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– 2 years ago
**files (15)**
– ## Psych421chapter5678.docx
– ## Chapter5.4_Informationprocessinginneuralnetworks-revisedfor3rdedition.ppt
– ## Chapter8.1_Architecturesforintelligentagents-revisedfor3rdedition.ppt
– ## Chapter5.1_Neurallyinspiredmodelsofinformationprocessing-revisedfor3rdedition.ppt
– Chapter5.3_Multilayernetworks-revisedfor3rdedition.ppt
– Chapter8.4_Hybridarchitectures-revisedfor3rdedition.ppt
– Chapter8.2_Fodoronthemodularityofmind-revisedfor3rdedition.pptx
– Chapter7.3_Neuroeconomics_Bayesinthebrain-createdfor3rdedition.ppt
– Chapter7.2_PerceptionasaBayesianproblem-createdfor3rdedition.ppt
– Chapter6.1_Cognitivescienceanddynamicalsystems-revisedfor3rdedition.ppt
– Chapter6.2_Applyingdynamicalsystems-revisedfor3rdedition.ppt
– Chapter5.2_Single-layernetworksandBooleanfunctions-revisedfor3rdedition.ppt
– Chapter8.3_Themassivemodularityhypothesis-revisedfor3rdedition.ppt
– pssh.pdf
– Chapter7.1_Bayesianism_Aprimer-createdfor3rdedition.ppt
**## Psych421chapter5678.docx**
**Chapter 5 – Artificially Modeling the mind (200 words)**
Hello; for this week, please, read chapter 5 and answer the following questions:
1.
**How do artificial neural network models work alongside neuroscientific techniques to help us study the brain? What do they add to our understanding?**
2.
**Why do you think training is such a key part of neural network modeling? How does it shape the way these models work?**
3.
**How similar do you think artificial neural networks are to the real brain? Are there aspects where they feel biologically realistic or where they fall short?**
4.
**What are the main features of artificial neural networks? How would you compare them to the physical symbol system model of information processing?**
**Chapter 6 – The Dynamical Mind (200 words)**
During this week, please, read Chapter 6 and answer the following questions:
1.
**What’s the main idea behind the dynamical systems hypothesis? Do you find it convincing, and why or why not?**
2.
**How does the example of the Watt governor help Van Gelder make his point? Can you think of other examples that might support his argument?**
3.
**Dynamical models seem great for studying how skills like walking develop. Do you think they could also be useful for exploring other cognitive processes? Why or why not?**
4.
**The A-not-B error is a fascinating phenomenon. Do you think there might be other plausible cognitive explanations for why it happens?**
**Resources for Chapter 6, others are attached**
[https://www.youtube.com/watch?v=LSHZ_b05W7o](https://www.youtube.com/watch?v=LSHZ_b05W7o)
[http://www.scholarpedia.org/article/Chinese_room_argument](http://www.scholarpedia.org/article/Chinese_room_argument)
**Chapter 7 – Bayesian Systems in the mind (200 words)**
For this week you will read Chapter 7. After that, answer the following questions:
1.
**Do you think the Bayesian idea that beliefs can have degrees of certainty makes sense? Why or why not?**
2.
**Can you explain how Bayes’s Rule works in simple terms? Why do you think it’s so widely used?**
3.
**What’s the connection between Bayesian thinking and Gestalt approaches to perception? How do they help us understand how we make sense of what we see?**
4.
**What’s the challenge posed by the St. Petersburg game? Do you think distinguishing between monetary value and utility helps solve it?**
**Chapter 8 – Modules to Build a Mind (200 words)**
For this week, please, read Chapter 8 and answer the following questions.
1.
**What are the main differences between the three agent architectures described in the text? How would you explain these differences to someone new to the topic?**
2.
**What are the key features of modular processing, according to Fodor? Why do you think these characteristics are important?**
3.
**How does the massive modularity thesis differ from Fodor’s ideas about modularity? Which approach do you find more convincing, and why?**
4.
