Dirk Wulff

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Verified

Dirk verified their affiliation via an institutional email.

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• PhD
• Senior Research Scientist & Head of Research Area at Max Planck Institute for Human Development

Taxonomic incommensurability denotes the difficulty in comparing scientific theories due to different uses of concepts and operationalizations. To tackle this problem in psychology, here we use language models to obtain semantic embeddings representing psychometric items, scales and construct labels in a vector space. This approach allows us to ana...

Large language models (LLMs) are arguably the most predictive models of human cognition available. Despite their impressive human-alignment, LLMs are often labeled as "*just* next-token predictors" that purportedly fall short of genuine cognition. We argue that these deflationary claims need further justification. Drawing on prominent cognitive and...

Large language models (LLMs) are being increasingly incorporated into scientific workflows. However, we have yet to fully grasp the implications of this integration. How should the advancement of large language models affect the practice of science? For this opinion piece, we have invited four diverse groups of scientists to reflect on this query,...

People’s understanding of topics and concepts such as risk, sustainability, and intelligence can be important for psychological researchers and policymakers alike. One underexplored way of accessing this information is to use free associations to map people’s mental representations. In this tutorial, we describe how free association responses can b...

This study explores the use of natural language processing to automate the evaluation of collaborative group engagement in collaborative learning settings. We evaluated two approaches involving large-language models (LLMs) and a set of interpretable linguistic markers to predict the four dimensions---behavioral, social, cognitive, and conceptual-to...

Semantic representations are integral to natural language processing, psycholinguistics, and artificial intelligence. Although often derived from internet text, recent years have seen a rise in the popularity of behavior-based (e.g., free associations) and brain-based (e.g., fMRI) representations, which promise improvements in our ability to measur...

To make profitable investment decisions, investors must know and understand their risks. They can learn about these risks in different ways. Evidence suggests that investors who learn from a ‘risk tool’ simulator perceive financial risk more accurately, feel more informed and confident, and thus take on more financial risk. We attempt a conceptual...

How do people search for information when they are given the opportunity to freely explore their options? Previous research has suggested that people focus on reducing uncertainty before making a decision, but it remains unclear how exactly they do so and whether they do so consistently. We present an analysis of over 1,000,000 information-search d...

Establishing a unified theory of cognition has been a major goal of psychology. While there have been previous attempts to instantiate such theories by building computational models, we currently do not have one model that captures the human mind in its entirety. Here we introduce Centaur, a computational model that can predict and simulate human b...

Accurately capturing individual differences in semantic networks is fundamental to advancing our mechanistic understanding of semantic memory. Past empirical attempts to construct individual-level semantic networks from behavioral paradigms may be limited by data constraints. To assess these limitations and propose improved designs for the measurem...

Collective intelligence underpins the success of groups, organizations, markets and societies. Through distributed cognition and coordination, collectives can achieve outcomes that exceed the capabilities of individuals-even experts-resulting in improved accuracy and novel capabilities. Often, collective intelligence is supported by information tec...

Large Language Models (LLMs) are advancing research across various disciplines. As applications in the behavioral and social sciences largely favor closed, proprietary models, we call for the increased adoption of powerful open LLMs, offering practical and ethical advantages.

Large language models (LLMs) have the potential to revolutionize behavioral science by accelerating and improving the research cycle, from conceptualization to data analysis. Unlike closed-source solutions, open-source frameworks for LLMs can enable transparency, reproducibility, and adherence to data protection standards, which gives them a crucia...

A number of labeling systems based on text have been proposed to help monitor work on the United Nations (UN) Sustainable Development Goals (SDGs). Here, we present a systematic comparison of prominent SDG labeling systems using a variety of text sources and show that these differ considerably in their sensitivity (i.e., true-positive rate) and spe...

Staff education measures applied during a clinical trial were associated with meaningful and lasting changes in institutional clinical practice.

