The importance of contrast features in rat vision

Abstract

Models of object recognition have mostly focused upon the hierarchical processing of objects from local edges up to more complex shape features. An alternative strategy that might be involved in pattern recognition centres around coarse-level contrast features. In humans and monkeys, the use of such features is most documented in the domain of face perception. Given prior suggestions that, generally, rodents might rely upon contrast features for object recognition, we hypothesized that they would pick up the typical contrast features relevant for face detection. We trained rats in a face-nonface categorization task with stimuli previously used in computer vision and tested for generalization with new, unseen stimuli by including manipulations of the presence and strength of a range of contrast features previously identified to be relevant for face detection. Although overall generalization performance was low, it was significantly modulated by contrast features. A model taking into account the summed strength of contrast features predicted the variation in accuracy across stimuli. Finally, with deep neural networks, we further investigated and quantified the performance and representations of the animals. The findings suggest that rat behaviour in visual pattern recognition tasks is partially explained by contrast feature processing.

A computationally informed comparison between the strategies of rodents and humans in visual object recognition

“…This is the most direct evidence available in the literature that rats resolve object recognition tasks through a very different and computationally simpler strategy compared to humans. The CNN approach does not inform us how we can verbalize this simpler strategy, but based upon earlier work (Schnell and colleagues, 2023); Vermaercke & Op de Beeck, 2012) we would hypothesize that rats rely upon visual contrast features (e.g., this area is darker/lighter than that other area). Such contrast features are also used by humans and monkeys, e.g.…”

“…We performed this with the standardized DNN layer activations in the principal component space as inputs, before ReLU, to our 24 training stimuli (see Figure 1), i.e. all stimuli of the Training, Dimension learning and Transformations protocols The layers of AlexNet were divided into 13 sublayers, similar as in Schnell and colleagues (2023) and Vinken & Op de Beeck (2021).…”

“…The setup is identical to the one used by Schnell and colleagues (2019) and Schnell and colleagues (2023). A short description will follow here.…”

“…Supplemental Figure 4 The pixel dissimilarity matrix of the 4x11 stimulus grid. This matrix is calculated in the exact same way as (Schnell et al, 2023). The colourbar indicates pixel dissimilarity values, normalized between 0 and 100.…”

Rodents’ visual gamma as a biomarker of pathological neural conditions

“…Regarding cortical organization, rodent V1 neurons exhibit similarities with those of primates, including retinotopic maps (Gias et al., 2005; Zhuang et al., 2017), contrast sensitivity (Busse et al., 2011; Schnell, Vinken et al., 2023) and orientation selectivity (Ghodrati et al., 2016; Glickfeld et al., 2013; Niell & Stryker, 2008; Ohki et al., 2005) albeit with variations in spatial clustering (‘pinwheel‐like’ in primates and ‘salt‐and‐pepper’ in rodents (Ibbotson & Jung, 2020; Jang et al., 2020)). Interestingly, despite their limited visual acuity, the degree of rodent orientation selectivity is similar to that seen in the cat or monkey (Ghodrati et al., 2016; Niell & Stryker, 2008), suggesting that this may be a fundamental aspect of cortical computation (Van Hooser, 2007).…”