Dr Jonathan Nelson

Lecturer in Experimental Cognitive Psychology
+44 (0)1483 686890
34 AC 05
For office hours see

Academic and research departments

School of Psychology.


University roles and responsibilities

  • Serving on the FHMS Human Subjects Ethics Committee

    Affiliations and memberships

    Cognitive Science Society
    Judgement and Decision Making Society
    Mathematical Psychology Society
    Psychonomic Society
    Society for Neuroscience


    Research interests

    Research collaborations



    Charley M. Wu, Eric Schulz, Maarten Speekenbrink, Jonathan D. Nelson, Björn Meder (2018)Generalization guides human exploration in vast decision spaces, In: Nature Human Behaviour2(12)pp. 915-924 Nature Research

    From foraging for food to learning complex games, many aspects of human behaviour can be framed as a search problem with a vast space of possible actions. Under finite search horizons, optimal solutions are generally unobtainable. Yet, how do humans navigate vast problem spaces, which require intelligent exploration of unobserved actions? Using various bandit tasks with up to 121 arms, we study how humans search for rewards under limited search horizons, in which the spatial correlation of rewards (in both generated and natural environments) provides traction for generalization. Across various different probabilistic and heuristic models, we find evidence that Gaussian process function learning—combined with an optimistic upper confidence bound sampling strategy—provides a robust account of how people use generalization to guide search. Our modelling results and parameter estimates are recoverable and can be used to simulate human-like performance, providing insights about human behaviour in complex environments.

    DANIELLA LOUISE JONES, JONATHAN DAVID NELSON, BERTRAM OPITZ (2021)Increased Anxiety is Associated with Better Learning from Negative Feedback, In: Psychology Learning and Teaching20(1)pp. 76-90 Sage

    Anxiety is one of the most prevalent mental health problems; it is known to impede cognitive functioning. It is believed to alter preferences for feedback-based learning in anxious and non-anxious learners. Thus, the present study measured feedback processing in adults ( N = 30) with and without anxiety symptoms using a probabilistic learning task. Event-related potential (ERP) measures were used to assess how the bias for either positive or negative feedback learning is reflected by the feedback-related negativity component (FRN), an ERP extracted from the electroencephalogram. Anxious individuals, identified by means of the Penn State Worry Questionnaire, showed a diminished FRN and increased accuracy after negative compared to positive feedback. Non-anxious individuals exhibited the reversed pattern with better learning from positive feedback, highlighting their preference for positive feedback. Our ERP results imply that impairments with feedback-based learning in anxious individuals are due to alterations in the mesolimbic dopaminergic system. Our finding that anxious individuals seem to favor negative as opposed to positive feedback has important implications for teacher–student feedback communication.

    Björn Meder, Jonathan D. Nelson, Matt Jones, Azzurra Ruggeri (2019)Stepwise versus globally optimal search in children and adults, In: Cognition191103965pp. 1-18 Elsevier

    How do children and adults search for information when stepwise-optimal strategies fail to identify the most efficient query? The value of questions is often measured in terms of stepwise information gain (expected reduction of entropy on the next time step) or other stepwise-optimal methods. However, such myopic models are not guaranteed to identify the most efficient sequence of questions, that is, the shortest path to the solution. In two experiments we contrast stepwise methods with globally optimal strategies and study how younger children (around age 8, N = 52), older children (around age 10, N = 99), and adults (N = 101) search in a 20-questions game where planning ahead is required to identify the most efficient first question. Children searched as efficiently as adults, but also as myopically. Both children and adults tended to rely on heuristic stepwise-optimal strategies, focusing primarily on questions’ implications for the next time step, rather than planning ahead.

