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Predicting the actions and reactions of others is crucial to suc- cessful social interaction. When deciding whether to bluff in a game of poker, we consider the chances that the other players will fold or continue to play and unmask our bluff. When deciding whether to tell our boss that their plans are likely to have adverse effects, we consider a range of reac- tions, from being grateful for our honesty to being dismissed out of spite. Such predictions are highly uncertain and com- plex, not least because the other's (re)actions usually result from them making equally complex and uncertain inferences about us. Nevertheless, we are often remarkably successful – although sometimes utterly wrong – in our social inferences. How do we explain these successes and failures?

In social interactions, inferring the interaction partner's hidden mental state is crucial for predicting their actions and optimiz- ing our responses. Effective models for this inference must account for how these mental states evolve due to the interac- tion history and environmental changes. For example, recog- nizing someone's emotional state can help forecast their be- havior. Our study investigates how making these latent states visible influences decision-making in social interactions. Us- ing the repeated trust game paradigm, we show how to use hid- den Markov models (HMM) to formally represent latent state dependent strategies of the players. HMMs fitted to human dyadic play in the trust game are then used to specify adap- tive AI agents that simulate changes in mental dispositions of human players, such as the level of trust in the opponent, dur- ing a repeated interaction. Making these artificial HMM based agents take the role of the investor and interact with real hu- man trustees, we then explore how displaying “emotion” cues to the opponent's latent state affects people's actions. We find that the presence of cues was associated with more cooperative behavior from the human trustees, and that patterns of behav- ior that promote the maintenance of cooperation emerged in the presence of latent state cues and were transferred to set- tings where the cues were subsequently hidden.

In this study, we investigated human causal learning in a continuous time and space setting. Our experiments revealed that people are capable causal learners in such contexts, and that standard Bayesian updating of prior beliefs partially explains how priors impact their judgments. Specifically, individuals with strong prior beliefs consistent with the ground truth are less likely to misinterpret indirect effects as direct in structures like causal chains. When priors were inconsistent with the ground truth however, participants performed worse on average, choosing less informative interventions. Computational modelling revealed that to deal with the abundance of data in this setting, participants use interventions to mark informative data and focus on direct outgoing links from the variable they intervene on. This task-decomposition strategy, when paired with participants' interventions, achieves comparable accuracy to using the entirety of the dataset, despite ignoring almost half of the available data. These findings are in line with the resource rational framework, where discarding data outside of interventions saves computational costs and the full inference problem about the graph is broken down into sub-problems of inferring individual links between variables. Overall, our study reinforces the idea that humans are frugal and intuitive active learners who combine actions and inference to optimize learning while minimizing effort.

The goal of the WiECG project is to create a prototype device that makes it possible to perform a 12-lead ECG measurement on patients without wires from the patient to a monitor. The solution consists of a transmitter and receiver, one of which is close or on the patients body and the other is connected to a monitor. This thesis describes the design and implementation of a subsystem of the prototype device that performs digitization, digital processing and reconstruction of the measured 12-lead ECG signal. This concerns converting nine 0 to 3.3V analog signals to the digital domain by using Analog-to-Digital converters, real-time filtering of nine signals with multiple digital IIR filters and reconstructing nine digital signals to the analog domain using Digital-to-Analog converters. Furthermore, component selection, design decisions and the implementation process will be detailed in this document. The subsystem proposed in this paper is able to successfully sample, efficiently filter and reconstruct nine signals in real time. Recommendations on improving the implementation to better adhere to the lower power requirements for a longer battery life are provided as future research prospects., Computer Engineering

Human learning transfer takes advantage of important cognitive building blocks such as an abstract representation of concepts underlying tasks and causal models of the environment. One way to build abstract representations of the environment when the task involves interactions with others is to build a model of the opponent that may inform what actions they are likely to take next. In this study, we explore opponent modelling and its role in learning transfer by letting human participants play different games against the same computer agent, who possesses human-like theory of mind abilities with a limited degree of iterated reasoning. We find that participants deviate from Nash equilibrium play and learn to adapt to the opponent's strategy to exploit it. Moreover, we show that participants transfer their learning to new games and that this transfer is moderated by the level of sophistication of the opponent. Computational modelling shows that it is likely that players start each game using a model-based learning strategy that facilitates generalisation and opponent model transfer, but then switch to behaviour that is consistent with a model-free learning strategy in the later stages of the interaction.

