Jonathan D. Cohen's research while affiliated with Princeton University and other places

Publications (395)

Article
In patch foraging tasks, animals must decide whether to remain with a depleting resource or to leave it in search of a potentially better source of reward. In such tasks, animals consistently follow the general predictions of optimal foraging theory (the Marginal Value Theorem; MVT): to leave a patch when the reward rate in the current patch deplet...
Article
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One of the most striking features of human cognition is the ability to plan. Two aspects of human planning stand out—its efficiency and flexibility. Efficiency is especially impressive because plans must often be made in complex environments, and yet people successfully plan solutions to many everyday problems despite having limited cognitive resou...
Preprint
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Strong inductive biases are a key component of human intelligence, allowing people to quickly learn a variety of tasks. Although meta-learning has emerged as an approach for endowing neural networks with useful inductive biases, agents trained by meta-learning may acquire very different strategies from humans. We show that co-training these agents...
Article
Real-time fMRI (RT-fMRI) neurofeedback has been shown to be effective in treating neuropsychiatric disorders and holds tremendous promise for future breakthroughs, both with regard to basic science and clinical applications. However, the prevalence of its use has been hampered by computing hardware requirements, the complexity of setting up and run...
Preprint
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The ability to acquire abstract knowledge is a hallmark of human intelligence and is believed by many to be one of the core differences between humans and neural network models. Agents can be endowed with an inductive bias towards abstraction through meta-learning, where they are trained on a distribution of tasks that share some abstract structure...
Preprint
The N-back task is often considered to be a canonical example of a task that relies on working memory (WM), requiring both active maintenance of representations of previously-presented stimuli and also processing of these representations. In particular, the set-size effect in this task (e.g., poorer performance on 3-back than 2-back judgments), as...
Preprint
Delayed gratification refers to the willingness to sacrifice smaller, short-term reward for greater, long-term reward. There are suggestions that this proclivity may be impacted by stress and can be predictive of other real-world behaviors. In this article, we outline four large-scale online experiments (total $N = 12,906$) we conducted over the co...
Article
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Applying machine learning algorithms to automatically infer relationships between concepts from large‐scale collections of documents presents a unique opportunity to investigate at scale how human semantic knowledge is organized, how people use it to make fundamental judgments (“How similar are cats and bears?”), and how these judgments depend on t...
Preprint
Real-time fMRI (RT-fMRI) neurofeedback has been shown to be effective in treating neuropsychiatric disorders and holds tremendous promise for future breakthroughs, both with regard to basic science and clinical applications. However, the prevalence of its use has been hampered by computing hardware requirements, the complexity of setting up and run...
Article
Full-text available
Functional magnetic resonance imaging (fMRI) offers a rich source of data for studying the neural basis of cognition. Here, we describe the Brain Imaging Analysis Kit (BrainIAK), an open-source, free Python package that provides computationally optimized solutions to key problems in advanced fMRI analysis. A variety of techniques are presently incl...
Article
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To make informed decisions in natural environments that change over time, humans must update their beliefs as new observations are gathered. Studies exploring human inference as a dynamical process that unfolds in time have focused on situations in which the statistics of observations are history-independent. Yet, temporal structure is everywhere i...
Article
Cognitive fatigue and boredom are two phenomenological states that reflect overt task disengagement. In this article, we present a rational analysis of the temporal structure of controlled behavior, which provides a formal account of these phenomena. We suggest that in controlling behavior, the brain faces competing behavioral and computational imp...
Article
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Experimental design is a key ingredient of reproducible empirical research. Yet, given the increasing complexity of experimental designs, researchers often struggle to implement ones that allow them to measure their variables of interest without confounds. SweetPea (https://sweetpea-org.github.io/) is an open-source declarative language in Python,...
Article
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Humans are remarkably limited in: (i) how many control-dependent tasks they can execute simultaneously, and (ii) how intensely they can focus on a single task. These limitations are universal assumptions of most theories of cognition. Yet, a rationale for why humans are subject to these constraints remains elusive. This feature review draws on rece...
Preprint
Full-text available
In patch foraging tasks, animals must decide whether to remain with a depleting resource or to leave it in search of a potentially better source of reward. In such tasks, animals consistently follow the general predictions of optimal foraging theory (the Marginal Value Theorem; MVT): to leave a patch when the reward rate in the current patch deplet...
