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

It combines insights from psychology, neuroscience, computer science, and artificial intelligence to build mathematical and computational models of cognitive processes. In the context of decision making, these models can help us understand how we evaluate options, make choices, and learn from the outcomes of our decisions.



We will explore various computational models of decision making, including expected utility theory, prospect theory, and reinforcement learning. These models provide different perspectives on decision making, reflecting the complexity and diversity of this cognitive process. For instance, expected utility theory models decision making as a rational process of maximizing expected outcomes, while prospect theory takes into account the psychological biases that can influence our decisions.



We will also discuss the neural basis of decision making, examining how the brain implements these computational models. This will involve a discussion of the role of different brain regions, such as the prefrontal cortex and the basal ganglia, in decision making. We will also look at how neuroimaging techniques like fMRI can be used to study the neural correlates of decision making.



Finally, we will consider the implications of computational models of decision making for artificial intelligence. By understanding how humans make decisions, we can design more intelligent and human-like AI systems. This has applications in a wide range of areas, from autonomous vehicles to personalized recommendation systems.



In this chapter, we aim to provide a comprehensive overview of decision making from a computational cognitive science perspective. We hope that this will not only deepen your understanding of this fascinating cognitive process, but also inspire you to explore this field further.



### Section: 19.1 Decision making theories



In this section, we will delve into the theoretical underpinnings of decision making, focusing on two key theories: the Normal Distribution Model and the Priority Heuristic. These theories provide a mathematical and computational framework for understanding how decisions are made in different contexts.



#### 19.1.1 Normal Distribution Model



The Normal Distribution Model is a statistical approach to decision making that is often used in the context of a two-alternative forced choice (2AFC) task. In this task, an individual is presented with two stimuli, $x_1$ and $x_2$, which are random variables from two different categories, $a$ and $b$. The individual's task is to decide which stimulus belongs to which category.



The Normal Distribution Model assumes that the stimuli come from normal distributions $N(\mu_a, \sigma_a)$ and $N(\mu_b, \sigma_b)$. The optimal decision strategy, according to this model, is to determine which of two bivariate normal distributions is more likely to produce the tuple $x_1, x_2$: the joint distributions of $a$ and $b$, or of $b$ and $a$.



The probability of error with this ideal decision strategy is given by the generalized chi-square distribution: $p(e)=p\left(\tilde{\chi}^2_{\boldsymbol{w}, \boldsymbol{k}, \boldsymbol{\lambda},0,0}\right)<0$, where $\boldsymbol{w}=\begin{bmatrix} \sigma_a^2 & -\sigma_b^2 \end{bmatrix}, \; \boldsymbol{k}=\begin{bmatrix} 1 & 1 \end{bmatrix}, \; \boldsymbol{\lambda}=\frac{\mu_a-\mu_b}{\sigma_a^2-\sigma_b^2} \begin{bmatrix} \sigma_a^2 & \sigma_b^2 \end{bmatrix}$.



This model can be extended to cases where each of the two stimuli is a multivariate normal vector, and to situations where the two categories have different prior probabilities, or the decisions are biased due to different values attached to the possible outcomes.



#### 19.1.2 Priority Heuristic



The Priority Heuristic is a decision-making theory that has been empirically supported by a number of studies. This heuristic correctly predicted the majority choice in all one-stage gambles in a study by Kahneman and Tversky (1979). Across four different data sets with a total of 260 problems, the heuristic predicted the majority choice.



The Priority Heuristic is a simple decision-making rule that prioritizes options based on a set of criteria, such as the probability of a positive outcome or the magnitude of potential gains or losses. This heuristic reflects the fact that humans often make decisions based on a limited set of salient factors, rather than conducting a comprehensive evaluation of all available information.



In the next sections, we will delve deeper into these theories, exploring their mathematical foundations, empirical support, and implications for our understanding of decision making.



