Learning goals for the course Flashcards
Can you answer and fulfill the learning goals for the entire course?
The historical background for the modern philosophy of science (1900s onwards)
Dualism, Empiricism and Idealism
Understand what is/was regarded as justified beliefs
Analytic, synthetic, a priori, a posteriori
Understanding of how science and
pseudoscience can be differentiated, using
creationism and evolutionary theory as examples
(The demarcation problem)
The demarcation problem tackles how to differentiate science from pseudoscience.
Science relies on testing and explaining the natural world through evidence, while pseudoscience often lacks these qualities.
For instance, creationism, which asserts divine creation, falls into pseudoscience, whereas evolutionary theory is scientific, being based on empirical evidence and testability.
Understanding some of the vulnerabilities of our
own field – the importance of good practices,
using precognition research as an example
Certain practices or beliefs can lead to pseudoscience rather than genuine scientific inquiry.
Precognition, the supposed ability to predict future events, is often considered untestable and lacks empirical evidence to support its claims.
Understanding how logical positivism separates
meaningful from meaningless sentences
Logical positivism distinguishes meaningful from meaningless sentences based on empirical verifiability and logical coherence.
Meaningless statements lack empirical grounding or violate logical principles.
Protocol sentences, reflecting direct observations, are deemed meaningful.
Theoretical statements must derive from or be testable against observations to be considered meaningful.
Understanding the problems facing logical positivism, e.g.
verification of general sentences and the Quine-Duhem
thesis
Logical positivism faces difficulties when trying to verify general statements, such as theoretical ones, using only observational evidence.
The Quine-Duhem thesis further complicates this by suggesting that when a theory fails to match observations, it’s not always clear which part of the theory is at fault, as multiple interconnected assumptions are involved.
Understanding the induction problem
The “induction problem” refers to the challenge of justifying the process of drawing general conclusions from specific observations.
It’s about how we can make reliable predictions or generalizations about the world based on past experiences.
Understanding how falsification follows deductive
procedures
Falsification, which involves attempting to prove a theory wrong rather than confirming it, fits into deductive logic, where the truth of conclusions is guaranteed by the truth of premises.
(Karl popper)
Understanding how Popper understands
falsifiability as a characteristic that separates
scientific from non-scientific statements (solves the
demarcation problem)
Popper’s idea that scientific statements should be open to falsification through empirical testing.
Scientific theories should make predictions that can be tested against observations, and if those predictions are proven false, it indicates that the theory is flawed or incomplete.
Understanding Kuhn’s idea of paradigms and
paradigm shifts
Paradigm Shifts: These occur when there’s a fundamental change in the prevailing scientific framework, often catalyzed by significant revolutions or breakthroughs.
Holism and Perception: Kuhn’s notion of holism suggests that our understanding of the world is holistic, shaped by the theoretical frameworks and paradigms we subscribe to.
Understanding why paradigms may be
incommensurable
Incommensurability: This concept suggests that paradigms are not directly comparable or commensurable because they entail different underlying assumptions, methods, and ways of interpreting phenomena.
Capability to reflect on what amounts to truth
Different paradigms influence our perception of truth in these disciplines.
E.g. The Millikan oil drop experiment and the cognitive revolution
Understanding how behaviourism is an externalist paradigm
In behaviorism, being an externalist paradigm means focusing solely on observable behavior and its relationship with the environment.
This approach disregards internal mental states and emphasizes controlling the environment to explain, predict, and control behavior.
Behaviorism operates on the premise that behavior is a response to stimuli, and understanding and influencing behavior requires manipulating external factors rather than delving into the mind’s inner workings.
Understanding what operant conditioning is, and how it
differs from classical conditioning
Operant conditioning focuses on how behavior is shaped by its consequences, such as reinforcement or punishment, without considering inner mental processes.
Classical conditioning deals with involuntary responses triggered by stimuli, where associations are formed between stimuli and responses.
Understanding the connection to empiricism and logical
positivism
Behaviorism asserts that the proper study of the mind is the study of behavior, which can be understood as operations on the environment. This aligns with empiricism.
Logical positivism stresses intersubjective and external verification, denying the significance of inner states.
Being able to critically reflect on the assumptions of
behaviourism
Critics, like Dennett, argue against behaviorism’s dismissal of inner causes, suggesting that it oversimplifies complex behaviors and ignores cognitive processes.
Being able to see how behaviourism is still relevant today
It provides practical methods for understanding and modifying behavior, applicable in various fields such as education, therapy, and organizational management.
It continues to shape psychological research and applications,
Understanding the intellectual history leading to the
cognitive revolution
The intellectual history leading to the cognitive revolution is characterized by the dominance of behaviorism
Behaviorism, influenced by empiricism and logical positivism, advocated for the study of behavior as operations on the environment and focused on predicting and controlling behavior without delving into inner states.
Understanding the assumptions of
computationalism
Cognition as Computation: The core assumption is that cognitive processes can be understood as computations on mental representations.
Implementation Independence: Computationalism assumes that computation is independent of the hardware it runs on.
Systematically Interpretable Symbol Manipulation: Computation, within the framework of computationalism, involves symbol manipulation following strict syntax rules.