**Why do Cosmides and Tooby argue that a module for detecting cheaters evolved? How convincing do you find their reasoning?**
**## Chapter5.4_Informationprocessinginneuralnetworks-revisedfor3rdedition.ppt**
Chapter 5.4:
Information processing in neural networks
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
Level of analysis
– Neural networks are models of information-processing at the algorithmic level
• alternatives to symbolic algorithms
• not implementations of symbolic algorithms
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
*
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
Modeling information-processing
• Neural networks are algorithmic in a limited sense
• algorithms for updating activation levels
• learning rules are algorithmic
• But not algorithmic in the same way as PSS
• algorithms are not task-specific
• algorithms do not operate over representations
• Algorithms change weights and thresholds at the level of individual units
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
*
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
Representations
• Representations in a neural network need not be located in distinct physical locations
• The network’s “knowledge” lies in its pattern of weights and thresholds
• The power of distributed (as opposed to localist) networks comes from the fact that the network doesn’t need a separate unit to code every feature to which it is sensitive
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
*
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
Problem
• In what sense do neural networks actually contain representations?
Superpositional storage
• Once a network has been trained, all its knowledge is encoded in a single set of weights
• Each instance of information-processing involves an input vector and the weight vector
• This makes it difficult to think about the network’s knowledge as composed of discrete items (e.g. particular beliefs)
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
*
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
No clear distinction between information storage and information processing
• Parallel rather than serial processing as activation spreads through a network
• Knowledge distributed across a network (rather than stored in discrete symbol structures)
• Processing does not reply on explicit rules (other than those governing how activation flows through the network)
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
*
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
Biological plausibility: compare to PSS
– Many cognitive abilities are difficult to model in a rule-based way
Context effects in perception (explosion of rules)
Pattern completion
Language (particularly languages such as English with many irregular forms)
– Connectionist networks are very successful on pattern recognition tasks (e.g. mine/rock)
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
*
**## Chapter8.1_Architecturesforintelligentagents-revisedfor3rdedition.ppt**
Chapter 8.1:
Architectures for intelligent agents
*
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
Overview
• review general issue of mental architecture
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
Agent architectures
– Agent architectures illustrate the different components that make up an agent and how those components are organized
– What is an agent?
Definition: an agent is a system that perceives its environment through sensory systems of some type and acts upon that environment through effector systems.
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
Note that mental architectures were introduced in the slides to Chapter 5.
*
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
Agent architectures
– How do cognitive scientists decide which sorts of agents are worth modeling?
– Three main types of agents
Simple reflex
Goal-based
Learning
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
*
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
Simple reflex agent
– Production rules (e.g., IF… THEN rules)
– Not a cognitive system
No information processing
Simply acting on information
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
*
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
Simple reflex agent
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
*
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
Goal-based agent
– Does not simply act on information
– Works out the consequences of different possible actions and then evaluates those consequences in light of its goals
– However, no capacity for learning
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
*
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
Goal-based agent
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
*
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
Learning agent
– Can detect errors
– Can experiment with different ways of achieving its goals, in light of past failures
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
*
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
Learning agent
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
*
