Decoding and predicting intentions

Recent advances in neurotechnology-based biohybrid robots

Biohybrid robots retain the innate biological characteristics and behavioral traits of animals, making them valuable in applications such as disaster relief, exploration of unknown terrains, and medical care. This review aims to comprehensively discuss the evolution of biohybrid robots, their key technologies and applications, and the challenges they face. By analyzing studies conducted on terrestrial, aquatic, and aerial biohybrid robots, we gain a deeper understanding of how these technologies have made significant progress in simulating natural organisms, improving mechanical performance, and intelligent control. Additionally, we address challenges associated with the application of electrical stimulation technology, the precision of neural signal monitoring, and the ethical considerations for biohybrid robots. We highlight the importance of future research focusing on developing more sophisticated and biocompatible control methods while prioritizing animal welfare. We believe that exploring multimodal monitoring and stimulation technologies holds the potential to enhance the performance of biohybrid robots. These efforts are expected to pave the way for biohybrid robotics technology to introduce greater innovation and well-being to human society in the future.

The multisensory nature of human action imagery

Imagination can appeal to all our senses and may, therefore, manifest in very different qualities (e.g., visual, tactile, proprioceptive, or kinesthetic). One line of research addresses action imagery that refers to a process by which people imagine the execution of an action without actual body movements. In action imagery, visual and kinesthetic aspects of the imagined action are particularly important. However, other sensory modalities may also play a role. The purpose of the paper will be to address issues that include: (i) the creation of an action image, (ii) how the brain generates images of movements and actions, (iii) the richness and vividness of action images. We will further address possible causes that determine the sensory impression of an action image, like task specificity, instruction and experience. In the end, we will outline open questions and future directions.

The place of Free Will: the freedom of the prisoner

Debates about the concept of Free Will date back to ancient times. About 40 years ago, Benjamin Libet designed an experiment showing that the conscious intention to move is preceded by a specific pattern of brain activation. His finding suggested that unconscious processes determine our decisions. Libet-style experiments have continued to dominate the debate about Free Will, pushing some authors to argue that the existence of Free Will is a mere illusion. We believe that this dispute is because we often measure Free Will using arbitrary human decisions rather than deliberate actions. After reviewing the definition of Free Will and the related literature, we conclude that the scientific evidence does not disprove the existence of Free Will. However, our will encounters several constraints and limitations that should be considered when evaluating our deeds' personal responsibility.

Libet's legacy: A primer to the neuroscience of volition

The neuroscience of volition is an emerging subfield of the brain sciences, with hundreds of papers on the role of consciousness in action formation published each year. This makes the state-of-the-art in the discipline poorly accessible to newcomers and difficult to follow even for experts in the field. Here we provide a comprehensive summary of research in this field since its inception that will be useful to both groups. We also discuss important ideas that have received little coverage in the literature so far. We systematically reviewed a set of 2220 publications, with detailed consideration of almost 500 of the most relevant papers. We provide a thorough introduction to the seminal work of Benjamin Libet from the 1960s to 1980s. We also discuss common criticisms of Libet's method, including temporal introspection, the interpretation of the assumed physiological correlates of volition, and various conceptual issues. We conclude with recent advances and potential future directions in the field, highlighting modern methodological approaches to volition, as well as important recent findings.

A meta-analysis of Libet-style experiments

In the seminal Libet experiment (Libet et al., 1983), unconscious brain activity preceded the self-reported, conscious intention to move. This was repeatedly interpreted as challenging the view that (conscious) mental states cause behavior and, prominently, as challenging the existence of free will. Extensive discussions in philosophy, psychology, neuroscience, and jurisprudence followed, but further empirical findings were heterogeneous. However, a quantitative review of the literature summarizing the evidence of Libet-style experiments is lacking. The present meta-analysis fills this gap. The results revealed a temporal pattern that is largely consistent with the one found by Libet and colleagues. Remarkably, there were only k = 6 studies for the time difference between unconscious brain activity and the conscious intention to move - the most crucial time difference regarding implications about conscious causation and free will. Additionally, there was a high degree of uncertainty associated with this meta-analytic effect. We conclude that some of Libet et al.'s findings appear more fragile than anticipated in light of the substantial scientific work that built on them.

The Roles of Statistics in Human Neuroscience

Statistics plays three important roles in brain studies. They are (1) the study of differences between brains in distinctive populations; (2) the study of the variability in the structure and functioning of the brain; and (3) the study of data reduction on large-scale brain data. I discuss these concepts using examples from past and ongoing research in brain connectivity, brain information flow, information extraction from large-scale neuroimaging data, and neural predictive modeling. Having dispensed with the past, I attempt to present a few areas where statistical science facilitates brain decoding and to write prospectively, in the light of present knowledge and in the quest for artificial intelligence, about questions that statistical and neurobiological communities could work closely together to address in the future.