Preprint
One of the most striking features of human cognition is the capacity to plan. Two aspects of human planning stand out: its efficiency, even in complex environments, and its flexibility, even in changing environments. Efficiency is especially impressive because directly computing an optimal plan is intractable, even for modestly complex tasks, and y...
Article
Full-text available
The demonstration that human decision-making can systematically violate the laws of rationality has had a wide impact on behavioural sciences. In this study, we use a pupillary index to adjudicate between two existing hypotheses about how irrational biases emerge: the hypothesis that biases result from fast, effortless processing and the hypothesis...
Article
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The ability to learn new tasks and generalize performance to others is one of the most remarkable characteristics of the human brain and of recent AI systems. The ability to perform multiple tasks simultaneously is also a signature characteristic of large-scale parallel architectures, that is evident in the human brain, and has been exploited effec...
Article
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A Correction to this paper has been published: https://doi.org/10.1038/s41567-021-01212-4.
Article
Working memory (WM) maintains task-relevant information in a state ready for processing. While traditional theories assume that sustained neuronal activity is responsible for WM, the Activity Silent WM (ASWM) account proposes that maintenance can also be supported by short-term synaptic weight changes. Here, we argue that the evidence for ASWM can...
Preprint
Full-text available
To make informed decisions in natural environments that change over time, humans must update their beliefs as new observations are gathered. Studies exploring human inference as a dynamical process that unfolds in time have focused on situations in which the statistics of observations are history-independent. Yet temporal structure is everywhere in...
Article
Full-text available
How do people learn when to allocate how much cognitive control to which task? According to the Learned Value of Control (LVOC) model, people learn to predict the value of alternative control allocations from features of a situation. This suggests that people may generalize the value of control learned in one situation to others with shared feature...
Preprint
A key aspect of human intelligence is the ability to infer abstract rules directly from high-dimensional sensory data, and to do so given only a limited amount of training experience. Deep neural network algorithms have proven to be a powerful tool for learning directly from high-dimensional data, but currently lack this capacity for data-efficient...
Preprint
Human intelligence is characterized by a remarkable ability to infer abstract rules from experience and apply these rules to novel domains. As such, designing neural network algorithms with this capacity is an important step toward the development of deep learning systems with more human-like intelligence. However, doing so is a major outstanding c...
Preprint
Full-text available
Functional magnetic resonance imaging (fMRI) offers a rich source of data for studying the neural basis of cognition. Here, we describe the Brain Imaging Analysis Kit (BrainIAK), an open-source, free Python package that provides computationally-optimized solutions to key problems in advanced fMRI analysis. A variety of techniques are presently incl...
Article
Humans often simultaneously pursue multiple plans at different time scales, a capacity known as prospective memory (PM). The successful realization of non-immediate plans (e.g., post package after work) requires keeping track of a future plan while accomplishing other intermediate tasks (e.g., write a paper). Prospective memory capacity requires th...
Preprint
Full-text available
Humans are remarkably limited in (a) how many control-dependent tasks they can execute simultaneously, and (b) how intensely they can focus on a single task. These limitations are universal assumptions of most theories of cognition. Yet, a rationale for why humans are subject to these constraints remains elusive. This review draws on recent insight...
Preprint
Full-text available
Experimental design is a key ingredient of reproducible empirical research. Yet, given the increasing complexity of experimental designs, researchers often struggle to implement ones that allow them to measure their variables of interest without confounds. SweetPea is an open-source declarative language in Python, in which researchers can describe...
Preprint
Humans understand a set of canonical geometric transformations (such as translation and rotation) that support generalization by being untethered to any specific object. We explore inductive biases that help a neural network model learn these transformations in pixel space in a way that can generalize out-of-domain. Specifically, we find that high...
Preprint
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One of the most fundamental and striking limitations of human cognition appears to be a constraint in the number of control-dependent processes that can be executed at one time. This constraint motivates one of the most influential tenets of cognitive psychology: that cognitive control relies on a central, limited capacity processing mechanism that...
Preprint
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Learning requires changing the brain. This typically occurs through experience, study, or instruction. We report a new way of acquiring conceptual knowledge by directly sculpting activity patterns in the human brain. We used a non-invasive technique (closed-loop real-time functional magnetic resonance imaging) to create novel categories of visual o...