To what extent do human reward learning and decision-making rely on the ability to represent and generate richly structured relationships between options? We provide evidence that structure learning and the principle of compositionality play crucial roles in human reinforcement learning. In a new multi-armed bandit paradigm, we found evidence that participants are able to learn representations of different reward structures and combine them to make correct generalizations about options in novel contexts. Moreover, we found substantial evidence that participants transferred knowledge of simpler reward structures to make compositional generalizations about rewards in complex contexts. This allowed participants to accumulate more rewards earlier, and to explore less whenever such knowledge transfer was possible. We also provide a computational model which is able to generalize and compose knowledge for complex reward structures. This model describes participant behaviour in the compositional generalization task better than various other models of decision-making and transfer learning.

Background: Current guidelines on coronary anomalies are primarily based on expert consensus and a limited number of trials. A gold standard for diagnosis and a consensus on the treatment strategy in this patient group are lacking, especially for patients with an anomalous origin of a coronary artery from the opposite sinus of Valsalva (ACAOS) with an interarterial course. Aim: To provide evidence-substantiated recommendations for diagnostic work-up, treatment and follow-up of patients with anomalous coronary arteries. Methods: A clinical care pathway for patients with ACAOS was established by six Dutch centres. Prospectively included patients undergo work-up according to protocol using computed tomography (CT) angiography, ischaemia detection, echocardiography and coronary angiography with intracoronary measurements to assess anatomical and physiological characteristics of the ACAOS. Surgical and functional follow-up results are evaluated by CT angiography, ischaemia detection and a quality-of-life questionnaire. Patient inclusion for the first multicentre study on coronary anomalies in the Netherlands started in 2020 and will continue for at least 3 years with a minimum of 2 years of follow-up. For patients with a right or left coronary artery originating from the pulmonary artery and coronary arteriovenous fistulas a registry is maintained. Results: Primary outcomes are: (cardiac) death, myocardial ischaemia attributable to the ACAOS, re-intervention after surgery and intervention after initially conservative treatment. The influence of work-up examinations on treatment choice is also evaluated. Conclusions: Structural evidence for the appropriate management of patients with coronary anomalies, especially (interarterial) ACAOS, is lacking. By means of a structured care pathway in a multicentre setting, we aim to provide an evidence-based strategy for the diagnostic evaluation and treatment of this patient group.

Item does not contain fulltext, BACKGROUND: Current guidelines on coronary anomalies are primarily based on expert consensus and a limited number of trials. A gold standard for diagnosis and a consensus on the treatment strategy in this patient group are lacking, especially for patients with an anomalous origin of a coronary artery from the opposite sinus of Valsalva (ACAOS) with an interarterial course. AIM: To provide evidence-substantiated recommendations for diagnostic work-up, treatment and follow-up of patients with anomalous coronary arteries. METHODS: A clinical care pathway for patients with ACAOS was established by six Dutch centres. Prospectively included patients undergo work-up according to protocol using computed tomography (CT) angiography, ischaemia detection, echocardiography and coronary angiography with intracoronary measurements to assess anatomical and physiological characteristics of the ACAOS. Surgical and functional follow-up results are evaluated by CT angiography, ischaemia detection and a quality-of-life questionnaire. Patient inclusion for the first multicentre study on coronary anomalies in the Netherlands started in 2020 and will continue for at least 3 years with a minimum of 2 years of follow-up. For patients with a right or left coronary artery originating from the pulmonary artery and coronary arteriovenous fistulas a registry is maintained. RESULTS: Primary outcomes are: (cardiac) death, myocardial ischaemia attributable to the ACAOS, re-intervention after surgery and intervention after initially conservative treatment. The influence of work-up examinations on treatment choice is also evaluated. CONCLUSIONS: Structural evidence for the appropriate management of patients with coronary anomalies, especially (interarterial) ACAOS, is lacking. By means of a structured care pathway in a multicentre setting, we aim to provide an evidence-based strategy for the diagnostic evaluation and treatment of this patient group.

