The aim of the current thesis was to investigate whether individuals with Autism Spectrum Disorders (ASDs) who show good visuospatial abilities, such as superior processing of local structures (Happ? & Frith, 2006; Mottron, Dawson, Souli?res, Hubert, & Burack, 2006), may also show intact or even superior learning of visuospatial information. In a series of experiments, with adolescents and adults with ASD and a comparison group of Typically Developing (TD) individuals, learning of spatial context was investigated using a visual search task, known as contextual cueing (Chun & Jiang, 1998). Contextual cueing refers to faster target detection in a visual search task with repeated exposure to a visual configuration (context), compared to configurations presented only once. Experiments 1 to 3 indicated that implicit learning may be reduced in ASD, however explicit learning was found to be preserved in ASD. In Experiments 4 to 6 implicit learning was re-examined. Results showed that when attention was oriented to the local parts of the display, individuals with ASD showed superior but atypical implicit learning of context relative to TDs (Experiment 4). However, when attention was directed to spatially distant, non-local contexts, performance was no different than for TD individuals (Experiment 5). Experiment 6 revealed superior implicit learning of local context in ASD and superior implicit learning of global context in TD individuals. Finally, Experiment 6 supported the view that contextual cueing is a local processing task, since both groups attended to local cues for longer periods of time. It is concluded that individuals with ASD show preserved or even superior implicit learning under conditions that involve attention to the local patterns.

The human cognitive system is stunningly powerful in some respects yet surprisingly limited in others. We can recognize an object or a face in a single glimpse and type 70 words per minute, yet we cannot hold more than a few objects at a time in visual working memory or split our attention to several locations. Attention and working memory impose major capacity limitations in cognitive processing. This ARO funded project examines the role of implicit learning in overcoming cognitive limitations. It hinges on the observation that humans process a visual display more quickly when it is encountered for a second time. The project addresses three fundamental properties about spatial learning. First, does learning have a capacity limit? Second, is learning reduced when attention is tied up by a secondary load? Third, how much does the learning ability vary across individuals, and what are the cognitive and brain mechanisms that separate good learners from poor learners? We found that spatial context learning is automatic, flexible, has high capacity, and applies to most individuals. This mechanism can potentially overcome cognitive limitations in human attention and working memory, and may assist soldiers in spatial navigation.

Previous research has shown that people can implicitly learn repeated visual contexts and use this information when locating relevant items. For example, when people are presented with repeated spatial configurations of distractor items or distractor identities in visual search, they become faster to find target stimuli in these repeated contexts over time (Chun and Jiang, 1998; 1999). Given that people learn these repeated distractor configurations and identities, might they also implicitly encode semantic information about distractors, if this information is predictive of the target location? We investigated this question with a series of visual search experiments using real-world stimuli within a contextual cueing paradigm (Chun and Jiang, 1998). Specifically, we tested whether participants could learn, through experience, that the target images they are searching for are always located near specific categories of distractors, such as food items or animals. We also varied the spatial consistency of target locations, in order to rule out implicit learning of repeated target locations. Results suggest that participants implicitly learned the target-predictive categories of distractors and used this information during search, although these results failed to reach significance. This lack of significance may have been due the relative simplicity of the search task, however, and several new experiments are proposed to further investigate whether repeated category information can benefit search.

Attention and Implicit Learning provides a comprehensive overview of the research conducted in this area. The book is conceived as a multidisciplinary forum of discussion on the question of whether implicit learning may be depicted as a process that runs independently of attention. The volume also deals with the complementary question of whether implicit learning affects the dynamics of attention, and it addresses these questions from perspectives that range from functional to neuroscientific and computational approaches. The view of implicit learning that arises from these pages is not that of a mysterious faculty, but rather that of an elementary ability of the cognitive systems to extract the structure of their environment as it appears directly through experience, and regardless of any intention to do so. Implicit learning, thus, is taken to be a process that may shape not only our behavior, but also our representations of the world, our attentional functions, and even our conscious experience. (Series B)

Vision and memory are two of the most intensively studied topics in psychology and neuroscience. This book provides a state-of-the-art account of visual memory systems. Each chapter is written by an internationally renowned researcher, who has made seminal contributions to the topic.

This volume looks at the latest research techniques to study the interaction of visual spatial learning and attention guidance with behavioral, psychophysiological, and imaging methods. Part One (behavioral methods) focuses on different paradigms of visual search like visual foraging and contextual cueing, and also methods like feature distribution analysis and search in virtual reality. Part Two (psychophysiological methods) integrates innovative uses of classical potential changes like the CDA and N2pc, with multivariate analysis methods and multi-method designs. Part Three (functional imaging) covers lesion-behavior mapping, retinotopic and grid cell mapping methods for human fMRI, as well as functional registration by hyperalignment and simultaneous eye-tracking and fMRI. In Neuromethods series style, chapters include the kind of detail and key advice from the specialists needed to get successful results in your laboratory. Cutting-edge and comprehensive, Spatial Learning and Attention Guidance is a valuable resource for all researchers and scientists who are interested in learning more about the relationship between attention and memory.