Pillars of judgment : how memory abilities, task feedback, and cognitive load guide judgment strategies

Making judgments is an essential part of everyday life and how people form a judgment has instigated a plethora of research. Research in judgment and categorization has particularly contrasted two types of judgment strategies: rule-based and similarity-based strategies. Recent research suggests that people can make use of both rule- and similaritybased strategies and frequently shift between these strategies. To select between strategies, contingency approaches propose that people trade off the strategies’ accuracy against the effort needed to execute strategy so that the selected strategy matches the demands of the task environment and the capabilities of the decision maker. This dissertation presents three papers investigating how accuracy-effort trade-offs between rule-based and similarity-based judgment strategies change strategy selection in judgment and categorization tasks. The first paper studies how reducing working memory by imposing a cognitive load may foster shifts to a less demanding similarity-based strategy and, in turn, enhances judgment performance in tasks well solved by a similarity-based strategy, but not in tasks for which rules are better suited. The second paper compares judgment strategies to strategies people apply in categorization. It shows that the same task characteristics, namely the number of cues and the functional relationship between cues and criterion, foster shifts between rulebased and similarity-based strategies in judgment and categorization. The third manuscript explores which memory abilities underlie rule-based and similarity-based judgments. Specifically, it shows that working memory predicts to a stronger degree how well people solve rule-based judgment tasks, whereas episodic memory is more closely linked to judgment performance in similarity-based tasks. Furthermore, episodic memory also predicts selecting a similarity-based strategy, but not working memory

Regulating AI:Applying Insights from Behavioural Economics and Psychology to the Application of Article 5 of the EU AI Act

Article 5 of the European Union’s Artificial Intelligence Act is intended to regulate AI use to prevent potentially harmful consequences. Nevertheless, applying this legislation practically is likely to be challenging because of ambiguously used terminologies and because it fails to specify which manipulation techniques may be invoked by AI, potentially leading to significant harm. This paper aims to bridge this gap by defining key terms and demonstrating how AI may invoke these techniques, drawing from insights in psychology and behavioural economics. First, this paper provides definitions of the terms “subliminal techniques", “manipulative techniques" and “deceptive techniques". Secondly, we identified from the literature in cognitive psychology and behavioural economics three subliminal and five manipulative techniques and exemplify how AI might implement these techniques to manipulate users in real-world case scenarios. These illustrations may serve as a practical guide for stakeholders to detect cases of AI manipulation and consequently devise preventive measures. Article 5 has also been criticised for offering inadequate protection. We critically assess the protection offered by Article 5, proposing specific revisions to paragraph 1, points (a) and (b) of Article 5 to increase its protective effectiveness

Electron Paramagnetic Resonance and Electron Spin Echo Studies of Co2+ Coordination by Nicotinamide Adenine Dinucleotide (NAD+) in Water Solution

Co(2+) binding to the nicotinamide adenine dinucleotide (NAD(+)) molecule in water solution was studied by electron paramagnetic resonance (EPR) and electron spin echo at low temperatures. Cobalt is coordinated by NAD(+) when the metal is in excess only, but even in such conditions, the Co/NAD(+) complexes coexist with Co(H(2)O)(6) complexes. EPR spin-Hamiltonian parameters of the Co/NAD(+) complex at 6 K are g(z) = 2.01, g(x) = 2.38, g(y) = 3.06, A(z) = 94 × 10(−4) cm(−1), A(x) = 33 × 10(−4) cm(−1) and A(y) = 71 × 10(−4) cm(−1). They indicate the low-spin Co(2+) configuration with S = 1/2. Electron spin echo envelope modulation spectroscopy with Fourier transform of the modulated spin echo decay shows a strong coordination by nitrogen atoms and excludes the coordination by phosphate and/or amide groups. Thus, Co(2+) ion is coordinated in pseudo-tetrahedral geometry by four nitrogen atoms of adenine rings of two NAD(+) molecules

“Nanofiltration” Enabled by Super-Absorbent Polymer Beads for Concentrating Microorganisms in Water Samples

Detection and quantification of pathogens in water is critical for the protection of human health and for drinking water safety and security. When the pathogen concentrations are low, large sample volumes (several liters) are needed to achieve reliable quantitative results. However, most microbial identification methods utilize relatively small sample volumes. As a consequence, a concentration step is often required to detect pathogens in natural waters. Herein, we introduce a novel water sample concentration method based on superabsorbent polymer (SAP) beads. When SAP beads swell with water, small molecules can be sorbed within the beads, but larger particles are excluded and, thus, concentrated in the residual non-sorbed water. To illustrate this approach, millimeter-sized poly(acrylamide-co-itaconic acid) (P(AM-co-IA)) beads are synthesized and successfully applied to concentrate water samples containing two model microorganisms: Escherichia coli and bacteriophage MS2. Experimental results indicate that the size of the water channel within water swollen P(AM-co-IA) hydrogel beads is on the order of several nanometers. The millimeter size coupled with a negative surface charge of the beads are shown to be critical in order to achieve high levels of concentration. This new concentration procedure is very fast, effective, scalable, and low-cost with no need for complex instrumentation

