Identifying resource-rational heuristics for risky choice

Perfectly rational decision making is almost always out of reach for people because their computational resources are limited. Instead, people may rely on computationally frugal heuristics that usually yield good outcomes. Although previous research has identified many such heuristics, discovering good heuristics and predicting when they will be used remains challenging. Here, we present a theoretical framework that allows us to use methods from machine learning to automatically derive the best heuristic to use in any given situation by considering how to make the best use of limited cognitive resources. To demonstrate the generalizability and accuracy of our method, we compare the heuristics it discovers against those used by people across a wide range of multi-attribute risky choice environments in a behavioral experiment that is an order of magnitude larger than any previous experiments of its type. Our method rediscovered known heuristics, identifying them as rational strategies for specific environments, and discovered novel heuristics that had been previously overlooked. Our results show that people adapt their decision strategies to the structure of the environment and generally make good use of their limited cognitive resources, although their strategy choices do not always fully exploit the structure of the environment. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Gamification of Behavior Change: Mathematical Principle and Proof-of-Concept Study

BACKGROUND

Many people want to build good habits to become healthier, live longer, or become happier but struggle to change their behavior. Gamification can make behavior change easier by awarding points for the desired behavior and deducting points for its omission.

OBJECTIVE

In this study, we introduced a principled mathematical method for determining how many points should be awarded or deducted for the enactment or omission of the desired behavior, depending on when and how often the person has succeeded versus failed to enact it in the past. We called this approach optimized gamification of behavior change.

METHODS

As a proof of concept, we designed a chatbot that applies our optimized gamification method to help people build healthy water-drinking habits. We evaluated the effectiveness of this gamified intervention in a 40-day field experiment with 1 experimental group (n=43) that used the chatbot with optimized gamification and 2 active control groups for which the chatbot's optimized gamification feature was disabled. For the first control group (n=48), all other features were available, including verbal feedback. The second control group (n=51) received no feedback or reminders. We measured the strength of all participants' water-drinking habits before, during, and after the intervention using the Self-Report Habit Index and by asking participants on how many days of the previous week they enacted the desired habit. In addition, all participants provided daily reports on whether they enacted their water-drinking intention that day.

RESULTS

A Poisson regression analysis revealed that, during the intervention, users who received feedback based on optimized gamification enacted the desired behavior more often (mean 14.71, SD 6.57 times) than the active (mean 11.64, SD 6.38 times; P<.001; incidence rate ratio=0.80, 95% CI 0.71-0.91) or passive (mean 11.64, SD 5.43 times; P=.001; incidence rate ratio=0.78, 95% CI 0.69-0.89) control groups. The Self-Report Habit Index score significantly increased in all conditions (P<.001 in all cases) but did not differ between the experimental and control conditions (P>.11 in all cases). After the intervention, the experimental group performed the desired behavior as often as the 2 control groups (P≥.17 in all cases).

CONCLUSIONS

Our findings suggest that optimized gamification can be used to make digital behavior change interventions more effective.

TRIAL REGISTRATION

Open Science Framework (OSF) H7JN8; https://osf.io/h7jn8.

Automatic discovery and description of human planning strategies

Scientific discovery concerns finding patterns in data and creating insightful hypotheses that explain these patterns. Traditionally, each step of this process required human ingenuity. But the galloping development of computer chips and advances in artificial intelligence (AI) make it increasingly more feasible to automate some parts of scientific discovery. Understanding human planning is one of the fields in which AI has not yet been utilized. State-of-the-art methods for discovering new planning strategies still rely on manual data analysis. Data about the process of human planning is often used to group similar behaviors together. Researchers then use this data to formulate verbal descriptions of the strategies which might underlie those groups of behaviors. In this work, we leverage AI to automate these two steps of scientific discovery. We introduce a method for automatic discovery and description of human planning strategies from process-tracing data collected with the Mouselab-MDP paradigm. Our method utilizes a new algorithm, called Human-Interpret, that performs imitation learning to describe sequences of planning operations in terms of a procedural formula and then translates that formula to natural language. We test our method on a benchmark data set that researchers have previously scrutinized manually. We find that the descriptions of human planning strategies that we obtain automatically are about as understandable as human-generated descriptions. They also cover a substantial proportion of relevant types of human planning strategies that had been discovered manually. Our method saves scientists' time and effort, as all the reasoning about human planning is done automatically. This might make it feasible to more rapidly scale up the search for yet undiscovered cognitive strategies that people use for planning and decision-making to many new decision environments, populations, tasks, and domains. Given these results, we believe that the presented work may accelerate scientific discovery in psychology, and due to its generality, extend to problems from other fields.