    Eric Schulz, Lara Bertram, Matthias Hofer, Jonathan D. Nelson (2019)Exploring the space of human exploration using Entropy Mastermind, In: Exploring the space of human exploration using Entropy Mastermindpp. 2762-2768 The Cognitive Science Society

    What drives people’s exploration in complex scenarios where they have to actively acquire information? How do people adapt their selection of queries to the environment? We explore these questions using Entropy Mastermind, a novel variant of the Mastermind code-breaking game, in which participants have to guess a secret code by making useful queries. Participants solved games more efficiently if the entropy of the game environment was low; moreover, people adapted their initial queries to the scenario they were in. We also investigated whether it would be possible to predict participants’ queries within the generalized Sharma-Mittal information-theoretic framework. Although predicting individual queries was difficult, the modeling framework offered important insights on human behavior. Entropy Mastermind opens up rich possibilities for modeling and behavioral research.

    JB Jarecki, B Meder, JD Nelson (2017)Naïve and Robust: Class-Conditional Independence in Human Classification Learning, In: Cognitive Science42(1)pp. 4-42 Wiley

    Humans excel in categorization. Yet from a computational standpoint, learning a novel probabilistic classification task involves severe computational challenges. The present paper investigates one way to address these challenges: assuming class-conditional independence of features. This feature independence assumption simplifies the inference problem, allows for informed inferences about novel feature combinations, and performs robustly across different statistical environments. We designed a new Bayesian classification learning model (the dependence-independence structure and category learning model, DISC-LM) that incorporates varying degrees of prior belief in class-conditional independence, learns whether or not independence holds, and adapts its behavior accordingly. Theoretical results from two simulation studies demonstrate that classification behavior can appear to start simple, yet adapt effectively to unexpected task structures. Two experiments — designed using optimal experimental design principles — were conducted with human learners. Classification decisions of the majority of participants were best accounted for by a version of the model with very high initial prior belief in class-conditional independence, before adapting to the true environmental structure. Class-conditional independence may be a strong and useful default assumption in category learning tasks.

    Flavia Filimon, Jonathan D. Nelson, Terrence J. Sejnowski, Martin I. Sereno, Garrison W. Cottrell (2020)The ventral striatum dissociates information expectation, reward anticipation, and reward receipt, In: Proceedings of the National Academy of Sciences117(26)pp. 15200-15208 National Academy of Sciences

    Do dopaminergic reward structures represent the expected utility of information similarly to a reward? Optimal experimental design models from Bayesian decision theory and statistics have proposed a theoretical framework for quantifying the expected value of information that might result from a query. In particular, this formulation quantifies the value of information before the answer to that query is known, in situations where payoffs are unknown and the goal is purely epistemic: That is, to increase knowledge about the state of the world. Whether and how such a theoretical quantity is represented in the brain is unknown. Here we use an event-related functional MRI (fMRI) task design to disentangle information expectation, information revelation and categorization outcome anticipation, and response-contingent reward processing in a visual probabilistic categorization task. We identify a neural signature corresponding to the expectation of information, involving the left lateral ventral striatum. Moreover, we show a temporal dissociation in the activation of different reward-related regions, including the nucleus accumbens, medial prefrontal cortex, and orbitofrontal cortex, during information expectation versus reward-related processing.

    F Filimon, MG Philiastides, JD Nelson, NA Kloosterman, HR Heekeren (2013)How Embodied Is Perceptual Decision Making? Evidence for Separate Processing of Perceptual and Motor Decisions, In: Journal of Neuroscience33(5)pp. 2121-2136

    The extent to which different cognitive processes are “embodied” is widely debated. Previous studies have implicated sensorimotor regions such as lateral intraparietal (LIP) area in perceptual decision making. This has led to the view that perceptual decisions are embodied in the same sensorimotor networks that guide body movements. We use event-related fMRI and effective connectivity analysis to investigate whether the human sensorimotor system implements perceptual decisions. We show that when eye and hand motor preparation is disentangled from perceptual decisions, sensorimotor areas are not involved in accumulating sensory evidence toward a perceptual decision. Instead, inferior frontal cortex increases its effective connectivity with sensory regions representing the evidence, is modulated by the amount of evidence, and shows greater task-positive BOLD responses during the perceptual decision stage. Once eye movement planning can begin, however, an intraparietal sulcus (IPS) area, putative LIP, participates in motor decisions. Moreover, sensory evidence levels modulate decision and motor preparation stages differently in different IPS regions, suggesting functional heterogeneity of the IPS. This suggests that different systems implement perceptual versus motor decisions, using different neural signatures.

