No scientific conclusion follows automatically from a statistically non-significant result, yet people routinely use non-significant results to guide conclusions about the status of theories (or the effectiveness of practices). To know whether a non-significant result counts against a theory, or if it just indicates data insensitivity, researchers must use one of: power, intervals (such as confidence or credibility intervals), or else an indicator of the relative evidence for one theory over another, such as a Bayes factor. I argue Bayes factors (...) allow theory to be linked to data in a way that overcomes the weaknesses of the other approaches. Specifically, Bayes factors use the data themselves to determine their sensitivity in distinguishing theories (unlike power), and they make use of those aspects of a theory’s predictions that are often easiest to specify (unlike power and intervals, which require specifying the minimal interesting value in order to address theory). Bayes factors provide a coherent approach to determining whether non-significant results support a null hypothesis over a theory, or whether the data are just insensitive. They allow accepting and rejecting the null hypothesis to be put on an equal footing. Concrete examples are provided to indicate the range of application of a simple online Bayes calculator, which reveal both the strengths and weaknesses of Bayes factors. (shrink)

The target article sought to question the common belief that our decisions are often biased by unconscious influences. While many commentators offer additional support for this perspective, others question our theoretical assumptions, empirical evaluations, and methodological criteria. We rebut in particular the starting assumption that all decision making is unconscious, and that the onus should be on researchers to prove conscious influences. Further evidence is evaluated in relation to the core topics we reviewed (multiple-cue judgment, deliberation without attention, and decisions (...) under uncertainty), as well as priming effects. We reiterate a key conclusion from the target article, namely, that it now seems to be generally accepted that awareness should be operationally defined as reportable knowledge, and that such knowledge can only be evaluated by careful and thorough probing. We call for future research to pay heed to the different ways in which awareness can intervene in decision making (as identified in our lens model analysis) and to employ suitable methodology in the assessment of awareness, including the requirements that awareness assessment must be reliable, relevant, immediate, and sensitive. (shrink)

Previous research showed that the repeated approaching of one stimulus and avoiding of another stimulus typically leads to more positive evaluations of the former stimuli. In the current study, we examined whether approach and avoidance training effects on evaluations of neutral stimuli can be modulated by introducing a regularity between the approach-avoidance actions and a positive or negative stimulus. In an AAT task, participants repeatedly approached one neutral non-word and avoided another neutral non-word. Half of the participants also approached a (...) negative fear-conditioned stimulus and avoided a conditioned safe stimulus. The other half of the participants avoided the CS+ and approached the CS−. Whereas participants in the avoid CS+ condition exhibited a typical AAT effect, participants in the approach CS+ condition exhibited a reversed AAT effect. These findings provide evidence for the malleability of the AAT effect when strongly valenced stimuli are approached or avoided. We discuss the practical and theoretical implications of our findings. (shrink)

The present work is concerned with a phenomenon referred to as contextual cueing. In visual search, if a searched-for target object is consistently encountered within a stable spatial arrangement of distractor objects, detecting the target becomes more efficient over time, relative to non-repeated, random arrangements. This effect is attributed to learned target-distractor spatial associations stored in long-term memory, which expedite visual search. This Thesis investigates four aspects of contextual cueing: Study 1 tackled the implicit-explicit debate of contextual cueing from a (...) new perspective. Previous studies tested explicit access to learned displays by applying a recognition test, asking observers whether they have seen a given display in the previous search task. These tests, however, typically yield mixed findings and there is an on-going controversy whether contextual cueing can be described as an implicit or an explicit effect. The current study applied the new perspective of metacognition to contextual cueing and combined a contextual cueing task with metacognitive ratings about the clarity of the visual experience, either of the display configuration or the target stimulus. Bayesian analysis revealed that there was an effect of repeated context on metacognitive sensitivity for configuration, but not target, ratings. It was concluded that effects of contextual memory on metacognition are content-specific and lead to increased metacognitive access to the display configuration, but not to the target stimulus. The more general implication is that from the perspective of metacognition, contextual cueing can be considered as an explicit effect. Study 2 aimed at testing how explicit knowledge affects memory-guided visual search. Two sets of search displays were shown to participants: explicit and implicit displays. Explicit displays were introduced prior to the search experiment, in a dedicated learning session, and observers should deliberately learn these displays. Implicit displays, on the other hand, were first shown in the search experiment and learning was incidental through repeated exposure to these displays. Contextual cueing arising from explicit and implicit displays was assessed relative to a baseline condition of non-repeated displays. The results showed a standard contextual cueing effect for explicit displays and, interestingly, a negative cueing effect for implicit displays. Recognition performance was above chance for both types of repeated displays; however, it was higher for explicit displays. This pattern of results confirmed – in part – the predictions of a single memory model of attention-moderated associative learning, in which different display types compete for behavior and explicit representations block the retrieval of implicit representations. Study 3 investigates interactions between long-term contextual memory with short-term perceptual hypotheses. Both types of perceptual memory share high similarities with respect to their content, therefore the hypothesis was formulated that they share a common memory resource. In three experiments of interrupted search with repeated and non-repeated displays, it was shown that contextual cueing expedites performance in interrupted search; however, there was no interaction of contextual cueing with the generation or the confirmation of perceptual hypotheses. Rather, the analysis of fixational eye movements showed that long-term memory exerts its influence on search performance upon the first glance of a given display, essentially affecting the starting point of the search process. The behavior of approaching the target stimulus is then a product of generating and confirming perceptual hypotheses with these processes being unaffected by long-term contextual memory. It was concluded that long-term and short-term memory representations of the same search display are independent and exhibit additive effects on search performance. Study 4 is concerned with the effects of reward on perceptual learning. It was argued that rewarding repeated displays in a contextual cueing paradigm leads to an acceleration of the learning effect; however, it was not considered whether reward also has an effect in non-repeated displays. In these displays, at least the target position is kept constant while distractor configurations are random across repetitions. Usually this is done in order to account for target position-specific probability learning in contextual cueing. However, it is possible that probability learning itself is modulated by reward. The current experiment introduced high or low reward to repeated and importantly, also non-repeated displays. It was shown that reward had a huge effect on non-repeated displays, indicating that rewarding certain target positions, irrespective of the distractor layout, facilitates RT performance. Interestingly, reward effects were even larger for non-repeated compared to repeated displays. It was concluded that reward has a strong effect on probability-, and not context learning. (shrink)