Optimal feedback improves behavioral focus during self-regulated computer-based work

Distractions are omnipresent and can derail our attention, which is a precious and very limited resource. To achieve their goals in the face of distractions, people need to regulate their attention, thoughts, and behavior; this is known as self-regulation. How can self-regulation be supported or strengthened in ways that are relevant for everyday work and learning activities? To address this question, we introduce and evaluate a desktop application that helps people stay focused on their work and train self-regulation at the same time. Our application lets the user set a goal for what they want to do during a defined period of focused work at their computer, then gives negative feedback when they get distracted, and positive feedback when they reorient their attention towards their goal. After this so-called focus session, the user receives overall feedback on how well they focused on their goal relative to previous sessions. While existing approaches to attention training often use artificial tasks, our approach transforms real-life challenges into opportunities for building strong attention control skills. Our results indicate that optimal attentional feedback can generate large increases in behavioral focus, task motivation, and self-control-benefitting users to successfully achieve their long-term goals.

The Computational Challenges of Means Selection Problems: Network Structure of Goal Systems Predicts Human Performance

We study human performance in two classical NP-hard optimization problems: Set Cover and Maximum Coverage. We suggest that Set Cover and Max Coverage are related to means selection problems that arise in human problem-solving and in pursuing multiple goals: The relationship between goals and means is expressed as a bipartite graph where edges between means and goals indicate which means can be used to achieve which goals. While these problems are believed to be computationally intractable in general, they become more tractable when the structure of the network resembles a tree. Thus, our main prediction is that people should perform better with goal systems that are more tree-like. We report three behavioral experiments which confirm this prediction. Our results suggest that combinatorial parameters that are instrumental to algorithm design can also be useful for understanding when and why people struggle to choose between multiple means to achieve multiple goals.

A computational process-tracing method for measuring people's planning strategies and how they change over time

One of the most unique and impressive feats of the human mind is its ability to discover and continuously refine its own cognitive strategies. Elucidating the underlying learning and adaptation mechanisms is very difficult because changes in cognitive strategies are not directly observable. One important domain in which strategies and mechanisms are studied is planning. To enable researchers to uncover how people learn how to plan, we offer a tutorial introduction to a recently developed process-tracing paradigm along with a new computational method for measuring the nature and development of a person's planning strategies from the resulting process-tracing data. Our method allows researchers to reveal experience-driven changes in people's choice of individual planning operations, planning strategies, strategy types, and the relative contributions of different decision systems. We validate our method on simulated and empirical data. On simulated data, its inferences about the strategies and the relative influence of different decision systems are accurate. When evaluated on human data generated using our process-tracing paradigm, our computational method correctly detects the plasticity-enhancing effect of feedback and the effect of the structure of the environment on people's planning strategies. Together, these methods can be used to investigate the mechanisms of cognitive plasticity and to elucidate how people acquire complex cognitive skills such as planning and problem-solving. Importantly, our methods can also be used to measure individual differences in cognitive plasticity and examine how different types (pedagogical) interventions affect the acquisition of cognitive skills.

Resource-rational Models of Human Goal Pursuit

Goal-directed behavior is a deeply important part of human psychology. People constantly set goals for themselves and pursue them in many domains of life. In this paper, we develop computational models that characterize how humans pursue goals in a complex dynamic environment and test how well they describe human behavior in an experiment. Our models are motivated by the principle of resource rationality and draw upon psychological insights about people's limited attention and planning capacities. We find that human goal pursuit is qualitatively different and substantially less efficient than optimal goal pursuit in our simulated environment. Models of goal pursuit based on the principle of resource rationality capture human behavior better than both a model of optimal goal pursuit and heuristics that are not resource-rational. We conclude that the way humans pursue goals is shaped by the need to achieve goals effectively as well as cognitive costs and constraints on planning and attention. Our findings are an important step toward understanding humans' goal pursuit as cognitive limitations play a crucial role in shaping people's goal-directed behavior.