Patients undergoing surgical aortic valve replacement (SAVR) are at high risk for atrial fibrillation (AF) and stroke after surgery. There is an unmet clinical need to improve stroke prevention in this patient population. The LAA-CLOSURE trial aims to assess the efficacy and safety of prophylactic surgical closure of the left atrial appendage for stroke and cardiovascular death prevention in patients undergoing bioprosthetic SAVR. This randomized, open-label, prospective multicenter trial will enroll 1,040 patients at 13 European sites. The primary endpoint is a composite of cardiovascular mortality, stroke and systemic embolism at 5 years. Secondary endpoints include cardiovascular mortality, stroke, systemic embolism, bleed fulfilling academic research consortium (BARC) criteria, hospitalization for decompensated heart failure and health economic evaluation. Sample size is based on 30% risk reduction in time to event analysis of primary endpoint. Prespecified reports include 30-day safety analysis focusing on AF occurrence and short-term outcomes and interim analyses at

How do people recognize and learn about complex functional structure? Taking inspiration from other areas of cognitive science, we propose that this is achieved by harnessing compositionality: complex structure is decomposed into simpler building blocks. We formalize this idea within the framework of Bayesian regression using a grammar over Gaussian process kernels, and compare this approach with other structure learning approaches. Participants consistently chose compositional (over non-compositional) extrapolations and interpolations of functions. Experiments designed to elicit priors over functional patterns revealed an inductive bias for compositional structure. Compositional functions were perceived as subjectively more predictable than non-compositional functions, and exhibited other signatures of predictability, such as enhanced memorability and reduced numerosity. Taken together, these results support the view that the human intuitive theory of functions is inherently compositional.

Background: Current guidelines on coronary anomalies are primarily based on expert consensus and a limited number of trials. A gold standard for diagnosis and a consensus on the treatment strategy in this patient group are lacking, especially for patients with an anomalous origin of a coronary artery from the opposite sinus of Valsalva (ACAOS) with an interarterial course. Aim: To provide evidence-substantiated recommendations for diagnostic work-up, treatment and follow-up of patients with anomalous coronary arteries. Methods: A clinical care pathway for patients with ACAOS was established by six Dutch centres. Prospectively included patients undergo work-up according to protocol using computed tomography (CT) angiography, ischaemia detection, echocardiography and coronary angiography with intracoronary measurements to assess anatomical and physiological characteristics of the ACAOS. Surgical and functional follow-up results are evaluated by CT angiography, ischaemia detection and a quality-of-life questionnaire. Patient inclusion for the first multicentre study on coronary anomalies in the Netherlands started in 2020 and will continue for at least 3 years with a minimum of 2 years of follow-up. For patients with a right or left coronary artery originating from the pulmonary artery and coronary arteriovenous fistulas a registry is maintained. Results: Primary outcomes are: (cardiac) death, myocardial ischaemia attributable to the ACAOS, re-intervention after surgery and intervention after initially conservative treatment. The influence of work-up examinations on treatment choice is also evaluated. Conclusions: Structural evidence for the appropriate management of patients with coronary anomalies, especially (interarterial) ACAOS, is lacking. By means of a structured care pathway in a multicentre setting, we aim to provide an evidence-based strategy for the diagnostic evaluation and treatment of this patient group.