**## Chapter5.1_Neurallyinspiredmodelsofinformationprocessing-revisedfor3rdedition.ppt**
Chapter 5.1:
Neurally inspired models of information processing
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
Overview
– Introduce basic principles of connectionist networks
– Introduce different types of activation function
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
Features of connectionist networks
• Exploit parallel processing
• Can be used to model multiple satisfaction of soft constraints
• Do not feature explicit (content-specific) rules
• Exhibit graceful degradation
• Intended as models of information-processing at the algorithmic level
• Capable of learning
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
Note that this was introduced in chapter 3
*
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
Neurons and network units
Fig. 2
An artificial neuron:
Ii Input i
Wi The weight attached to input i
T The threshold of the neuron
X The total input to the neuron
S The output signal
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
Activation functions
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
Networks and layers
• Basic distinction
Single-unit networks [a.k.a. single layer networks]
Multilayer networks
• Different learning rules
• Only multilayer networks have hidden units
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
**Chapter5.3_Multilayernetworks-revisedfor3rdedition.ppt**
This file is too large to display. [View in new window](https://www.sweetstudy.com/files/chapter5-3-multilayernetworks-revisedfor3rdedition-ppt-10226695)
**Chapter8.4_Hybridarchitectures-revisedfor3rdedition.ppt**
This file is too large to display. [View in new window](https://www.sweetstudy.com/files/chapter8-4-hybridarchitectures-revisedfor3rdedition-ppt)
**Chapter8.2_Fodoronthemodularityofmind-revisedfor3rdedition.pptx**
This file is too large to display. [View in new window](https://www.sweetstudy.com/files/chapter8-2-fodoronthemodularityofmind-revisedfor3rdedition-pptx)
**Chapter7.3_Neuroeconomics_Bayesinthebrain-createdfor3rdedition.ppt**
This file is too large to display. [View in new window](https://www.sweetstudy.com/files/chapter7-3-neuroeconomics-bayesinthebrain-createdfor3rdedition-ppt-10226707)
**Chapter7.2_PerceptionasaBayesianproblem-createdfor3rdedition.ppt**
This file is too large to display. [View in new window](https://www.sweetstudy.com/files/chapter7-2-perceptionasabayesianproblem-createdfor3rdedition-ppt-10226705)
**Chapter6.1_Cognitivescienceanddynamicalsystems-revisedfor3rdedition.ppt**
This file is too large to display. [View in new window](https://www.sweetstudy.com/files/chapter6-1-cognitivescienceanddynamicalsystems-revisedfor3rdedition-ppt-10226699)
**Chapter6.2_Applyingdynamicalsystems-revisedfor3rdedition.ppt**
This file is too large to display. [View in new window](https://www.sweetstudy.com/files/chapter6-2-applyingdynamicalsystems-revisedfor3rdedition-ppt-10226701)
**Chapter5.2_Single-layernetworksandBooleanfunctions-revisedfor3rdedition.ppt**
This file is too large to display. [View in new window](https://www.sweetstudy.com/files/chapter5-2-single-layernetworksandbooleanfunctions-revisedfor3rdedition-ppt-10226693)
**Chapter8.3_Themassivemodularityhypothesis-revisedfor3rdedition.ppt**
This file is too large to display. [View in new window](https://www.sweetstudy.com/files/chapter8-3-themassivemodularityhypothesis-revisedfor3rdedition-ppt-10226713)
**pssh.pdf**
This file is too large to display. [View in new window](https://www.sweetstudy.com/files/pssh-pdf-10226717)
**Chapter7.1_Bayesianism_Aprimer-createdfor3rdedition.ppt**
This file is too large to display. [View in new window](https://www.sweetstudy.com/files/chapter7-1-bayesianism-aprimer-createdfor3rdedition-ppt)
**Chapter7.1_Bayesianism_Aprimer-createdfor3rdedition.ppt**
This file is too large to display. [View in new window](https://www.sweetstudy.com/files/chapter7-1-bayesianism-aprimer-createdfor3rdedition-ppt)
**## Psych421chapter5678.docx**
**Chapter 5 – Artificially Modeling the mind (200 words)**
Hello; for this week, please, read chapter 5 and answer the following questions:
1.
**How do artificial neural network models work alongside neuroscientific techniques to help us study the brain? What do they add to our understanding?**
2.
**Why do you think training is such a key part of neural network modeling? How does it shape the way these models work?**
3.
**How similar do you think artificial neural networks are to the real brain? Are there aspects where they feel biologically realistic or where they fall short?**
4.
**What are the main features of artificial neural networks? How would you compare them to the physical symbol system model of information processing?**
**Chapter 6 – The Dynamical Mind (200 words)**
During this week, please, read Chapter 6 and answer the following questions:
1.
**What’s the main idea behind the dynamical systems hypothesis? Do you find it convincing, and why or why not?**
2.
**How does the example of the Watt governor help Van Gelder make his point? Can you think of other examples that might support his argument?**
3.