Testing Learning Mechanisms of Rule-Based Judgment

Weighing the importance of different pieces of information is a key determinant of making accurate judgments. In social judgment theory, these weighting processes have been successfully described with linear models. How people learn to make judgments has received less attention. Although the hitherto proposed delta learning rule can perfectly learn to solve linear problems, reanalyzing a previous experiment showed that it does not adequately describe human learning. To provide a more accurate description of learning processes we amended the delta learning rule with three learning mechanisms-a decay, an attentional learning mechanism, and a capacity limitation. An additional study tested the different learning mechanisms in predicting learning in linear judgment tasks. In this study, participants first learned to predict a continuous criterion based on four cues. To test the three learning mechanisms rigorously against each other, we changed the importance of the cues after 200 trials so that the mechanisms make different predictions with regard to how fast people adapt to the new environment. On average, judgment accuracy improved from Trial 1 to Trial 200, dropped when the task environment changed, but improved again until the end of the task. The capacity-restricted learning model, restricting how much people update the cue weights on a single trial, best described and predicted the learning curve of the majority of participants. Taken together, these results suggest that considering cognitive constraints within learning models may help to understand how humans learn when making inferences.</p

3D-printed flow cells for aptamer-based impedimetric detection of e. coli crooks strain

Electrochemical spectroscopy enables rapid, sensitive, and label-free analyte detection without the need of extensive and laborious labeling procedures and sample preparation. In addition, with the emergence of commercially available screen-printed electrodes (SPEs), a valuable, disposable alternative to costly bulk electrodes for electrochemical (bio-)sensor applications was established in recent years. However, applications with bare SPEs are limited and many applications demand additional/supporting structures or flow cells. Here, high-resolution 3D printing technology presents an ideal tool for the rapid and flexible fabrication of tailor-made, experiment-specific systems. In this work, flow cells for SPE-based electrochemical (bio-)sensor applications were designed and 3D printed. The successful implementation was demonstrated in an aptamer-based impedimetric biosensor approach for the detection of Escherichia coli (E. coli) Crooks strain as a proof of concept. Moreover, further developments towards a 3D-printed microfluidic flow cell with an integrated micromixer also illustrate the great potential of high-resolution 3D printing technology to enable homogeneous mixing of reagents or sample solutions in (bio-)sensor applications

Testing learning mechanisms of rule-based judgment

Weighing the importance of different pieces of information is a key determinant of making accurate judgments. In social judgment theory, these weighting processes have been successfully described with linear models. How people learn to make judgments has received less attention. Although the hitherto proposed delta learning rule can perfectly learn to solve linear problems, reanalyzing a previous experiment showed that it does not adequately describe human learning. To provide a more accurate description of learning processes we amended the delta learning rule with three learning mechanisms—a decay, an attentional learning mechanism, and a capacity limitation. An additional study tested the different learning mechanisms in predicting learning in linear judgment tasks. In this study, participants first learned to predict a continuous criterion based on four cues. To test the three learning mechanisms rigorously against each other, we changed the importance of the cues after 200 trials so that the mechanisms make different predictions with regard to how fast people adapt to the new environment. On average, judgment accuracy improved from Trial 1 to Trial 200, dropped when the task environment changed, but improved again until the end of the task. The capacity-restricted learning model, restricting how much people update the cue weights on a single trial, best described and predicted the learning curve of the majority of participants. Taken together, these results suggest that considering cognitive constraints within learning models may help to understand how humans learn when making inferences

Pillars of Judgment:How Memory Abilities Affect Performance in Rule-Based and Exemplar-Based Judgments

Making accurate judgments is an essential skill in everyday life. Although how different memory abilities relate to categorization and judgment processes has been hotly debated, the question is far from resolved. We contribute to the solution by investigating how individual differences in memory abilities affect judgment performance in 2 tasks that induced rule-based or exemplar-based judgment strategies. In a study with 279 participants, we investigated how working memory and episodic memory affect judgment accuracy and strategy use. As predicted, participants switched strategies between tasks. Furthermore, structural equation modeling showed that the ability to solve rule-based tasks was predicted by working memory, whereas episodic memory predicted judgment accuracy in the exemplar-based task. Last, the probability of choosing an exemplar-based strategy was related to better episodic memory, but strategy selection was unrelated to working memory capacity. In sum, our results suggest that different memory abilities are essential for successfully adopting different judgment strategies

Tracing the path of forgetting in rule abstraction and exemplar retrieval

People often forget acquired knowledge over time such as names of former classmates. Which knowledge people can access, however, may modify the judgement process and affect judgement accuracy. Specifically, we hypothesised that judgements based on retrieving past exemplars from long-term memory may be more vulnerable to forgetting than remembering rules that relate the cues to the criterion. Experiment 1 systematically tracked the individual course of forgetting from initial learning to later tests (immediate, 1 day, and 1 week) in a linear judgement task facilitating rule-based strategies and a multiplicative judgement task facilitating exemplar-based strategies. Practising the acquired judgement strategy in repeated tests helped participants to consistently apply the learnt judgement strategy and retain a high judgement accuracy even after a week. Yet, whereas a long retention interval did not affect judgements in the linear task, a long retention interval impaired judgements in the multiplicative task. If practice was restricted as in Experiment 2, judgement accuracy suffered in both tasks. In addition, after a week without practice, participants tried to reconstruct their judgements by applying rules in the multiplicative task. These results emphasise that the extent to which decision makers can still retrieve previously learned knowledge limits their ability to make accurate judgements and that the preferred strategies change over time if the opportunity for practice is limited