On the necessity of recurrent processing during object recognition: it depends on the need for scene segmentation

• Published in: The Journal of neuroscience Vol. 41; no. 29; pp. 6281 - 6289
• Main Authors: Seijdel, Noor, Loke, Jessica, van de Klundert, Ron, van der Meer, Matthew, Quispel, Eva, van Gaal, Simon, de Haan, Edward H F, Scholte, H Steven
• Format: Journal Article
• Language: English
• Published: United States Society for Neuroscience 21.07.2021
• Subjects: Artificial neural networks; Complexity; EEG; Electroencephalography; Evoked response (psychophysiology); Exploratory behavior; Feature extraction; Impairment; Information processing; Neural networks; Object recognition; Pattern recognition; Segmentation
• ISSN: 0270-6474; 1529-2401
• DOI: 10.1523/JNEUROSCI.2851-20.2021

While feed-forward activity may suffice for recognizing objects in isolation, additional visual operations that aid object recognition might be needed for real-world scenes. One such additional operation is figure-ground segmentation; extracting the relevant features and locations of the target object while ignoring irrelevant features. In this study of 60 human participants (female and male), we show objects on backgrounds of increasing complexity to investigate whether recurrent computations are increasingly important for segmenting objects from more complex backgrounds. Three lines of evidence show that recurrent processing is critical for recognition of objects embedded in complex scenes. First, behavioral results indicated a greater reduction in performance after masking objects presented on more complex backgrounds; with the degree of impairment increasing with increasing background complexity. Second, electroencephalography (EEG) measurements showed clear differences in the evoked response potentials (ERPs) between conditions around time points beyond feed-forward activity and exploratory object decoding analyses based on the EEG signal indicated later decoding onsets for objects embedded in more complex backgrounds. Third, Deep Convolutional Neural Network performance confirmed this interpretation; feed-forward and less deep networks showed a higher degree of impairment in recognition for objects in complex backgrounds compared to recurrent and deeper networks. Together, these results support the notion that recurrent computations drive figure-ground segmentation of objects in complex scenes. The incredible speed of object recognition suggests that it relies purely on a fast feed-forward build-up of perceptual activity. However, this view is contradicted by studies showing that disruption of recurrent processing leads to decreased object recognition performance. Here we resolve this issue by showing that how object recognition is resolved, and whether recurrent processing is crucial, depends on the context in which it is presented. For objects presented in isolation or in 'simple' environments, feed-forward activity could be sufficient for successful object recognition. However, when the environment is more complex, additional processing seems necessary to select the elements that belong to the object, and by that segregate them from the background.