Rational use of cognitive resources in human planning

Making good decisions requires thinking ahead, but the huge number of actions and outcomes one could consider makes exhaustive planning infeasible for computationally constrained agents, such as humans. How people are nevertheless able to solve novel problems when their actions have long-reaching consequences is thus a long-standing question in cognitive science. To address this question, we propose a model of resource-constrained planning that allows us to derive optimal planning strategies. We find that previously proposed heuristics such as best-first search are near optimal under some circumstances but not others. In a mouse-tracking paradigm, we show that people adapt their planning strategies accordingly, planning in a manner that is broadly consistent with the optimal model but not with any single heuristic model. We also find systematic deviations from the optimal model that might result from additional cognitive constraints that are yet to be uncovered.

Leveraging artificial intelligence to improve people's planning strategies

SignificanceMany bad decisions and their devastating consequences could be avoided if people used optimal decision strategies. Here, we introduce a principled computational approach to improving human decision making. The basic idea is to give people feedback on how they reach their decisions. We develop a method that leverages artificial intelligence to generate this feedback in such a way that people quickly discover the best possible decision strategies. Our empirical findings suggest that a principled computational approach leads to improvements in decision-making competence that transfer to more difficult decisions in more complex environments. In the long run, this line of work might lead to apps that teach people clever strategies for decision making, reasoning, goal setting, planning, and goal achievement.

A rational reinterpretation of dual-process theories

Highly influential "dual-process" accounts of human cognition postulate the coexistence of a slow accurate system with a fast error-prone system. But why would there be just two systems rather than, say, one or 93? Here, we argue that a dual-process architecture might reflect a rational tradeoff between the cognitive flexibility afforded by multiple systems and the time and effort required to choose between them. We investigate what the optimal set and number of cognitive systems would be depending on the structure of the environment. We find that the optimal number of systems depends on the variability of the environment and the difficulty of deciding when which system should be used. Furthermore, we find that there is a plausible range of conditions under which it is optimal to be equipped with a fast system that performs no deliberation ("System 1") and a slow system that achieves a higher expected accuracy through deliberation ("System 2"). Our findings thereby suggest a rational reinterpretation of dual-process theories.

Learning to Overexert Cognitive Control in a Stroop Task

How do people learn when to allocate how much cognitive control to which task? According to the Learned Value of Control (LVOC) model, people learn to predict the value of alternative control allocations from features of a situation. This suggests that people may generalize the value of control learned in one situation to others with shared features, even when demands for control are different. This makes the intriguing prediction that what a person learned in one setting could cause them to misestimate the need for, and potentially overexert, control in another setting, even if this harms their performance. To test this prediction, we had participants perform a novel variant of the Stroop task in which, on each trial, they could choose to either name the color (more control-demanding) or read the word (more automatic). Only one of these tasks was rewarded each trial and could be predicted by one or more stimulus features (the color and/or word). Participants first learned colors and then words that predicted the rewarded task. Then, we tested how these learned feature associations transferred to novel stimuli with some overlapping features. The stimulus-task-reward associations were designed so that for certain combinations of stimuli, transfer of learned feature associations would incorrectly predict that more highly rewarded task would be color-naming, even though the actually rewarded task was word-reading and therefore did not require engaging control. Our results demonstrated that participants overexerted control for these stimuli, providing support for the feature-based learning mechanism described by the LVOC model.

