Apart from positive priming effects, masked prime stimuli can impair responses to a subsequent target stimulus which shares response-critical features in contrast to a target assigned to the opposite response. This counterintuitive phenomenon is called inverse priming . Here we examine the generality of this phenomenon beyond priming of motor responses. We used a non-motor cue-priming paradigm to study the underlying mechanism of inverse priming for relevant features masks which include task-relevant stimulus features and for irrelevant masks which omit task-relevant (...) features. We found inverse cue-priming effects with both types of masks. With task-irrelevant masks inverse cue-priming was emphasized in those participants being unable to perceive the prime. The existence of inverse non-motor priming under conditions where simple perceptual interactions between the stimuli are ruled out as the source of inverse priming is at odds with the view that inverse priming reflects motor inhibition. Alternatives are discussed. (shrink)

In masked priming tasks responses are usually faster when prime and target require identical rather than different responses. Previous research has extensively manipulated the nature and number of response-affording stimuli. However, little is known about the constraints of masked priming regarding the nature and number of response alternatives. The present study explored the limits of masked priming in a six-choice reaction time task, where responses from different fingers of both hands were required. We studied participants that were either experts for (...) the type of response or novices. Masked primes facilitated responding to targets that required the same response, responses with a different finger of the same hand, and with a homologous finger of the other hand. These effects were modulated by expertise. The results show that masked primes facilitate responding especially for experts in the S–R mapping and with increasing similarity of primed and required response. (shrink)

At the core of the Ouroboros Model lies a self-referential recursive process with alternating phases of data acquisition and evaluation. Memory entries are organized in schemata. Activation at a time of part of a schema biases the whole structure and, in particular, missing features, thus triggering expectations. An iterative recursive monitor process termed ‘consumption analysis’ is then checking how well such expectations fit with successive activations. A measure for the goodness of fit, “emotion”, provides feedback as (self-) monitoring signal. Contradictions (...) between anticipations based on previous experience and actual current data are highlighted as well as minor gaps and deficits. The basic algorithm can be applied to goal directed movements as well as to abstract rational reasoning when weighing evidence for and against some remote theories. A sketch is provided how the Ouroboros Model can shed light on rather different characteristics of human behavior including learning and meta-learning. Partial implementations proved effective in dedicated safety systems. (shrink)