Can you tickle yourself if you swap bodies with someone else?

The effect of the body transfer illusion on the perceived strength of self- and externally-generated “tickle” sensations was investigated. As expected, externally generated movement produced significantly higher ratings of tickliness than those associated with self-generated movements. Surprisingly, the body transfer illusion had no influence on the ratings of tickliness, suggesting that highly surprising, and therefore hard to predict, experiences of body image and first-person perspective do not abolish the attenuation of tickle sensations. In addition, evidence was found that a version of the rubber hand illusion exists within the body transfer illusion. We situate our findings within the larger debate over sensory attenuation: (1) there is an attenuation of prediction errors that depends upon the context in which sensory input is predicted (i.e., efference copy), and (2) sensory attenuation is a necessary consequence of self-generated movement irrespective of context (i.e., active inference). The results support the notion of active inference.     

Inference and coherence in causal-based artifact categorization

In four experiments, we tested conditions under which artifact concepts support inference and coherence in causal categorization. In all four experiments, participants categorized scenarios in which we systematically varied information about artifacts’ associated design history, physical structure, user intention, user action and functional outcome, and where each property could be specified as intact, compromised or not observed. Consistently across experiments, when participants received complete information (i.e., when all properties were observed), they categorized based on individual properties and did not show evidence of using coherence to categorize. In contrast, when the state of some property was not observed, participants gave evidence of using available information to infer the state of the unobserved property, which increased the value of the available information for categorization. Our data offers answers to longstanding questions regarding artifact categorization, such as whether there are underlying causal models for artifacts, which properties are part of them, whether design history is an artifact’s causal essence, and whether physical appearance or functional outcome is the most central artifact property.     

Inference and the taking condition

It has recently been argued that inference essentially involves the thinker taking his premises to support his conclusion and drawing his conclusion because of this fact. However, this Taking Condition has also been criticized: If taking is interpreted as believing, it seems to lead to a vicious regress and to overintellectualize the act of inferring. In this paper, I examine and reject various attempts to salvage the Taking Condition, either by interpreting inferring as a kind of rule‐following, or by finding an innocuous role for the taking‐belief. Finally, I propose an alternative account of taking, according to which it is not a separate belief, but rather an aspect of the attitude of believing: Believing that p implies not only taking p to be true and taking oneself to believe that p, but also taking one's reasons q to support p, when the belief in question is held on account of an inference.     

Counterfactual comparison modulates fairness consideration in the mini‐ultimatum game: An event‐related potentials study

Existing literature on the mini‐ultimatum game indicates that counterfactual comparison between chosen and unchosen alternatives is of great importance for individual's fairness consideration. However, it is still unclear how counterfactual comparison influences the electrophysiological responses to unfair chosen offers. In conjunction with event‐related potentials’ (ERPs) technique, the current study aimed to explore the issue by employing a modified version of the mini‐ultimatum game where a fixed set of two alternatives (unfair offer vs. fair alternative, unfair vs. hyperfair alternative, unfair offer vs. hyperunfair alternative) was presented before the chosen offer. The behavioral results showed that participants were more likely to accept unfair chosen offers when the unchosen alternative was hyperunfair than when the unchosen alternative was fair or hyperfair. The ERPs results showed that the feedback‐related negativity (FRN) elicited by unfair chosen offers was insensitive to the type of unchosen alternative when correcting for possible overlap with other components. In contrast, unfair chosen offers elicited larger P300 amplitudes when the unchosen alternative was hyperunfair than when the unchosen alternative was fair or hyperfair. These findings suggest that counterfactual comparison may take effect at later stages of fairness consideration as reflected by the P300.     

Causal Networks or Causal Islands? The Representation of Mechanisms and the Transitivity of Causal Judgment

Knowledge of mechanisms is critical for causal reasoning. We contrasted two possible organizations of causal knowledge—an interconnected causal network, where events are causally connected without any boundaries delineating discrete mechanisms; or a set of disparate mechanisms—causal islands—such that events in different mechanisms are not thought to be related even when they belong to the same causal chain. To distinguish these possibilities, we tested whether people make transitive judgments about causal chains by inferring, given A causes B and B causes C, that A causes C. Specifically, causal chains schematized as one chunk or mechanism in semantic memory (e.g., exercising, becoming thirsty, drinking water) led to transitive causal judgments. On the other hand, chains schematized as multiple chunks (e.g., having sex, becoming pregnant, becoming nauseous) led to intransitive judgments despite strong intermediate links ((Experiments 1–3). Normative accounts of causal intransitivity could not explain these intransitive judgments (Experiments 4 and 5).     

A Duet for one

This paper considers communication in terms of inference about the behaviour of others (and our own behaviour). It is based on the premise that our sensations are largely generated by other agents like ourselves. This means, we are trying to infer how our sensations are caused by others, while they are trying to infer our behaviour: for example, in the dialogue between two speakers. We suggest that the infinite regress induced by modelling another agent – who is modelling you – can be finessed if you both possess the same model. In other words, the sensations caused by others and oneself are generated by the same process. This leads to a view of communication based upon a narrative that is shared by agents who are exchanging sensory signals. Crucially, this narrative transcends agency – and simply involves intermittently attending to and attenuating sensory input. Attending to sensations enables the shared narrative to predict the sensations generated by another (i.e. to listen), while attenuating sensory input enables one to articulate the narrative (i.e. to speak). This produces a reciprocal exchange of sensory signals that, formally, induces a generalised synchrony between internal (neuronal) brain states generating predictions in both agents. We develop the arguments behind this perspective, using an active (Bayesian) inference framework and offer some simulations (of birdsong) as proof of principle.     

Prior distributions for random choice structures

We study various axioms of discrete probabilistic choice, measuring how restrictive they are, both alone and in the presence of other axioms, given a specific class of prior distributions over a complete collection of finite choice probabilities. We do this by using Monte Carlo simulation to compute, for a range of prior distributions, probabilities that various simple and compound axioms hold. For example, the probability of the triangle inequality is usually many orders of magnitude higher than the probability of random utility. While neither the triangle inequality nor weak stochastic transitivity imply the other, the conditional probability that one holds given the other holds is greater than the marginal probability, for all priors in the class we consider. The reciprocal of the prior probability that an axiom holds is an upper bound on the Bayes factor in favor of a restricted model, in which the axiom holds, against an unrestricted model. The relatively high prior probability of the triangle inequality limits the degree of support that data from a single decision maker can provide in its favor. The much lower probability of random utility implies that the Bayes factor in favor of it can be much higher, for suitable data.