The importance of visual features in generic vs. specialized object recognition: a computational study

Contrast and luminance adaptation alter neuronal coding and perception of stimulus orientation

Sensory systems face a barrage of stimulation that continually changes along multiple dimensions. These simultaneous changes create a formidable problem for the nervous system, as neurons must dynamically encode each stimulus dimension, despite changes in other dimensions. Here, we measured how neurons in visual cortex encode orientation following changes in luminance and contrast, which are critical for visual processing, but nuisance variables in the context of orientation coding. Using information theoretic analysis and population decoding approaches, we find that orientation discriminability is luminance and contrast dependent, changing over time due to firing rate adaptation. We also show that orientation discrimination in human observers changes during adaptation, in a manner consistent with the neuronal data. Our results suggest that adaptation does not maintain information rates per se, but instead acts to keep sensory systems operating within the limited dynamic range afforded by spiking activity, despite a wide range of possible inputs.

Sensory systems produce stable stimulus representations despite constant changes across multiple stimulus dimensions. Here, the authors reveal dynamic neural coding mechanisms by testing how coding of one dimension (orientation) changes with adaptations to other dimensions (luminance and contrast).

A specialized face-processing model inspired by the organization of monkey face patches explains several face-specific phenomena observed in humans

Converging reports indicate that face images are processed through specialized neural networks in the brain –i.e. face patches in monkeys and the fusiform face area (FFA) in humans. These studies were designed to find out how faces are processed in visual system compared to other objects. Yet, the underlying mechanism of face processing is not completely revealed. Here, we show that a hierarchical computational model, inspired by electrophysiological evidence on face processing in primates, is able to generate representational properties similar to those observed in monkey face patches (posterior, middle and anterior patches). Since the most important goal of sensory neuroscience is linking the neural responses with behavioral outputs, we test whether the proposed model, which is designed to account for neural responses in monkey face patches, is also able to predict well-documented behavioral face phenomena observed in humans. We show that the proposed model satisfies several cognitive face effects such as: composite face effect and the idea of canonical face views. Our model provides insights about the underlying computations that transfer visual information from posterior to anterior face patches.

Time-varying effective connectivity during visual object naming as a function of semantic demands

Accumulating evidence suggests that visual object understanding involves a rapid feedforward sweep, after which subsequent recurrent interactions are necessary. The extent to which recurrence plays a critical role in object processing remains to be determined. Recent studies have demonstrated that recurrent processing is modulated by increasing semantic demands. Differentially from previous studies, we used dynamic causal modeling to model neural activity recorded with magnetoencephalography while 14 healthy humans named two sets of visual objects that differed in the degree of semantic accessing demands, operationalized in terms of the values of basic psycholinguistic variables associated with the presented objects (age of acquisition, frequency, and familiarity). This approach allowed us to estimate the directionality of the causal interactions among brain regions and their associated connectivity strengths. Furthermore, to understand the dynamic nature of connectivity (i.e., the chronnectome; Calhoun et al., 2014) we explored the time-dependent changes of effective connectivity during a period (200-400 ms) where adding semantic-feature information improves modeling and classifying visual objects, at 50 ms increments. First, we observed a graded involvement of backward connections, that became active beyond 200 ms. Second, we found that semantic demands caused a suppressive effect in the backward connection from inferior frontal cortex (IFC) to occipitotemporal cortex over time. These results complement those from previous studies underscoring the role of IFC as a common source of top-down modulation, which drives recurrent interactions with more posterior regions during visual object recognition. Crucially, our study revealed the inhibitory modulation of this interaction in situations that place greater demands on the conceptual system.