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1. Region-specific fMRI dictionary for decoding face verification in humans

   https://doi.org/10.1109/IJCNN.2017.7966337  

   Yadav, Daksha; Kohli, Naman; Nagpal, Shruti; Singh, Maneet; Pandey, Prateekshit; Vatsa, Mayank; Singh, Richa; Noore, Afzel; Prabhakaran, Gokulraj; Mahajan, Harsh (May 2017, International Joint Conference on Biometrics (IJCB))

   This paper focuses on decoding the process of face verification in the human brain using fMRI responses. 2400 fMRI responses are collected from different participants while they perform face verification on genuine and imposter stimuli face pairs. The first part of the paper analyzes the responses covering both cognitive and fMRI neuro-imaging results. With an average verification accuracy of 64.79% by human participants, the results of the cognitive analysis depict that the performance of female participants is significantly higher than the male participants with respect to imposter pairs. The results of the neuroimaging analysis identifies regions of the brain such as the left fusiform gyrus, caudate nucleus, and superior frontal gyrus that are activated when participants perform face verification tasks. The second part of the paper proposes a novel two-level fMRI dictionary learning approach to predict if the stimuli observed is genuine or imposter using the brain activation data for selected regions. A comparative analysis with existing machine learning techniques illustrates that the proposed approach yields at least 4.5% higher classification accuracy than other algorithms. It is envisioned that the result of this study is the first step in designing brain-inspired automatic face verification algorithms. 

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2. The infant’s view redefines the problem of referential uncertainty in early word learning

   https://doi.org/10.1073/pnas.2107019118  

   Yu, Chen; Zhang, Yayun; Slone, Lauren K.; Smith, Linda B. (December 2021, Proceedings of the National Academy of Sciences)

   The learning of first object names is deemed a hard problem due to the uncertainty inherent in mapping a heard name to the intended referent in a cluttered and variable world. However, human infants readily solve this problem. Despite considerable theoretical discussion, relatively little is known about the uncertainty infants face in the real world. We used head-mounted eye tracking during parent–infant toy play and quantified the uncertainty by measuring the distribution of infant attention to the potential referents when a parent named both familiar and unfamiliar toy objects. The results show that infant gaze upon hearing an object name is often directed to a single referent which is equally likely to be a wrong competitor or the intended target. This bimodal gaze distribution clarifies and redefines the uncertainty problem and constrains possible solutions. 

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3. Photo Sleuth: Combining Collective Intelligence and Computer Vision to Identify Historical Portraits

   Mohanty, Vikram; Thames, David; Luther, Kurt (January 2018, ACM Conference on Collective Intelligence (CI 2018))

   Identifying people in photographs is a critical task in a wide variety of domains, from national security [7] to journalism [14] to human rights investigations [1]. The task is also fundamentally complex and challenging. With the world population at 7.6 billion and growing, the candidate pool is large. Studies of human face recognition ability show that the average person incorrectly identifies two people as similar 20–30% of the time, and trained police detectives do not perform significantly better [11]. Computer vision-based face recognition tools have gained considerable ground and are now widely available commercially, but comparisons to human performance show mixed results at best [2,10,16]. Automated face recognition techniques, while powerful, also have constraints that may be impractical for many real-world contexts. For example, face recognition systems tend to suffer when the target image or reference images have poor quality or resolution, as blemishes or discolorations may be incorrectly recognized as false positives for facial landmarks. Additionally, most face recognition systems ignore some salient facial features, like scars or other skin characteristics, as well as distinctive non-facial features, like ear shape or hair or facial hair styles. This project investigates how we can overcome these limitations to support person identification tasks. By adjusting confidence thresholds, users of face recognition can generally expect high recall (few false negatives) at the cost of low precision (many false positives). Therefore, we focus our work on the “last mile” of person identification, i.e., helping a user find the correct match among a large set of similarlooking candidates suggested by face recognition. Our approach leverages the powerful capabilities of the human vision system and collaborative sensemaking via crowdsourcing to augment the complementary strengths of automatic face recognition. The result is a novel technology pipeline combining collective intelligence and computer vision. We scope this project to focus on identifying soldiers in photos from the American Civil War era (1861– 1865). An estimated 4,000,000 soldiers fought in the war, and most were photographed at least once, due to decreasing costs, the increasing robustness of the format, and the critical events separating friends and family [17]. Over 150 years later, the identities of most of these portraits have been lost, but as museums and archives increasingly digitize and publish their collections online, the pool of reference photos and information has never been more accessible. Historians, genealogists, and collectors work tirelessly to connect names with faces, using largely manual identification methods [3,9]. Identifying people in historical photos is important for preserving material culture [9], correcting the historical record [13], and recognizing contributions of marginalized groups [4], among other reasons. 

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4. A Flexible Neural Representation of Faces in the Human Brain

   https://doi.org/10.1093/texcom/tgaa055  

   Cao, Runnan; Li, Xin; Todorov, Alexander; Wang, Shuo (January 2020, Cerebral Cortex Communications)

   null (Ed.)

   Abstract An important question in human face perception research is to understand whether the neural representation of faces is dynamically modulated by context. In particular, although there is a plethora of neuroimaging literature that has probed the neural representation of faces, few studies have investigated what low-level structural and textural facial features parametrically drive neural responses to faces and whether the representation of these features is modulated by the task. To answer these questions, we employed 2 task instructions when participants viewed the same faces. We first identified brain regions that parametrically encoded high-level social traits such as perceived facial trustworthiness and dominance, and we showed that these brain regions were modulated by task instructions. We then employed a data-driven computational face model with parametrically generated faces and identified brain regions that encoded low-level variation in the faces (shape and skin texture) that drove neural responses. We further analyzed the evolution of the neural feature vectors along the visual processing stream and visualized and explained these feature vectors. Together, our results showed a flexible neural representation of faces for both low-level features and high-level social traits in the human brain. 

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5. A distinct neurogenomic response to a trade-off between social challenge and opportunity in male sticklebacks ( Gasterosteus aculeatus )

   https://doi.org/10.1098/rsbl.2023.0253  

   Barbasch, T. A.; Behrens, C.; McLain, M.; Arredondo, E.; Bell, A. M. (November 2023, Biology Letters)

   Animals frequently make adaptive decisions about what to prioritize when faced with multiple, competing demands simultaneously. However, the proximate mechanisms of decision-making in the face of competing demands are not well understood. We explored this question using brain transcriptomics in a classic model system: threespined sticklebacks, where males face conflict between courtship and territorial defence. We characterized the behaviour and brain gene expression profiles of males confronted by a trade-off between courtship and territorial defence by comparing them to males not confronted by this trade-off. When faced with the trade-off, males behaviourally prioritized defence over courtship, and this decision was reflected in their brain gene expression profiles. A distinct set of genes and biological processes was recruited in the brain when males faced a trade-off and these responses were largely non-overlapping across two brain regions. Combined, these results raise new questions about the interplay between the neural and molecular mechanisms involved in decision-making. 

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