### Section: 19.2 Decision making processes



Decision making is a complex cognitive process that involves the evaluation of different alternatives and the selection of an optimal or satisfactory solution. This process can be influenced by a variety of factors, including the individual's values, preferences, beliefs, and knowledge. In this section, we will explore the different processes involved in decision making, with a focus on the Analytic Network Process (ANP) and Multiple-Criteria Decision Analysis (MCDA).



#### 19.2.1 Analytic Network Process



The Analytic Network Process (ANP) is a decision-making tool that allows for the consideration of a complex set of criteria and alternatives. It is particularly useful in situations where the decision-making process involves interdependent relationships among the decision elements.



The ANP process involves several steps:



1. **Modeling the decision problem**: This involves identifying the decision elements (criteria, alternatives, etc.) and their interrelationships. These elements are then organized into a network structure.



2. **Pairwise comparisons**: Each pair of elements within a cluster is compared in terms of their importance or preference. This is usually done using a scale from 1 (equal importance or preference) to 9 (extreme importance or preference).



3. **Supermatrix formation**: The results of the pairwise comparisons are used to form a supermatrix, which represents the relationships among the decision elements.



4. **Priority calculation**: The priorities of the decision elements are calculated by raising the supermatrix to limiting powers until the weights stabilize.



5. **Synthesis and decision**: The final decision is made by synthesizing the results and selecting the alternative with the highest priority.



#### 19.2.2 Multiple-Criteria Decision Analysis



Multiple-Criteria Decision Analysis (MCDA) is a decision-making approach that involves the evaluation of a set of alternatives based on multiple criteria. This approach is particularly useful in complex decision-making situations where trade-offs among conflicting criteria need to be considered.



The MCDA process involves several steps:



1. **Problem structuring**: This involves defining the decision problem, identifying the decision alternatives, and specifying the evaluation criteria.



2. **Modeling preferences**: The decision-maker's preferences are modeled using a value function or a utility function.



3. **Scoring alternatives**: Each alternative is scored on each criterion based on the decision-maker's preferences.



4. **Aggregating scores**: The scores are aggregated to obtain an overall score for each alternative.



5. **Ranking and selection**: The alternatives are ranked based on their overall scores, and the best alternative is selected.



These decision-making processes provide a structured approach to making complex decisions, allowing for the consideration of multiple factors and the explicit modeling of preferences and trade-offs.



### Section: 19.3 Decision making and cognition



Decision making is a cognitive process that is deeply intertwined with other cognitive functions such as memory, attention, and perception. In this section, we will explore how these cognitive functions interact with decision making, with a particular focus on the role of memory in decision making.



#### 19.3a Decision making and memory



Memory plays a crucial role in decision making. It provides the necessary information for evaluating alternatives and making choices. The relationship between memory and decision making can be understood through the lens of the "remember" versus "know" judgments.



In the context of decision making, "remember" judgments involve the recall of specific details about an event or item, while "know" judgments involve a sense of familiarity without the recall of specific details. This distinction is important because it reflects the different ways in which information is stored and retrieved in memory, which in turn influences how decisions are made.



For instance, in a "remember" judgment, the decision to choose a particular alternative might be based on the recall of specific details about that alternative. This could involve the recall of episodic details, such as when and where the alternative was encountered, or semantic details, such as facts or knowledge about the alternative.



On the other hand, in a "know" judgment, the decision might be based on a sense of familiarity with the alternative, without the recall of specific details. This could involve a general sense of recognition, which is activated by an augmented memory for a part of the stimulus, as suggested by the eye movement method.



In both cases, the decision-making process involves the retrieval of information from memory. However, the nature of the information and the way it is retrieved can influence the decision outcome. For example, a decision based on a "remember" judgment might be more accurate if the recalled details are relevant and accurate, while a decision based on a "know" judgment might be more efficient if the sense of familiarity is strong and reliable.



In the next section, we will explore how other cognitive functions, such as attention and perception, interact with decision making.



#### 19.3b Decision making and perception



Perception, like memory, plays a significant role in decision making. It is through perception that we interpret and understand the world around us, and this interpretation influences our decisions. Perception can be defined as the process by which we interpret sensory information to give meaning to our environment.



