The surprising efficiency of triple conjunction search has created a puzzle for modelers who link visual feature binding to selective attention, igniting an ongoing debate on whether features are bound with or without attention. Nordfang and Wolfe (2014) identified feature sharing and grouping as important factors in solving the puzzle and thereby established new constraints for models of visual search. Here we extend our neural dynamic model of scene perception and visual search (Grieben et al., 2020) to account for these constraints without the need for preattentive binding. By demonstrating that visual search is not only guided top-down, but that its efficiency is affected by bottom-up salience, we address a major theoretical weakness of models of conjunctive visual search (Proulx, 2007). We show how these complex interactions emerge naturally from the underlying neural dynamics.

We present a neural dynamic model that perceptually grounds nested noun phrases, i.e., noun phrases that contain further (possibly also nested) noun phrases as parts. The model receives input from the visual array and a representation of a noun phrase from language processing. It organizes a search for the denoted object in the visual scene. The model is a neural dynamic architecture of interacting neural populations which has clear interfaces with perceptual processes. It solves a set of theoretical challenges, including the problem of keeping a nested structure in short-term memory in a way that solves the problem of 2 and massive binding problem emphasized by Jackendoff (2002). The model organizes a search for the objects that are referenced in that structure. We motivate the model, demonstrate simulation results, and discuss how it differs from related models.

We extend our previous neural dynamic models of visual search and scene memory (Grieben et al. (2020); Grieben and Schöner (2021)) to move beyond classical “laboratory” stimuli. The new model can autonomously explore a natural scene and build a scene memory of recognized objects and their locations. It is also capable of guided visual search for object categories in the scene. This is achieved by learning object templates for object recognition, and feature guidance templates for visual search and associating them to categorical concepts. We address how preattentive shape can be extracted from the visual input and how scene guidance, specifically, scene gramar (Võ, 2021), emerge. For the first time, we embed feature extraction by a headless deep convolutional neural network (CNN) in a neural dynamic (DFT) architecture by learning a mapping from the distributed feature representation of the CNN to the localist representation of a dynamic neural field.