Loading...

A neurocomputational model of decision and confidence in object recognition task

Roshan, S. S ; Sharif University of Technology | 2024

30 Viewed
  1. Type of Document: Article
  2. DOI: 10.1016/j.neunet.2024.106318
  3. Publisher: 2024
  4. Abstract:
  5. How does the brain process natural visual stimuli to make a decision? Imagine driving through fog. An object looms ahead. What do you do? This decision requires not only identifying the object but also choosing an action based on your decision confidence. In this circumstance, confidence is making a bridge between seeing and believing. Our study unveils how the brain processes visual information to make such decisions with an assessment of confidence, using a model inspired by the visual cortex. To computationally model the process, this study uses a spiking neural network inspired by the hierarchy of the visual cortex in mammals to investigate the dynamics of feedforward object recognition and decision-making in the brain. The model consists of two modules: a temporal dynamic object representation module and an attractor neural network-based decision-making module. Unlike traditional models, ours captures the evolution of evidence within the visual cortex, mimicking how confidence forms in the brain. This offers a more biologically plausible approach to decision-making when encountering real-world stimuli. We conducted experiments using natural stimuli and measured accuracy, reaction time, and confidence. The model's estimated confidence aligns remarkably well with human-reported confidence. Furthermore, the model can simulate the human change-of-mind phenomenon, reflecting the ongoing evaluation of evidence in the brain. Also, this finding offers decision-making and confidence encoding share the same neural circuit. © 2024
  6. Keywords:
  7. Spiking convolutional neural network ; Humans ; Models, Neurological ; Neural Networks, Computer ; Pattern Recognition, Visual ; Photic Stimulation ; Recognition, Psychology ; Visual Perception ; Brain ; Convolutional neural networks ; Feedforward neural networks ; Mammals ; Object recognition ; Recurrent neural networks ; Change-of-mind ; Confidence ; Convolutional neural network ; Decision-making modeling ; Objects recognition ; Perceptual decision making ; Recurrent attractor-based decision-making model ; Temporal object recognition ; Temporal objects ; Adult ; Computer model ; Feed forward neural network ; Female ; Human ; Human experiment ; Male ; Normal human ; Reaction time ; Spiking neural network ; Visual cortex ; Visual memory ; Visual stimulation ; Artificial neural network ; Biological model ; Computer simulation ; Photostimulation ; Physiology ; Procedures ; Recognition ; Vision ; Visual pattern recognition ; Decision making
  8. Source: Neural Networks ; Volume 175 , 2024 ; 08936080 (ISSN)
  9. URL: https://pubmed.ncbi.nlm.nih.gov/38643618