A simple and cheap setup for timing tapping responses synchronized to auditory stimuli.

Measuring human capabilities to synchronize in time, adapt to perturbations to timing sequences, or reproduce time intervals often requires experimental setups that allow recording response times with millisecond precision. Most setups present auditory stimuli using either MIDI devices or specialized hardware such as Arduino and are often expensive or require calibration and advanced programming skills. Here, we present in detail an experimental setup that only requires an external sound card and minor electronic skills, works on a conventional PC, is cheaper than alternatives, and requires almost no programming skills. It is intended for presenting any auditory stimuli and recording tapping response times with within 2-ms precision (up to - 2 ms lag). This paper shows why desired accuracy in recording response times against auditory stimuli is difficult to achieve in conventional computer setups, presents an experimental setup to overcome this, and explains in detail how to set it up and use the provided code. Finally, the code for analyzing the recorded tapping responses was evaluated, showing that no spurious or missing events were found in 94% of the analyzed recordings.

Fast Distributed Dynamics of Semantic Networks via Social Media.

We investigate the dynamics of semantic organization using social media, a collective expression of human thought. We propose a novel, time-dependent semantic similarity measure (TSS), based on the social network Twitter. We show that TSS is consistent with static measures of similarity but provides high temporal resolution for the identification of real-world events and induced changes in the distributed structure of semantic relationships across the entire lexicon. Using TSS, we measured the evolution of a concept and its movement along the semantic neighborhood, driven by specific news/events. Finally, we showed that particular events may trigger a temporary reorganization of elements in the semantic network.

Do not fear your opponent: suboptimal changes of a prevention strategy when facing stronger opponents.

The time spent making a decision and its quality define a widely studied trade-off. Some models suggest that the time spent is set to optimize reward, as verified empirically in simple-decision making experiments. However, in a more complex perspective compromising components of regulation focus, ambitions, fear, risk and social variables, adjustment of the speed-accuracy trade-off may not be optimal. Specifically, regulatory focus theory shows that people can be set in a promotion mode, where focus is on seeking to approach a desired state (to win), or in a prevention mode, focusing to avoid undesired states (not to lose). In promotion, people are eager to take risks increasing speed and decreasing accuracy. In prevention, strategic vigilance increases, decreasing speed and improving accuracy. When time and accuracy have to be compromised, one can ask which of these 2 strategies optimizes reward, leading to optimal performance. This is investigated here in a unique experimental environment. Decision making is studied in rapid-chess (180 s per game), in which the goal of a player is to mate the opponent in a finite amount of time or, alternatively, time-out of the opponent with sufficient material to mate. In different games, players face strong and weak opponents. It was observed that (a) players adopt a more conservative strategy when facing strong opponents, with slower and more accurate moves, and (b) this strategy is suboptimal: Players increase their winning likelihood against strong opponents using the policy they adopt when confronting opponents with similar strength.

Response time distributions in rapid chess: a large-scale decision making experiment.

Rapid chess provides an unparalleled laboratory to understand decision making in a natural environment. In a chess game, players choose consecutively around 40 moves in a finite time budget. The goodness of each choice can be determined quantitatively since current chess algorithms estimate precisely the value of a position. Web-based chess produces vast amounts of data, millions of decisions per day, incommensurable with traditional psychological experiments. We generated a database of response times (RTs) and position value in rapid chess games. We measured robust emergent statistical observables: (1) RT distributions are long-tailed and show qualitatively distinct forms at different stages of the game, (2) RT of successive moves are highly correlated both for intra- and inter-player moves. These findings have theoretical implications since they deny two basic assumptions of sequential decision making algorithms: RTs are not stationary and can not be generated by a state-function. Our results also have practical implications. First, we characterized the capacity of blunders and score fluctuations to predict a player strength, which is yet an open problem in chess softwares. Second, we show that the winning likelihood can be reliably estimated from a weighted combination of remaining times and position evaluation.