In the context of decision making, perception can influence the way we evaluate alternatives and make choices. For instance, the way we perceive the risk associated with a particular alternative can influence our decision to choose that alternative. This is often referred to as risk perception.



Risk perception is a cognitive process that involves evaluating the potential harm or danger associated with a particular decision. It is influenced by a variety of factors, including personal experiences, cultural beliefs, and cognitive biases. For example, people tend to overestimate the risk of rare events and underestimate the risk of common events, a cognitive bias known as the availability heuristic (Tversky & Kahneman, 1973).



Perception can also influence decision making through the phenomenon of framing. Framing refers to the way information is presented or "framed". The same information can lead to different decisions depending on how it is framed. For instance, people are more likely to choose a medical treatment if its success rate is framed in terms of survival (e.g., "90% of patients survive") rather than in terms of mortality (e.g., "10% of patients die") (Tversky & Kahneman, 1981).



The role of perception in decision making is further highlighted in the visual discrimination test and the Iowa Gambling Task. In the visual discrimination test, participants' decisions are influenced by their perception of the pictures and the associated rewards and punishments. Similarly, in the Iowa Gambling Task, participants' decisions are influenced by their perception of the risk and reward associated with each deck of cards.



In conclusion, perception plays a crucial role in decision making by influencing how we evaluate alternatives and make choices. Understanding the role of perception in decision making can help us make better decisions and avoid cognitive biases.



#### References



Tversky, A., & Kahneman, D. (1973). Availability: A heuristic for judging frequency and probability. Cognitive Psychology, 5(2), 207-232.



Tversky, A., & Kahneman, D. (1981). The framing of decisions and the psychology of choice. Science, 211(4481), 453-458.



#### 19.3c Decision making and emotion



Emotion plays a crucial role in decision making, often influencing the choices we make and the risks we are willing to take. Emotions can either facilitate or hinder the decision-making process, depending on their nature and intensity. 



#### Influence of Emotion on Decision Making



Emotion influences decision making in three primary ways: 



1. Your current emotional state: How you feel while you are making a decision can significantly influence the choices you make. For instance, if you are in a positive emotional state, you may be more likely to take risks and make optimistic decisions. Conversely, if you are in a negative emotional state, you may be more risk-averse and make pessimistic decisions (Lerner & Keltner, 2000).



2. Your past emotional state: How you felt in the past can also influence your current decision-making process. Past experiences, particularly those that elicited strong emotions, can shape our perceptions and attitudes towards risk, thereby influencing our decisions (Phelps, Lempert & Sokol-Hessner, 2014).



3. Your anticipated future emotional state: The anticipated emotional outcomes of a decision can significantly influence the choices we make. For instance, if a particular decision is expected to result in positive emotions in the future, we may be more likely to choose that option. Conversely, if a decision is expected to result in negative emotions, we may be more likely to avoid that option (Mellers, Schwartz & Ritov, 1999).



#### Emotion and Risk Aversion



Risk aversion, a concept deeply rooted in the field of economics, is also influenced by emotion. As discussed in the context, risk aversion refers to the tendency to prefer certain outcomes over uncertain ones, even when the uncertain outcomes may have a higher expected utility. This behavior is often driven by fear or anxiety about potential losses, highlighting the role of emotion in decision making (Kahneman & Tversky, 1979).



#### Emotion and the Iowa Gambling Task



The Iowa Gambling Task (IGT) is a psychological task designed to simulate real-life decision making. The task involves four decks of cards, each associated with different levels of risk and reward. Research has shown that individuals with damage to the orbitofrontal cortex, a brain area associated with emotional processing, struggle with this task, suggesting that emotion plays a crucial role in effective decision making (Bechara, Damasio & Damasio, 2000).



