In everyday life, people categorize objects so
easily that they do not realize how complex this task is. Objects from
the same category are very different from one another. Even the same
object never projects the exact same image to the retina, because of
changes in the surrounding illumination, the particular viewpoint from
which the person is looking at the object, how close the person is to
the object, etc. Furthermore, we are capable of extracting different
forms of categorical information from the same objects, depending on
environmental demands (e.g., from a single face we can extract
information about age, gender, race, etc.).
How are faces and other objects encoded in the brain?
processing in the brain results in a re-description of the sensory
input in terms of a number of "high-level" object properties. Much
effort has been dedicated to understanding which object properties are
represented "independently" or “separably” from other properties, and
which properties are represented in a more "holistic" or “integral”
manner. Given that most researchers agree on the importance of this
distinction between separable and integral object properties, it is
surprising how little effort has been dedicated to improve the way in
which these concepts are tested and measured. By far the most common
approach is to use operational definitions of independence, which are
linked to rather vague conceptual definitions. A disadvantage of this
approach is that different researchers use different operational
definitions, often leading to contradictory conclusions. General
recognition theory (GRT; Ashby & Soto, 2015)
is a multidimensional extension of signal detection theory that has
solved such problems in psychophysics, by providing a unified
theoretical framework in which different forms of independence can be
defined and linked to operational tests.
An important line of research in our lab has extended GRT to allow the
study of dimensional separability through a variety of behavioral and
neuroimaging tests, all linked within a coherent framework. Our first
step in this direction was the development of GRT with Individual
Differences (GRT-wIND; Soto et al., 2015; Soto & Ashby; 2015), an extension of GRT that allows a better dissociation between perceptual
and decisional forms of separability than traditional models. These
advances in GRT have been accompanied by the development of a
user-friendly R package (grtools; see Soto et al., 2017),
which allows scientists without a computational background to easily
apply GRT-wIND and other GRT analyses in their own research.
Our second step has been to extend GRT to study the
separability of brain representations through neuroimaging. We have
accomplished this by linking GRT to neural encoding
odels from computational neuroscience (Soto, Vucovich, & Ashby, 2018; see also Ashby & Soto, 2016).
The resulting theoretical framework allowed us, for the first time, to
link behavioral and brain measures of separability. Unlike previous
approaches, our framework formally specifies the relation between these
different levels of perceptual and brain representation, providing the
ols for a truly integrative research approach in the study of dimensional independence.
We are currently working on validating this extended framework and applying it to the study of face dimensions.
How does learning about objects influence their neural encoding?
We have developed a Bayesian theory of generalization (Soto, Gershman, & Niv, 2014; Soto et al., 2015)
proposing that separable dimensions exist mostly because they allow
easy classification of natural objects. This suggests that category
learning itself might influence the way in which objects are encoded,
facilitating their representation in terms of independent dimensions
that have been useful for categorization in the past. In line with this
idea, we have shown that although unfamiliar morphed face dimensions
are non-separable (i.e., integral), extensive categorization training
with stimuli varying in such dimensions makes them more psychologically
privileged and increases their separability (Soto & Ashby; 2015). Relatedly, categorization training makes object recognition more independent to changes in viewpoint (Soto & Wasserman; 2016).
We have used reverse correlation and visual adaptation techniques to
more precisely characterize how categorization training influences the
visual representation of face identity (Soto, 2018).
We are currently performing an fMRI experiment aimed at determining how
the neural representation of face identity changes with categorization
training. We will analyze the data from this experiment using both
traditional multivariate analyses and our own GRT tests of neural separability.
Learning about the reward value or emotional valence of objects can
also influence their neural representation. For example, attention is
automatically biased towards visual stimuli and properties that predict
positive feedback or reward. Neurophysiological and neuroimaging data
suggest that the tail of the caudate could be the site where learning
of such reward-driven attentional biases is implemented. We have
recently proposed a neurocomputational model suggesting that learning
of associations between visual representations and rewards in the
caudate may influence those same visual representations via closed
loops involving visual cortex and the basal ganglia. The model can
explain the basic behavioral effect of value-based attentional capture,
and we are currently working on simulating related neurophysiological
and behavioral phenomena. This model also makes specific predictions
about how reward learning should change the encoding of the rewarded
and unrewarded stimuli, which we expect to test in the near future.
Do depression and anxiety influence face encoding?
Faces are some of the most important objects encountered by people in
their everyday lives, and several psychological disorders (e.g.,
depression) involve abnormal processing of face categories (in
particular, emotional expression). More specifically, previous research
shown that emotional expression interferes with processing of other
face dimensions (e.g., gender) to a greater degree in people with
depressive symptoms than in people without such symptoms. We are
currently studying this effect using tools from multidimensional
signal detection theory to determine the independence of face