Automatic discovery of interpretable planning strategies

When making decisions, people often overlook critical information or are overly swayed by irrelevant information. A common approach to mitigate these biases is to provide decision-makers, especially professionals such as medical doctors, with decision aids, such as decision trees and flowcharts. Designing effective decision aids is a difficult problem. We propose that recently developed reinforcement learning methods for discovering clever heuristics for good decision-making can be partially leveraged to assist human experts in this design process. One of the biggest remaining obstacles to leveraging the aforementioned methods for improving human decision-making is that the policies they learn are opaque to people. To solve this problem, we introduce AI-Interpret: a general method for transforming idiosyncratic policies into simple and interpretable descriptions. Our algorithm combines recent advances in imitation learning and program induction with a new clustering method for identifying a large subset of demonstrations that can be accurately described by a simple, high-performing decision rule. We evaluate our new AI-Interpret algorithm and employ it to translate information-acquisition policies discovered through metalevel reinforcement learning. The results of three large behavioral experiments showed that providing the decision rules generated by AI-Interpret as flowcharts significantly improved people’s planning strategies and decisions across three different classes of sequential decision problems. Moreover, our fourth experiment revealed that this approach is significantly more effective at improving human decision-making than training people by giving them performance feedback. Finally, a series of ablation studies confirmed that our AI-Interpret algorithm was critical to the discovery of interpretable decision rules and that it is ready to be applied to other reinforcement learning problems. We conclude that the methods and findings presented in this article are an important step towards leveraging automatic strategy discovery to improve human decision-making. The code for our algorithm and the experiments is available at https://github.com/RationalityEnhancement/InterpretableStrategyDiscovery.

Cognitive prostheses for goal achievement

Procrastination takes a considerable toll on people's lives, the economy and society at large. Procrastination is often a consequence of people's propensity to prioritize their immediate experiences over the long-term consequences of their actions. This suggests that aligning immediate rewards with long-term values could be a promising way to help people make more future-minded decisions and overcome procrastination. Here we develop an approach to decision support that leverages artificial intelligence and game elements to restructure challenging sequential decision problems in such a way that it becomes easier for people to take the right course of action. A series of four increasingly realistic experiments suggests that this approach can enable people to make better decisions faster, procrastinate less, complete their work on time and waste less time on unimportant tasks. These findings suggest that our method is a promising step towards developing cognitive prostheses that help people achieve their goals.

Rational metareasoning and the plasticity of cognitive control

The human brain has the impressive capacity to adapt how it processes information to high-level goals. While it is known that these cognitive control skills are malleable and can be improved through training, the underlying plasticity mechanisms are not well understood. Here, we develop and evaluate a model of how people learn when to exert cognitive control, which controlled process to use, and how much effort to exert. We derive this model from a general theory according to which the function of cognitive control is to select and configure neural pathways so as to make optimal use of finite time and limited computational resources. The central idea of our Learned Value of Control model is that people use reinforcement learning to predict the value of candidate control signals of different types and intensities based on stimulus features. This model correctly predicts the learning and transfer effects underlying the adaptive control-demanding behavior observed in an experiment on visual attention and four experiments on interference control in Stroop and Flanker paradigms. Moreover, our model explained these findings significantly better than an associative learning model and a Win-Stay Lose-Shift model. Our findings elucidate how learning and experience might shape people’s ability and propensity to adaptively control their minds and behavior. We conclude by predicting under which circumstances these learning mechanisms might lead to self-control failure.

The human brain has the impressive ability to adapt how it processes information to high level goals. While it is known that these cognitive control skills are malleable and can be improved through training, the underlying plasticity mechanisms are not well understood. Here, we derive a computational model of how people learn when to exert cognitive control, which controlled process to use, and how much effort to exert from a formal theory of the function of cognitive control. Across five experiments, we find that our model correctly predicts that people learn to adaptively regulate their attention and decision-making and how these learning effects transfer to novel situations. Our findings elucidate how learning and experience might shape people’s ability and propensity to adaptively control their minds and behavior. We conclude by predicting under which circumstances these learning mechanisms might lead to self-control failure.

Overrepresentation of extreme events in decision making reflects rational use of cognitive resources

People's decisions and judgments are disproportionately swayed by improbable but extreme eventualities, such as terrorism, that come to mind easily. This article explores whether such availability biases can be reconciled with rational information processing by taking into account the fact that decision makers value their time and have limited cognitive resources. Our analysis suggests that to make optimal use of their finite time decision makers should overrepresent the most important potential consequences relative to less important, put potentially more probable, outcomes. To evaluate this account, we derive and test a model we call utility-weighted sampling. Utility-weighted sampling estimates the expected utility of potential actions by simulating their outcomes. Critically, outcomes with more extreme utilities have a higher probability of being simulated. We demonstrate that this model can explain not only people's availability bias in judging the frequency of extreme events but also a wide range of cognitive biases in decisions from experience, decisions from description, and memory recall. (PsycINFO Database Record

Rational use of cognitive resources: levels of analysis between the computational and the algorithmic

