Phillip Hintikka Kieval
Biographic Data
| ID | 5997095 |
|---|---|
| NAME | Phillip Hintikka Kieval |
| GIVEN NAMES | Phillip Hintikka |
| FAMILY NAME | Kieval |
| SIGNATURE | KIEVAL P H |
| AFFILIATIONS | University of Cambridge |
| ORCID | 0000-0001-5369-0322 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 2 |
| AUTHOR COUNT | 3 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2022 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 1 |
Neural Scaling Laws and Science Without Theory
Mapping representational mechanisms with deep neural networks
The predominance of machine learning based techniques in cognitive neuroscience raises a host of philosophical and methodological concerns. Given the messiness of neural activity, modellers must make choices about how to structure their raw data to make inferences about encoded representations. This leads to a set of standard methodological assumptions about when abstraction is appropriate in neuroscientific practice. Yet, when made uncritically …
Permission to believe is not permission to believe at will
According to doxastic involuntarism, we cannot believe at will. In this paper, I argue that permissivism, the view that, at times, there is more than one way to respond rationally to a given body of evidence, is consistent with doxastic involuntarism. Rober (Mind 128(511):837–859, 2019a, Philos Phenom Res 1–17, 2019b) argues that, since permissive situations are possible, cognitively healthy agents can believe at will. However, Roeber (Philos Phe…
Mapping representational mechanisms with deep neural networks
The predominance of machine learning based techniques in cognitive neuroscience raises a host of philosophical and methodological concerns. Given the messiness of neural activity, modellers must make choices about how to structure their raw data to make inferences about encoded representations. This leads to a set of standard methodological assumptions about when abstraction is appropriate in neuroscientific practice. Yet, when made uncritically …
Mapping representational mechanisms with deep neural networks
The predominance of machine learning based techniques in cognitive neuroscience raises a host of philosophical and methodological concerns. Given the messiness of neural activity, modellers must make choices about how to structure their raw data to make inferences about encoded representations. This leads to a set of standard methodological assumptions about when abstraction is appropriate in neuroscientific practice. Yet, when made uncritically …
Permission to believe is not permission to believe at will
According to doxastic involuntarism, we cannot believe at will. In this paper, I argue that permissivism, the view that, at times, there is more than one way to respond rationally to a given body of evidence, is consistent with doxastic involuntarism. Rober (Mind 128(511):837–859, 2019a, Philos Phenom Res 1–17, 2019b) argues that, since permissive situations are possible, cognitively healthy agents can believe at will. However, Roeber (Philos Phe…
Neural Scaling Laws and Science Without Theory
Computer Science (2 works) · Epistemology (2 works) · Metaphysics (2 works) · Philosophy of language (2 works) · Philosophy of science (2 works) · Abstraction (1 works) · Action Observation and Synchronization (1 works) · Artificial Intelligence (1 works) · Artificial Intelligence (1 works) · Artificial neural network (1 works)