Preprint
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Modern machine learning systems struggle with sample efficiency and are usually trained with enormous amounts of data for each task. This is in sharp contrast with humans, who often learn with very little data. In recent years, meta-learning, in which one trains on a family of tasks (i.e. a task distribution), has emerged as an approach to improvin...
Article
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Depressed individuals show an attentional bias toward negatively valenced stimuli and thoughts. In this proof-of-concept study, we present a novel closed-loop neurofeedback procedure intended to remediate this bias. Internal attentional states were detected in real-time by applying machine learning techniques to fMRI data on a cloud server; these a...
Preprint
Full-text available
Cognitive fatigue and boredom are two phenomenological states widely associated with limitations in cognitive control. In this paper, we present a rational analysis of the temporal structure of controlled behavior, which provides a new framework for providing a formal account of these phenomena. We suggest that in controlling behavior, the brain fa...
Preprint
The terms multi-task learning and multitasking are easily confused. Multi-task learning refers to a paradigm in machine learning in which a network is trained on various related tasks to facilitate the acquisition of tasks. In contrast, multitasking is used to indicate, especially in the cognitive science literature, the ability to execute multiple...
Preprint
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Extrapolation -- the ability to make inferences that go beyond the scope of one's experiences -- is a hallmark of human intelligence. By contrast, the generalization exhibited by contemporary neural network algorithms is largely limited to interpolation between data points in their training corpora. In this paper, we consider the challenge of learn...
Preprint
A striking limitation of human cognition is our inability to execute some tasks simultaneously. Recent work suggests that such limitations can arise from a fundamental tradeoff in network architectures that is driven by the sharing of representations between tasks: sharing promotes quicker learning, at the expense of interference while multitasking...
Article
Full-text available
The scale of human interaction is larger than ever before—people regularly interact with and learn from others around the world, and everyone impacts the global environment. We develop an evolutionary game theory model to ask how the scale of interaction affects the evolution of cognition. Our agents make decisions using automatic (e.g., reflexive)...
Preprint
Full-text available
Theories of dACC function have to contend with an increasingly long and diverse list of signals that have been tied to this region. To account for this apparent heterogeneity, we recently proposed a theory of dACC function that embraces that heterogeneity and offers a unifying function focused on the evaluation, motivation and allocation of cogniti...
Preprint
Depressed individuals show an attentional bias toward negatively valenced stimuli and thoughts. Here we present a novel closed-loop neurofeedback procedure that seeks to remediate this bias. Internal attentional states were detected by applying machine learning techniques to fMRI data in real-time, and externalized using a visually presented stimul...
Article
We review research that measures time preferences—i.e., preferences over intertemporal trade—offs. We distinguish between studies using financial flows, which we call “money earlier or later” (MEL) decisions, and studies that use time-dated consumption/effort. Under different structural models, we show how to translate what MEL experiments directly...
Article
Planning is useful. It lets people take actions that have desirable long-term consequences. But, planning is hard. It requires thinking about consequences, which consumes limited computational and cognitive resources. Thus, people should plan their actions, but they should also be smart about how they deploy resources used for planning their action...
Preprint
This note introduces mathematical foundations for modeling of human multitask performance. Using basic definitions from set theory and graph theory, we introduce formal definitions of the environment in which multitasks are performed, of an agent which attempts to perform a multitask, and of the success rate of the agent on a multitask. Drawing on...
Preprint
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Many decisions involve a choice between exploring unknown opportunities and exploiting well-known options. Work across a variety of domains, from animal foraging to human decision making, has suggested that animals solve such ``explore-exploit dilemmas'' with a mixture of two strategies: one driven by information seeking (directed exploration) and...
Preprint
How do people learn when to allocate how much cognitive control to which task? According to the Learned Value of Control (LVOC) model, people learn to predict the value of alternative control allocations from features of a given situation. This suggests that people may generalize the value of control learned in one situation to other situations wit...
Article
Full-text available
With advances in methods for collecting and analyzing fMRI data, there is a concurrent need to understand how to reliably evaluate and optimally use these methods. Simulations of fMRI data can aid in both the evaluation of complex designs and the analysis of data. We present fmrisim, a new Python package for standardized, realistic simulation of fM...
