Change blindness: eradication of gestalt strategies

Arrays of eight, texture-defined rectangles were used as stimuli in a one-shot change blindness (CB) task where there was a 50% chance that one rectangle would change orientation between two successive presentations separated by an interval. CB was eliminated by cueing the target rectangle in the first stimulus, reduced by cueing in the interval and unaffected by cueing in the second presentation. This supports the idea that a representation was formed that persisted through the interval before being 'overwritten' by the second presentation (Landman et al, 2003 Vision Research 43149–164]. Another possibility is that participants used some kind of grouping or Gestalt strategy. To test this we changed the spatial position of the rectangles in the second presentation by shifting them along imaginary spokes (by ±1 degree) emanating from the central fixation point. There was no significant difference seen in performance between this and the standard task [F(1,4)=2.565, p=0.185]. This may suggest two things: (i) Gestalt grouping is not used as a strategy in these tasks, and (ii) it gives further weight to the argument that objects may be stored and retrieved from a pre-attentional store during this task

Functional smiles: tools for love, sympathy, and war

The smile is the most frequent facial expression, but not all smiles are equal. A social functional account holds that smiles of reward, affiliation, and dominance resolve basic social tasks, including rewarding behavior, social bonding, and hierarchy negotiation. Here we explore facial expression patterns associated with the three smiles. We modeled the expressions using a data-driven approach and showed that reward smiles are symmetrical and accompanied by eyebrow raising, affiliative smiles involve lip pressing, and asymmetrical dominance smiles contain nose wrinkling and upper lip raising. A Bayesian classifier analysis and a detection task revealed that the three smile types are highly distinct facial expressions. Finally, social judgments made by a separate participant group showed that the different smile type models convey different social messages. Our results provide the first detailed description of the physical form and social messages conveyed by the three functional smiles, documenting the versatility of these facial expressions

Interpreting EEG and MEG signal modulation in response to facial features: the influence of top-down task demands on visual processing strategies

The visual processing of faces is a fast and efficient feat that our visual system usually accomplishes many times a day. The N170 (an Event-Related Potential) and the M170 (an Event-Related Magnetic Field) are thought to be prominent markers of the face perception process in the ventral stream of visual processing that occur ~ 170 ms after stimulus onset. The question of whether face processing at the time window of the N170 and M170 is automatically driven by bottom-up visual processing only, or whether it is also modulated by top-down control, is still debated in the literature. However, it is known from research on general visual processing, that top-down control can be exerted much earlier along the visual processing stream than the N170 and M170 take place. I conducted two studies, each consisting of two face categorization tasks. In order to examine the influence of top-down control on the processing of faces, I changed the task demands from one task to the next, while presenting the same set of face stimuli. In the first study, I recorded participants’ EEG signal in response to faces while they performed both a Gender task and an Expression task on a set of expressive face stimuli. Analyses using Bubbles (Gosselin & Schyns, 2001) and Classification Image techniques revealed significant task modulations of the N170 ERPs (peaks and amplitudes) and the peak latency of maximum information sensitivity to key facial features. However, task demands did not change the information processing during the N170 with respect to behaviourally diagnostic information. Rather, the N170 seemed to integrate gender and expression diagnostic information equally in both tasks. In the second study, participants completed the same behavioural tasks as in the first study (Gender and Expression), but this time their MEG signal was recorded in order to allow for precise source localisation. After determining the active sources during the M170 time window, a Mutual Information analysis in connection with Bubbles was used to examine voxel sensitivity to both the task-relevant and the task-irrelevant face category. When a face category was relevant for the task, sensitivity to it was usually higher and peaked in different voxels than sensitivity to the task-irrelevant face category. In addition, voxels predictive of categorization accuracy were shown to be sensitive to task-relevant, behaviourally diagnostic facial features only. I conclude that facial feature integration during both N170 and M170 is subject to top-down control. The results are discussed against the background of known face processing models and current research findings on visual processing

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

Generation of realistic human behaviour

As the use of computers and robots in our everyday lives increases so does the need for better interaction with these devices. Human-computer interaction relies on the ability to understand and generate human behavioural signals such as speech, facial expressions and motion. This thesis deals with the synthesis and evaluation of such signals, focusing not only on their intelligibility but also on their realism. Since these signals are often correlated, it is common for methods to drive the generation of one signal using another. The thesis begins by tackling the problem of speech-driven facial animation and proposing models capable of producing realistic animations from a single image and an audio clip. The goal of these models is to produce a video of a target person, whose lips move in accordance with the driving audio. Particular focus is also placed on a) generating spontaneous expression such as blinks, b) achieving audio-visual synchrony and c) transferring or producing natural head motion. The second problem addressed in this thesis is that of video-driven speech reconstruction, which aims at converting a silent video into waveforms containing speech. The method proposed for solving this problem is capable of generating intelligible and accurate speech for both seen and unseen speakers. The spoken content is correctly captured thanks to a perceptual loss, which uses features from pre-trained speech-driven animation models. The ability of the video-to-speech model to run in real-time allows its use in hearing assistive devices and telecommunications. The final work proposed in this thesis is a generic domain translation system, that can be used for any translation problem including those mapping across different modalities. The framework is made up of two networks performing translations in opposite directions and can be successfully applied to solve diverse sets of translation problems, including speech-driven animation and video-driven speech reconstruction.Open Acces

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