**Dynamical models seem great for studying how skills like walking develop. Do you think they could also be useful for exploring other cognitive processes? Why or why not?**
4.
**The A-not-B error is a fascinating phenomenon. Do you think there might be other plausible cognitive explanations for why it happens?**
**Resources for Chapter 6, others are attached**
[https://www.youtube.com/watch?v=LSHZ_b05W7o](https://www.youtube.com/watch?v=LSHZ_b05W7o)
[http://www.scholarpedia.org/article/Chinese_room_argument](http://www.scholarpedia.org/article/Chinese_room_argument)
**Chapter 7 – Bayesian Systems in the mind (200 words)**
For this week you will read Chapter 7. After that, answer the following questions:
1.
**Do you think the Bayesian idea that beliefs can have degrees of certainty makes sense? Why or why not?**
2.
**Can you explain how Bayes’s Rule works in simple terms? Why do you think it’s so widely used?**
3.
**What’s the connection between Bayesian thinking and Gestalt approaches to perception? How do they help us understand how we make sense of what we see?**
4.
**What’s the challenge posed by the St. Petersburg game? Do you think distinguishing between monetary value and utility helps solve it?**
**Chapter 8 – Modules to Build a Mind (200 words)**
For this week, please, read Chapter 8 and answer the following questions.
1.
**What are the main differences between the three agent architectures described in the text? How would you explain these differences to someone new to the topic?**
2.
**What are the key features of modular processing, according to Fodor? Why do you think these characteristics are important?**
3.
**How does the massive modularity thesis differ from Fodor’s ideas about modularity? Which approach do you find more convincing, and why?**
4.
**Why do Cosmides and Tooby argue that a module for detecting cheaters evolved? How convincing do you find their reasoning?**
**## Chapter5.4_Informationprocessinginneuralnetworks-revisedfor3rdedition.ppt**
Chapter 5.4:
Information processing in neural networks
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
Level of analysis
– Neural networks are models of information-processing at the algorithmic level
• alternatives to symbolic algorithms
• not implementations of symbolic algorithms
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
*
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
Modeling information-processing
• Neural networks are algorithmic in a limited sense
• algorithms for updating activation levels
• learning rules are algorithmic
• But not algorithmic in the same way as PSS
• algorithms are not task-specific
• algorithms do not operate over representations
• Algorithms change weights and thresholds at the level of individual units
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
*
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
Representations
• Representations in a neural network need not be located in distinct physical locations
• The network’s “knowledge” lies in its pattern of weights and thresholds
• The power of distributed (as opposed to localist) networks comes from the fact that the network doesn’t need a separate unit to code every feature to which it is sensitive
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
*
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
Problem
• In what sense do neural networks actually contain representations?
Superpositional storage
• Once a network has been trained, all its knowledge is encoded in a single set of weights
• Each instance of information-processing involves an input vector and the weight vector
• This makes it difficult to think about the network’s knowledge as composed of discrete items (e.g. particular beliefs)
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
*
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
No clear distinction between information storage and information processing
• Parallel rather than serial processing as activation spreads through a network
• Knowledge distributed across a network (rather than stored in discrete symbol structures)
• Processing does not reply on explicit rules (other than those governing how activation flows through the network)
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
*
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
Biological plausibility: compare to PSS
– Many cognitive abilities are difficult to model in a rule-based way
Context effects in perception (explosion of rules)
Pattern completion
Language (particularly languages such as English with many irregular forms)
– Connectionist networks are very successful on pattern recognition tasks (e.g. mine/rock)
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
*
**## Chapter8.1_Architecturesforintelligentagents-revisedfor3rdedition.ppt**
Chapter 8.1:
Architectures for intelligent agents
*
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
Overview
• review general issue of mental architecture
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
Agent architectures
– Agent architectures illustrate the different components that make up an agent and how those components are organized
– What is an agent?
Definition: an agent is a system that perceives its environment through sensory systems of some type and acts upon that environment through effector systems.
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
Note that mental architectures were introduced in the slides to Chapter 5.
*
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
Agent architectures
– How do cognitive scientists decide which sorts of agents are worth modeling?
