Extensive training leads to temporal and spatial shifts of cortical activity underlying visual category selectivity

Neurocognitive effects of a training program for poor face recognizers using shape and texture caricatures: A pilot investigation

Recent research suggested disproportional usage of shape information by people with poor face recognition, although texture information appears to be more important for familiar face recognition. Here, we tested a training program with faces that were selectively caricatured in either shape or texture parameters. Forty-eight young adults with poor face recognition skills (1 SD below the mean in at least 2/3 face processing tests: CFMT, GFMT, BFFT) were pseudo-randomly assigned to either one of two training groups or a control group (n = 16 each). Training comprised six sessions over three weeks. Per session, participants studied ten unfamiliar facial identities whose shape or texture characteristics were caricatured. Before and after training (or waiting in the control group), all participants completed EEG experiments on face learning and famous face recognition, and behavioral face processing tests. Results showed small but specific training-induced improvements: Whereas shape training improved face matching (training tasks, and to some extent GFMT), texture training elicited marked improvements in face learning (CFMT). Moreover, for the texture training group the N170 ERP was enhanced for novel faces post-training, suggesting training-induced changes in early markers of face processing. Although further research is necessary, this suggests that parameter-specific caricature training is a promising way to improve performance in people with poor face recognition skills.

Unraveling neural coding of dynamic natural visual scenes via convolutional recurrent neural networks

Traditional models of retinal system identification analyze the neural response to artificial stimuli using models consisting of predefined components. The model design is limited to prior knowledge, and the artificial stimuli are too simple to be compared with stimuli processed by the retina. To fill in this gap with an explainable model that reveals how a population of neurons work together to encode the larger field of natural scenes, here we used a deep-learning model for identifying the computational elements of the retinal circuit that contribute to learning the dynamics of natural scenes. Experimental results verify that the recurrent connection plays a key role in encoding complex dynamic visual scenes while learning biological computational underpinnings of the retinal circuit. In addition, the proposed models reveal both the shapes and the locations of the spatiotemporal receptive fields of ganglion cells.

Recurrent neural networks can explain flexible trading of speed and accuracy in biological vision

Deep feedforward neural network models of vision dominate in both computational neuroscience and engineering. The primate visual system, by contrast, contains abundant recurrent connections. Recurrent signal flow enables recycling of limited computational resources over time, and so might boost the performance of a physically finite brain or model. Here we show: (1) Recurrent convolutional neural network models outperform feedforward convolutional models matched in their number of parameters in large-scale visual recognition tasks on natural images. (2) Setting a confidence threshold, at which recurrent computations terminate and a decision is made, enables flexible trading of speed for accuracy. At a given confidence threshold, the model expends more time and energy on images that are harder to recognise, without requiring additional parameters for deeper computations. (3) The recurrent model's reaction time for an image predicts the human reaction time for the same image better than several parameter-matched and state-of-the-art feedforward models. (4) Across confidence thresholds, the recurrent model emulates the behaviour of feedforward control models in that it achieves the same accuracy at approximately the same computational cost (mean number of floating-point operations). However, the recurrent model can be run longer (higher confidence threshold) and then outperforms parameter-matched feedforward comparison models. These results suggest that recurrent connectivity, a hallmark of biological visual systems, may be essential for understanding the accuracy, flexibility, and dynamics of human visual recognition.

Bilateral engagement of the occipito-temporal cortex in response to dance kinematics in experts

Previous evidence has shown neuroplastic changes in brain anatomy and connectivity associated with the acquisition of professional visuomotor skills. Reduced hemispherical asymmetry was found in the sensorimotor and visual areas in expert musicians and athletes compared with non-experts. Moreover, increased expertise with faces, body, and objects resulted in an enhanced engagement of the occipito-temporal cortex (OTC) during stimulus observation. The present study aimed at investigating whether intense and extended practice with dance would result in an enhanced symmetric response of OTC at an early stage of action processing. Expert ballet dancers and non-dancer controls were presented with videos depicting ballet steps during EEG recording. The observation of the moving dancer elicited a posterior N2 component, being larger over the left hemisphere in dancers than controls. The source reconstruction (swLORETA) of the negativity showed the engagement of the bilateral inferior and middle temporal regions in experts, while right-lateralized activity was found in controls. The dancers also showed an early P2 and enhanced P300 responses, indicating faster stimulus processing and subsequent recognition. This evidence seemed to suggest expertise-related increased sensitivity of the OTC in encoding body kinematics. Thus, we speculated that long-term whole-body practice would result in enriched and refined action processing.

Magnetoencephalography adaptation reveals depth-cue-invariant object representations in the visual cortex

Independent of edges and 2-D shape that can be highly informative of object identity, depth cues alone can also give rise to vivid and effective object percepts. The processing of different depth cues engages segregated cortical areas, and an efficient object representation would be one that is invariant to depth cues. Here, we investigated depth-cue invariance of object representations by measuring the category-specific response to faces-the M170 response measured with magnetoencephalography. The M170 response is strongest to faces and is sensitive to adaptation, such that repeated presentation of a face diminishes subsequent M170 responses. We used this feature of the M170 and measured the degree to which the adaptation effect is affected by variations in depth cue and 3-D object shape. Subjects viewed a rapid presentation of two stimuli-an adaptor and a test stimulus. The adaptor was either a face, a chair, or a face-like oval surface, and rendered with a single depth cue (shading, structure from motion, or texture). The test stimulus was always a shaded face of a random identity, thus completely controlling for low-level influences on the M170 response to the test stimulus. In the left fusiform face area, we found strong M170 adaptation when the adaptor was a face regardless of its depth cue. This adaptation was marginal in the right fusiform and negligible in the occipital regions. Our results support the presence of depth-cue-invariant representations in the human visual system, alongside size, position, and viewpoint invariance.

Enhancement of face-sensitive ERPs in older adults induced by face recognition training

A common cognitive problem reported by older people is compromised face recognition, which is often paralleled by age-related changes in face-sensitive and memory-related components in event-related brain potentials (ERPs). We developed a new training using photorealistic caricatures based on evidence that caricatures are beneficial for people with compromised face processing. Twenty-four older participants (62-75 yrs, 13 female) completed 12 training sessions (3 per week, 60 min each) and 24 older participants (61-76 yrs, 12 female) acted as controls. Before and after training (or waiting), participants took part in a diagnostic test battery for face processing abilities, and in ERP experiments on face learning and recognition. Although performance improvements during the training provided little evidence for generalization to other face processing tasks, ERPs showed substantial training-related enhancements of face-sensitive ERPs. Specifically, we observed marked increases of the N170, P200 and N250 components, which may indicate training-induced enhancement of face detection and activation of identity-specific representations. Thus, neuronal correlates of face processing are plastic in older age, and can be modulated by caricature training.