Modularity of Mind
By Martin Palecek. 2017
717 citations recorded by the source corpus.
Subfields: Experimental and Cognitive Psychology, Social Psychology, Sociology and Political Science
View Paper: https://www.semanticscholar.org/paper/2d581e44f9cecd8074955769afdd138c2cb947d6
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Experimental and Cognitive Psychology
- Stephen C. Levinson
- Sotaro Kita
- Marianne Gullberg
- Noah D. Goodman
- Dan Sperber
- Michael C. Frank
- Morten H. Christiansen
- N. Chater
In Social Psychology
In Sociology and Political Science
Citations in the corpus
- Direct-fit to nature: an evolutionary perspective on biological (and artificial) neural networks (2019, background): “First, the brain’s computational resources are limited, and the underlying neural code must be optimized for particular functions (e.g., Chomsky, 1980; Fodor, 1983).”
- MMBench: Is Your Multi-modal Model an All-around Player? (2023, background): “Together, they form the basis of most tasks in the real world, including recognizing objects, solving problems, and making decisions [36, 16].”
- Hierarchical Control of Cognitive Processes (2011, neutral): “The inner loop is informationally encapsulated (Fodor, 1983), so the outer loop does not know how the inner loop does what it does.”
- The relation between statistical power and inference in fMRI (2017, neutral): “Although the once-heated philosophical debate surrounding modularity has subsided in recent years [43,44], a version of the question remains salient for neuroimaging researchers: to what extent should we expect standard experimental contrasts to identify individual brain regions versus diffuse netwo”
- On the Human "Interaction Engine" (2020, methodology): “The model is a Jean Tinguely kinetic sculpture built of bric-a-brac, not a Fodorean mental module (Fodor 1983), let alone a Chomskyan point v mutation (Bickerton 1998; Hauser et al. 2002).”
- Transforming task representations to perform novel tasks (2020, neutral): “49, 90.32]), while the non-homoiconic model is achieving an average expected reward of 79.49% (bootstrap 95%-CI [69.”