Out of one's mind: A study of involuntary semantic memories

Peer reviewe

Integrative priming occurs rapidly and uncontrollably during lexical processing

Lexical priming, whereby a prime word facilitates recognition of a related target word (e.g., nurse ? doctor), is typically attributed to association strength, semantic similarity, or compound familiarity. Here, the authors demonstrate a novel type of lexical priming that occurs among unassociated, dissimilar, and unfamiliar concepts (e.g., horse ? doctor). Specifically, integrative priming occurs when a prime word can be easily integrated with a target word to create a unitary representation. Across several manipulations of timing (stimulus onset asynchrony) and list context (relatedness proportion), lexical decisions for the target word were facilitated when it could be integrated with the prime word. Moreover, integrative priming was dissociated from both associative priming and semantic priming but was comparable in terms of both prevalence (across participants) and magnitude (within participants). This observation of integrative priming challenges present models of lexical priming, such as spreading activation, distributed representation, expectancy, episodic retrieval, and compound cue models. The authors suggest that integrative priming may be explained by a role activation model of relational integration

Cognitive processes in categorical and associative priming: a diffusion model analysis

Cognitive processes and mechanisms underlying different forms of priming were investigated using a diffusion model approach. In a series of 6 experiments, effects of prime-target associations and of a semantic and affective categorical match of prime and target were analyzed for different tasks. Significant associative and categorical priming effects were found in standard analyses of response times (RTs) and error frequencies. Results of diffusion model analyses revealed that priming effects of associated primes were mapped on the drift rate parameter (v), while priming effects of a categorical match on a task-relevant dimension were mapped on the extradecisional parameters (t(0) and d). These results support a spreading activation account of associative priming and an explanation of categorical priming in terms of response competition. Implications for the interpretation of priming effects and the use of priming paradigms in cognitive psychology and social cognition are discussed

Translating expert system rules into Ada code with validation and verification

The purpose of this ongoing research and development program is to develop software tools which enable the rapid development, upgrading, and maintenance of embedded real-time artificial intelligence systems. The goals of this phase of the research were to investigate the feasibility of developing software tools which automatically translate expert system rules into Ada code and develop methods for performing validation and verification testing of the resultant expert system. A prototype system was demonstrated which automatically translated rules from an Air Force expert system was demonstrated which detected errors in the execution of the resultant system. The method and prototype tools for converting AI representations into Ada code by converting the rules into Ada code modules and then linking them with an Activation Framework based run-time environment to form an executable load module are discussed. This method is based upon the use of Evidence Flow Graphs which are a data flow representation for intelligent systems. The development of prototype test generation and evaluation software which was used to test the resultant code is discussed. This testing was performed automatically using Monte-Carlo techniques based upon a constraint based description of the required performance for the system

A distributional model of semantic context effects in lexical processinga

One of the most robust findings of experimental psycholinguistics is that the context in which a word is presented influences the effort involved in processing that word. We present a novel model of contextual facilitation based on word co-occurrence prob ability distributions, and empirically validate the model through simulation of three representative types of context manipulation: single word priming, multiple-priming and contextual constraint. In our simulations the effects of semantic context are mod eled using general-purpose techniques and representations from multivariate statistics, augmented with simple assumptions reflecting the inherently incremental nature of speech understanding. The contribution of our study is to show that special-purpose m echanisms are not necessary in order to capture the general pattern of the experimental results, and that a range of semantic context effects can be subsumed under the same principled account.›

Neurocognitive Informatics Manifesto.

Informatics studies all aspects of the structure of natural and artificial information systems. Theoretical and abstract approaches to information have made great advances, but human information processing is still unmatched in many areas, including information management, representation and understanding. Neurocognitive informatics is a new, emerging field that should help to improve the matching of artificial and natural systems, and inspire better computational algorithms to solve problems that are still beyond the reach of machines. In this position paper examples of neurocognitive inspirations and promising directions in this area are given

Seeing the invisible: The scope and limits of unconscious processing in binocular rivalry

When an image is presented to one eye and a very different image is presented to the corresponding location of the other eye, they compete for conscious representation, such that only one image is visible at a time while the other is suppressed. Called binocular rivalry, this phenomenon and its deviants have been extensively exploited to study the mechanism and neural correlates of consciousness. In this paper, we propose a framework, the unconscious binding hypothesis, to distinguish unconscious processing from conscious processing. According to this framework, the unconscious mind not only encodes individual features but also temporally binds distributed features to give rise to cortical representation, but unlike conscious binding, such unconscious binding is fragile. Under this framework, we review evidence from psychophysical and neuroimaging studies, which suggests that: (1) for invisible low level features, prolonged exposure to visual pattern and simple translational motion can alter the appearance of subsequent visible features (i.e. adaptation); for invisible high level features, although complex spiral motion cannot produce adaptation, nor can objects/words enhance subsequent processing of related stimuli (i.e. priming), images of tools can nevertheless activate the dorsal pathway; and (2) although invisible central cues cannot orient attention, invisible erotic pictures in the periphery can nevertheless guide attention, likely through emotional arousal; reciprocally, the processing of invisible information can be modulated by attention at perceptual and neural levels

Understanding Image Virality

Virality of online content on social networking websites is an important but esoteric phenomenon often studied in fields like marketing, psychology and data mining. In this paper we study viral images from a computer vision perspective. We introduce three new image datasets from Reddit, and define a virality score using Reddit metadata. We train classifiers with state-of-the-art image features to predict virality of individual images, relative virality in pairs of images, and the dominant topic of a viral image. We also compare machine performance to human performance on these tasks. We find that computers perform poorly with low level features, and high level information is critical for predicting virality. We encode semantic information through relative attributes. We identify the 5 key visual attributes that correlate with virality. We create an attribute-based characterization of images that can predict relative virality with 68.10% accuracy (SVM+Deep Relative Attributes) -- better than humans at 60.12%. Finally, we study how human prediction of image virality varies with different `contexts' in which the images are viewed, such as the influence of neighbouring images, images recently viewed, as well as the image title or caption. This work is a first step in understanding the complex but important phenomenon of image virality. Our datasets and annotations will be made publicly available.Comment: Pre-print, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 201

TLA+ Proofs

TLA+ is a specification language based on standard set theory and temporal logic that has constructs for hierarchical proofs. We describe how to write TLA+ proofs and check them with TLAPS, the TLA+ Proof System. We use Peterson's mutual exclusion algorithm as a simple example to describe the features of TLAPS and show how it and the Toolbox (an IDE for TLA+) help users to manage large, complex proofs.Comment: A shorter version of this article appeared in the proceedings of the conference Formal Methods 2012 (FM 2012, Paris, France, Springer LNCS 7436, pp. 147-154