– Three main types of agents
Simple reflex
Goal-based
Learning
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
*
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
Simple reflex agent
– Production rules (e.g., IF… THEN rules)
– Not a cognitive system
No information processing
Simply acting on information
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
*
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
Simple reflex agent
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
*
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
Goal-based agent
– Does not simply act on information
– Works out the consequences of different possible actions and then evaluates those consequences in light of its goals
– However, no capacity for learning
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
*
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
Goal-based agent
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
*
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
Learning agent
– Can detect errors
– Can experiment with different ways of achieving its goals, in light of past failures
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
*
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
Learning agent
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
*
**## Chapter5.1_Neurallyinspiredmodelsofinformationprocessing-revisedfor3rdedition.ppt**
Chapter 5.1:
Neurally inspired models of information processing
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
Overview
– Introduce basic principles of connectionist networks
– Introduce different types of activation function
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
Features of connectionist networks
• Exploit parallel processing
• Can be used to model multiple satisfaction of soft constraints
• Do not feature explicit (content-specific) rules
• Exhibit graceful degradation
• Intended as models of information-processing at the algorithmic level
• Capable of learning
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
Note that this was introduced in chapter 3
*
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
Neurons and network units
Fig. 2
An artificial neuron:
Ii Input i
Wi The weight attached to input i
T The threshold of the neuron
X The total input to the neuron
S The output signal
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
Activation functions
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
Networks and layers
• Basic distinction
Single-unit networks [a.k.a. single layer networks]
Multilayer networks
• Different learning rules
• Only multilayer networks have hidden units
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
**Chapter5.3_Multilayernetworks-revisedfor3rdedition.ppt**
This file is too large to display. [View in new window](https://www.sweetstudy.com/files/chapter5-3-multilayernetworks-revisedfor3rdedition-ppt-10226695)
**Chapter8.4_Hybridarchitectures-revisedfor3rdedition.ppt**
This file is too large to display. [View in new window](https://www.sweetstudy.com/files/chapter8-4-hybridarchitectures-revisedfor3rdedition-ppt)
**Chapter8.2_Fodoronthemodularityofmind-revisedfor3rdedition.pptx**
This file is too large to display. [View in new window](https://www.sweetstudy.com/files/chapter8-2-fodoronthemodularityofmind-revisedfor3rdedition-pptx)
**Chapter7.3_Neuroeconomics_Bayesinthebrain-createdfor3rdedition.ppt**
This file is too large to display. [View in new window](https://www.sweetstudy.com/files/chapter7-3-neuroeconomics-bayesinthebrain-createdfor3rdedition-ppt-10226707)
**Chapter7.2_PerceptionasaBayesianproblem-createdfor3rdedition.ppt**
This file is too large to display. [View in new window](https://www.sweetstudy.com/files/chapter7-2-perceptionasabayesianproblem-createdfor3rdedition-ppt-10226705)
**Chapter6.1_Cognitivescienceanddynamicalsystems-revisedfor3rdedition.ppt**
This file is too large to display. [View in new window](https://www.sweetstudy.com/files/chapter6-1-cognitivescienceanddynamicalsystems-revisedfor3rdedition-ppt-10226699)
**Chapter6.2_Applyingdynamicalsystems-revisedfor3rdedition.ppt**
This file is too large to display. [View in new window](https://www.sweetstudy.com/files/chapter6-2-applyingdynamicalsystems-revisedfor3rdedition-ppt-10226701)
**Chapter5.2_Single-layernetworksandBooleanfunctions-revisedfor3rdedition.ppt**
This file is too large to display. [View in new window](https://www.sweetstudy.com/files/chapter5-2-single-layernetworksandbooleanfunctions-revisedfor3rdedition-ppt-10226693)
**Chapter8.3_Themassivemodularityhypothesis-revisedfor3rdedition.ppt**
This file is too large to display. [View in new window](https://www.sweetstudy.com/files/chapter8-3-themassivemodularityhypothesis-revisedfor3rdedition-ppt-10226713)
**pssh.pdf**
This file is too large to display. [View in new window](https://www.sweetstudy.com/files/pssh-pdf-10226717)
**Chapter7.1_Bayesianism_Aprimer-createdfor3rdedition.ppt**
This file is too large to display. [View in new window](https://www.sweetstudy.com/files/chapter7-1-bayesianism-aprimer-createdfor3rdedition-ppt)
**## Psych421chapter5678.docx**
**Chapter 5 – Artificially Modeling the mind (200 words)**
Hello; for this week, please, read chapter 5 and answer the following questions:
1.