In conclusion, emotion is a critical component of the decision-making process. It influences our perceptions of risk and reward, our willingness to take risks, and our overall decision-making effectiveness. Understanding the role of emotion in decision making can provide valuable insights into human behavior and can help improve decision-making strategies in various fields, including economics, psychology, and neuroscience.



### Conclusion



In this chapter, we have delved into the fascinating world of decision making from a computational cognitive science perspective. We have explored how computational models can be used to understand and predict human decision-making processes. These models, grounded in mathematical and statistical principles, provide a framework for interpreting the complex cognitive processes that underlie our everyday decisions.



We have also examined the role of uncertainty in decision making, and how computational models can help us navigate this uncertainty. By incorporating elements of probability theory and Bayesian inference, these models can account for the inherent variability and unpredictability of human behavior.



Moreover, we have discussed the implications of these models for various fields, from artificial intelligence to economics. The insights gained from computational cognitive science can inform the design of intelligent systems, guide economic policy, and even shed light on psychiatric disorders.



In conclusion, computational cognitive science offers a powerful tool for understanding decision making. By combining rigorous mathematical modeling with insights from cognitive psychology, it provides a comprehensive framework for studying the mind in action.



### Exercises



#### Exercise 1

Consider a simple decision-making scenario and describe how you would model it using the principles of computational cognitive science. What factors would you take into account? How would you represent uncertainty?



#### Exercise 2

Explain the role of Bayesian inference in decision making. How does it help us deal with uncertainty? Provide an example to illustrate your points.



#### Exercise 3

Discuss the implications of computational cognitive science for artificial intelligence. How can insights from this field inform the design of intelligent systems?



#### Exercise 4

Choose a real-world economic policy issue and discuss how computational cognitive science could be used to inform policy decisions. What insights could this approach provide?



#### Exercise 5

Explore the potential applications of computational cognitive science in psychiatry. How could models of decision making be used to understand and treat psychiatric disorders?



## Chapter: Problem Solving



### Introduction



Problem solving is a fundamental aspect of cognitive science, and it is a process that we engage in every day, whether we are trying to find the best route to work, deciding on the best strategy to complete a task, or even figuring out the most effective way to solve a complex mathematical problem. This chapter, "Problem Solving", will delve into the computational aspects of this cognitive process, exploring how we can model and understand problem-solving strategies using computational tools and techniques.



The field of computational cognitive science provides a unique lens through which we can examine problem solving. By using computational models, we can simulate the cognitive processes involved in problem solving, providing us with insights that are not easily obtained through traditional psychological experiments. These models can help us understand how we generate solutions to problems, how we select between different strategies, and how we learn from our past experiences to improve our future problem-solving efforts.



In this chapter, we will explore various computational models of problem solving, from simple heuristic models to more complex machine learning algorithms. We will discuss how these models are constructed, how they work, and how they can be used to predict and explain human problem-solving behavior. We will also examine the strengths and limitations of these models, and discuss how they can be improved and extended to better capture the complexity of human problem solving.



As we delve into the computational aspects of problem solving, we will also touch on the role of problem representation, the importance of goal setting, and the impact of cognitive biases on problem-solving performance. These factors play a crucial role in problem solving, and understanding them can help us build more accurate and realistic computational models.



In conclusion, this chapter aims to provide a comprehensive overview of the computational approach to problem solving in cognitive science. By the end of this chapter, you should have a solid understanding of how computational models can be used to study problem solving, and how these models can contribute to our understanding of this complex cognitive process.



### Section: 20.1 Problem solving strategies



Problem-solving strategies are the methods or processes that individuals or groups use to find solutions to problems. These strategies can be simple, such as trial and error, or more complex, such as using algorithms or heuristics. In this section, we will explore some of the most common problem-solving strategies and discuss how they can be modeled computationally.



#### 20.1.1 Trial and Error



Trial and error is one of the simplest problem-solving strategies. It involves trying different solutions until one works. This strategy can be effective for simple problems, but it can be time-consuming and inefficient for more complex problems.