Large language models (LLMs) are often labeled as just next-token predictors devoid of cognitive capacities such as thought or understanding. We argue that these deflationary claims are premature. Drawing on prominent theoretical and philosophical frameworks in cognitive science, we critically evaluate different forms of Justaism that dismiss LLM c...

Large language models (LLMs) are arguably the most predictive models of human cognition available. Despite their impressive human-alignment, LLMs are often labeled as "*just* next-token predictors" that purportedly fall short of genuine cognition. We argue that these deflationary claims need further justification. Drawing on prominent cognitive and...

Objectives Numerous theories exist regarding age differences in risk preference and related constructs, yet many of them offer conflicting predictions and fail to consider convergence between measurement modalities or constructs. To pave the way for conceptual clarification and theoretical refinement, in this preregistered study we aimed to compreh...

We assess whether the classic psychometric paradigm of risk perception can be improved or supplanted by novel approaches relying on language embeddings. To this end, we introduce the Basel Risk Norms, a large data set covering 1004 distinct sources of risk (e.g., vaccination, nuclear energy, artificial intelligence) and compare the psychometric par...

People’s understanding of topics and concepts such as risk, sustainability, and intelligence can be important for psychological researchers and policymakers alike. One underexplored way of accessing this information is to use free associations to map people’s mental representations. In this tutorial, we describe how free association responses can b...

Science communication is evolving: Increasingly, it is directed at the public rather than academic peers. Understanding the circumstances under which the public engages with scientific content is therefore crucial to improving science communication. In this article, we investigate the role of affect on audience engagement with a modern form of scie...

Large language models (LLMs) have the potential to revolutionize behavioral science by accelerating and improving the research cycle, from conceptualization to data analysis. Unlike closed-source solutions, open-source frameworks for LLMs can enable transparency, reproducibility, and adherence to data protection standards, which gives them a crucia...

Large language models (LLMs) are being increasingly incorporated into scientific workflows. However, we have yet to fully grasp the implications of this integration. How should the advent of large language models affect the practice of science? For this opinion piece, we have invited four diverse groups of scientists to reflect on this query, shari...

Taxonomic incommensurability denotes the difficulty in comparing scientific theories due to incompatible use of concepts and operationalizations. We show that item, scale, and label embeddings—representations of psychometric items, scales, and construct labels in a vector space obtained from language models—can help tackle this problem in psycholog...

We assess whether the classic psychometric paradigm of risk perception can be improved or supplanted by novel approaches relying on language embeddings. To this end, we introduce the Basel Risk Norms, a large data set covering 1,004 distinct sources of risk (e.g., vaccination, nuclear energy, artificial intelligence) and compare the psychometric pa...

How do people search for information when they are given the opportunity to freely explore their options? Past research has suggested an important role of uncertainty minimization but has not clarified how people implement it and whether they do so consistently. We present an analysis of over 1,000,000 information-search decisions made by over 2,50...

A number of labeling systems based on text have been proposed to help monitor work on the United Nations (UN) Sustainable Development Goals (SDGs). Here, we present a systematic comparison of systems using a variety of text sources and show that systems differ considerably in their specificity (i.e., true-positive rate) and sensitivity (i.e., true-...

Cognitive science invokes semantic networks to explain diverse phenomena, from memory retrieval to creativity. Research in these areas often assumes a single underlying semantic network that is shared across individuals. Yet, recent evidence suggests that content, size, and connectivity of semantic networks are experience-dependent, implying sizabl...

To make profitable investment decisions, investors must know and understand their risks. They can learn about these risks in different ways. Evidence suggests that investors who learn from a “risk tool” simulator perceive financial risk more accurately, feel more informed and confident, and thus take on more financial risk. We attempt a conceptual...

Are decisions associated with risk and uncertainty made differently across the life span? In this preregistered study we performed a multiverse analysis to exhaustively examine age effects on risk preference, impulsivity, and low self-control using different assessment modalities (N=148, 55% female, 16-81 years, mean age=46 years, SD=19). The main...