Marr's levels of analysis-computational, algorithmic, and implementation-have served cognitive science well over the last 30 years. But the recent increase in the popularity of the computational level raises a new challenge: How do we begin to relate models at different levels of analysis? We propose that it is possible to define levels of analysis that lie between the computational and the algorithmic, providing a way to build a bridge between computational- and algorithmic-level models. The key idea is to push the notion of rationality, often used in defining computational-level models, deeper toward the algorithmic level. We offer a simple recipe for reverse-engineering the mind's cognitive strategies by deriving optimal algorithms for a series of increasingly more realistic abstract computational architectures, which we call "resource-rational analysis."

A neurocomputational model of the mismatch negativity

The mismatch negativity (MMN) is an event related potential evoked by violations of regularity. Here, we present a model of the underlying neuronal dynamics based upon the idea that auditory cortex continuously updates a generative model to predict its sensory inputs. The MMN is then modelled as the superposition of the electric fields evoked by neuronal activity reporting prediction errors. The process by which auditory cortex generates predictions and resolves prediction errors was simulated using generalised (Bayesian) filtering--a biologically plausible scheme for probabilistic inference on the hidden states of hierarchical dynamical models. The resulting scheme generates realistic MMN waveforms, explains the qualitative effects of deviant probability and magnitude on the MMN - in terms of latency and amplitude--and makes quantitative predictions about the interactions between deviant probability and magnitude. This work advances a formal understanding of the MMN and--more generally--illustrates the potential for developing computationally informed dynamic causal models of empirical electromagnetic responses.

Modelling trial-by-trial changes in the mismatch negativity

The mismatch negativity (MMN) is a differential brain response to violations of learned regularities. It has been used to demonstrate that the brain learns the statistical structure of its environment and predicts future sensory inputs. However, the algorithmic nature of these computations and the underlying neurobiological implementation remain controversial. This article introduces a mathematical framework with which competing ideas about the computational quantities indexed by MMN responses can be formalized and tested against single-trial EEG data. This framework was applied to five major theories of the MMN, comparing their ability to explain trial-by-trial changes in MMN amplitude. Three of these theories (predictive coding, model adjustment, and novelty detection) were formalized by linking the MMN to different manifestations of the same computational mechanism: approximate Bayesian inference according to the free-energy principle. We thereby propose a unifying view on three distinct theories of the MMN. The relative plausibility of each theory was assessed against empirical single-trial MMN amplitudes acquired from eight healthy volunteers in a roving oddball experiment. Models based on the free-energy principle provided more plausible explanations of trial-by-trial changes in MMN amplitude than models representing the two more traditional theories (change detection and adaptation). Our results suggest that the MMN reflects approximate Bayesian learning of sensory regularities, and that the MMN-generating process adjusts a probabilistic model of the environment according to prediction errors.

The ability to predict one's environment is crucial for adaptive and proactive behaviour. It requires learning a mental model that captures the environment's statistical regularities. A process of this sort is thought to be reflected by the mismatch negativity (MMN) potential, a non-invasive electrophysiological measure of the neural response to regularity violation by sensory stimuli. However, the exact computational processes reflected by the MMN remain a matter of debate. We developed a modelling framework in which competing hypotheses about these processes can be objectively compared by their ability to predict single-trial MMN amplitudes. We applied this framework to formalize five major MMN theories and propose a unifying view on three distinct theories which explain the MMN as a reflection of prediction errors, model adjustment, and novelty detection, respectively. We assessed our models of the five theories with EEG data from eight healthy volunteers. Our results are consistent with the idea that the MMN arises from prediction error driven adjustments of a probabilistic mental model of the environment.

Effect of transcoronary ablation of septal hypertrophy on clinical outcome in hypertrophic obstructive cardiomyopathy associated with atrial fibrillation

BACKGROUND

Relatively few reports on the clinical impact of atrial fibrillation (AF) in hypertrophic obstructive cardiomyopathy (HOCM) are available. The aims of our study are to report the effect of transcoronary ablation of septal hypertrophy (TASH) on clinical outcome in HOCM associated with AF and to evaluate the influence of AF on symptoms and quality of life in HOCM.