In computational terms, trial and error can be modeled as a brute force algorithm. This type of algorithm tries all possible solutions until it finds one that works. For example, a brute force algorithm for solving a Sudoku puzzle would try all possible combinations of numbers until it finds a combination that satisfies all the rules of the game.



#### 20.1.2 Algorithms



An algorithm is a step-by-step procedure for solving a problem. Algorithms are precise, deterministic, and they always produce a correct solution if one exists. They are commonly used in computer science and mathematics, but they can also be used in cognitive science to model problem-solving processes.



For example, the Dijkstra's algorithm is a well-known algorithm for finding the shortest path between two nodes in a graph. This algorithm can be used to model how we solve problems such as finding the shortest route to a destination.



#### 20.1.3 Heuristics



Heuristics are rules of thumb or mental shortcuts that we use to make problem-solving easier. They are not guaranteed to produce a correct solution, but they can often lead to a good solution in a reasonable amount of time.



In computational terms, heuristics can be modeled as approximation algorithms. These algorithms do not always find the optimal solution, but they can find a solution that is close to optimal in a much shorter time than a brute force algorithm.



For example, the A* search algorithm is a heuristic algorithm that is used in pathfinding and graph traversal. It uses a heuristic to estimate the cost of reaching the goal from a given node, which allows it to prioritize nodes that are likely to lead to a solution.



#### 20.1.4 Collaborative Problem Solving



As discussed in the previous chapter, problem solving is not always an individual process. Often, it involves collaboration between multiple individuals or groups. This is especially true for complex problems that require different types of expertise.



In computational terms, collaborative problem solving can be modeled using multi-agent systems. These systems consist of multiple autonomous agents that work together to solve a problem. Each agent has its own knowledge and capabilities, and they communicate and coordinate their actions to achieve a common goal.



For example, a multi-agent system could be used to model how a team of scientists collaborates to solve a complex research problem. Each scientist could be modeled as an agent with their own expertise and resources, and the system could simulate how they share information, make decisions, and coordinate their actions to solve the problem.



In the following sections, we will delve deeper into these problem-solving strategies, exploring their strengths, limitations, and applications in computational cognitive science.



### Section: 20.2 Problem solving and cognition



Problem solving is a cognitive process that involves identifying, analyzing, and resolving problems. It is a fundamental aspect of human cognition and is central to our ability to function effectively in the world. In this section, we will explore the relationship between problem solving and cognition, and discuss how computational cognitive science can help us understand this relationship.



#### 20.2.1 Cognitive Processes in Problem Solving



Problem solving involves a number of cognitive processes, including perception, memory, attention, and reasoning. These processes work together to help us understand the problem, generate potential solutions, evaluate these solutions, and implement the best one.



For example, when we encounter a problem, we first need to perceive and understand it. This involves using our sensory systems to gather information about the problem, and our cognitive systems to interpret this information. We then use our memory to recall relevant information that might help us solve the problem. This could include previous solutions to similar problems, or knowledge about the problem domain.



Next, we need to generate potential solutions to the problem. This involves using our reasoning abilities to combine and manipulate the information we have gathered and recalled. We might use logical reasoning to deduce potential solutions, or creative reasoning to come up with new and innovative solutions.



Once we have generated potential solutions, we need to evaluate them to determine which one is the best. This involves using our judgment and decision-making abilities to compare the solutions and assess their merits and drawbacks. Finally, we need to implement the chosen solution, which involves using our motor systems to carry out the necessary actions.



#### 20.2.2 Computational Modeling of Problem Solving



Computational cognitive science provides a powerful tool for understanding the cognitive processes involved in problem solving. By creating computational models of these processes, we can simulate problem solving in a controlled and systematic way, and test hypotheses about how these processes work.



For example, we might create a computational model of memory to investigate how information is stored and retrieved during problem solving. This model could simulate the process of encoding, storing, and retrieving information, and allow us to manipulate variables such as the amount of information, the type of information, and the timing of retrieval.



Similarly, we might create a computational model of reasoning to investigate how potential solutions are generated and evaluated