How does time pressure influence attitudes towards uncer-tainty? When time is limited, do people engage in differentexploration strategies? We study human exploration in a rangeof four-armed bandit tasks with different reward distributionsand manipulate the available time for each decision (limitedvs. unlimited). Through multiple behavioral and model-basedanalyses, we show that reactions towards uncertainty are influ-enced by time pressure. Specifically, participants seek out un-certain options when time is unlimited, but avoid uncertaintyunder time pressure. Moreover, larger relative differences inuncertainty between options slowed down reaction times anddampened the drift rate of a linear ballistic accumulator model.These results shed new light on the differential effect of uncer-tainty and time pressure on human exploration.

Background: 75% of all mental health problems have their onset before the end of adolescence. Therefore, adolescence may be a particularly sensitive time period for preventing mental health problems. Affective control, the capacity to engage with goal relevant and inhibit distracting information in affective contexts, has been proposed as a potential target for prevention. In this study, we will explore the impact of improving adolescents’ affective control capacity on their mental health. Methods: The proof-of-principle double-blind randomized controlled trial will compare the effectiveness of an app-based affective control training (AC-Training) to a placebo training (P-Training) app. In total, 200 (~50% females) adolescents (11-19 years) will train for 14 days on their training app. The AC-Training will include three different n-back tasks: visuospatial, auditory and dual (i.e., including both modalities). These tasks require participants to flexibly engage and disengage with affective and neutral stimuli (i.e., faces and words). The P-Training will present participants with a perceptual matching task. The three versions of the P-Training tasks vary in the stimuli included (i.e., shapes, words and faces). The two training groups will be compared on gains in affective control, mental health, emotion regulation and self-regulation, immediately after training, one month and one year after training. Discussion: If, as predicted, the proposed study finds that AC-Training successfully improves affective control in adolescents, there would be significant potential benefits to adolescent mental health. As a free app, the training would also be scalable and easy to disseminate across a wide range of settings. Trial registration: The trial was registered on December 10th 2018 with the International Standard Randomised Controlled Trial Number (Registration number: ISRCTN17213032).

The relationship between DNA sequence, biochemical function, and molecular evolution is relatively well-described for protein-coding regions of genomes, but far less clear in noncoding regions, particularly, in eukaryote genomes. In part, this is because we lack a complete description of the essential noncoding elements in a eukaryote genome. To contribute to this challenge, we used saturating transposon mutagenesis to interrogate the Schizosaccharomyces pombe genome. We generated 31 million transposon insertions, a theoretical coverage of 2.4 insertions per genomic site. We applied a five-state hidden Markov model (HMM) to distinguish insertion-depleted regions from insertion biases. Both raw insertion-density and HMM-defined fitness estimates showed significant quantitative relationships to gene knockout fitness, genetic diversity, divergence, and expected functional regions based on transcription and gene annotations. Through several analyses, we conclude that transposon insertions produced fitness effects in 66–90% of the genome, including substantial portions of the noncoding regions. Based on the HMM, we estimate that 10% of the insertion depleted sites in the genome showed no signal of conservation between species and were weakly transcribed, demonstrating limitations of comparative genomics and transcriptomics to detect functional units. In this species, 3′- and 5′-untranslated regions were the most prominent insertion-depleted regions that were not represented in measures of constraint from comparative genomics. We conclude that the combination of transposon mutagenesis, evolutionary, and biochemical data can provide new insights into the relationship between genome function and molecular evolution.