What are the defining features of lay people’s semantic representation of risk? We contribute to mapping the semantics of risk based on word associations to provide insight into both universal and individual differences in the representation of risk. Specifically, we introduce a mini-snowball word association paradigm and use the tools of network a...

We investigate a novel link between self-concept clarity and social decision making performance. Drawing on theories of goal pursuit and the self, we posit that self-concept clarity, a concept combining the organization and accessibility of self-related memory representations, can be linked to better decision making performance in situations invol...

We report data from a proof-of-concept study involving the concurrent assessment of large-scale individual semantic networks and cognitive performance. The data include10,800 free associations—collected using a dedicated web-based platform over the course of several weeks—and responses to several cognitive tasks, including verbal fluency, episodic...

People undergo many idiosyncratic experiences throughout their lives that may contribute to individual differences in the size and structure of their knowledge representations. Ultimately, these can have important implications for individuals' cognitive performance. We review evidence that suggests a relationship between individual experiences, the...

Science communication is changing. It is increasingly directed not only at peers but at the public in general. Accordingly, understanding the circumstances under which audience members engage with scientific content is crucial to improving science communication. In this article, we investigate the role of affect on audience engagement with a modern...

Movement tracking is a novel process tracing method promising unique access to the temporal dynamics of cognitive processes. The method involves high-resolution tracking of the hand or handheld devices, e.g., a computer mouse, while they are used to make a choice. In contrast to other process tracing methods, which mostly focus on information acqui...

The modern world holds countless risks for humanity, both large-scale and intimately personal—from cyberwarfare, pandemics, and climate change to sexually transmitted diseases and drug use and abuse. Many risks have prompted institutional, regulatory, and technological countermeasures, the success of which depends to some extent on how individuals...

BACKGROUND Intraoperative arterial hypotension is strongly associated with postoperative major adverse cardio- vascular events (MACE); however, whether targeting higher intraoperative mean arterial blood pressures (MAPs) may prevent adverse events remains unclear. OBJECTIVES This study sought to determine whether targeting higher intraoperative MAP...

Monitoring progress on the United Nations Sustainable Development Goals (SDGs) is important for both academic and non-academic organizations. Existing approaches to monitoring SDGs have focused on specific data types, namely, publications listed in proprietary research databases. We present the text2sdg R package, a user-friendly, open-source packa...

There is great theoretical and applied interest in understanding the psychology of risk-but what are defining features of lay people's semantic representation of this concept? We contribute a new approach to mapping the semantics of risk based on word associations that promises to provide insight into individual and group differences. Specifically,...

We report data from a proof-of-concept study involving the concurrent assessment of large-scale individual semantic networks and cognitive performance. The data include 10,800 free associations-collected using a dedicated web-based platform over the course of 2-4 weeks-and responses to several cognitive tasks, including verbal fluency, episodic mem...

People undergo many idiosyncratic experiences throughout their lives that may contribute to individual differences in the size and structure of their knowledge representations. Ultimately, these can have important implications for individuals' cognitive performance. We review evidence that suggests a relationship between individual experiences, the...

We compare three sequential sampling models, the Race model, the leaky competing accumulator model (LCA) and the drift diffusion model (DDM), as novel computational accounts of choices and response times in semantic relatedness decisions. We focus on two empirical benchmarks, the relatedness effect, denoting faster ”related” than ”unrelated” decisi...

Governments use taxes to discourage undesired behaviors and encourage desired ones. One target of such interventions is reckless behavior, such as texting while driving, which in most cases is harmless but sometimes leads to catastrophic outcomes. Past research has demonstrated how interventions can backfire when the tax on one reckless behavior is...

We improve instability-based methods for the selection of the number of clusters k in cluster analysis by developing a corrected clustering distance that corrects for the unwanted influence of the distribution of cluster sizes on cluster instability. We show that our corrected instability measure outperforms current instability-based measures acros...

The modern world holds countless risks for humanity, both large-scale and intimately personal—from cyber warfare, pandemics, and climate change to sexually transmitted diseases and drug use and abuse. Many risks have prompted institutional, regulatory, and technological countermeasures, the success of which depends to some extent on how individuals...

