تقرير
The minimal computational substrate of fluid intelligence
| العنوان: | The minimal computational substrate of fluid intelligence |
|---|---|
| المؤلفون: | Nelson, Amy PK, Mole, Joe, Pombo, Guilherme, Gray, Robert J, Ruffle, James K, Chan, Edgar, Rees, Geraint E, Cipolotti, Lisa, Nachev, Parashkev |
| سنة النشر: | 2023 |
| المجموعة: | Computer Science Quantitative Biology |
| مصطلحات موضوعية: | Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Quantitative Biology - Neurons and Cognition |
| الوصف: | The quantification of cognitive powers rests on identifying a behavioural task that depends on them. Such dependence cannot be assured, for the powers a task invokes cannot be experimentally controlled or constrained a priori, resulting in unknown vulnerability to failure of specificity and generalisability. Evaluating a compact version of Raven's Advanced Progressive Matrices (RAPM), a widely used clinical test of fluid intelligence, we show that LaMa, a self-supervised artificial neural network trained solely on the completion of partially masked images of natural environmental scenes, achieves human-level test scores a prima vista, without any task-specific inductive bias or training. Compared with cohorts of healthy and focally lesioned participants, LaMa exhibits human-like variation with item difficulty, and produces errors characteristic of right frontal lobe damage under degradation of its ability to integrate global spatial patterns. LaMa's narrow training and limited capacity -- comparable to the nervous system of the fruit fly -- suggest RAPM may be open to computationally simple solutions that need not necessarily invoke abstract reasoning. Comment: 26 pages, 5 figures |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2308.07039 |
| رقم الأكسشن: | edsarx.2308.07039 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |