1. A recurrent neural network for human object recognition
Ultra-rapid categorization (Wu, et al., 2015) has been considered as primarily feed-forward (Serre, Oliva & Poggio, 2007). However, we show that the behavior of a recurrent neural network is still compatible with the empirical findings. These results provide evidence that object recognition is supported by interactive processes in the brain.
- [ CogSci 2016 Poster, CogSci 2016 Abstract, Code ]
Lu, Q., Cox, C., Rogers, T. T., Lambon Ralph, M.A., Takahashi R. (manuscript in preparation). An interactive account for human vision: a recurrent neural network explains neural and behavioral temporal dynamics of object recognition process.
Lu, Q., & Rogers, T. T. (2016). An interactive model accounts for both ultra-rapid superordinate classification and basic-level advantage in object recognition. Poster presented at the 38th Annual Meeting of the Cognitive Science Society, Philadelphia, PA.
2. A reinforcement learning network for "counting"
Counting skill is a foundation for more sophisticated math concepts, and it takes children several years to learn to do it well. To understand this learning process, we explored how social feedback helps a DQN-like (Mnih, et al., 2015) agent to learn a counting-related task.
- [ NCPW 2016 Poster, NCPW 2016 Abstract, Code ]
Lu, Q., & McClelland, J.L. (2016). Teaching a neural network to count: reinforcement learning with “social scaffolding”. Poster presented at the 15th Neural Computation and Psychology Workshop, Philadelphia, PA.
3. Discover distributed representation with sparse MVPA methods
We developed a sparse multivoxel pattern analysis (MVPA) method that identifies task-relevant signal directly from whole-brain fMRI data. For the neural representations of faces, places, and objects, besides some classic ROIs (e.g. Kanwisher, McDermott, and Chun, 1997), we also found many additional brain regions that are spatially distributed and idiosyncratic across subjects.
- [ CNS 2015 Poster , Code ]
Cox, C. R., Lu, Q., & Rogers, T. T. (2015). Iterative Lasso: An even-handed approach to whole brain multivariate pattern analysis.
Poster presented at the 22nd Cognitive Neuroscience Society Annual Conference, San Francisco, CA.
Cox, C. R., Lu, Q., & Rogers, T. T. (2015). Iterative Lasso: An even-handed approach to whole brain multivariate pattern analysis. Poster presented at the Neuroimaging, Computational Neuroscience and Neuroengineering Workshop, Madison, WI.Other ongoing MVPA projects: