Boosting or choking – How conscious and unconscious reward processing modulate the active maintenance of goal-relevant information

Zedelius, Claire M.; Veling, Harm; Aarts, Henk

doi:10.1016/j.concog.2010.05.001

Cited by 47 publications

(72 citation statements)

References 23 publications

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“…Thus, in this context, awareness of the reward outcome was necessary to influence performance. Zedelius et al (2011) (see also Zedelius et al, 2014 for a review) also studied the role of consciously and unconsciously perceived rewards on the active maintenance of goal-relevant information. They used a word span task and presented either supraliminal or subliminal reward value before or after participants processed the target words.…”

Section: Does Motivational Attention Require Consciousness?mentioning

confidence: 99%

How motivation and reward learning modulate selective attention

Bourgeois

Chelazzi

Vuilleumier

2016

Progress in Brain Research

109

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Section: Does Motivational Attention Require Consciousness?mentioning

confidence: 99%

How motivation and reward learning modulate selective attention

Bourgeois

Chelazzi

Vuilleumier

2016

Progress in Brain Research

109

View full text Add to dashboard Cite

“…An example of a within-subject experiment is Zedelius, Veling, and Aarts (2011). These researchers investigated the effect of interfering information and reward on memory.…”

Section: P-population and Wp-populationmentioning

confidence: 99%

“…For simplifications in the remainder of this paper, π is omitted in the notation of all examples using the conditions from Zedelius et al (2011). Alternatively, for each person i the hypothesis could be formulated as:…”

Section: P-population and Wp-populationmentioning

confidence: 99%

“…The difference in use of H i u and will be elaborated further in the section on Bayes factors. Zedelius et al (2011) formulated several expectations concerning the ordering of success probabilities over the experimental conditions. The main expectation was that high-reward trials would have a higher success probability than low-reward trials.…”

Section: N = 1: How To Analyze the Data Of One Personmentioning

confidence: 99%

“…After each trial, the participant was given a score of 1 if all five words were recalled and 0 if not. Zedelius et al (2011) specified expectations regarding the ordering of success probabilities that can be translated in many different hypotheses. One example of an informative hypothesis based on the expectations of Zedelius et al (2011) is…”

Section: P-population and Wp-populationmentioning

confidence: 99%

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All for one or some for all? Evaluating informative hypotheses using multiple N = 1 studies

et al. 2017

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Analyses are mostly executed at the population level, whereas in many applications the interest is on the individual level instead of the population level. In this paper, multiple N = 1 experiments are considered, where participants perform multiple trials with a dichotomous outcome in various conditions. Expectations with respect to the performance of participants can be translated into so-called informative hypotheses. These hypotheses can be evaluated for each participant separately using Bayes factors. A Bayes factor expresses the relative evidence for two hypotheses based on the data of one individual. This paper proposes to "average" these individual Bayes factors in the gP-BF, the average relative evidence. The gP-BF can be used to determine whether one hypothesis is preferred over another for all individuals under investigation. This measure provides insight into whether the relative preference of a hypothesis from a pre-defined set is homogeneous over individuals. Two additional measures are proposed to support the interpretation of the gP-BF: the evidence rate (ER), the proportion of individual Bayes factors that support the same hypothesis as the gP-BF, and the stability rate (SR), the proportion of individual Bayes factors that express a stronger support than the gP-BF. These three statistics can be used to determine the relative support in the data for the informative hypotheses entertained. Software is available that can be used to execute the approach proposed in this paper and to determine the sensitivity of the outcomes with respect to the number of participants and within condition replications.

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Distinct neural responses to conscious versus unconscious monetary reward cues

Bijleveld

Custers

Stigchel

et al. 2014

Human Brain Mapping

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Human reward pursuit is often assumed to involve conscious processing of reward information. However, recent research revealed that reward cues enhance cognitive performance even when perceived without awareness. Building on this discovery, the present functional MRI study tested two hypotheses using a rewarded mental-rotation task. First, we examined whether subliminal rewards engage the ventral striatum (VS), an area implicated in reward anticipation. Second, we examined differences in neural responses to supraliminal versus subliminal rewards. Results indicated that supraliminal, but not subliminal, high-value reward cues engaged brain areas involved in reward processing (VS) and task performance (supplementary motor area, motor cortex, and superior temporal gyrus). This pattern of findings is striking given that subliminal rewards improved performance to the same extent as supraliminal rewards. So, the neural substrates of conscious versus unconscious reward pursuit are vastly different—but despite their differences, conscious and unconscious reward pursuit may still produce the same behavioral outcomes.

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Boosting or choking – How conscious and unconscious reward processing modulate the active maintenance of goal-relevant information

Cited by 47 publications

References 23 publications

How motivation and reward learning modulate selective attention

How motivation and reward learning modulate selective attention

All for one or some for all? Evaluating informative hypotheses using multiple N = 1 studies

Distinct neural responses to conscious versus unconscious monetary reward cues

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