Novel and familiar object recognition rely on the same ability.

Abstract

There is recent evidence for a domain-general object recognition ability, called O, which is distinct from general intelligence and other cognitive and personality constructs. We extend the study of O by characterizing how it generalizes to the ability to recognize familiar objects and to the ability to make judgments of the average identity of ensembles of objects. We applied latent variable modeling to data collected from a sample of adults (N = 284) in three different tasks and for six different object domains (three novel and three familiar). The results replicated prior work in finding that on average 88% of the variance of lower-order factors could be accounted by O for novel objects. The latent constructs recruited by the higher-order factor for novel objects and for familiar objects were almost perfectly correlated and therefore functionally identical. A latent factor for ensemble perception shared about 42% of the variance with O, suggesting at least strong overlap between abilities supporting judgments about individual objects and ensemble of objects. This work extends the theoretical reach of O by showing generalization across two dimensions (familiar vs. novel objects; individual vs. ensemble object perception). With respect to the structure of individual differences in high-level vision, researchers would benefit from accounting for the contribution of O when seeking to understand various domain-specific abilities.

Visual object recognition ability is not related to experience with visual arts

“…Measures of o highly similar to ours (using the same objects and tasks) have shown good divergent validity relative to measures of g and other cognitive constructs as well as personality traits ( Richler et al, 2019 ) and convergent validity with recognition of decisions about groups of objects ( Sunday et al, 2022 ) and with haptic object recognition ( Chow et al, 2022 ). Indeed, o , which is typically measured using novel objects, as in this study, is nearly perfectly correlated in latent variable modeling with object recognition ability measured with familiar objects ( Sunday et al, 2022 ). In addition, o has good predictive validity for learning to recognize nodules in chest radiographs ( Sunday et al, 2018 ).…”

“…Evidence for o was initially obtained using structural equation modeling, supporting a higher level factor accounting for performance in three different visual object recognition tasks with five different novel object categories ( Richler et al, 2019 ). The initial work found that o could account for almost 90% of the variance in lower order factors, and another study replicated this result with novel objects and extended it to show that o represents an ability that equally applies to familiar objects like birds or planes ( Sunday, Tomarken, Cho, & Gauthier, 2022 ). Importantly, o is only weakly related to general intelligence, and it is not correlated with individual differences in visual working memory capacity or global/local perceptual style ( Richler et al, 2019 ).…”

Can art change the way we see?

“…A latent-variable framework is a powerful statistical approach to identify such variability, by measuring the shared variance of several indicators for a construct of interest. This has been productive in the area of high-level vision, for instance in measuring latent factors associated with face recognition based on different tasks (Hildebrandt, Wilhelm, Herzmann, & Sommer, 2013; Ćepulić et al, 2018) or, more recently, a visual object recognition ability, o , accounting for performance across different tasks (e.g., recognition, matching, and even ensemble judgments) and categories of objects (both familiar and novel; Richler et al, 2019; Sunday, 2019). Such a domain-general factor as o is ripe for exploration of relations to other domains and could be used in future art-related studies.…”

“…Recent efforts to investigate such individual differences has used a latent variable framework to find evidence of a domain-general ability, distinct from general intelligence, which accounts for more than 85% of the variance in performance on a variety of tasks and categories of novel objects (Richler et al, 2019). The same ability can account for recognition of both novel and familiar objects, and neural correlates of this ability are distributed in several areas within the visual system (Sunday, 2019). It remains unknown what genetic and environmental factors influence this domain-general visual ability.…”

Domain-general ability underlies complex object ensemble processing.

“…This makes it clear that any suggestion that ensemble coding with faces benefit from mechanisms specifically related to social processing would require evidence that is currently lacking. In the current work, we were inspired by recent evidence for domain-general object recognition abilities (Richler et al, 2017; 2019; Sunday et al, in press). In some of these studies, structural equation modeling revealed that more than 85% of the variance in performance for different tasks, with a range of novel and familiar objects, is accounted for by a higher-level factor.…”