How do people plan ahead when searching for rewards? Weinvestigate planning in a foraging task in which participantssearch for rewards on an infinite two-dimensional grid. Ourresults show that their search is best-described by a modelwhich searches at least 3 steps ahead. Furthermore, partici-pants do not seem to update their beliefs during planning, butrather treat their initial beliefs as given, a strategy similar to aheuristic called root-sampling. This planning algorithm corre-sponds well with participants’ behavior in test problems withrestricted movement and varying degrees of information, out-performing more complex models. These results enrich ourunderstanding of adaptive planning in complex environments

We introduce the spatially correlated multi-armed banditas a task coupling function learning with the exploration-exploitation trade-off. Participants interacted with bi-variatereward functions on a two-dimensional grid, with the goal ofeither gaining the largest average score or finding the largestpayoff. By providing an opportunity to learn the underly-ing reward function through spatial correlations, we modelto what extent people form beliefs about unexplored payoffsand how that guides search behavior. Participants adapted toassigned payoff conditions, performed better in smooth thanin rough environments, and—surprisingly—sometimes per-formed equally well in short as in long search horizons. Ourmodeling results indicate a preference for local search options,which when accounted for, still suggests participants werebest-described as forming local inferences about unexploredregions, combined with a search strategy that directly tradedoff between exploiting high expected rewards and exploring toreduce uncertainty about the spatial structure of rewards.

Variance of the outcomes associated with an option often provides a measure of the riskiness of that option. Hence, it is important for organisms are able to detect any sudden changes in outcome variance. In Experiment 1, we presented people with graphs of share price time series or water level time series. In half the graphs, variance (financial or flooding risk) changed at some point. People were better at detecting increases than decreases in risk - maybe because it is more important to detect increases in danger than decreases in it. However, in Experiment 2, people were still better at detecting increases than decreases in variance even when those changes did not reflect altered levels of risk. Our findings may reflect the fact that the actual change in variance exceeds the change needed to identify a regime change in variance by a larger amount for upward than for downward changes.

The estimation of duration has been shown to follow Bayesian inference, where people use their prior belief to calibrate the estimation. This explains timing biases such as the range bias where a duration is reproduced as longer when previously encountered durations were longer than shorter. However, it is unclear whether prior belief is based on previously perceived or reproduced durations. In 4 experiments, we show that the range bias occurs between short and long reproduction ranges but not between short and long perception ranges. Further analyses also show that the prior is updated by the most recent reproduced (but not perceived) duration. Together these results support a task-oriented Bayesian inference account of time reproduction, where people use the perceived duration and their past reproduction experience to make an inference about how much time to reproduce.

How do people explore in order to gain rewards in uncer-tain dynamical systems? Within a reinforcement learningparadigm, control normally involves trading off between ex-ploration (i.e. trying out actions in order to gain more knowl-edge about the system) and exploitation (i.e. using currentknowledge of the system to maximize reward). We study anovel control task in which participants must steer a boat ona grid, aiming to follow a path of high reward whilst learninghow their actions affect the boat’s position. We find that partic-ipants explore strategically yet conservatively, exploring morewhen mistakes are less costly and practicing actions that willbe required later on.

How do people decide whether to try out novel options? We argue that they utilize contextual information toefficiently generalize from learned functional relations in order to decide between known or novel options. In a contextualmulti-armed bandit task, in which rewards are a noisy function of observable features, we assess participants’ preferences fornewly introduced options. We show that participants preferably choose a novel option if its features indicate high rewards,but shun the option if its features indicate low rewards, a behavior that can only be explained by functional generalization.Moreover, we assess people’s preferences for novel options that have medium rewards to test whether they prefer options lesssimilar to experienced options, consistent with choices guided by uncertainty. Given that novel options normally come withobservable features, we argue that contextual learning is a parsimonious yet powerful explanation of behavior in the face ofnovelty.

Warning labels can be considered as descriptions added to repeated decisions-from-experience. Limited researchso far has looked at the theoretical integration of decisions from descriptions and decisions from experience when the two areavailable concurrently. We explore how the presence and timing of such warning labels influence behaviour. We expected theprovision of warning labels to subsequently reduce risk taking, and that more prior experience before the appearance of suchlabels would lead to stronger habit formation and reduce their behavioural impact. Instead, we show how the appearance ofdescriptions warning against risks can have a perverse effect of increasing risk taking. And counter-intuitively, we also observethat the amount of previous experience prior to the appearance of descriptions does not impact behaviour. Briefly presentedwarning labels also have the same effect as constantly present ones. All of these findings have strong implications on the designof effective warning labels.