PATIENT AND METHODS

In 80 consecutive patients (38 f, mean age 56 +/- 17 years) with severely symptomatic HOCM referred for interventional treatment, we analyzed the prevalence of AF based on 240 Holter ECG recordings and patients' history, retrospectively. Symptoms, quality of life, number of hospital admissions and hemodynamic performance were obtained in all patients before and after TASH. Mortality was additionally investigated by letter and telephone contact.

RESULTS

The overall prevalence of AF was 29%. Paroxysmal AF was detected in 17 pts (21.3%), persistent AF in 5 pts (6.3%). Only 1 pt (1.3%) suffered from permanent AF. Symptoms due to AF were present in 52.6% of the AF patients. Quality of life score was markedly improved after TASH (15.9 +/- 3.8 vs. 20.7 +/- 3.8, p < 0.001) with no difference between sinus rhythm and atrial fibrillation. However, hospital admissions were more frequent in the AF group (0.85 +/- 1.84 vs. 0.28 +/- 0.81, p = 0.03) in 32 +/- 13 months. AF patients suffered more often from syncope before TASH (30 +/- 70% vs. 10 +/- 30%, p = 0.008). Two patients with sinus rhythm at baseline died after 32 +/- 13 months from cardiovascular cause.

CONCLUSIONS

Atrial fibrillation is the major cardiac arrhythmia in severe HOCM. The majority of AF patients demonstrate AF specific symptoms. The paroxysmal type of atrial fibrillation dominates by far. Both patients with and without atrial fibrillation showed similar quality of life with marked improvement after TASH.

Die Effektivität von schriftlichen und graphischen Warnhinweisen auf Zigarettenschachteln

Zusammenfassung: In der vorliegenden Studie wurde die Effektivität von furchterregenden Warnhinweisen bei jugendlichen Rauchern und Raucherinnen analysiert. 336 Raucher/-innen (Durchschnittsalter: 15 Jahre) wurden schriftliche oder graphische Warnhinweise auf Zigarettenpackungen präsentiert (Experimentalbedingungen; n = 96, n = 119), oder sie erhielten keine Warnhinweise (Kontrollbedingung; n = 94). Anschließend wurden die Modellfaktoren des revidierten Modells der Schutzmotivation ( Arthur & Quester, 2004 ) erhoben. Die Ergebnisse stützen die Hypothese, dass die Faktoren “Schweregrad der Schädigung” und “Wahrscheinlichkeit der Schädigung” die Verhaltenswahrscheinlichkeit, weniger oder leichtere Zigaretten zu rauchen, vermittelt über den Mediator “Furcht” beeinflussen. Die Verhaltenswahrscheinlichkeit wurde dagegen nicht von den drei experimentellen Bedingungen beeinflusst. Auch konnten die Faktoren “Handlungswirksamkeitserwartungen” und “Selbstwirksamkeitserwartungen” nicht als Moderatoren des Zusammenhangs zwischen Furcht und Verhaltenswahrscheinlichkeit bestätigt werden.

Transcoronary ablation of septal hypertrophy (TASH): a new treatment option for hypertrophic obstructive cardiomyopathy