No Intentions in the Brain: A Wittgensteinian Perspective on the Science of Intention

In their paper “Why we may not find intentions in the brain,” Uithol et al. (2014) convincingly argue that “the processes underlying action initiation and control are considerably more dynamic and context sensitive than the concept of intention can allow for.” Their paper could be seen as a critical note to the widespread tendency to search for identifiable neurocorrelates of mental concepts. Their more specific suggestion is that the absence of clear neural correlates undermines the traditional understanding of intention. In this paper I will try to take their argument a step further. First of all, I will argue that our folk psychology leaves room for various understandings of intentions, and that the concept of intention discussed by Uithol et al. is an academic concept that has its roots in the causal theory of action and in functionalist approaches to cognition. I will argue that both these paradigms are contested, and that there seems to be theoretical wiggle room for alternative understandings of intention. Subsequently I outline such an alternative perspective based on Wittgensteinian philosophy of psychology, emphasizing the regulative role of intention talk. However, the proposed understanding raises the question how to think about neural realization: is intention talk “just” talk, or do intentions really exist? I will propose that intention talk should be understood as a form of pattern recognition, and that the patterns involved are extended in both space and time. The conclusion outlines some important implications for the neuroscientific investigation of intentions.

Subjective vividness of motor imagery has a neural signature in human premotor and parietal cortex

Motor imagery (MI) is the process in which subjects imagine executing a body movement with a strong kinesthetic component from a first-person perspective. The individual capacity to elicit such mental images is not universal but varies within and between subjects. Neuroimaging studies have shown that these inter-as well as intra-individual differences in imagery quality mediate the amplitude of neural activity during MI on a group level. However, these analyses were not sensitive to forms of representation that may not map onto a simple modulation of overall amplitude. Therefore, the present study asked how far the subjective impression of motor imagery vividness is reflected by a spatial neural code, and how patterns of neural activation in different motor regions relate to specific imagery impressions. During fMRI scanning, 20 volunteers imagined three different types of right-hand actions. After each imagery trial, subjects were asked to evaluate the perceived vividness of their imagery. A correlation analysis compared the rating differences and neural dissimilarity values of the rating groups separately for each region of interest. Results showed a significant positive correlation in the left vPMC and right IPL, indicating that these regions particularly reflect perceived imagery vividness in that similar rated trials evoke more similar neural patterns. A decoding analysis revealed that the vividness of the motor image related systematically to the action specificity of neural activation patterns in left vPMC and right SPL. Imagined actions accompanied by higher vividness ratings were significantly more distinguishable within these areas. Altogether, results showed that spatial patterns of neural activity within the human motor cortices reflect the individual vividness of imagined actions. Hence, the findings reveal a link between the subjective impression of motor imagery vividness and objective physiological markers.

Emotional Theory of Rationality

In recent decades, the existence of a close relationship between emotional phenomena and rational processes has certainly been established, yet there is still no unified definition or effective model to describe them. To advance our understanding of the mechanisms governing the behavior of living beings, we must integrate multiple theories, experiments, and models from both fields. In this article we propose a new theoretical framework that allows integrating and understanding the emotion-cognition duality, from a functional point of view. Based on evolutionary principles, our reasoning adds to the definition and understanding of emotion, justifying its origin, explaining its mission and dynamics, and linking it to higher cognitive processes, mainly with attention, cognition, decision-making, and consciousness. According to our theory, emotions are the mechanism for brain function optimization, aside from the contingency and stimuli prioritization system. As a result of this approach, we have developed a dynamic systems-level model capable of providing plausible explanations for certain psychological and behavioral phenomena and establishing a new framework for the scientific definition of some fundamental psychological terms.

Free Will, Determinism, and Epiphenomenalism

This paper articulates a non-epiphenomenal, libertarian kind of free will—a kind of free will that's incompatible with both determinism and epiphenomenalism—and responds to scientific arguments against the existence of this sort of freedom. In other words, the paper argues that we don't have any good empirical scientific reason to believe that human beings don't possess a non-epiphenomenal, libertarian sort of free will.

The Berlin Brain-Computer Interface: Progress Beyond Communication and Control

The combined effect of fundamental results about neurocognitive processes and advancements in decoding mental states from ongoing brain signals has brought forth a whole range of potential neurotechnological applications. In this article, we review our developments in this area and put them into perspective. These examples cover a wide range of maturity levels with respect to their applicability. While we assume we are still a long way away from integrating Brain-Computer Interface (BCI) technology in general interaction with computers, or from implementing neurotechnological measures in safety-critical workplaces, results have already now been obtained involving a BCI as research tool. In this article, we discuss the reasons why, in some of the prospective application domains, considerable effort is still required to make the systems ready to deal with the full complexity of the real world.

