Predictive minds in Ouija board sessions

Andersen M, Nielbo KL, Schjoedt U, Pfeiffer T, Roepstorff A, Sørensen J. Predictive minds in Ouija board sessions. Phenomenology and the Cognitive Sciences. 2018;18(3):577-588.Ouija board sessions are illustrious examples of how subjective feelings of control the Sense of Agency (SoA) - can be manipulated in real life settings. We present findings from a field experiment at a paranormal conference, where Ouija enthusiasts were equipped with eye trackers while using the Ouija board. Our results show that participants have a significantly lower probability at visually predicting letters in a Ouija board session compared to a condition in which they are instructed to deliberately spell out words with the Ouija board planchette. Our results also show that Ouija board believers report lower SoA compared to sceptic participants. These results support previous research which claim that low sense of agency is caused by a combination of retrospective inference and an inhibition of predictive processes. Our results show that users in Ouija board sessions become increasingly better at predicting letters as responses unfold over time, and that meaningful responses from the Ouija board can only be accounted for when considering interactions that goes on at the participant pair level. These results suggest that meaningful responses from the Ouija board may be an emergent property of interacting and predicting minds that increasingly impose structure on initially random events in Ouija sessions

The Fractality of Sentiment Arcs for Literary Quality Assessment: the Case of Nobel Laureates

In the few works that have used NLP to study literary quality, sentiment and emotion analysis have often been considered valuable sources of information. At the same time, the idea that the nature and polarity of the sentiments expressed by a novel might have something to do with its perceived quality seems limited at best. In this paper, we argue that the fractality of narratives, specifically the longterm memory of their sentiment arcs, rather than their simple shape or average valence, might play an important role in the perception of literary quality by a human audience. In particular, we argue that such measure can help distinguish Nobel-winning writers from control groups in a recent corpus of English language novels. To test this hypothesis, we present the results from two studies: (i) a probability distribution test, where we compute the probability of seeing a title from a Nobel laureate at different levels of arc fractality; (ii) a classification test, where we use several machine learning algorithms to measure the predictive power of both sentiment arcs and their fractality measure. Lastly, we perform another experiment to examine whether arc fractality may be used to distinguish more or less popular works within the Nobel canon itself, looking at the probability of higher GoodReads’ ratings at different levels of arc fractality. Our findings seem to indicate that despite the competitive and complex nature of the task, the populations of Nobel and non-Nobel laureates seem to behave differently and can to some extent be told apart by a classifier. Moreover, the probability of Nobel titles having better ratings appears higher at different levels of arc fractality

The Fractality of Sentiment Arcs for Literary Quality Assessment: the Case of Nobel Laureates

In the few works that have used NLP to study literary quality, sentiment and emotion analysis have often been considered valuable sources of information. At the same time, the idea that the nature and polarity of the sentiments expressed by a novel might have something to do with its perceived quality seems limited at best. In this paper, we argue that the fractality of narratives, specifically the longterm memory of their sentiment arcs, rather than their simple shape or average valence, might play an important role in the perception of literary quality by a human audience. In particular, we argue that such measure can help distinguish Nobel-winning writers from control groups in a recent corpus of English language novels. To test this hypothesis, we present the results from two studies: (i) a probability distribution test, where we compute the probability of seeing a title from a Nobel laureate at different levels of arc fractality; (ii) a classification test, where we use several machine learning algorithms to measure the predictive power of both sentiment arcs and their fractality measure. Lastly, we perform another experiment to examine whether arc fractality may be used to distinguish more or less popular works within the Nobel canon itself, looking at the probability of higher GoodReads’ ratings at different levels of arc fractality. Our findings seem to indicate that despite the competitive and complex nature of the task, the populations of Nobel and non-Nobel laureates seem to behave differently and can to some extent be told apart by a classifier. Moreover, the probability of Nobel titles having better ratings appears higher at different levels of arc fractality

Collective-goal ascription increases cooperation in humans.

BACKGROUND: Cooperation is necessary in many types of human joint activity and relations. Evidence suggests that cooperation has direct and indirect benefits for the cooperators. Given how beneficial cooperation is overall, it seems relevant to investigate the various ways of enhancing individuals' willingness to invest in cooperative endeavors. We studied whether ascription of a transparent collective goal in a joint action promotes cooperation in a group. METHODS: A total of 48 participants were assigned in teams of 4 individuals to either a "transparent goal-ascription" or an "opaque goal-ascription" condition. After the manipulation, the participants played an anonymous public goods game with another member of their team. We measured the willingness of participants to cooperate and their expectations about the other player's contribution. RESULTS: Between subjects analyses showed that transparent goal ascription impacts participants' likelihood to cooperate with each other in the future, thereby greatly increasing the benefits from social interactions. Further analysis showed that this could be explained with a change in expectations about the partner's behavior and by an emotional alignment of the participants. CONCLUSION: The study found that a transparent goal ascription is associated with an increase of cooperation. We propose several high-level mechanisms that could explain the observed effect: general affect modulation, trust, expectation and perception of collective efficacy

In the transparent condition participants' investment is 34.6% higher than in the opaque condition, and the median investment is 100, compared to a median of only 50.

<p>In the transparent condition participants' expectations are 20% higher than the opaque condition, and the median on expectations is 100, compared to a median of only 50.</p