Editorial: Neural correlates of visual learning and object representation in inferior temporal lobe

Mark A. G. Eldridge ^1* Yasuko Sugase-Miyamoto ²

• ¹ Newcastle University, Newcastle upon Tyne, United Kingdom
• ² Human Informatics and Interaction Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Ibaraki, Japan

The inferior temporal cortex (IT) sits at the apex of the ventral visual stream - the neural pathway that specializes in the processing of object identity (Mishkin et al., 1983). IT receives dense anatomical inputs from visual areas V4 and V2 and sends projections to the perirhinal cortex, medial temporal lobe, frontal cortex, and many subcortical regions (Distler et al., 1993; Webster et al., 1991). Neurons in IT tend to have narrow tuning for complex conjunctions of features, and their response properties are generally stable across multiple presentations of the same stimulus (Desimone et al., 1984; Gross et al., 1972). These observations are consistent with a critical role of IT in object perception. However, IT has also been shown to be critical for several forms of visual object memory, and the activity of neurons in this region can be modulated during learning (Fahy et al., 1993; Pearl et al., 2024).Despite the growing body of literature demonstrating a role for subregions of the inferior temporal cortex in the learning of tasks that place demands on visual memory (Emadi & Esteky, 2014; Fuster & Jervey, 1981; Koida & Komatsu, 2007; Sigala & Logothetis, 2002), relatively little is known about the specific mechanisms via which neuronal activity in these regions subserves the behavioral functions attributed to them. The current Research Topic collection includes articles that explore such mechanisms in the inferior temporal cortex of nonhuman primates (NHP) using multi-electrode array recordings, electrocorticography, and aspiration lesions, along with a review of the responses of IT neurons to visual experience, and computational modeling of object recognition in the ventral visual stream. Shimizu et al. (2024) recorded from subregions of IT – Areas TE and TEO – using multielectrode arrays while monkeys learned to categorize images based on the similarity of perceptual features. Neurons in TE encoded category learning more strongly than those in TEO. The time-course of neural responses in TE was consistent with a feedback component from other brain areas. Ichwansyah et al. (2024) collected electrocorticography (ECoG) recordings from IT and dorso-medial prefrontal cortex (dmPFC) while monkeys learned to categorize video clips of animate and inanimate objects. Subregions of both areas carried information necessary for animacy category decoding, but an inter-connected network of subregions within IT demonstrated the highest category selectivity, in several frequency bands. Like the Shimizu study, Ichwansyah and colleagues also found evidence that feedback projections to temporal cortex - to a region in the superior temporal sulcus, in particular - contributed to categorization accuracy. Li et al. (2024) performed aspiration removals of Area TE or Rhinal cortex, and compared the abilities of these two groups to control subjects in a test of rapid categorization based on visual perceptual features. Consistent with Shimizu et al., and several published studies (Matsumoto et al, 2016; Eldridge et al., 2018; Setogawa et al., 2021), Li et al. observed impaired performance in the group with TE removals. In contrast, the group with Rhinal removals performed the task as accurately as controls, but appeared to make decisions more slowly, implicating Rhinal cortex in the process of rapid categorization, of the type needed when distinguishing predator from prey in the wild.Yamane (2024) presents a comprehensive review of the neural mechanisms observed in IT that have the potential to support visual experience/memory across multiple timescales. Among the changes in neural activity reported, repetition suppression is suggested as a fundamental, task-invariant, characteristic of neurons in IT, but similar mechanisms may exist in other (earlier) regions of the visual system.Quaia and Krauzlis (2024) present a model of recognition memory demonstrating that a V1-like layer can recognize objects with greater than 80% accuracy. Thus, they propose that visual recognition may be a parallel process, wherein earlier regions rapidly detect stimuli in the periphery at low resolution thereby enabling fine foveal recognition supported by IT. This model provides clues as to how IT might contribute to object/face recognition, and is consistent with the conclusions of the four other papers in this collection. Overall, this collection of studies sheds light on the causal links between neural activity and behavioural outcomes, and provides thought-provoking ideas about how IT functions in concert with other brain regions to support visual memory.