Modelling Face Memory Reveals Task-generalizable Representations

Current cognitive theories are cast in terms of information-processing mechanisms that use mental representations. For example, people use their mental representations to identify familiar faces under various conditions of pose, illumination and ageing, or to draw resemblance between family members. Yet, the actual information contents of these representations are rarely characterized, which hinders knowledge of the mechanisms that use them. Here, we modelled the three-dimensional representational contents of 4 faces that were familiar to 14 participants as work colleagues. The representational contents were created by reverse-correlating identity information generated on each trial with judgements of the face’s similarity to the individual participant’s memory of this face. In a second study, testing new participants, we demonstrated the validity of the modelled contents using everyday face tasks that generalize identity judgements to new viewpoints, age and sex. Our work highlights that such models of mental representations are critical to understanding generalization behaviour and its underlying information-processing mechanisms

Grounding deep neural network predictions of human categorization behavior in understandable functional features: the case of face identity

Deep neural networks (DNNs) can resolve real-world categorization tasks with apparent human-level performance. However, true equivalence of behavioral performance between humans and their DNN models requires that their internal mechanisms process equivalent features of the stimulus. To develop such feature equivalence, our methodology leveraged an interpretable and experimentally controlled generative model of the stimuli (realistic three-dimensional textured faces). Humans rated the similarity of randomly generated faces to four familiar identities. We predicted these similarity ratings from the activations of five DNNs trained with different optimization objectives. Using information theoretic redundancy, reverse correlation, and the testing of generalization gradients, we show that DNN predictions of human behavior improve because their shape and texture features overlap with those that subsume human behavior. Thus, we must equate the functional features that subsume the behavioral performances of the brain and its models before comparing where, when, and how these features are processed

Less than meets the eye: the diagnostic information for visual categorization

Current theories of visual categorization are cast in terms of information processing mechanisms that use mental representations. However, the actual information contents of these representations are rarely characterized, which in turn hinders knowledge of mechanisms that use them. In this thesis, I identified these contents by extracting the information that supports behavior under given tasks - i.e., the task-specific diagnostic information. In the first study (Chapter 2), I modelled the diagnostic face information for familiar face identification, using a unique generative model of face identity information combined with perceptual judgments and reverse correlation. I then demonstrated the validity of this information using everyday perceptual tasks that generalize face identity and resemblance judgments to new viewpoints, age, and sex with a new group of participants. My results showed that human participants represent only a proportion of the objective identity information available, but what they do represent is both sufficiently detailed and versatile to generalize face identification across diverse tasks successfully. In the second study (Chapter 3), I modelled the diagnostic facial movement for facial expressions of emotion recognition. I used the models that characterize the mental representations of six facial expressions of emotion (Happy, Surprise, Fear, Anger, Disgust, and Sad) in individual observers. I validated them on a new group of participants. With the validated models, I derived main signal variants for each emotion and their probabilities of occurrence within each emotion. Using these variants and their probability, I trained a Bayesian classifier and showed that the Bayesian classifier mimics human observers’ categorization performance closely. My results demonstrated that such emotion variants and their probabilities of occurrence comprise observers’ mental representations of facial expressions of emotion. In the third study (Chapter 4), I investigated how the brain reduces high dimensional visual input into low dimensional diagnostic representations to support a scene categorization. To do so, I used an information theoretic framework called Contentful Brain and Behavior Imaging (CBBI) to tease apart stimulus information that supports behavior (i.e., diagnostic) from that which does not (i.e., nondiagnostic). I then tracked the dynamic representations of both in magneto-encephalographic (MEG) activity. Using CBBI, I demonstrated a rapid (~170 ms) reduction of nondiagnostic information occurs in the occipital cortex and the progression of diagnostic information into right fusiform gyrus where they are constructed to support distinct behaviors. My results highlight how CBBI can be used to investigate the information processing from brain activity by considering interactions between three variables (stimulus information, brain activity, behavior), rather than just two, as is the current norm in neuroimaging studies. I discussed the task-specific diagnostic information as individuals’ dynamic and experienced-based representation about the physical world, which provides us the much-needed information to search and understand the black box of high-dimensional, deep and biological brain networks. I also discussed the practical concerns about using the data-driven approach to uncover diagnostic information

Information theoretic perspectives on en- and decoding in audition and vision

In cognitive neuroscience, encoding and decoding models mathematically relate stimuli in the outside world to neuronal or behavioural responses. While both stimuli and responses can be multidimensional variables, these models are on their own limited to bivariate descriptions of correspondences. In order to assess the cognitive or neuroscientific significance of such correspondences, a key challenge is to set them in relation to other variables. This thesis uses information theory to contextualise encoding and decoding models in example cases of audition and vision. In the first example, encoding models based on a certain operationalisation of the stimulus are relativised by models based on other operationalisations of the same stimulus material that are conceptually simpler and shown to predict the same neuronal response variance. This highlights the ambiguity inherent in an individual model. In the second example, a methodological contribution is made to the problem of relating the bivariate dependency of stimuli and responses to the history of response components with high degrees of predictability. This perspective demonstrates that only a subset of all stimulus-correlated response variance can be expected to be genuinely caused by the stimulus, while another subset is the consequence of the response’s own dynamics. In the third and final example, complex models are used to predict behavioural responses. Their predictions are grounded in experimentally controlled stimulus variance, such that interpretations of what the models predicted responses with are facilitated. Together, these three perspectives underscore the need to go beyond bivariate descriptions of correspondences in order to understand the process of perception