Governments often use taxes to discourage undesired behaviors or encourage desired ones. One target of such interventions are reckless behaviors such as texting while driving, which in most cases are harmless but sometimes lead to catastrophic outcomes. Past research has demonstrated how interventions can backfire when taxes for specific options ar...

Natural motor behavior is usually refined by ongoing sensory input in closed feedback loops. Research has suggested that humans make systematic errors when localizing touch on the skin, and that perceptual body representations underlying these behaviors are distorted. However, experimental procedures usually prevent participants from touching the t...

Memory search has long been pictured as taking place on a high-dimensional landscape. However, if people are able to cut corners in this landscape by dynamically shifting attention between the space's dimensions to connect distant locations, then this may give rise to wormholes in memory much like those of Einstein-Rosen in external space. Alternat...

An examination of the cognitive tools that the mind uses to grapple with uncertainty in the real world. How do humans navigate uncertainty, continuously making near-effortless decisions and predictions even under conditions of imperfect knowledge, high complexity, and extreme time pressure? Taming Uncertainty argues that the human mind has develope...

The field of cognitive aging has seen considerable advances in describing the linguistic and semantic changes that happen during the adult life span to uncover the structure of the mental lexicon (i.e., the mental repository of lexical and conceptual representations). Nevertheless, there is still debate concerning the sources of these changes, incl...

Network science provides a set of quantitative methods to investigate complex systems, including human cognition. Although cognitive theories in different domains are strongly based on a network perspective, the application of network science methodologies to quantitatively study cognition has so far been limited in scope. This review demonstrates...

The field of cognitive aging has seen considerable advances in describing the linguistic and semantic changes that happen during the adult life span to uncover the structure of the mental lexicon (i.e., the mental repository of lexical and conceptual representations). Nevertheless, there is still debate concerning the sources of these changes, incl...

Many natural behaviors involve closed feedback loops in which ongoing sensory input refines motor behavior. Previous research on tactile localization, however, has implemented localization as open-loop behavior. For instance, participants indicate a touched position on a silhouette shape of the body or on an occluding board mounted above the hand....

We investigate a novel link between self-concept and social decision making. Motivated by theories of evolutionary psychology and memory representation, we posit that self-concept clarity, a concept combining the organization and accessibility of self-related memory representations, can promote better decision making in situations involving other p...

The field of cognitive aging has seen considerable advances in describing the linguistic and semantic changes that happen during the adult life span to uncover the structure of the mental lexicon (i.e., the mental repository of lexical and conceptual representations). Nevertheless, there is still debate concerning the sources of these changes, incl...

Risk preference is one of the most important building blocks of choice theories in the behavioural sciences. In economics, it is often conceptualized as preferences concerning the variance of monetary payoffs, whereas in psychology, risk preference is often thought to capture the propensity to engage in behaviour with the potential for loss or harm...

Efficient implementations of network science tools to facilitate research into human (semantic) memory. In its current version, the package contains several methods to infer networks from verbal fluency data, various network growth models, diverse (switcher-) random walk processes, and tools to analyze and visualize networks. To deliver maximum per...

Mouse-tracking – the analysis of mouse movements in computerized experiments – is becoming increasingly popular in psychological research. Mouse movements are taken as an indicator of commitment to or conflict between choice options during the decision process. Using mouse-tracking, researchers have gained insight into the temporal development of c...

Cognitive science invokes semantic networks to explain diverse phenomena from reasoning to memory retrieval and creativity. While diverse approaches are available, researchers commonly assume a single underlying semantic network that is shared across individuals. Yet, semantic networks are considered the product of experience implying that individu...

Dieser Artikel gibt eine Einführung in das „Description-Experience Gap“ – der Beobachtung eines systematischen Unterschieds in Risikoentschei- dungen aufgrund symbolischer Beschreibungen bzw. sequentieller Erfah- rungen. Die „Kluft“ wird besonders deutlich bei seltenen Ereignissen, die zu viel Gewicht (Beschreibung) bzw. zu wenig Gewicht (Erfahrung...