In 1991, our group started to develop a catheter interventional therapy for hypertrophic obstructive cardiomyopathy (HOCM). The new concept was proposed in 1994. It is based on the conventional PTCA technique with the aim of inducing an artificial myocardial infarction by instillation of 96% ethanol into the most proximally situated septal branch of the left anterior descending coronary artery. This leads to a subaortic contraction disorder with subsequent decrease of the intraventricular pressure gradient, shrinkage of the hypertrophied septal bulge and widening of the outflow tract ("therapeutic remodeling"). The subaortic defect is small and well demarcated as assessed by left ventricular angiography, transesophageal echocardiography and 18 F-glucose positron emission tomography. The term transcoronary ablation of septum hypertrophy (TASH) was suggested. Our patient cohort that now comprises 215 therapeutic procedures in 187 patients underwent a large variety of prospective studies (maximum follow-up 4.5 years) including invasive controls at regular intervals, investigation of hemodynamics at rest and at exercise, transesophageal and transthoracic echocardiography, Doppler echocardiography during bicycle exercise, electrophysiologic testing, Holter monitoring and measurement of myocardial metabolism and perfusion, assessment of microembolic events by transcranial Doppler sonography and histological examinations. This article gives an overview and reports our increasing experience in applying TASH. The following post-TASH findings were obtained: significant hemodynamic and clinical improvement at rest and at exercise, decrease of septum thickness, increase of outflow tract area and decrease of induced ventricular tachycardia. There were well-demarcated, histologically atypical subaortic myocardial defects, no microembolic events, abnormal early peak of infarct related enzymes, and no change of baroreflex sensitivity. Pre-/post-TASH evaluations of the patients should be based in particular on clinical symptoms correlated to the intraventricular gradient measured by bicycle exercise Doppler echocardiography and to outflow tract area as assessed by transesophageal echocardiography. Since 1994, as a roughly estimate, worldwide 1000 patients in 20 countries have been treated. According to published articles, abstract presentations and workshops, TASH consistently leads to a pronounced clinical and hemodynamic benefit for patients with HOCM. TASH has become an established technique. At least in centers with a high level of expertise, it is no longer experimental but a routinely performed alternative to surgical treatment for HOCM, i.e., the previous gold standard of therapy. Of course, patient outcome needs further careful clinical and prognostic evaluation. With respect to complications, TASH appears to be superior to surgery (transaortic septal myectomy) for HOCM. Like surgical treatment, TASH is currently indicated in critically ill patients with typical HOCM (subaortic form), who exhibit with drug refractory symptoms, including patients, who preferred DDD pacemaker therapy as a first therapeutic step but in whom this produced no subsequent clinical benefit.

Urinary excretion of mutagens in smokers of cigarettes with various tar and nicotine yields, black tobacco, and cigars

Frameshift mutagens were isolated and concentrated from smokers' urine employing a method recently described. Urine concentrates of the habitual smokers and non-smokers who smoked cigarettes with low-, medium-, and high tar/nicotine yields, RCN (Reduced Condensate and Nicotine; artificial cigarettes containing cotobacco materials), black tobacco, and cigars were tested for mutagenicity in the Salmonella/mammalian microsome assay using Salmonella typhimurium TA98. Non-smokers who smoked 5 and habitual smokers who smoked 10 cigarettes of various tar and nicotine yields excreted more mutagens in urine with low-tar cigarettes than with medium- or high-tar cigarettes. Consuming more than 10 cigarettes a day resulted in a higher urinary excretion of mutagens with medium-tar cigarettes than with high-tar cigarettes. Smoking 5 RCN cigarettes a day by habitual smokers resulted in a higher urinary excretion of mutagens than smoking 5 commercial brand of cigarettes. In contrast, smoking 10 RCN cigarettes resulted in a lower urinary excretion of mutagens than smoking 10 commercial brand of cigarettes. The highest mutagenic activity was found with the urine of a habitual black tobacco smoker. Smoking cigars by non-smokers resulted in a very weak mutagenic activity of urine.

Piracetam in alcoholic organic mental disorder: a placebo controlled study comparing two dosages

The following paper presents an investigation of the efficacy of piracetam in alcoholic organic mental disorder. A double-blind placebo-controlled study design was used to compare 2 dosages of the substance (2 X 3 g vs. 2 X 12 g). Cognitive function was assessed on days 0, 7, 14, 28, 42. Analysis of the results from 24 patients showed a clear-cut amelioration of cognitive functions in all 3 treatment groups. No difference could be demonstrated between the administration of placebo and the lower dose of piracetam. Patients on the higher dose showed an earlier improvement on one of the tests, but the final scores were similar in all three treatment groups.

Isolation of frameshift mutagens from smokers' urine: experiences with three concentration methods

Frameshift mutagens were isolated and concentrated from cigarette smokers' urine by the use of three different extraction methods; XAD-2 resin chromatography, chloroform and blue cotton extraction systems. The extracts were tested for mutagenicity in the Salmonella/mammalian microsome test employing Salmonella typhimurium TA98. By the use of XAD-2 resin chromatography, no clear correlation was found between urine mutagenicity versus number of cigarettes smoked and no detectable mutagenic activity was observed when fewer than four cigarettes a day were consumed. A clear correlation was found between urine mutagenicity versus number of cigarettes smoked when urine samples were extracted by the use of chloroform or blue cotton. Interference of free histidine in mutagenicity assays could be excluded.