How do people learn about complex functional structure? Taking inspiration from other areas of cognitive science, we propose that this is accomplished by harnessing compositionality: complex structure is decomposed into simpler building blocks. We formalize this idea within the framework of Bayesian regression using a grammar over Gaussian process kernels. We show that participants prefer compositional over non-compositional function extrapolations, that samples from the human prior over functions are best described by a compositional model, and that people perceive compositional functions as more predictable than their non-compositional but otherwise similar counterparts. We argue that the compositional nature of intuitive functions is consistent with broad principles of human cognition.

Exploration-exploitation of functions, that is learningand optimizing a mapping between inputs and expectedoutputs, is ubiquitous to many real world situations.These situations sometimes require us to avoid certainoutcomes at all cost, for example because they arepoisonous, harmful, or otherwise dangerous. We testparticipants’ behavior in scenarios in which they haveto find the optimum of a function while at the sametime avoid outputs below a certain threshold. Intwo experiments, we find that Safe-Optimization, aGaussian Process-based exploration-exploitation algo-rithm, describes participants’ behavior well and thatparticipants seem to care first about whether a point issafe and then try to pick the optimal point from all suchsafe points. This means that their trade-off betweenexploration and exploitation indicates intelligent,approximate, and homeostasis-driven behavior

How can heuristic strategies emerge from smaller build-ing blocks? We propose Approximate Bayesian Com-putation (ABC) as a computational solution to thisproblem. As a first proof of concept, we demonstratehow a heuristic decision strategy such as Take The Best(TTB) can be learned from smaller, probabilisticallyupdated building blocks. Based on a self-reinforcingsampling scheme, different building blocks are com-bined and, over time, tree-like non-compensatory heuris-tics emerge. This new algorithm, coined ApproximatelyBayesian Computed Take The Best (ABC-TTB), is ableto recover data that was generated by TTB, leads tosensible inferences about cue importance and cue direc-tions, can outperform traditional TTB, and allows totrade-off performance and computational effort explic-itly.

Exploration-exploitation of functions, that is learning and optimizing a mapping between inputs and expected outputs, is ubiquitous to many real world situations. These situations sometimes require us to avoid certain outcomes at all cost, for example because they are poisonous, harmful, or otherwise dangerous. We test participants' behavior in scenarios in which they have to find the optimum of a function while at the same time avoid outputs below a certain threshold. In two experiments, we find that Safe-Optimization, a Gaussian Process-based exploration-exploitation algorithm, describes participants' behavior well and that participants seem to care firstly whether a point is safe and then try to pick the optimal point from all such safe points. This means that their trade-off between exploration and exploitation can be seen as an intelligent, approximate, and homeostasis-driven strategy., Comment: 6 pages, submitted to Cognitive Science Conference

In real-life decision environments people learn from their direct experience with alternative courses of action. Yet they can accelerate their learning by using functional knowledge about the features characterizing the alternatives. We designed a novel contextual multi-armed bandit task where decision makers chose repeatedly between multiple alternatives characterized by two informative features. We compared human behavior in this contextual task with a classic multi-armed bandit task without feature information. Behavioral analysis showed that participants in the contextual bandit task used the feature information to direct their exploration of promising alternatives. Ex post, we tested participants’ acquired functional knowledge in one-shot multi-feature choice trilemmas. We compared a novel function-learning-based reinforcement learning model to a classic reinforcement learning. Although reinforcement learning models predicted behavior better in the learning phase, the new models did better in predicting the trilemma choices

How do we perceive the predictability of functions? We derive a rational measure of a function's predictabil- ity based on Gaussian process learning curves. Using this measure, we show that the smoothness of a func- tion can be more important to predictability judgments than the variance of additive noise or the number of samples. These patterns can be captured well by the learning curve for Gaussian process regression, which in turn crucially depends on the eigenvalue spectrum of the covariance function. Using approximate bounds on the learning curve, we model participants' predictabil- ity judgments about sampled functions and ?nd that smoothness is indeed a better predictor for perceived predictability than both the variance and the sample size. This means that it can sometimes be preferable to learn about noisy but smooth functions instead of deterministic complex ones.

