The effects of immediate versus delayed feedback on complex concept learning

We report three experiments that examine whether immediate versus delayed feedback produce differential concept learning. Subjects were shown hypothetical experiment scenarios and were asked to determine whether each was a true experiment. Correct-answer feedback was used for all three experiments; Experiments 2 and 3 also included detailed explanations. In all three experiments, subjects who received immediate feedback were shown the correct answer after each response. In Experiments 1 and 2, subjects in the delayed feedback condition were shown feedback after responding to all of the scenarios. All subjects then completed a posttest with novel scenarios. Experiment 3 was three parts (each session was 2 days apart). Subjects in the immediate feedback condition completed the posttest on the second session; subjects in the delayed feedback condition were given feedback on the second session and completed the posttest on the third session. Although no posttest differences were observed between the feedback conditions in Experiments 1 and 2, a delayed feedback advantage was found in Experiment 3. We propose that longer intervals in delayed feedback (relative to shorter intervals) might allow learners to forget the incorrect hypotheses they form during learning, which might thereby enhance the processing of feedback.

Dark matter induced Brownian motion

We discuss a novel approach for directional, light dark matter searches inspired by the high precision position measurements achieved in gravitational wave detectors. If dark matter interacts with ordinary matter, movable masses are subject to an effect similar to Brownian motion induced by the scattering with dark matter particles which exhibits certain characteristics and could be observed. We provide estimates for the sensitivity of a hypothetical experiment looking for that motion. Interestingly, if successful, our approach would allow to constrain the local distribution of dark matter momentum.

Equivalence Projective Simulation as a Framework for Modeling Formation of Stimulus Equivalence Classes

Stimulus equivalence (SE) and projective simulation (PS) study complex behavior, the former in human subjects and the latter in artificial agents. We apply the PS learning framework for modeling the formation of equivalence classes. For this purpose, we first modify the PS model to accommodate imitating the emergence of equivalence relations. Later, we formulate the SE formation through the matching-to-sample (MTS) procedure. The proposed version of PS model, called the equivalence projective simulation (EPS) model, is able to act within a varying action set and derive new relations without receiving feedback from the environment. To the best of our knowledge, it is the first time that the field of equivalence theory in behavior analysis has been linked to an artificial agent in a machine learning context. This model has many advantages over existing neural network models. Briefly, our EPS model is not a black box model, but rather a model with the capability of easy interpretation and flexibility for further modifications. To validate the model, some experimental results performed by prominent behavior analysts are simulated. The results confirm that the EPS model is able to reliably simulate and replicate the same behavior as real experiments in various settings, including formation of equivalence relations in typical participants, nonformation of equivalence relations in language-disabled children, and nodal effect in a linear series with nodal distance five. Moreover, through a hypothetical experiment, we discuss the possibility of applying EPS in further equivalence theory research.

Bayesian Optimal Design of Experiments for Inferring the Statistical Expectation of Expensive Black-Box Functions

Bayesian optimal design of experiments (BODEs) have been successful in acquiring information about a quantity of interest (QoI) which depends on a black-box function. BODE is characterized by sequentially querying the function at specific designs selected by an infill-sampling criterion. However, most current BODE methods operate in specific contexts like optimization, or learning a universal representation of the black-box function. The objective of this paper is to design a BODE for estimating the statistical expectation of a physical response surface. This QoI is omnipresent in uncertainty propagation and design under uncertainty problems. Our hypothesis is that an optimal BODE should be maximizing the expected information gain in the QoI. We represent the information gain from a hypothetical experiment as the Kullback–Liebler (KL) divergence between the prior and the posterior probability distributions of the QoI. The prior distribution of the QoI is conditioned on the observed data, and the posterior distribution of the QoI is conditioned on the observed data and a hypothetical experiment. The main contribution of this paper is the derivation of a semi-analytic mathematical formula for the expected information gain about the statistical expectation of a physical response. The developed BODE is validated on synthetic functions with varying number of input-dimensions. We demonstrate the performance of the methodology on a steel wire manufacturing problem.

The causal meaning of Hamilton’s rule

Hamilton’s original derivation of his rule for the spread of an altruistic gene ( rb > c ) assumed additivity of costs and benefits. Recently, it has been argued that an exact version of the rule holds under non-additive pay-offs, so long as the cost and benefit terms are suitably defined, as partial regression coefficients. However, critics have questioned both the biological significance and the causal meaning of the resulting rule. This paper examines the causal meaning of the generalized Hamilton’s rule in a simple model, by computing the effect of a hypothetical experiment to assess the cost of a social action and comparing it to the partial regression definition. The two do not agree. A possible way of salvaging the causal meaning of Hamilton’s rule is explored, by appeal to R. A. Fisher’s ‘average effect of a gene substitution’.

Winning counterterrorism's version of Pascal's wager, but struggling to open the purse

Lankford's essential empirical argument, which is based on evidence such as psychological autopsies, is that suicide attacks are caused by suicidality. By operationalizing this causal claim in a hypothetical experiment, I show the claim to be provable, and I contend that its truth is supported by Lankford's data. However, I question his ensuing arguments about beauty and goodness, and thereby the practical value of his work in counterterrorist propaganda.