Engineering data compendium. Human perception and performance. User's guide

The concept underlying the Engineering Data Compendium was the product of a research and development program (Integrated Perceptual Information for Designers project) aimed at facilitating the application of basic research findings in human performance to the design and military crew systems. The principal objective was to develop a workable strategy for: (1) identifying and distilling information of potential value to system design from the existing research literature, and (2) presenting this technical information in a way that would aid its accessibility, interpretability, and applicability by systems designers. The present four volumes of the Engineering Data Compendium represent the first implementation of this strategy. This is the first volume, the User's Guide, containing a description of the program and instructions for its use

Compensating inaccurate annotations to train 3D facial landmark localisation models

In this paper we investigate the impact of inconsistency in manual annotations when they are used to train automatic models for 3D facial landmark localization. We start by showing that it is possible to objectively measure the consistency of annotations in a database, provided that it contains replicates (i.e. repeated scans from the same person). Applying such measure to the widely used FRGC database we find that manual annotations currently available are suboptimal and can strongly impair the accuracy of automatic models learnt therefrom. To address this issue, we present a simple algorithm to automatically correct a set of annotations and show that it can help to significantly improve the accuracy of the models in terms of landmark localization errors. This improvement is observed even when errors are measured with respect to the original (not corrected) annotations. However, we also show that if errors are computed against an alternative set of manual annotations with higher consistency, the accuracy of the models constructed using the corrections from the presented algorithm tends to converge to the one achieved by building the models on the alternative,more consistent set

Towards Active Event Recognition

Directing robot attention to recognise activities and to anticipate events like goal-directed actions is a crucial skill for human-robot interaction. Unfortunately, issues like intrinsic time constraints, the spatially distributed nature of the entailed information sources, and the existence of a multitude of unobservable states affecting the system, like latent intentions, have long rendered achievement of such skills a rather elusive goal. The problem tests the limits of current attention control systems. It requires an integrated solution for tracking, exploration and recognition, which traditionally have been seen as separate problems in active vision.We propose a probabilistic generative framework based on a mixture of Kalman filters and information gain maximisation that uses predictions in both recognition and attention-control. This framework can efficiently use the observations of one element in a dynamic environment to provide information on other elements, and consequently enables guided exploration.Interestingly, the sensors-control policy, directly derived from first principles, represents the intuitive trade-off between finding the most discriminative clues and maintaining overall awareness.Experiments on a simulated humanoid robot observing a human executing goal-oriented actions demonstrated improvement on recognition time and precision over baseline systems

Multisensory Oddity Detection as Bayesian Inference

A key goal for the perceptual system is to optimally combine information from all the senses that may be available in order to develop the most accurate and unified picture possible of the outside world. The contemporary theoretical framework of ideal observer maximum likelihood integration (MLI) has been highly successful in modelling how the human brain combines information from a variety of different sensory modalities. However, in various recent experiments involving multisensory stimuli of uncertain correspondence, MLI breaks down as a successful model of sensory combination. Within the paradigm of direct stimulus estimation, perceptual models which use Bayesian inference to resolve correspondence have recently been shown to generalize successfully to these cases where MLI fails. This approach has been known variously as model inference, causal inference or structure inference. In this paper, we examine causal uncertainty in another important class of multi-sensory perception paradigm – that of oddity detection and demonstrate how a Bayesian ideal observer also treats oddity detection as a structure inference problem. We validate this approach by showing that it provides an intuitive and quantitative explanation of an important pair of multi-sensory oddity detection experiments – involving cues across and within modalities – for which MLI previously failed dramatically, allowing a novel unifying treatment of within and cross modal multisensory perception. Our successful application of structure inference models to the new ‘oddity detection’ paradigm, and the resultant unified explanation of across and within modality cases provide further evidence to suggest that structure inference may be a commonly evolved principle for combining perceptual information in the brain

Anterior Prefrontal Cortex Contributes to Action Selection through Tracking of Recent Reward Trends

The functions of prefrontal cortex remain enigmatic, especially for its anterior sectors, putatively ranging from planning to self-initiated behavior, social cognition, task switching, and memory. A predominant current theory regarding the most anterior sector, the frontopolar cortex (FPC), is that it is involved in exploring alternative courses of action, but the detailed causal mechanisms remain unknown. Here we investigated this issue using the lesion method, together with a novel model-based analysis. Eight patients with anterior prefrontal brain lesions including the FPC performed a four-armed bandit task known from neuroimaging studies to activate the FPC. Model-based analyses of learning demonstrated a selective deficit in the ability to extrapolate the most recent trend, despite an intact general ability to learn from past rewards. Whereas both brain-damaged and healthy controls used comparisons between the two most recent choice outcomes to infer trends that influenced their decision about the next choice, the group with anterior prefrontal lesions showed a complete absence of this component and instead based their choice entirely on the cumulative reward history. Given that the FPC is thought to be the most evolutionarily recent expansion of primate prefrontal cortex, we suggest that its function may reflect uniquely human adaptations to select and update models of reward contingency in dynamic environments

Relating behavioral context to acoustic parameters of bottlenose dolphin (Tursiops truncatus) vocalizations

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution August 2001This thesis presents methods to analyze the function of vocalizations of the bottlenose dolphin, Tursiops truncatus. The thesis uses the social interaction as the basic unit of analysis, and maintains a deliberate focus on quantitative and replicable analyses throughout. A method for determining identity of the vocalizing animal in a lagoon was developed. This method combined passive acoustic localization with video sampling to determine which animal vocalized. It fills an urgent need for unbiased identification of vocalizations of undisturbed dolphins where details of social interactions can be followed without affecting the behavior of the subjects. This method was implemented in a captive lagoon with 6 dolphins: two adult females, their two male calves, and a juvenile male and a juvenile female. This thesis also reviews the current state of analysis of the bottlenose dolphin acoustic repertoire, highlighting the need for a detailed, quantitative, and consistent study of the entire vocal repertoire. It does not attempt to do a comprehensive repertoire study, but uses several new quantitative methods to parameterize vocalizations and relate these to behavior from dolphins. Vocalizations within the lagoon tended to occur around the time of onset of behaviors produced by the focal dolphin. A comparison of vocalizations during affiliative and agonistic interactions revealed that the association of group vocalizations with the behavior of a focal animal was related to agonistic but not affiliative interactions. Using the localization/video method, vocalizations in a time window around submissive behaviors were localized and classified as having come from either dolphins engaged in the interaction or dolphins not engaged in the interaction. Vocalizations were emitted by interactants more often than expected, and by non-interactants less often than expected. Use of different vocalization types was found to vary depending on the context of the agonistic interaction. In addition, the sequence of vocalizations with respect to behaviors within the interaction mattered, with more vocalizations occurring after than before submissive behaviors. These results demonstrated that group-based analyses of vocalizations are insufficient and one must use techniques designed to focus on the level of the interaction in order to study communication and social behavior in dolphins.Funding was provided by the Waikoloa Marine Life fund, Grant No. IBN-9975523 from the National Science Foundation, a graduate student fellowship for R. Thomas from the National Science Foundation, and an Ocean Ventures Fund Grant for R. Thomas from the Woods Hole Oceanographic Institution