Within-category representational stability through the lens of manipulable objects

Abstract

Our ability to recognize an object amongst many exemplars is one of our most important features, and one that putatively distinguishes humans from non-human animals and potentially from (current) computational and artificial intelligence models. We can recognize objects consistently regardless of when we see them suggesting that we have stable representations across time and different contexts. Importantly, little is known about how humans can replicate within-category object representations across time. Here, we investigate neural stability of within-category object representations by computing the similarity between representational geometries of activity patterns for 80 images of tools obtained on different functional magnetic resonance imaging (fMRI) scanning days. We show that within-category representational stability is observable in regions that span lateral and ventral temporal cortex, inferior and superior parietal cortex, and premotor cortex – regions typically associated with tool processing and visuospatial processing. We then focus on what kinds of representations best explain the representational geometries within these regions. We test the similarity of these geometries with those coming from the different layers of a convolutional neural network, and those coming from perceived and veridical visual similarity models. We find that regions supporting within-category representational stability show stronger relationship with higher-level visual/semantic features, suggesting that neural replicability is derived from perceived and higher-level visual information. Within category representational stability may thus originate from long-range cross talk between category-specific regions (and in this case strongly within ventral and lateral temporal cortex) over more abstract, rather than veridical/lower-level, visual (sensorial) representations, and perhaps in the service of object-centered representations.