Contextual Multi-Armed Bandit (CMAB) tasks are a novel framework to assess decision making in uncertain environments. In a CMAB task, participants are presented with multiple options (arms) which are characterized by a number of features (context) related to the reward as- sociated with the arms. By choosing arms repeatedly and observing the reward, participants can learn about the relation between context and reward and improve their decision strategy. We present two studies on how people behave in CMAB tasks. Within a stationary en- vironment, we ?nd that participants are best described by Thompson Sampling-based Gaussian Process mod- els. This decision rule incorporates probability match- ing to the expected outcomes derived from a rational model of the task and it is especially well-adapted to non-stationary environments. In a dynamic CMAB task we again ?nd that participants are best described by probability matching of Gaussian Process expectations. Our ?ndings imply that behavior previously referred to as \irrational" can actually be seen as a well-adapted strategy based on powerful inference algorithms. Keywords: Decision Making, Learning, Exploration- Exploitation, Contextual Multi-Armed Bandits

The fronto-striatal circuits are considered to mediate inhibitory control over action. The aim of this study was to investigate the contribution of the internal segment of the pallidum (GPi), one of the final output pathways from the basal ganglia to the cortex, in inhibition. We examined the effect of deep brain stimulation (DBS) of the GPi (GPi-DBS) in patients with Parkinson's disease who performed a conditional stop signal task, with DBS on and off. Modulation of GPi activity was associated with significantly faster Go reaction times with DBS on than off, but stop signal reaction times were not altered. Application of the drift diffusion model indicated that GPi-DBS was associated with significantly lower response thresholds compared to GPi-DBS off. However, the drift rate was significantly lower than healthy controls with both GPi-DBS on and off. These results suggest that the GPi plays a crucial role in the 'Go' pathway, perhaps facilitating reaching the required threshold to initiate actions. However, GPi-DBS does not alter the functioning of the indirect 'NoGo' pathway, and other basal ganglia nuclei, such as the STN, may play a greater role in reactive response inhibition and conflict resolution.

The fronto-striatal circuits are considered to mediate inhibitory control over action. The aim of this study was to investigate the contribution of the internal segment of the pallidum (GPi), one of the final output pathways from the basal ganglia to the cortex, in inhibition. We examined the effect of deep brain stimulation (DBS) of the GPi (GPi-DBS) in patients with Parkinson's disease who performed a conditional stop signal task, with DBS on and off. Modulation of GPi activity was associated with significantly faster Go reaction times with DBS on than off, but stop signal reaction times were not altered. Application of the drift diffusion model indicated that GPi-DBS was associated with significantly lower response thresholds compared to GPi-DBS off. However, the drift rate was significantly lower than healthy controls with both GPi-DBS on and off. These results suggest that the GPi plays a crucial role in the 'Go' pathway, perhaps facilitating reaching the required threshold to initiate actions. However, GPi-DBS does not alter the functioning of the indirect 'NoGo' pathway, and other basal ganglia nuclei, such as the STN, may play a greater role in reactive response inhibition and conflict resolution.

