Model-Based Theorizing in Cognitive Neuroscience
Bibliographic Data
| ID | 8397382 |
|---|---|
| Authors | Elizabeth E Irvine (0000-0003-1861-9533, Cardiff University, corresponding author), Elizabeth Irvine |
| Year | 2016 |
| Volume | 67 |
| Issue | 1 |
| Pages | 143-168 |
| Publication date | 2016-03-01 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | The British Journal for the Philosophy of Science (JOURNAL) |
| Journal identifiers | ISSN: 0007-0882 • E-ISSN: 1464-3537 |
| Publisher | Oxford University Press (PUBLISHER • GB) |
| DOI | 10.1093/bjps/axu034 |
| OpenAlex | W2073085828 |
| Language | EN |
| Citations received | 3 |
| References cited | 42 |
Weisberg ( [2006] ) and Godfrey-Smith ( [2006] , [2009] ) distinguish between two forms of theorizing: data-driven ‘abstract direct representation’ and modelling. The key difference is that when using a data-driven approach, theories are intended to represent specific phenomena and so directly represent them, while models may not be intended to represent anything and so represent targets indirectly, if at all. The aim here is to compare and analyse these practices, in order to outline an account of model-based theorizing that involves direct representational relationships. This is based on the way that computational templates are now used in cognitive neuroscience, and draws on the dynamic and tentative process of any kind of theory construction, and the idea of partial, purpose-relative representation. 1 Introduction2 Modelling and Abstract Direct Representation 2.1 Abstract direct representation (data-driven) 2.2 Model-based theorizing 2.3 Similarities and differences 2.3.1 Similarities 2.3.2 Differences3 Model-Based Theorizing in Cognitive Neuroscience 3.1 The key ideas: Templates and plausibility 3.2 Steps of theorizing4 What Kind of Representation?5 Theorizing and Robustness6 Conclusion
Cognition · Cognitive science · Computational neuroscience · Epistemology · Key (lock) · Mental representation · Process (computing) · Representation (politics) · Artificial Intelligence · Biomedical Text Mining and Ontologies · Cognitive Neuroscience · Computer Science · Functional Brain Connectivity Studies · Neuroscience · Philosophy · Philosophy and History of Science · Psychology
Science in the Age of Computer Simulation
Simulation and Similarity
Explaining Science
Re-Engineering Philosophy for Limited Beings
Some varieties of robustness
A theory of cortical responses
How persuasive is a good fit? A comment on theory testing.
Probabilistic models of cognition
Credible worlds
A Neural Substrate of Prediction and Reward
The Neural Basis of Decision Making
Bayesian Fundamentalism or Enlightenment? On the explanatory status and theoretical contributions of Bayesian models of cognition
The strategy of model-based science
Learning from the existence of models
Modelling and representing
The Robust Volterra Principle
Robustness Analysis
Computational Models
No Place for Particles in Relativistic Quantum Theories
Who is a Modeler
Economic Modelling as Robustness Analysis
| Unique citing works | 3 |
|---|---|
| Citations per year | 0,27 |
| Citation span | 2015 - 2025 (11) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 1 |