Review: Object vision in a structured world

In natural vision, objects appear at typical locations, both with respect to visual space (e.g., an airplane in the upper part of a scene) and other objects (e.g., a lamp above a table). Recent studies have shown that object vision is strongly adapted to such positional regularities. In this review we synthesize these developments, highlighting that adaptations to positional regularities facilitate object detection and recognition, and sharpen the representations of objects in visual cortex. These effects are pervasive across various types of high-level content. We posit that adaptations to real-world structure collectively support optimal usage of limited cortical processing resources. Taking positional regularities into account will thus be essential for understanding efficient object vision in the real world

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

Aerospace Medicine and Biology. A continuing bibliography with indexes

This bibliography lists 244 reports, articles, and other documents introduced into the NASA scientific and technical information system in February 1981. Aerospace medicine and aerobiology topics are included. Listings for physiological factors, astronaut performance, control theory, artificial intelligence, and cybernetics are included

Sampling in human cognition

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Brain and Cognitive Sciences, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 117-126).Bayesian Decision Theory describes optimal methods for combining sparse, noisy data with prior knowledge to build models of an uncertain world and to use those models to plan actions and make novel decisions. Bayesian computational models correctly predict aspects of human behavior in cognitive domains ranging from perception to motor control and language. However the predictive success of Bayesian models of cognition has highlighted long-standing challenges in bridging the computational and process levels of cognition. First, the computations required for exact Bayesian inference are incommensurate with the limited resources available to cognition (e.g., computational speed; and memory). Second, Bayesian models describe computations but not the processes that carry out these computations and fail to accurately predict human behavior under conditions of cognitive load or deficits. I suggest a resolution to both challenges: The mind approximates Bayesian inference by sampling. Experiments across a wide range of cognition demonstrate Monte-Carlo-like behavior by human observers; moreover, models of cognition based on specific Monte Carlo algorithms can describe previously elusive cognitive phenomena such as perceptual bistability and probability matching. When sampling algorithms are treated as process models of human cognition, the computational and process levels can be modeled jointly to shed light on new and old cognitive phenomena..by Edward Vul.Ph.D

Aerospace Medicine and Biology: A continuing bibliography with indexes, supplement 257

This bibliography lists 331 reports, articles and other documents introduced into the NASA scientific and technical information system in March 1984

Breaking Computational Barriers to Perform Time Series Pattern Mining at Scale and at the Edge

Uncovering repeated behavior in time series is an important problem in many domains such as medicine, geophysics, meteorology, and many more. With the continuing surge of smart/embedded devices generating time series data, there is an ever growing need to perform analysis on datasets of increasing size. Additionally, there is an increasing need for analysis at low power edge devices due to latency problems inherent to the speed of light and the sheer amount of data being recorded. The matrix profile has proven to be a tool highly suitable for pattern mining in time series; however, a naive approach to computing the matrix profile makes it impossible to use effectively in both the cloud and at the edge. This dissertation shows how, through the use of GPUs and machine learning, the matrix profile is computed more feasibly, both at cloud-scale and at sensor-scale. In addition, it illustrates why both of these types of computation are important and what new insights they can provide to practitioners working with time series data

The development of perceptual priors

Bayesian inference has come to be regarded as the best, statistically optimal, way to deal with the sensory uncertainty inherent in our natural environment. One way to cope with such uncertainty is to incorporate our pre-existing knowledge about the world. However, we know very little about the circumstances in which human observers integrate sensory information with prior knowledge in a way that is close to optimal. We understand even less about how the developing brain adapts to the environmental statistics, learns to use them efficiently, and what factors may underlie the development of this critical perceptual skill. We addressed these questions though a series of psychophysical experiments, in which adults and 6- to 11-year-old children estimated the location of unseen targets based on a noisy sensory cue and a prior distribution that can be learned over the course of the experiment. In Chapter 2, we showed that adult observers weighted sensory and prior information by their reliabilities but were far from optimal and struggled to generalise to untrained reliabilities in complex situations. The findings of Chapter 3 showed that 6- to 8-year-olds also weighted priors in proportion to their reliability, but they were slow to tune their behaviour to the statistics over time and remained furthest from optimal. Six- to -eight-year-olds’ performance reached adult-like levels when the priors were explicitly shown. Conversely, when the decision rule was made more complex, 6- to 8-year-olds’ abilities to distinguish between the priors broke down and adults’ performance became more child-like. These findings prompted us to investigate whether individual differences, specifically in working memory, may predict performance in adults. The distance from optimal was not predicted by working memory capacity, beyond general cognitive abilities. Together, these studies offer fresh insights into the capacity and limitations both adults and 6-11-year-old children have in learning and efficiently using novel environmental statistics