    JD Nelson, CRM McKenzie, GW Cottrell, TJ Sejnowski (2010)Experience matters: information acquisition optimizes probability gain, In: Psychological Science21(7)pp. 960-969 Sage

    Deciding which piece of information to acquire or attend to is fundamental to perception, categorization, medical diagnosis, and scientific inference. Four statistical theories of the value of information—information gain, Kullback-Liebler distance, probability gain (error minimization), and impact—are equally consistent with extant data on human information acquisition. Three experiments, designed via computer optimization to be maximally informative, tested which of these theories best describes human information search. Experiment 1, which used natural sampling and experience-based learning to convey environmental probabilities, found that probability gain explained subjects’ information search better than the other statistical theories or the probability-of-certainty heuristic. Experiments 1 and 2 found that subjects behaved differently when the standard method of verbally presented summary statistics (rather than experience-based learning) was used to convey environmental probabilities. Experiment 3 found that subjects’ preference for probability gain is robust, suggesting that the other models contribute little to subjects’ search behavior.

    C Wu, B Meder, F Filimon, JD Nelson (2017)Asking better questions: How presentation formats influence information search, In: Journal of Experimental Psychology: Learning, Memory, and Cognition43(8)pp. 1274-1297 American Psychological Association

    While the influence of presentation formats have been widely studied in Bayesian reasoning tasks, we present the first systematic investigation of how presentation formats influence information search decisions. Four experiments were conducted across different probabilistic environments, where subjects (N 2,858) chose between 2 possible search queries, each with binary probabilistic outcomes, with the goal of maximizing classification accuracy. We studied 14 different numerical and visual formats for presenting information about the search environment, constructed across 6 design features that have been prominently related to improvements in Bayesian reasoning accuracy (natural frequencies, posteriors, complement, spatial extent, countability, and part-to-whole information). The posterior variants of the icon array and bar graph formats led to the highest proportion of correct responses, and were substantially better than the standard probability format. Results suggest that presenting information in terms of posterior probabilities and visualizing natural frequencies using spatial extent (a perceptual feature) were especially helpful in guiding search decisions, although environments with a mixture of probabilistic and certain outcomes were challenging across all formats. Subjects who made more accurate probability judgments did not perform better on the search task, suggesting that simple decision heuristics may be used to make search decisions without explicitly applying Bayesian inference to compute probabilities. We propose a new take-the-difference (TTD) heuristic that identifies the accuracy-maximizing query without explicit computation of posterior probabilities.

    David R. Mandel, Gorka Navarrete, Nathan Dieckmann, Jonathan Nelson (2019)Editorial: Judgment and Decision Making Under Uncertainty: Descriptive, Normative, and Prescriptive Perspectives, In: Frontiers in Psychology101506pp. 1-3 Frontiers Media

    Judgment and Decision Making Under Uncertainty: Descriptive, Normative, and Prescriptive Perspectives was motivated by our interest in better understanding why people judge and decide as they do (descriptive perspective), how they ideally ought to judge and decide (normative perspective), and how their judgment and decision-making processes might be improved in practice (prescriptive perspective). We sought papers that addressed some aspect of judgment and decision making from one or more of these three theoretical perspectives. We further sought contributions that examined judgment and decision making under conditions of uncertainty, which we intentionally left loosely defined. Our focus on uncertainty reflects the fact that the vast majority of decisions people make in life are not made under conditions of complete certainty, and the uncertainties may be more or less well-defined. Indeed, different components of a single judgment or decision may have multiple uncertainties associated with it, some of which may be fuzzier than others. Following our call for papers, we received 32 submissions, 17 of which were accepted. The latter set comprises this book. There are 11 original research articles, 2 hypothesis and theory articles, 2 perspectives, and 1 book review and systematic review each.

