In seeking to replicate the complex functioning of human sensory systems, neuroscience and artificial intelligence researchers face a persistent challenge: the disparity in invariances between computational models and human perception. As highlighted in recent studies, including one by a team of scientists, artificial neural networks designed to mimic the various functions of the human visual and auditory systems often exhibit invariances that do not align with those found in sensory perception. human. This contradiction raises questions about the underlying principles that guide the development of these models and their applicability in real-world scenarios.
Historically, attempts to address the issue of invariance discrepancies between computational models and human perception have involved investigating areas such as model vulnerability to adverse perturbations or the impact of noise and translations on model judgments.
Model metamers: The concept of model metamers is inspired by human perceptual metamers, which are stimuli that, although physically distinct, produce indistinguishable responses at certain stages of the sensory system. In the context of computational models, model metamers are synthetic stimuli with almost identical activations in a model to specific natural images or sounds. The critical question is whether humans can recognize that these model metamers belong to the same class as the biological signals they match.
The results of this study shed light on the significant divergence between the invariances present in computational models and those of human perception. The research team generated model metamers from various deep neural network vision and hearing models, including supervised and unsupervised learning models. In a surprising discovery, model metamers produced in the later stages of these models were consistently unrecognizable to human observers. This suggests that many invariances in these models are not shared with the human sensory system.
The effectiveness of these model metamers in exposing differences between models and humans is further demonstrated by their predictability. Interestingly, human recognition of model metamers was strongly correlated with their recognition by other models, suggesting that the gap between humans and models lies in the idiosyncratic invariances specific to each model.
In conclusion, the introduction of model metamers is an important step toward understanding and addressing disparities between computational models of sensory systems and human sensory perception. These synthetic stimuli offer new insight into the challenges researchers face in creating more biologically faithful models. While much work remains to be done, the concept of model metamers provides a promising benchmark for future model evaluation and the potential for improved artificial systems that better align with the complexities of human sensory perception.
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Niharika is a Technical Consulting Intern at Marktechpost. She is a third-year student currently pursuing her B.tech degree at the Indian Institute of technology (IIT), Kharagpur. She is a very enthusiastic person with a keen interest in machine learning, data science and artificial intelligence and an avid reader of the latest developments in these fields.
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