Vision informs our thoughts, emotions,
and actions. Visual input from the retina travels through a cascade of
processes in the neocortex to the highest echelons of the brain. An important
step to explaining these higher brain functions is to first understand and
quantitatively characterize the neuronal circuits behind the transformation of
the pixel-like visual input to the complex behaviorally relevant format in
higher brain centers.
Recently,
Rutishauser and colleagues courageously attacked this question by recording the
activity of individual neurons in the human brain while subjects view and act
upon images of faces [1]. The researchers studied the amygdala, a
region of the brain that plays a central role in processing emotions [2]. Higher brain centers that govern
complex behavior are typically difficult to study, and the amygdala is no
exception. Studies in rodents and non-human primates can take advantage of
electrophysiological techniques to monitor the activity of individual neurons,
but it is not always trivial to design behavioral paradigms that tap into the
rich repertoire of human emotions. Non-invasive studies of the human amygdala
suffer from poor spatial and/or temporal resolution. Rutishauser et al. [1] combined the best of both worlds by
examining neuronal activity in epileptic patients in whom electrodes had been
implanted for clinical reasons [3, 4]. This type of recording can provide insights about human
cognition at the level of individual neurons and local circuits.
Previous
single unit studies have revealed that neurons in the primate amygdala (in
humans and monkeys) respond to complex visual shapes including faces and other
stimuli [5-8]. However, it was not clear whether these responses require
visual presentation of the whole stimulus, or whether certain parts or features
of the stimulus are sufficient to elicit a selective response. Because the
amygdala is involved in recognizing emotions, the integration of different
features into a whole percept may provide clues about how emotions are
processed. Rutishauser et al. [1] hypothesized that the representation in
the amygdala may have ‘holistic’ characteristics: that is, that neurons might
be particularly sensitive to whole stimuli as opposed to stimulus parts. The
authors used an experimental paradigm in which face images are presented
through ‘bubbles’ such that only partial information is available to the viewer
who has to make a categorical discrimination based on the input.
What do neurons in the amygdala say about
all this holistic business? Rutishauser et
al. [1] found that several amygdala neurons
prefer whole stimuli as opposed to specific parts or features. These neurons
show surprising sensitivity in their firing rate responses to small degrees of
occlusion in the stimuli, suggesting a ‘holistic’ representation.
Computational models can help us
interpret the empirical findings. The problem of object completion from partial
information has received significant attention in the computational
neuroscience literature. Object completion is relevant to the current study
because the images were seen through bubbles, making object recognition from
partial information a necessary step for a putative ‘holistic’ representation.
Attractor networks show a remarkable ability to complete patterns by driving
activity according to well-specified dynamical rules that guide the system from
arbitrary starting points towards stored memories [9]. Some authors have speculated that the
neuronal responses in the hippocampus are reminiscent of the dynamical patterns
described by attractor networks [10]. The extent to which these similarities
extend to the amygdala is not clear but it is tempting to speculate that this
type of recurrent connectivity is at the heart of the holistic representations
that Rutishauser described.
The
computational models also highlight the difficulties inherent in definitions
about wholes and parts. There is often an anthropomorphic distinction between
wholes and parts. Further inspection shows that these definitions are far from
trivial. Isn’t a face a part of a whole individual? Or why not consider the
eyes as a separate whole? Is ‘F’ a whole letter or is it part of the letter ‘E’?
Perhaps the distinction between features and wholes can be accounted, at least
partly, by experience with particular combinations of features that tend to
appear together in certain configurations. In contrast to other professions, in
Science, good work can lead to more work. Several questions emerge from the
work of Rutishauser et al.
*An
expanded version of this article appeared in Current Biology. Tang H and Kreiman G. (2011). Face Recognition:
Vision and Emotions beyond the Bubble. Current Biology 21:21.
