Caffeine administration does not alter salivary α-amylase activity in young male daily caffeine consumers.

BACKGROUND: To follow up on a recent report from our lab [Hum Psychopharmacol 25:359-367, 2010.] we examined the effects of caffeine on salivary α-amylase (sAA) activity in response to an engaging, non-stressful task in healthy young males (age 18-30 yrs) who consumed caffeine on a daily basis. Using a placebo-controlled, double-blind, between-subjects design, 45 men received either placebo, 200 mg or 400 mg of caffeine (Vivarin®). Participants then rested for 20 minutes, and performed a 20-minute computerized air traffic controller-like task that was cognitively engaging but not stressful. Saliva samples (assayed for sAA and cortisol), blood pressure, and heart rate were taken before (baseline) and 15 minutes after the computerized task. RESULTS: Systolic and diastolic blood pressure and sAA activity increased across the laboratory session (F's > 9.20, p's < 0.05); salivary cortisol levels decreased (F = 16.17, p < 0.05). There were no main effects for caffeine administration on sAA, salivary cortisol, or cardiovascular measures, and caffeine did not interact with the task to alter these measures. CONCLUSIONS: Laboratory administered caffeine does not alter sAA activity, even when sAA activity is stimulated by participating in a cognitively engaging task. These data demonstrate that caffeine administration does not affect sAA activity, at least in healthy young men who regularly consume caffeine. Results support recent findings that basal caffeine levels in habitual caffeine users are not associated with basal sAA activity and that daily caffeine intake and diurnal sAA activity are not related.

Simplifying the interaction between cognitive models and task environments with the JSON Network Interface.

Process models of cognition, written in architectures such as ACT-R and EPIC, should be able to interact with the same software with which human subjects interact. By eliminating the need to simulate the experiment, this approach would simplify the modeler's effort, while ensuring that all steps required of the human are also required by the model. In practice, the difficulties of allowing one software system to interact with another present a significant barrier to any modeler who is not also skilled at this type of programming. The barrier increases if the programming language used by the modeling software differs from that used by the experimental software. The JSON Network Interface simplifies this problem for ACT-R modelers, and potentially, modelers using other systems.

Goal-proximity decision-making.

Reinforcement learning (RL) models of decision-making cannot account for human decisions in the absence of prior reward or punishment. We propose a mechanism for choosing among available options based on goal-option association strengths, where association strengths between objects represent previously experienced object proximity. The proposed mechanism, Goal-Proximity Decision-making (GPD), is implemented within the ACT-R cognitive framework. GPD is found to be more efficient than RL in three maze-navigation simulations. GPD advantages over RL seem to grow as task difficulty is increased. An experiment is presented where participants are asked to make choices in the absence of prior reward. GPD captures human performance in this experiment better than RL.

The soft constraints hypothesis: a rational analysis approach to resource allocation for interactive behavior.

Soft constraints hypothesis (SCH) is a rational analysis approach that holds that the mixture of perceptual-motor and cognitive resources allocated for interactive behavior is adjusted based on temporal cost-benefit tradeoffs. Alternative approaches maintain that cognitive resources are in some sense protected or conserved in that greater amounts of perceptual-motor effort will be expended to conserve lesser amounts of cognitive effort. One alternative, the minimum memory hypothesis (MMH), holds that people favor strategies that minimize the use of memory. SCH is compared with MMH across 3 experiments and with predictions of an Ideal Performer Model that uses ACT-R's memory system in a reinforcement learning approach that maximizes expected utility by minimizing time. Model and data support the SCH view of resource allocation; at the under 1000-ms level of analysis, mixtures of cognitive and perceptual-motor resources are adjusted based on their cost-benefit tradeoffs for interactive behavior.

ProtoMatch: a tool for analyzing high-density, sequential eye gaze and cursor protocols.

ProtoMatch is a software tool for integrating and analyzing fixed-location and movement eye gaze and cursor data. It provides a comprehensive collection of protocol analysis tools that support sequential data analyses for eye fixations and scanpaths as well as for cursor "fixations" (dwells at one location) and "cursorpaths" (movements between locations). ProtoMatch is modularized software that integrates both eye gaze and cursor protocols into a unified stream of data and provides an assortment of filters and analyses. ProtoMatch subsumes basic analyses (i.e., fixation duration, number of fixations, etc.) and introduces a method of objectively computing the similarity between scanpaths or cursorpaths using sequence alignment. The combination of filters, basic analyses, and sequence alignment in ProtoMatch provides researchers with a versatile system for performing both confirmatory and exploratory sequential data analyses (Sanderson & Fisher, 1994).

Using Brunswikian theory and a longitudinal design to study how hierarchical teams adapt to increasing levels of time pressure.

Brunswikian theory and a longitudinal design were used to study how three-person, hierarchical teams adapted to increasing levels of time pressure and, thereby, try to understand why previous team research has not necessarily found a direct relationship between team processes and performance with increasing time pressure. We obtained four principal findings. First, team members initially adapted to increasing time pressure without showing any performance decrements by accelerating their cognitive processing, increasing the amount of their implicit coordination by sending more information without being asked and, to a lesser extent, filtering (omitting) certain activities. Second, teams began and continued to perform the task differently with increasing time pressure, yet often achieved comparable levels of performance. Third, time pressure did affect performance because there was a level of time pressure beyond which performance could not be maintained, although that level differed for different teams. And, fourth, some adaptation strategies were more effective than others at the highest time pressure level. Taken together, these findings support the Brunswikian perspective that one should not necessarily expect a direct relationship between team processes and performance with increasing time pressure because teams adapt their processes in different, yet often equally effective ways, in an effort to maintain high and stable performance.