RTify: Aligning Deep Neural Networks with Human Behavioral Decisions
Authors: Yu-Ang Cheng, Ivan F Rodriguez Rodriguez, Sixuan Chen, Kohitij Kar, Takeo Watanabe, Thomas Serre
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The approach is extensively evaluated against various psychophysics experiments. We also show that the approximation can be used to optimize an ideal-observer RNN model to achieve an optimal tradeoff between speed and accuracy without human data. The resulting model is found to account well for human RT data. |
| Researcher Affiliation | Academia | Yu-Ang Cheng 1, Ivan Felipe Rodriguez 1, Sixuan Chen1, Kohitij Kar2, Takeo Watanabe1, Thomas Serre1 1 Brown University 2 York University |
| Pseudocode | Yes | Algorithm 1 Forward Function of WW circuit for Random Dot Motion 1: Input: Image, threshold 2: Output: decision times 3: Initialize s1, s2, decision times 4: while s1 < threshold and s2 < threshold do 5: I1, I2 fw(ζ(Image)) 6: Combining excitatory and inhibitory currents 7: H1 f(J11 s1 J12 s2 + I0 + I1 + IOU noise1) 8: H2 f(J22 s2 J21 s1 + I0 + I2 + IOU noise2) 9: Calculate rate of change τs + H1 (1 s1) τs + H2 (1 s2) 12: Update s1, s2, decision times 13: end while 14: return decision times |
| Open Source Code | Yes | Code and data are available at https://github.com/ Yu-Ang Cheng/RTify. |
| Open Datasets | Yes | We validate our RTify framework on two psychophysics datasets: the RDM dataset [24, 38] and a natural image categorization dataset [39]. ... taken from the COCO dataset [47] |
| Dataset Splits | No | Here, histograms are computed for RTs for correct and incorrect trials corresponding to individual experimental conditions (such as coherence levels shown here; see section 3 for details). |
| Hardware Specification | Yes | As a side note, all models were trained on single Nvidia RTX GPUs (Titan/3090/A6000) with 24/24/48GB of memory each. |
| Software Dependencies | No | The paper does not specify software dependencies with version numbers (e.g., PyTorch, TensorFlow, Python versions). |
| Experiment Setup | Yes | First, we trained an RNN consisting of 5 convolutional blocks (Convolution, Batch Norm, Re LU, Max pooling) and a 4096-unit LSTM with BPTT. ... The RNN was trained for 100 epochs using the Adam optimizer with a learning rate of 1e-4 at full coherence (c = 99.9%) for the first 10 epochs as a warm-up and 1e-5 at all coherence levels for the remaining 90 epochs. Next, we trained our two different RTify modules. For fitting human RTs, it was trained for 10,000 epochs, and for self-penalty, it was trained for 20,000 epochs. In both cases, the Adam optimizer were used, and the weights of the task-optimized RNN were frozen while training the RTify modules. |