Preprint
Planning is useful. It lets people take actions that have desirable long-term consequences. But, planning is hard. It requires thinking about consequences, which consumes limited computational and cognitive resources. Thus, people should plan their actions, but they should also be smart about how they deploy resources used for planning their action...
Article
Full-text available
Researchers and educators have long wrestled with the question of how best to teach their clients be they humans, non-human animals or machines. Here, we examine the role of a single variable, the difficulty of training, on the rate of learning. In many situations we find that there is a sweet spot in which training is neither too easy nor too hard...
Preprint
Understanding how human semantic knowledge is organized and how people use it to judge fundamental relationships, such as similarity between concepts, has proven difficult. Theoretical models have consistently failed to provide accurate predictions of human judgments, as has the application of machine learning algorithms to large-scale, text-based...
Article
Full-text available
The target article by Lee et al. (in review) highlights the ways in which ongoing concerns about research reproducibility extend to model-based approaches in cognitive science. Whereas Lee et al. focus primarily on the importance of research practices to improve model robustness, we propose that the transparent sharing of model specifications, incl...
Article
Full-text available
Animals, including humans, consistently exhibit myopia in two different contexts: foraging, in which they harvest locally beyond what is predicted by optimal foraging theory, and intertemporal choice, in which they exhibit a preference for immediate vs. delayed rewards beyond what is predicted by rational (exponential) discounting. Despite the simi...
Conference Paper
Full-text available
Researchers have long been interested in using laboratory measures of cognitive control to predict a person’s cognitive control/self control success outside the lab. We used a computational approach to identify which lab-based performance measures provide the most valid individual difference measures of one’s ability and/or motivation to exert cogn...
Conference Paper
Full-text available
One of the most fundamental and striking limitations of human cognitive function is the constraint on the number of control-dependent processes that can be executed simultaneously. However, the sources of this capacity constraint remain largely unexplored. Previous work has attributed the constraints on control-dependent processing to the sharing o...
Conference Paper
Full-text available
Several studies reported that it is harder to switch from a difficult task to an easy task than vice versa. Previous studies explain this paradoxical effect in terms of differences in task strength, by letting participants switch between different types of tasks. However, these studies failed to isolate the effects of task strength from task identi...
Conference Paper
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Constraints on control-dependent processing have become a fundamental concept in general theories of cognition that explain human behavior in terms of rational adaptations to these constraints. However, theories miss a rationale for why such constraints would exist in the first place. Recent work suggests that constraints on the allocation of contr...
Preprint
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In past decades, the Bayesian paradigm has gained traction as a principled account of human behavior in inference tasks. Yet this success is tainted by the ubiquity of behavioral suboptimality and variability. We explore these discrepancies using an online inference task, in which we modulate the temporal statistics of hidden change points. We show...
Preprint
The Target Article by Lee et al. (2019) highlights the ways in which ongoing concerns about research reproducibility extend to model-based approaches in cognitive science. Whereas Lee et al. focus primarily on the importance of research practices to improve model robustness, we propose that the transparent sharing of model specifications, including...
Article
Recent fMRI research shows that perceptual and cognitive representations are instantiated in high-dimensional multivoxel patterns in the brain. However, the methods for detecting these representations are limited. Topological data analysis (TDA) is a new approach, based on the mathematical field of topology, that can detect unique types of geometri...
Preprint
The scale of human interaction patterns is larger now than ever before – people regularly interact with and learn from others around the world, and we each have the ability to impact the global environment that is shared by all. The consequences of local versus global interaction - particularly for the evolution of cooperation - have been studied e...
Article
The scale of human interaction is larger than ever before—people regularly interact with and learn from others around the world, and everyone impacts the global environment. We develop an evolutionary game theory model to ask how the scale of interaction affects the evolution of cognition. Our agents make decisions using automatic (e.g., reflexive)...
Preprint
Full-text available
A bstract Humans often simultaneously pursue multiple plans at different time scales. The successful realization of non-immediate plans (e.g., post package after work) requires keeping track of a future plan while accomplishing other intermediate tasks (e.g., write a paper), a capacity known as prospective memory . This capacity requires the integr...
Preprint
Full-text available
We compare the performance of non-human primates and deep reinforcement learning agents in a virtual pursuit-avoidance task, as part of an effort to understand the role that cognitive control plays in the deeply evolved skill of chase and escape behavior. Here we train two agents, a deep Q network and an actor-critic model, on a video game in which...