Understanding what (mental) representations are
Mental representations refer to the internal structures or symbols that the mind uses to represent external stimuli, concepts, or knowledge
Understanding David Marr’s three levels of analysis
Computational Theory: This level deals with understanding the goal of the computation, why it’s appropriate, and the logic behind the strategy used to achieve it.
Representation and Algorithm: At this level, the focus is on implementing the computational theory. It addresses questions like what representation is used for the input and output, and what algorithm is employed for the transformation.
Hardware Implementation: This level concerns itself with realizing the representation and algorithm physically.
Understanding the impact of the Chinese Room
Argument
This argument raises questions about whether computational processes alone can account for genuine understanding and consciousness.
Searle presents a scenario where a person who does not understand Chinese is inside a room, receiving Chinese symbols and producing responses according to a rule book. Despite appearing to understand Chinese to an outside observer, the person inside the room does not actually comprehend the language.
Understanding the Symbol Grounding Problem
The Symbol Grounding Problem refers to the challenge of understanding how symbols, such as those used in language or computation, acquire their meanings.
The problem: Explaining how these symbols come to represent real-world objects, concepts, or events. (Grounding in real life)
Understanding the different levels of Turing
indistinguishability
The different levels of Turing indistinguishability refer to the ability of a computational model to mimic human cognitive processes to such an extent that it cannot be distinguished from human cognition.
T1: Basic simulation with limited resemblance to human behavior.
T2: Improved simulation with some nuanced aspects of cognition.
T3: Advanced simulation closely resembling human cognition in various tasks.
T4: Virtually indistinguishable from human cognition, with high-level capabilities.
Ability to critically reflect on the scope of the Chinese
Room argument
Implications on the Symbol Grounding Problem and Turing indistinguishability: Chinese Room targeting limited functional mimicry (T2).
Whole-room-argument:
Understanding might emerge from the system as a whole or through causal interaction with the world.
Understanding the limitations of the computationalist
paradigm seen from the connectionist paradigm
Neural network dynamics over internal computation and questions the independence of algorithmic and implementation levels.
Connectionism prioritizes empirical evidence over imagination in understanding AI capabilities.
Advantages such as speed and flexibility in addressing contextual challenges.
Understanding how neural networks work on a broad
level
Neural networks operate as black boxes, taking input data, processing it through layers of interconnected units (neurons), and producing an output.
Through training, the network learns to adjust the weights of connections between neurons to minimize the difference between its output and the desired output.
This process, called backpropagation, allows neural networks to learn complex patterns and relationships in data without explicit programming.
Understanding how semantic content may emerge from
the network, i.e. how symbols may get grounded
Semantic content emerges from interconnected units, like neurons, below the representational level in computationalism, where neural networks underlie behaviors attributed to internal computation.
Classical AI, exemplified by Shakey the robot, faces challenges representing vision due to slower processing compared to the human brain.
Understanding how perception can be seen as inference based on
sensory input
Perceptual inference is done following Bayes’ rule. (Prior knowledge)
Understanding how action is necessary to select the best inference
Best inference = the inference that minimises prediction error
The capability to reflect on the relevance of this for the symbol
grounding problem and artificial intelligence
Perception isn’t direct but probabilistic, influenced by prior beliefs and current observations.
Action refines perception, as exemplified by viewing objects from different angles.
Predictive processing emphasizes updating hypotheses based on sensory input.
Appreciating how prediction error minimisation may explain mind
attributes such as emotion, introspection, privileged access and self
Emotion: PEM suggests emotions arise from the brain’s attempt to minimize prediction errors regarding internal states.
Introspection: PEM views introspection as inference on mental causes, where the brain tries to understand and predict its own cognitive processes.
Privileged Access: Individuals have direct access to their own prediction errors, leading to unique insights into their mental states.
The Self: PEM proposes that the self emerges from the brain’s construction of a self-model, continually updated based on sensory input and action outcomes.
Understanding the difference between access
consciousness and phenomenal consciousness
Access consciousness: Mental content availability for cognitive processes.
Phenomenal consciousness: Subjective “feel” of experience.
Understanding, how we may able to investigate
consciousness in cognitive science
Understanding consciousness involves investigating its neural correlates. Techniques like EEG reveal subconscious processing even when participants don’t report conscious awareness. For instance, the attentional blink phenomenon demonstrates subconscious processing of stimuli despite participants’ unawareness.
Philosophical considerations, like Thomas Nagel’s “What is it like to be a bat?” emphasize subjective experience and its irreducibility to functional or intentional states.
Capability to reflect on how consciousness is seen from
the viewpoints of behaviourism, computationalism,
connectionism and predictive processing
Behaviourism: Focuses on observable behavior, translating mental processes into actions. Challenges include operationalizing conscious experience for behavioral study.
Computationalism: Posits that consciousness is a form of information processing, implementable in any system capable of computation. Questions arise regarding why and how information processing gives rise to subjective experience.
Connectionism: Views consciousness as dependent on brain-like structures and distributed networks. However, it raises questions about how such models instantiate conscious experience.
Predictive processing: Models consciousness as hierarchical information processing, where perception provides prediction errors to update internal models of the world. Challenges include explaining why subjective experience emerges from such processing.