    M Moussaid, JD Nelson (2013)Simple Heuristics and the Modelling of Crowd Behaviours, In: U Weidmann, U Kirsch, M Schreckenberg (eds.), Pedestrian and Evacuation Dynamics 2012pp. 75-90

    A crowd of pedestrians is a complex system that exhibits a rich variety of self-organized collective behaviors, such as lane formation, stop-and-go waves, or crowd turbulence. Understanding the mechanisms of crowd dynamics requires establishing a link between the local behavior of pedestrians during interactions, and the global dynamics of the crowd at high density. For this, the elaboration of a model is necessary. In this contribution, we will make a distinction between two kinds of modelling methods: outcome models that are often based on analogies with Newtonian mechanics, and process models based on concepts of cognitive science. While outcome models describe directly the movements of a pedestrian by means of repulsive forces or probabilities to move from one place to another, process models generate the movement from the bottom-up by describing the underlying cognitive process used by the pedestrian during navigation. Here, we will describe and compare two representatives of outcome and process models, namely the social force model on the one hand, and the heuristic model on the other hand. In particular, we will describe the strength and the limitations of each approach, and discuss possible future improvements for process models.

    Vincenzo Crupi, Jonathan Nelson, Bjorn Meder, Gustavo Cevolani, Katya Tentori (2018)Generalized Information Theory Meets Human Cognition: Introducing a Unified Framework to Model Uncertainty and Information Search, In: Cognitive Science42(5)pp. 1410-1456 Wiley-Blackwell

    Searching for information is critical in many situations. In medicine, for instance, careful choice of a diagnostic test can help narrow down the range of plausible diseases that the patient might have. In a probabilistic framework, test selection is often modeled by assuming that people’s goal is to reduce uncertainty about possible states of the world. In cognitive science, psychology, and medical decision making, Shannon entropy is the most prominent and most widely used model to formalize probabilistic uncertainty and the reduction thereof. However, a variety of alternative entropy metrics (Hartley, Quadratic, Tsallis, Renyi, and more) are popular in the social and the natural sciences, computer science, and philosophy of science. Particular entropy measures have been predominant in particular research areas, and it is often an open issue whether these divergences emerge from different theoretical and practical goals or are merely due to historical accident. Cutting across disciplinary boundaries, we show that several entropy and entropy reduction measures arise as special cases in a unified formalism, the Sharma–Mittal framework. Using mathematical results, computer simulations, and analyses of published behavioral data, we discuss four key questions: How do various entropy models relate to each other? What insights can be obtained by considering diverse entropy models within a unified framework? What is the psychological plausibility of different entropy models? What new questions and insights for research on human information acquisition follow? Our work provides several new pathways for theoretical and empirical research, reconciling apparently conflicting approaches and empirical findings within a comprehensive and unified information-theoretic formalism.

    J Jarecki, B Meder, JD Nelson (2013)The Assumption of Class-conditional Independence in Category Learning, In: M Knauff, N Sebanz, M Pauen, I Wachsmuth (eds.), 35th Annual Meeting of the Cognitive Science Society (CogSci 2013): Cooperative Minds: Social Interaction and Group Dynamicspp. 2650-2655

    This paper investigates the role of the assumption of class-conditional independence of object features in human classification learning. This assumption holds that object feature values are statistically independent of each other, given knowledge of the object's true category. Treating features as class-conditionally independent can in many situations substantially facilitate learning and categorization even if the assumption is not perfectly true. Using optimal experimental design principles, we designed a task to test whether people have this default assumption when learning to categorize. Results provide some supporting evidence, although the data are mixed. What is clear is that classification behavior adapts to the structure of the environment: a category structure that is unlearnable under the assumption of class-conditional independence is learned by all participants.

    B Meder, JD Nelson (2012)Information search with situation-specific reward functions, In: Judgment and Decision Making7(2)pp. 119-148 Society for Judgment and Decision Making