Risk preference is one of the most important building blocks of choice theories in the behavioural sciences. In economics, it is often conceptualised as preferences concerning variance of monetary payoffs, whereas in psychology risk preference is often thought to capture the propensity to engage in behaviour with the potential for loss or harm. Bot...

We improve current instability-based methods for the selection of the number of clusters k in cluster analysis by developing a normalized cluster instability measure that corrects for the distribution of cluster sizes, a previously unaccounted driver of cluster instability. We show that our normalized instability measure outper- forms current insta...

Mouse- and hand-tracking has become a popular method for studying the cognitive processes involved in a variety of domains, including language processing, memory functions, social cognition, and preferential and moral decision making, to name just a few. The popularity of mouse- and hand-tracking derives from its promise to provide a window into th...

This chapter is a somewhat odd addition in a Handbook of Process-Tracing Methods. We will not revel at the ever growing and ever more sophisticated methods to trace processes nor will we conceive of still another technology. Nevertheless, our concern will be with “information search prior to choice”—the object of desire of what Schulte-Mecklenbeck...

Network science provides a set of quantitative methods to investigate complex systems, including human cognition. Although cognitive theories in different domains are strongly based on a network perspective, the application of network science methodologies to quantitatively study cognition has so far been limited in scope. This review demonstrates...

People can learn about the probabilistic consequences of their actions in two ways: One is by consulting descriptions of an action’s consequences and probabilities (e.g., reading up on a medication’s side effects). The other is by personally experiencing the probabilistic consequences of an action (e.g., beta testing software). In principle, people...

In a recent article, Ericsson and colleagues (2015) compared traditional utility-discounting models with a set of heuristic models of intertemporal choice using a cross-validation approach. Consistent with earlier reports, Ericsson and colleagues concluded that heuristic models (specifically their novel intertemporal choice heuristic or ITCH model)...

Botulinum toxin was shown to be effective in treatment of chronic migraine. We wanted to explore its efficacy and tolerability in chronic application under real-life conditions. For this, 27 consecutive patients (age 45.6 ± 10.8 years, 25 females, 2 males) received altogether 176 injection series (IS) with 189.7 ± 45.8MU onabotulinumtoxinA (Botox®)...

How does the mental lexicon, the network of learned words in our semantic memory, change in old age? To address this question, we employ a new network inference method to infer networks from verbal fluency data of a group of younger and older adults. We find that older adults produce more unique words in verbal fluency tasks than younger adults. In...

How does the mental lexicon, the network of learned words in our semantic memory, change in old age? To address this question, we employ a new network inference method to infer networks from verbal fluency data of a group of younger and older adults. We find that older adults produce more unique words in verbal fluency tasks than younger adults. In...

Botulinum toxin was shown to be effective in treatment of chronic migraine. We wanted to explore its efficacy and tolerability in chronic application under real-life conditions. For this, 27 consecutive patients (age 45.6 ± 10.8 years, 25 females, 2 males) received altogether 176 injection series (IS) with 189.7 ± 45.8MU onabotulinumtoxinA (Botox(®...

What are the cognitive mechanisms underlying subjective valuations formed on the basis of sequential experiences of an option’s possible outcomes? Ashby and Rakow (2014) have proposed a sliding window model (SWIM), according to which people’s valuations represent the average of a limited sample of recent experiences (the size of which is estimated...

To what extent do people adapt their information search policies and subsequent decisions to the long- and short-run consequences of choice environments? To address this question, we investigated exploration and exploitation policies in choice environments that involved single or multiple plays. We further compared behavior in these environments wi...

People can access information about choices in at least two ways: via summary descriptions that provide an overview of potential outcomes and their likelihood of occurrence or via sequential presentation of outcomes. Provided with the former, people make decisions from description; with the latter, they make decisions from experience. Recent invest...