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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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Published on: December 15, 2023

Emergent human-like covert attention in feedforward convolutional neural networks.

Sudhanshu Srivastava¹, William Yang Wang², Miguel P Eckstein³

¹Graduate Program in Dynamical Neuroscience, University of California, Santa Barbara, Santa Barbara, CA 93106, USA; Institute for Collaborative Biotechnologies, University of California, Santa Barbara, Santa Barbara, CA 93106, USA.

Current Biology : CB

|January 20, 2024

Summary

A convolutional neural network (CNN) optimized for accuracy learned human-like covert attention behaviors without explicit programming. These findings suggest attention signatures may emerge from optimizing task performance in neural networks.

Keywords:

Bayesian ideal observer Posner cueing computational models of attention contextual cueing convolutional neural networks covert visual attention limited resources set size statistical learning visual search

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Area of Science:

Cognitive Neuroscience
Computational Neuroscience
Artificial Intelligence

Background:

Covert attention enables visual selection without eye movements, with benefits often explained by limited resources.
Existing theories of attention are challenging to reconcile with neuronal population activity.
Understanding the neural basis of attention is crucial for explaining perception and cognition.

Purpose of the Study:

To investigate whether human-like covert attention behaviors can emerge in a feedforward convolutional neural network (CNN) without explicit attention mechanisms.
To determine if optimizing for target detection accuracy alone can replicate attentional cueing and contextual effects.
To explore the implications of these findings for theories of attention and its presence in simpler organisms.

Main Methods:

A feedforward CNN was trained on image datasets to optimize target detection accuracy.
The CNN was evaluated on its ability to utilize predictive cues and contexts in standard covert attention tasks (Posner cueing, set size, contextual cueing).
CNN performance was compared to human accuracy data and a Bayesian ideal observer model, with analyses across different training schemes and cue types.

Main Results:

The CNN successfully learned to utilize cues and contexts, mirroring human accuracy patterns in covert attention tasks.
These cueing and contextual effects were robust, generalizing across training variations, cue types, and task measures.
The CNN's effects persisted even when network resources were reduced and generalized to novel cue instances.

Conclusions:

Human-like behavioral signatures of covert attention may be an emergent property of optimizing task accuracy in neuronal populations.
Limited attentional resources may not be necessary to explain these attention phenomena.
The findings provide a potential explanation for attention-like effects observed in organisms lacking a neocortex.