    The goal of obtaining information to improve classification accuracy can strongly conflict with the goal of obtaining information for improving payoffs. Two environments with such a conflict were identified through computer optimization. Three subsequent experiments investigated people’s search behavior in these environments. Experiments 1 and 2 used a multiple-cue probabilistic category-learning task to convey environmental probabilities. In a subsequent search task subjects could query only a single feature before making a classification decision. The crucial manipulation concerned the search-task reward structure. The payoffs corresponded either to accuracy, with equal rewards associated with the two categories, or to an asymmetric payoff function, with different rewards associated with each category. In Experiment 1, in which learning-task feedback corresponded to the true category, people later preferentially searched the accuracy-maximizing feature, whether or not this would improve monetary rewards. In Experiment 2, an asymmetric reward structure was used during learning. Subjects searched the reward-maximizing feature when asymmetric payoffs were preserved in the search task. However, if search-task payoffs corresponded to accuracy, subjects preferentially searched a feature that was suboptimal for reward and accuracy alike. Importantly, this feature would have been most useful, under the learning-task payoff structure. Experiment 3 found that, if words and numbers are used to convey environmental probabilities, neither reward nor accuracy consistently predicts search. These findings emphasize the necessity of taking into account people’s goals and search-and-decision processes during learning, thereby challenging current models of information search.

    JD Nelson, B Divjak, G Gudmundsdottir, LF Martignon, B Meder (2014)Children’s sequential information search is sensitive to environmental probabilities, In: Cognition130(1)pp. 74-80 Elsevier

    We investigated 4th-grade children’s search strategies on sequential search tasks in which the goal is to identify an unknown target object by asking yes–no questions about its features. We used exhaustive search to identify the most efficient question strategies and evaluated the usefulness of children’s questions accordingly. Results show that children have good intuitions regarding questions’ usefulness and search adaptively, relative to the statistical structure of the task environment. Search was especially efficient in a task environment that was representative of real-world experiences. This suggests that children may use their knowledge of real-world environmental statistics to guide their search behavior. We also compared different related search tasks. We found positive transfer effects from first doing a number search task on a later person search task.

    Nichola Taylor, Matthias Hofer, Jonathan D. Nelson (2020)The Paradox of Help Seeking in the Entropy Mastermind Game, In: Frontiers in Education (Lausanne)5 Frontiers Media S.A

    Research on Bayesian reasoning suggests that humans make good use of available information. Similarly, research on human information acquisition suggests that Optimal Experimental Design models predict human queries well. This perspective contrasts starkly with educational research on help seeking, which suggests that many students wait excessively long to ask for help, or even decline help when it is offered. We bring these lines of work together, exploring when people seek help as a function of problem state in the Entropy Mastermind code breaking game. The Entropy Mastermind game is a probabilistic version of the classic code breaking game, involving inductive, deductive and scientific reasoning. Whether help in the form of a hint was available was manipulated within subjects. Results showed that participants tended to ask for help late in the game play, often when they already had all the necessary information needed to crack the code. These results pose a challenge for some versions of Bayesian and Optimal Experimental Design frameworks. Possible theoretical frameworks to understand the results, including from computer science approaches to the Mastermind game, are considered.

    Anna Coenen, Jonathan Nelson, Todd M Gureckis (2018)Asking the right questions about the psychology of human inquiry: Nine open challenges, In: Psychonomic Bulletin & Review Springer

    The ability to act on the world with the goal of gaining information is core to human adaptability and intelligence. Perhaps the most successful and influential account of such abilities is the Optimal Experiment Design (OED) hypothesis, which argues that humans intuitively perform experiments on the world similar to the way an effective scientist plans an experiment. The widespread application of this theory within many areas of psychology calls for a critical evaluation of the theory’s core claims. Despite many successes, we argue that the OED hypothesis remains lacking as a theory of human inquiry and that research in the area often fails to confront some of the most interesting and important questions. In this critical review, we raise and discuss nine open questions about the psychology of human inquiry.

    Gute Fragen zu stellen, ist eine grundlegende Kompetenz für das Lösen alltäglicher Aufgaben. Der vorliegende Beitrag skiz-ziert eine spielerische Möglichkeit, anhand von gut überlegten Fragen zu verschiedenen Merkmalen und Fabelwesen erste Kenntnisse über Information, sowie einfache Strategien (Heuristiken) der Informationssuche von Schüler/inne/n zu vermit-teln. Dabei geht es auch darum, elementare Kompetenzen im Umgang mit Kodierung und Dekodierung von Information von Kindern zu fördern.

    Additional publications