Ellen, Providence, Regina en New Astoria: een hotel-historie

<p><b>Experiment overview (a) and experimental design (b) for Experiment 2.</b> The general trial structure and experimental design of Experiment 2 are largely a replication of Experiment 1.</p

Na de goodwill, tijd voor actie : desinformatie bestrijden door structurele samenwerkingen

Vaccintwijfel, politieke advertenties op Facebook en geblokkeerde complotaccounts op Twitter: je hoeft niet ver te zoeken naar recente berichten over nepnieuws online en het gevecht dat daartegen wordt aangegaan. Zowel bij politici, technologiebedrijven, journalisten en academici als in het publieke debat staan betrouwbare informatie en nepnieuws bijgevolg hoog op de agenda. Elk van de genoemde actoren worstelt met het fenomeen desinformatie en probeert vanuit de eigen insteek de schade te beperken. Maar vandaag blijft het gevecht tegen nepnieuws helaas vaak hangen op versnipperde initiatieven door de verschillende spelers apart. Het gaat om op zich degelijke, maar geïsoleerde initiatieven, of in het beste geval over tijdelijke projectmatige samenwerkingen tussen enkele spelers. En dat voelt als een gemiste kans, want wij geloven dat duurzame oplossingen voor nepnieuws zich net daar situeren waar nu de blinde vlek van bestaande initiatieven zit: op het snijpunt van politiek, wetenschap, technologie, journalistiek én het publiek. Hoog tijd dus voor een structurele en duurzame samenwerking

The sound of beauty : how complexity determines aesthetic preference

Stimulus complexity is an important determinant of aesthetic preference. An influential idea is that increases instimulus complexity lead to increased preference up to an optimal point after which preference decreases (in-verted-U pattern). However, whereas some studies indeed observed this pattern, most studies instead showed anincreased preference for more complexity. One complicating issue is that it remains unclear how to definecomplexity. To address this, we approached complexity and its relation to aesthetic preference from a predictivecoding perspective. Here, low- and high-complexity stimuli would correspond to low and high levels of pre-diction errors, respectively. We expected participants to prefer stimuli which are neither too easy to predict (lowprediction error), nor too difficult (high prediction error). To test this, we presented two sequences of tones oneach trial that varied in predictability from highly regular (low prediction error) to completely random (highprediction error), and participants had to indicate which of the two sequences they preferred in a two-intervalforced-choice task. The complexity of each tone sequence (amount of prediction error) was estimated usingentropy. Results showed that participants tended to choose stimuli with intermediate complexity over those ofhigh or low complexity. This confirms the century-old idea that stimulus complexity has an inverted-U re-lationship to aesthetic preference

The time course of cognitive control implementation

Optimally recruiting cognitive control is a key factor in efficient task performance. In line with influential cognitive control theories, earlier work assumed that control is relatively slow. We challenge this notion and test whether control also can be implemented more rapidly by investigating the time course of cognitive control. In two experiments, a visual discrimination paradigm was applied. A reward cue was presented with variable intervals to target onset. The results showed that reward cues can rapidly improve performance. Importantly, the reward manipulation was orthogonal to the response, ensuring that the reward effect was due to fast cognitive control implementation rather than to automatic activation of rewarded S-R associations. We also empirically specify the temporal limits of cognitive control, because the reward cue had no effect when it was presented shortly after target onset, during task execution

Preparing for hard times: Scalp and intracranial physiological signatures of proactive cognitive control

Item does not contain fulltextAbstract Based on reward and difficulty information, people can strategically adjust proactive cognitive control. fMRI research shows that motivated proactive control is implemented through fronto-parietal control networks that are triggered by reward and difficulty cues. Here, we investigate electrophysiological signatures of proactive control. Previously, the contingent negative variation (CNV) in the ERPs and oscillatory power in the theta (4–8 Hz) and alpha band (8–14 Hz) have been suggested as signatures of control implementation. However, experimental designs did not always separate control implementation from motor preparation. Critically, we used a mental calculation task to investigate effects of proactive control implementation on the CNV and on theta and alpha power, in absence of motor preparation. In the period leading up to task onset, we found a more negative CNV, increased theta power, and decreased alpha power for hard versus easy calculations, showing increased proactive control implementation when a difficult task was expected. These three measures also correlated with behavioral performance, both across trials and across subjects. In addition to scalp EEG in healthy participants, we collected intracranial local field potential recordings in an epilepsy patient. We observed a slow-drift component that was more pronounced for hard trials in a hippocampal location, possibly reflecting task-specific preparation for hard mental calculations. The current study thus shows that difficulty information triggers proactive control in absence of motor preparation and elucidates its neurophysiological signatures

Preparing for hard times:scalp and intracranial physiological signatures of proactive cognitive control

\u3cp\u3eBased on reward and difficulty information, people can strategically adjust proactive cognitive control. fMRI research shows that motivated proactive control is implemented through fronto-parietal control networks that are triggered by reward and difficulty cues. Here, we investigate electrophysiological signatures of proactive control. Previously, the contingent negative variation (CNV) in the ERPs and oscillatory power in the theta (4–8 Hz) and alpha band (8–14 Hz) have been suggested as signatures of control implementation. However, experimental designs did not always separate control implementation from motor preparation. Critically, we used a mental calculation task to investigate effects of proactive control implementation on the CNV and on theta and alpha power, in absence of motor preparation. In the period leading up to task onset, we found a more negative CNV, increased theta power, and decreased alpha power for hard versus easy calculations, showing increased proactive control implementation when a difficult task was expected. These three measures also correlated with behavioral performance, both across trials and across subjects. In addition to scalp EEG in healthy participants, we collected intracranial local field potential recordings in an epilepsy patient. We observed a slow-drift component that was more pronounced for hard trials in a hippocampal location, possibly reflecting task-specific preparation for hard mental calculations. The current study thus shows that difficulty information triggers proactive control in absence of motor preparation and elucidates its neurophysiological signatures.\u3c/p\u3

Recognition accuracy (panel a through f; y-axis) and certainty ratings (panel g and h; y-axis).

<p>Recognition accuracy and certainty ratings as a function of the number of options (panel a through d; x-axis) or the SRPEs (panel e through h; x-axis) in the immediate test group (left column) and their equivalent in the delayed test group (right column). The results of Experiment 1 are indicated by the black full line; the results of Experiment 2 are plotted with a grey dashed lines (95% confidence intervals are indicated for Experiment 1 only). To elucidate the relation between panel a-d and panel e-f, empty circles represent the unrewarded trials and full circles the rewarded trials. Note that in the one-option condition the chosen translation was always rewarded (panel a through d). For each number of options and depending on the reward and delay (as well as for the SRPEs), the average recognition accuracy/certainty and its 95% confidence interval was estimated and superimposed. (a-f) Recognition increased significantly with an increasing number of options and recognition was enhanced for rewarded word pairs; thus recognition increased significantly with higher SRPEs. Performance at chance level is indicated by the gray dotted line at 25% accuracy. (g and h) SRPEs significantly predicted certainty ratings for correctly recognized word pairs (depicted in blue) but not for incorrectly recognized word pairs (depicted in orange).</p