**How do artificial neural network models work alongside neuroscientific techniques to help us study the brain? What do they add to our understanding?**
2.
**Why do you think training is such a key part of neural network modeling? How does it shape the way these models work?**
3.
**How similar do you think artificial neural networks are to the real brain? Are there aspects where they feel biologically realistic or where they fall short?**
4.
**What are the main features of artificial neural networks? How would you compare them to the physical symbol system model of information processing?**
**Chapter 6 – The Dynamical Mind (200 words)**
During this week, please, read Chapter 6 and answer the following questions:
1.
**What’s the main idea behind the dynamical systems hypothesis? Do you find it convincing, and why or why not?**
2.
**How does the example of the Watt governor help Van Gelder make his point? Can you think of other examples that might support his argument?**
3.
**Dynamical models seem great for studying how skills like walking develop. Do you think they could also be useful for exploring other cognitive processes? Why or why not?**
4.
**The A-not-B error is a fascinating phenomenon. Do you think there might be other plausible cognitive explanations for why it happens?**
**Resources for Chapter 6, others are attached**
[https://www.youtube.com/watch?v=LSHZ_b05W7o](https://www.youtube.com/watch?v=LSHZ_b05W7o)
[http://www.scholarpedia.org/article/Chinese_room_argument](http://www.scholarpedia.org/article/Chinese_room_argument)
**Chapter 7 – Bayesian Systems in the mind (200 words)**
For this week you will read Chapter 7. After that, answer the following questions:
1.
**Do you think the Bayesian idea that beliefs can have degrees of certainty makes sense? Why or why not?**
2.
**Can you explain how Bayes’s Rule works in simple terms? Why do you think it’s so widely used?**
3.
**What’s the connection between Bayesian thinking and Gestalt approaches to perception? How do they help us understand how we make sense of what we see?**
4.
**What’s the challenge posed by the St. Petersburg game? Do you think distinguishing between monetary value and utility helps solve it?**
**Chapter 8 – Modules to Build a Mind (200 words)**
For this week, please, read Chapter 8 and answer the following questions.
1.
**What are the main differences between the three agent architectures described in the text? How would you explain these differences to someone new to the topic?**
2.
**What are the key features of modular processing, according to Fodor? Why do you think these characteristics are important?**
3.
**How does the massive modularity thesis differ from Fodor’s ideas about modularity? Which approach do you find more convincing, and why?**
4.
**Why do Cosmides and Tooby argue that a module for detecting cheaters evolved? How convincing do you find their reasoning?**
**## Chapter5.4_Informationprocessinginneuralnetworks-revisedfor3rdedition.ppt**
Chapter 5.4:
Information processing in neural networks
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
Level of analysis
– Neural networks are models of information-processing at the algorithmic level
• alternatives to symbolic algorithms
• not implementations of symbolic algorithms
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
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Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
Modeling information-processing
• Neural networks are algorithmic in a limited sense
• algorithms for updating activation levels
• learning rules are algorithmic
• But not algorithmic in the same way as PSS
• algorithms are not task-specific
• algorithms do not operate over representations
• Algorithms change weights and thresholds at the level of individual units
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
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Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
Representations
• Representations in a neural network need not be located in distinct physical locations
• The network’s “knowledge” lies in its pattern of weights and thresholds
• The power of distributed (as opposed to localist) networks comes from the fact that the network doesn’t need a separate unit to code every feature to which it is sensitive
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
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Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
Problem
• In what sense do neural networks actually contain representations?
