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

Connectionist simulation of attitude learning: Asymmetries in the acquisition of positive and negative evaluations

Connectionist computer simulation was employed to explore the notion that, if attitudes guide approach and avoidance behaviors, false negative beliefs are likely to remain uncorrected for longer than false positive beliefs. In Study 1, the authors trained a three-layer neural network to discriminate "good" and "bad" inputs distributed across a two-dimensional space. "Full feedback" training, whereby connection weights were modified to reduce error after every trial, resulted in perfect discrimination. "Contingent feedback," whereby connection weights were only updated following outputs representing approach behavior, led to several false negative errors (good inputs misclassified as bad). In Study 2, the network was redesigned to distinguish a system for learning evaluations from a mechanism for selecting actions. Biasing action selection toward approach eliminated the asymmetry between learning of good and bad inputs under contingent feedback. Implications for various attitudinal phenomena and biases in social cognition are discussed

Expanding the boundaries of evaluative learning research: how intersecting regularities shape our likes and dislikes

Over the last 30 years, researchers have identified several types of procedures through which novel preferences may be formed and existing ones altered. For instance, regularities in the presence of a single stimulus (as in the case of mere exposure) or 2 or more stimuli (as in the case of evaluative conditioning) have been shown to influence liking. We propose that intersections between regularities represent a previously unrecognized class of procedures for changing liking. Across 4 related studies, we found strong support for the hypothesis that when environmental regularities intersect with one another (i.e., share elements or have elements that share relations with other elements), the evaluative properties of the elements of those regularities can change. These changes in liking were observed across a range of stimuli and procedures and were evident when self-report measures, implicit measures, and behavioral choice measures of liking were employed. Functional and mental explanations of this phenomenon are offered followed by a discussion of how this new type of evaluative learning effect can accelerate theoretical, methodological, and empirical development in attitude research

Inducing a Stroop Effect

We examine the conditions that lead to Stroop interference for a meaningless linguistic label. Tiffany's cognitive model of drug abuse implies that individuals will respond more slowly to drug-related words compared to neutral words in an emotional Stroop task, because the former have many automatic associations (e.g. positive expectancies). To examine this proposal, we trained participants to associate a meaningless label with either one other word or several other words and examined the induced Stroop interference for these meaningless labels. In two experiments, and contrary to expectations from Tiffany's work, we observed greatest Stroop interference for the meaningless label with just one association. These results are discussed in terms of associative learning theory

Reevaluating evaluative conditioning: A nonassociative explanation of conditioning effects in the visual evaluative conditioning paradigm

In 2 studies, the authors investigated whether evaluative conditioning (EC) is an associative phenomenon. Experiment 1 compared a standard EC paradigm with nonpaired and no-treatment control conditions. EC effects were obtained only when the conditioned stimulus (CS) and unconditioned stimulus (UCS) were rated as perceptually similar. However, similar EC effects were obtained in both control groups. An earlier failure to obtain EC effects was reanalyzed in Experiment 2. Conditioning-like effects were found when comparing a CS with the most perceptually similar UCSs used in the procedure but not when analyzing a CS rating with respect to the UCS with which it was paired during conditioning. The implications are that EC effects found in many studies are not due to associative learning and that the special characteristics of EC (conditioning without awareness and resistance to extinction) are probably nonassociative artifacts of the EC paradigm

Frontal midline theta and N200 amplitude reflect complementary information about expectancy and outcome evaluation

Feedback ERN (fERN) and frontal midline theta have both been proposed to index a dopamine-like reinforcement learning signal in anterior cingulate cortex (ACC). We investigated these proposals by comparing fERN amplitude and theta power with respect to their sensitivities to outcome valence and probability in a previously collected EEG dataset. Bayesian model comparison revealed a dissociation between the two measures, with fERN amplitude mainly sensitive to valence and theta power mainly sensitive to probability. Further, fERN amplitude was highly correlated with the portion of theta power that is consistent in phase across trials (i.e., evoked theta power). These results suggest that although both measures provide valuable information about cognitive function of frontal midline cortex, fERN amplitude is specifically sensitive to dopamine reinforcement learning signals whereas theta power reflects the ACC response to unexpected events

Extranoematic artifacts: neural systems in space and topology

During the past several decades, the evolution in architecture and engineering went through several stages of exploration of form. While the procedures of generating the form have varied from using physical analogous form-finding computation to engaging the form with simulated dynamic forces in digital environment, the self-generation and organization of form has always been the goal. this thesis further intend to contribute to self-organizational capacity in Architecture. The subject of investigation is the rationalizing of geometry from an unorganized point cloud by using learning neural networks. Furthermore, the focus is oriented upon aspects of efficient construction of generated topology. Neural network is connected with constraining properties, which adjust the members of the topology into predefined number of sizes while minimizing the error of deviation from the original form. The resulted algorithm is applied in several different scenarios of construction, highlighting the possibilities and versatility of this method