In the present study we address the following questions: (1) How is performance affected when patients with Parkinson's Disease (PD) perform a dynamic decision making task? (2) Does dopaminergic medication differentially affect dynamic decision making? To address these questions participants were trained with different goals during learning: either they made intervention-based decisions or prediction-based decisions during learning. The findings show that overall there is an advantage for those trained to intervene over those trained to predict. In addition, the results are the first demonstration that PD patients 'ON' (N=20) compared to 'OFF' L-Dopa (N=15) medication and also relative to healthy age matched controls (N=16) showed lower levels of relative improvement in the accuracy of their decisions in a dynamic decision making task, and tended to use sub-optimal strategies. These findings provide support for the 'Dopamine Overdose' hypothesis using a novel decision making task, and suggest that executive functions such as decision making can be adversely affected by dopaminergic medication in PD.

The aim of our study was to investigate two inter-related hypotheses about the role of the subthalamic nucleus. First that the subthalamic nucleus plays a role in adjusting response thresholds and speed-accuracy trade-offs and second that it is involved in reactive and proactive inhibition and conflict resolution. These were addressed by comparing the performance of 10 patients with Parkinson's disease treated with right subthalamotomy and 12 patients with left subthalamotomy, to 14 unoperated patients with Parkinson's disease and 23 age-matched healthy control participants on a conditional stop signal task and applying the drift diffusion model. Unilateral subthalamotomy significantly improved Parkinson's disease motor signs. Patients with right subthalamotomy had significantly faster Go reaction times with their contra-lesional hand than the unoperated patients and did not differ from the control participants, indicating their speed of response initiation was 'normalized'. However, operated patients made significantly more discrimination errors than unoperated patients and controls, suggesting that subthalamotomy influenced speed-accuracy trade-offs. This was confirmed by the drift diffusion model, revealing that while the unoperated patients had significantly lower drift rate and higher response thresholds than the control participants, the response thresholds for the operated groups did not differ from the controls and the patients with right subthalamotomy had a significantly higher drift rate than unoperated patients and similar to that of controls. The drift diffusion model further established that unlike the control participants, operated patients failed to show context-dependent strategic modulation of response thresholds. The patients with right subthalamotomy could not engage in late phase, fast inhibition of the response and showed minimal proactive inhibition when tested with the contra-lesional hand. These results provide strong evidence that the subth

Dit boek behandelt een visie op online media bij Nederlandse universiteiten. Daarbij beperk ik me tot die aspecten waarin online communicatie en/of online marketing een rol speelt. Dit boek behandelt niet de onderwijsgerelateerde aspecten van online media. Elektronische leeromgevingen, sociale media in het klaslokaal (ten behoeve van het onderwijs) komen in dit boek niet aan bod. Ook het onderzoek doen met behulp van online media is geen onderwerp in dit boek. Aan zowel studeren als onderzoek doen met online media zijn communicatie en/of marketing aspecten verbonden (zowel voor de instelling als voor de student/onderzoeker). Dat maakt wel deel uit van de onderwerpen in dit boek. Dit boek is geschreven met twee doelgroepen voor ogen: medewerkers van de TU Delft (met als belangrijkste de wetenschapper) en communicatieprofessionals. Dat neemt niet weg dat studenten, bestuurders, hogescholen en andere universiteiten voordeel kunnen hebben van dit boek. Het betekent wel dat ik niet een vertaalslag heb gemaakt naar andere doelgroepen, die moet je dus zelf maken. Feit blijft wel dat een groep communicatieprofessionals heel andere eigenschappen heeft dan de groep “medewerkers van de TU Delft”. De eerste categorie ondersteunt en leidt de tweede categorie op. Ik houd er in dit boek dan ook een pleidooi voor dat de communicatieprofessionals de (overige) medewerkers helpen bij hun online aanwezigheid en ik leg ook uit waarom ik denk dat dit een goede weg is om te bewandelen. De twee werelden lopen dusdanig door elkaar heen dat ik voor de keuze stond om of twee boekjes te maken of een boek met een rode draad. Ik heb gekozen voor de laatste variant waarbij de rode draad “personal branding” is., Marketing & Communicatie, Delft University of Technology