@article {5537, title = {How does the primate brain combine generative and discriminative computations in vision?}, journal = {arXiv}, year = {2024}, month = {01/2024}, abstract = {

Vision is widely understood as an inference problem. However, two con- trasting conceptions of the inference process have each been influential in research on biological vision as well as the engineering of machine vi- sion. The first emphasizes bottom-up signal flow, describing vision as a largely feedforward, discriminative inference process that filters and transforms the visual information to remove irrelevant variation and rep- resent behaviorally relevant information in a format suitable for down- stream functions of cognition and behavioral control. In this conception, vision is driven by the sensory data, and perception is direct because the processing proceeds from the data to the latent variables of interest. The notion of {\textquotedblleft}inference{\textquotedblright} in this conception is that of the engineering litera- ture on neural networks, where feedforward convolutional neural net- works processing images are said to perform inference. The alternative conception is that of vision as an inference process in Helmholtz{\textquoteright}s sense, where the sensory evidence is evaluated in the context of a generative model of the causal processes that give rise to it. In this conception, vi- sion inverts a generative model through an interrogation of the sensory evidence in a process often thought to involve top-down predictions of sensory data to evaluate the likelihood of alternative hypotheses. The authors include scientists rooted in roughly equal numbers in each of the conceptions and motivated to overcome what might be a false dichotomy between them and engage the other perspective in the realm of theory and experiment. The primate brain employs an unknown algorithm that may combine the advantages of both conceptions. We explain and clar- ify the terminology, review the key empirical evidence, and propose an empirical research program that transcends the dichotomy and sets the stage for revealing the mysterious hybrid algorithm of primate vision.

}, url = {https://arxiv.org/abs/2401.06005}, author = {Benjamin Peters and James J. DiCarlo and Todd Gureckis and Ralf Haefner and Leyla Isik and Joshua Tenenbaum and Talia Konkle and Thomas Naselaris and Kimberly Stachenfeld and Zenna Tavares and Doris Tsao and Ilker Yildirim and Nikolaus Kriegeskorte} } @article {1356, title = {Intelligent Information Loss: The Coding of Facial Identity, Head Pose, and Non-Face Information in the Macaque Face Patch System}, journal = {The Journal of Neuroscience }, volume = {35}, year = {2015}, month = {05/2015}, chapter = {7069}, abstract = {

Faces are a behaviorally important class of visual stimuli for primates. Recent work in macaque monkeys has identified six discrete face areas where most neurons have higher firing rates to images of faces compared with other objects (Tsao et al., 2006). While neurons in these areas appear to have different tuning (Freiwald and Tsao, 2010; Issa and DiCarlo, 2012), exactly what types of information and, consequently, which visual behaviors neural populations within each face area can support, is unknown. Here we use population decoding to better characterize three of these face patches (ML/MF, AL, and AM). We show that neural activity in all patches contains information that discriminates between the broad categories of face and nonface objects, individual faces, and nonface stimuli. Information is present in both high and lower firing rate regimes. However, there were significant differences between the patches, with the most anterior patch showing relatively weaker representation of nonface stimuli. Additionally, we find that pose-invariant face identity information increases as one moves to more anterior patches, while information about the orientation of the head decreases. Finally, we show that all the information we can extract from the population is present in patterns of activity across neurons, and there is relatively little information in the total activity of the population. These findings give new insight into the representations constructed by the face patch system and how they are successively transformed.

}, doi = {10.1523/JNEUROSCI.3086-14.2015}, url = {http://www.ncbi.nlm.nih.gov/pubmed/25948258}, author = {Ethan Meyers and Mia Borzello and W. A. Freiwald and Doris Tsao} }