Training choices toward low value options

Food decisions are driven by differences in value of choice alternatives such that high value items are preferred over low value items. However, recent research has demonstrated that by implementing the Cue-Approach Training (CAT) the odds of choosing low value items over high value items can be increased. This effect was explained by increased attention to the low value items induced by CAT. Our goal was to replicate the original findings and to address the question of the underlying mechanism by employing eye-tracking during participants’ choice making. During CAT participants were presented with images of food items and were instructed to quickly respond to some of them when an auditory cue was presented (cued items), and not without this cue (uncued items). Next, participants made choices between two food items that differed on whether they were cued during CAT (cued versus uncued) and in pre-training value (high versus low). As predicted, results showed participants were more likely to select a low value food item over a high value food item for consumption when the low value food item had been cued compared to when the low value item had not been cued. Important, and against our hypothesis, there was no significant increase in gaze time for low value cued items compared to low value uncued items. Participants did spend more time fixating on the chosen item compared to the unchosen alternative, thus replicating previous work in this domain. The present research thus establishes the robustness of CAT as means of facilitating choices for low value over high value food but could not demonstrate that this increased preference was due to increased attention for cued low value items. The present research thus raises the question how CAT may increase choices for low value options

Observing many researchers using the same data and hypothesis reveals a hidden universe of uncertainty

This study explores how researchers' analytical choices affect the reliability of scientific findings. Most discussions of reliability problems in science focus on systematic biases. We broaden the lens to emphasize the idiosyncrasy of conscious and unconscious decisions that researchers make during data analysis. We coordinated 161 researchers in 73 research teams and observed their research decisions as they used the same data to independently test the same prominent social science hypothesis: that greater immigration reduces support for social policies among the public. In this typical case of social science research, research teams reported both widely diverging numerical findings and substantive conclusions despite identical start conditions. Researchers' expertise, prior beliefs, and expectations barely predict the wide variation in research outcomes. More than 95% of the total variance in numerical results remains unexplained even after qualitative coding of all identifiable decisions in each team's workflow. This reveals a universe of uncertainty that remains hidden when considering a single study in isolation. The idiosyncratic nature of how researchers' results and conclusions varied is a previously underappreciated explanation for why many scientific hypotheses remain contested. These results call for greater epistemic humility and clarity in reporting scientific findings

On the (In)security of Stream Ciphers Based on Arrays and Modular Addition

Stream ciphers play an important role in symmetric cryptology because of their suitability in high speed applications where block ciphers fall short. A large number of fast stream ciphers or pseudorandom bit generators (PRBGs) can be found in the literature that are based on arrays and simple operations such as modular additions, rotations and memory accesses (e.g. RC4, RC4A, Py, Py6, ISAAC etc.). This paper investigates the security of array-based stream ciphers (or PRBGs) against certain types of distinguishing attacks in a unified way. We argue, counter-intuitively, that the most useful characteristic of an array, namely, the association of array-elements with unique indices, may turn out to be the origins of distinguishing attacks if adequate caution is not maintained. In short, an adversary may attack a cipher simply exploiting the dependence of array-elements on the corresponding indices. Most importantly, the weaknesses are not eliminated even if the indices and the array-elements are made to follow uniform distributions separately. Exploiting these weaknesses we build distinguishing attacks with reasonable advantage on five recent stream ciphers (or PRBGs), namely

Towards a General RC4-Like Keystream Generator

RC4 was designed in 1987 when 8-bit and 16-bit processors were commercially available. Today, most processors use 32-bit or 64bit words but using original RC4 with 32/64 bits is infeasible due to the large memory constraints and the number of operations in the key scheduling algorithm. In this paper we propose a new 32/64-bit RC4like keystream generator. The proposed generator produces 32 or 64 bits in each iteration and can be implemented in software with reasonable memory requirements. It has a huge internal state and offers higher resistance to state recovery attacks than the original 8-bit RC4. Further, on a 32-bit processor the generator is 3.1 times faster than original RC4. We also show that it can resist attacks that are successful on the original RC4. The generator is suitable for high speed software encryption