved in language, providing valuable insights into how humans process and understand language.



We have also discussed the importance of language in cognitive science, as it is not only a means of communication but also a tool for thinking and reasoning. We have seen how computational cognitive science can help us understand the intricate relationship between language and thought, and how language can shape our cognitive processes.



Finally, we have touched upon the challenges and future directions in the field of computational cognitive science of language. Despite the progress made so far, there is still much to learn about the cognitive processes involved in language. With the advancement of computational models and techniques, we can look forward to a deeper understanding of these processes in the future.



### Exercises



#### Exercise 1

Discuss the role of computational models in understanding language comprehension. How can these models help us understand the cognitive processes involved in comprehending language?



#### Exercise 2

Explain the relationship between language and thought from a computational cognitive science perspective. How can language shape our cognitive processes?



#### Exercise 3

Discuss the challenges in the field of computational cognitive science of language. What are some of the limitations of current computational models of language?



#### Exercise 4

Discuss the future directions in the field of computational cognitive science of language. How can the advancement of computational models and techniques contribute to a deeper understanding of the cognitive processes involved in language?



#### Exercise 5

Choose a specific aspect of language (e.g., syntax, semantics, pragmatics) and discuss how computational cognitive science can help us understand this aspect. What are some of the computational models that have been used to study this aspect of language?



## Chapter: Decision Making



### Introduction



Decision making is a fundamental cognitive process that underlies a wide range of human behaviors. From simple choices like what to eat for breakfast, to complex decisions such as planning a career or solving a mathematical problem, our ability to make decisions shapes our lives in profound ways. In this chapter, we delve into the fascinating world of decision making from a computational cognitive science perspective.



Computational cognitive science is a multidisciplinary field that uses computational models to understand how the mind works