Superpositional storage
• Once a network has been trained, all its knowledge is encoded in a single set of weights
• Each instance of information-processing involves an input vector and the weight vector
• This makes it difficult to think about the network’s knowledge as composed of discrete items (e.g. particular beliefs)
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
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Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
No clear distinction between information storage and information processing
• Parallel rather than serial processing as activation spreads through a network
• Knowledge distributed across a network (rather than stored in discrete symbol structures)
• Processing does not reply on explicit rules (other than those governing how activation flows through the network)
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
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Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
Biological plausibility: compare to PSS
– Many cognitive abilities are difficult to model in a rule-based way
Context effects in perception (explosion of rules)
Pattern completion
Language (particularly languages such as English with many irregular forms)
– Connectionist networks are very successful on pattern recognition tasks (e.g. mine/rock)
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
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**## Chapter8.1_Architecturesforintelligentagents-revisedfor3rdedition.ppt**
Chapter 8.1:
Architectures for intelligent agents
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Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
Overview
• review general issue of mental architecture
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
Agent architectures
– Agent architectures illustrate the different components that make up an agent and how those components are organized
– What is an agent?
Definition: an agent is a system that perceives its environment through sensory systems of some type and acts upon that environment through effector systems.
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
Note that mental architectures were introduced in the slides to Chapter 5.
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Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
Agent architectures
– How do cognitive scientists decide which sorts of agents are worth modeling?
– Three main types of agents
Simple reflex
Goal-based
Learning
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
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Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
Simple reflex agent
– Production rules (e.g., IF… THEN rules)
– Not a cognitive system
No information processing
Simply acting on information
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
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Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
Simple reflex agent
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
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Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
Goal-based agent
– Does not simply act on information
– Works out the consequences of different possible actions and then evaluates those consequences in light of its goals
– However, no capacity for learning
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
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Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
Goal-based agent
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
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Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
Learning agent
– Can detect errors
– Can experiment with different ways of achieving its goals, in light of past failures
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
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Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
Learning agent
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
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**## Chapter5.1_Neurallyinspiredmodelsofinformationprocessing-revisedfor3rdedition.ppt**
Chapter 5.1:
Neurally inspired models of information processing
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
Overview
– Introduce basic principles of connectionist networks
– Introduce different types of activation function
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
Features of connectionist networks
• Exploit parallel processing
• Can be used to model multiple satisfaction of soft constraints
• Do not feature explicit (content-specific) rules
• Exhibit graceful degradation
• Intended as models of information-processing at the algorithmic level
• Capable of learning
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
Note that this was introduced in chapter 3
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Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
Neurons and network units
Fig. 2
An artificial neuron:
Ii Input i
Wi The weight attached to input i
T The threshold of the neuron
X The total input to the neuron
S The output signal
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
Activation functions
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
Networks and layers
• Basic distinction
Single-unit networks [a.k.a. single layer networks]
Multilayer networks
• Different learning rules
• Only multilayer networks have hidden units
Cognitive Science José Luis Bermúdez / Cambridge University Press 2020
**Chapter5.3_Multilayernetworks-revisedfor3rdedition.ppt**
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**Chapter8.4_Hybridarchitectures-revisedfor3rdedition.ppt**
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**Chapter8.2_Fodoronthemodularityofmind-revisedfor3rdedition.pptx**
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**Chapter7.3_Neuroeconomics_Bayesinthebrain-createdfor3rdedition.ppt**
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**Chapter7.2_PerceptionasaBayesianproblem-createdfor3rdedition.ppt**
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**Chapter6.1_Cognitivescienceanddynamicalsystems-revisedfor3rdedition.ppt**
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**Chapter6.2_Applyingdynamicalsystems-revisedfor3rdedition.ppt**
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**Chapter5.2_Single-layernetworksandBooleanfunctions-revisedfor3rdedition.ppt**
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**Chapter8.3_Themassivemodularityhypothesis-revisedfor3rdedition.ppt**
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**pssh.pdf**
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**Chapter7.1_Bayesianism_Aprimer-createdfor3rdedition.ppt**
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