For any speech category there are multiple sources of information (both acoustic and contextual) that are relevant to categorization. Complicating matters further, these sources of information are not always available simultaneously, but present themselves over the course of several hundred milliseconds. These features of spoken language complicate an already difficult task, and raise three important questions: 1) how do listeners weight different cues to the same speech category, 2) how do listeners integrate asynchronous information during speech perception and 3) how do listeners cope with contextual variability. While these questions have been explored, to varying degrees, with adults, there have been very few attempts to explore these questions from a developmental perspective. Furthermore, some of the more complex interactions between these factors remain uncharted territory even in the adult literature. For example, while adult listeners compensate for context when categorizing speech, and utilize acoustic cues as soon as they become available, we still do not know how this process is affected by context. This dissertation addresses these lingering issues by assessing 7-year-olds', 12-year-olds' and adults' perception of the /s-ʃ/ contrast (one that is influenced by multiple acoustic cues and context) using eye-tracking and the visual world paradigm. This work demonstrates that there is considerable development between 7 and 12 years of age for the /s-ʃ/ contrast in terms of real-time cue integration, cue-weighting and context compensation, and that development likely continues beyond these ages. In addition, the adult work demonstrates, for the first time, a pattern of real-time cue integration in which listeners' (both adult and child) buffer acoustic cues. Finally, several hypotheses are considered that may account for these findings, including the possibility that the unique developmental pattern of fricative perception may play an important role in understanding why adults buffer this contrast, and the implications of buffered speech perception are discussed.

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When examining objects using tactile senses, individuals often incorporate multiple sources of haptic sensory information to estimate the object’s properties. How do our brains integrate various cues to form a single percept of the object? Previous research has indicated that integration from cues across sensory modalities is optimally achieved by weighting each cue according to its variance, such that more reliable cues have more weight in determining the percept. To explore this question in the context of a within-modality haptic setting, we assessed participants’ perception of edges that cross the index, middle, and ring fingers of the right hand. We used a 2-interval forced choice (2IFC) task to measure the acuity of each digit individually, as well as the acuity of all three digits working together, by asking participants to distinguish the locations of two closely spaced plastic edges. In examining the data, we considered three perceptual models, an optimal (Bayesian) model, an unweighted average model, and a winner-take-all model. The results indicate that participants perceived sub-optimally, such that the acuity of the three digits together did not exceed that of the best individual digit. We further investigated our question by having participants unknowingly undergo a 2IFC cue conflict condition, where they thought they were touching a straight edge which was actually staggered and thus gave each digit a different positional cue. Our analyses indicate that participants did not undertake optimal cue combination but are inconclusive with respect to which suboptimal strategy they employed. / Thesis / Master of Science (MSc) / This thesis investigates the neural mechanisms behind tactile perception, specifically how the brain combines multiple sensory cues to construct a unified percept when interacting with objects through touch. Typically, optimal sensory integration involves assigning more weight to more reliable cues. Our research focused on tactile integration by examining participants’ ability to perceive the positions of edges crossing their index, middle, and ring fingers simultaneously. The results indicated that, contrary to predictions, participants exhibited various sub-optimal cue integration strategies. Their ability to perceive the combined positions of all three fingers was not superior to that of the best-performing individual finger. We also explored cue conflict situations, where the locations of the tactile cues were no longer from a straight edge, unbeknown to participants, and the results here reinforced the finding that participants did not consistently employ optimal cue combination strategies. This research offers valuable insights into how the brain processes tactile information.

<p>This thesis presents methods useful in a bin picking application, such as detection and representation of local features, pose estimation and multi-cue integration.</p><p>The scene tensor is a representation of multiple line or edge segments and was first introduced by Nordberg in [30]. A method for estimating scene tensors from gray-scale images is presented. The method is based on orientation tensors, where the scene tensor can be estimated by correlations of the elements in the orientation tensor with a number of 1<em>D</em> filters. Mechanisms for analyzing the scene tensor are described and an algorithm for detecting interest points and estimating feature parameters is presented. It is shown that the algorithm works on a wide spectrum of images with good result.</p><p>Representations that are invariant with respect to a set of transformations are useful in many applications, such as pose estimation, tracking and wide baseline stereo. The scene tensor itself is not invariant and three different methods for implementing an invariant representation based on the scene tensor is presented. One is based on a non-linear transformation of the scene tensor and is invariant to perspective transformations. Two versions of a tensor doublet is presented, which is based on a geometry of two interest points and is invariant to translation, rotation and scaling. The tensor doublet is used in a framework for view centered pose estimation of 3<em>D</em> objects. It is shown that the pose estimation algorithm has good performance even though the object is occluded and has a different scale compared to the training situation.</p><p>An industrial implementation of a bin picking application have to cope with several different types of objects. All pose estimation algorithms use some kind of model and there is yet no model that can cope with all kinds of situations and objects. This thesis presents a method for integrating cues from several pose estimation algorithms for increasing the system stability. It is also shown that the same framework can also be used for increasing the accuracy of the system by using cues from several views of the object. An extensive test with several different objects, lighting conditions and backgrounds shows that multi-cue integration makes the system more robust and increases the accuracy.</p><p>Finally, a system for bin picking is presented, built from the previous parts of this thesis. An eye in hand setup is used with a standard industrial robot arm. It is shown that the system works for real bin-picking situations with a positioning error below 1 mm and an orientation error below 1^o degree for most of the different situations.</p> / Report code: LiU-TEK-LIC-2005:15.

This thesis presents methods useful in a bin picking application, such as detection and representation of local features, pose estimation and multi-cue integration. The scene tensor is a representation of multiple line or edge segments and was first introduced by Nordberg in [30]. A method for estimating scene tensors from gray-scale images is presented. The method is based on orientation tensors, where the scene tensor can be estimated by correlations of the elements in the orientation tensor with a number of 1D filters. Mechanisms for analyzing the scene tensor are described and an algorithm for detecting interest points and estimating feature parameters is presented. It is shown that the algorithm works on a wide spectrum of images with good result. Representations that are invariant with respect to a set of transformations are useful in many applications, such as pose estimation, tracking and wide baseline stereo. The scene tensor itself is not invariant and three different methods for implementing an invariant representation based on the scene tensor is presented. One is based on a non-linear transformation of the scene tensor and is invariant to perspective transformations. Two versions of a tensor doublet is presented, which is based on a geometry of two interest points and is invariant to translation, rotation and scaling. The tensor doublet is used in a framework for view centered pose estimation of 3D objects. It is shown that the pose estimation algorithm has good performance even though the object is occluded and has a different scale compared to the training situation. An industrial implementation of a bin picking application have to cope with several different types of objects. All pose estimation algorithms use some kind of model and there is yet no model that can cope with all kinds of situations and objects. This thesis presents a method for integrating cues from several pose estimation algorithms for increasing the system stability. It is also shown that the same framework can also be used for increasing the accuracy of the system by using cues from several views of the object. An extensive test with several different objects, lighting conditions and backgrounds shows that multi-cue integration makes the system more robust and increases the accuracy. Finally, a system for bin picking is presented, built from the previous parts of this thesis. An eye in hand setup is used with a standard industrial robot arm. It is shown that the system works for real bin-picking situations with a positioning error below 1 mm and an orientation error below 1o degree for most of the different situations. / <p>Report code: LiU-TEK-LIC-2005:15.</p>