Hybrid psychology as a human science

Resolving some major philosophical errors in relating behaviour to brain structures and processes can provide a firm foundation for a hybrid science that gives equal weight to both meaning making (Cultural Psychology) and brain activity (Neuroscience). Neuroscientists, however, still fall for two mereological fallacies: the first involving their use of predicates and the second in the projection onto a whole of products of interactions with whole people and it is a fallacy to project them back into that person as constituents. While brains are parts of human bodies it is not clear that they are parts of persons. Clarification is then sought through the identification of four “grammars” linked by three specific principles. Finally, arguments are developed to show that objections to the idea that brains and their constituent organs are tools are misplaced.

From Trust in Automation to Decision Neuroscience: Applying Cognitive Neuroscience Methods to Understand and Improve Interaction Decisions Involved in Human Automation Interaction

Human automation interaction (HAI) systems have thus far failed to live up to expectations mainly because human users do not always interact with the automation appropriately. Trust in automation (TiA) has been considered a central influence on the way a human user interacts with an automation; if TiA is too high there will be overuse, if TiA is too low there will be disuse. However, even though extensive research into TiA has identified specific HAI behaviors, or trust outcomes, a unique mapping between trust states and trust outcomes has yet to be clearly identified. Interaction behaviors have been intensely studied in the domain of HAI and TiA and this has led to a reframing of the issues of problems with HAI in terms of reliance and compliance. We find the behaviorally defined terms reliance and compliance to be useful in their functionality for application in real-world situations. However, we note that once an inappropriate interaction behavior has occurred it is too late to mitigate it. We therefore take a step back and look at the interaction decision that precedes the behavior. We note that the decision neuroscience community has revealed that decisions are fairly stereotyped processes accompanied by measurable psychophysiological correlates. Two literatures were therefore reviewed. TiA literature was extensively reviewed in order to understand the relationship between TiA and trust outcomes, as well as to identify gaps in current knowledge. We note that an interaction decision precedes an interaction behavior and believe that we can leverage knowledge of the psychophysiological correlates of decisions to improve joint system performance. As we believe that understanding the interaction decision will be critical to the eventual mitigation of inappropriate interaction behavior, we reviewed the decision making literature and provide a synopsis of the state of the art understanding of the decision process from a decision neuroscience perspective. We forward hypotheses based on this understanding that could shape a research path toward the ability to mitigate interaction behavior in the real world.

Predict or classify: The deceptive role of time-locking in brain signal classification

Several experimental studies claim to be able to predict the outcome of simple decisions from brain signals measured before subjects are aware of their decision. Often, these studies use multivariate pattern recognition methods with the underlying assumption that the ability to classify the brain signal is equivalent to predict the decision itself. Here we show instead that it is possible to correctly classify a signal even if it does not contain any predictive information about the decision. We first define a simple stochastic model that mimics the random decision process between two equivalent alternatives, and generate a large number of independent trials that contain no choice-predictive information. The trials are first time-locked to the time point of the final event and then classified using standard machine-learning techniques. The resulting classification accuracy is above chance level long before the time point of time-locking. We then analyze the same trials using information theory. We demonstrate that the high classification accuracy is a consequence of time-locking and that its time behavior is simply related to the large relaxation time of the process. We conclude that when time-locking is a crucial step in the analysis of neural activity patterns, both the emergence and the timing of the classification accuracy are affected by structural properties of the network that generates the signal.

The point of no return in vetoing self-initiated movements

In humans, spontaneous movements are often preceded by early brain signals. One such signal is the readiness potential (RP) that gradually arises within the last second preceding a movement. An important question is whether people are able to cancel movements after the elicitation of such RPs, and if so until which point in time. Here, subjects played a game where they tried to press a button to earn points in a challenge with a brain-computer interface (BCI) that had been trained to detect their RPs in real time and to emit stop signals. Our data suggest that subjects can still veto a movement even after the onset of the RP. Cancellation of movements was possible if stop signals occurred earlier than 200 ms before movement onset, thus constituting a point of no return.

Computational cognitive modelling of action awareness: prior and retrospective

This paper presents a computational cognitive model for action awareness focusing on action preparation and performance by considering its cognitive effects and affects from both prior and retrospective form relative to the action execution. How action selection and execution contribute to the awareness or vice versa is a research question, and from the findings of brain imaging and recording techniques more information has become available on this. Some evidence leads to a hypothesis that awareness of action selection is not directly causing the action execution (or behaviour) but comes afterwards as an effect of unconscious processes of action preparation. In contrast, another hypothesis claims that both predictive and inferential processes related to the action preparation and execution may contribute to the conscious awareness of the action, and furthermore, this awareness of an action is a dynamic combination of both prior awareness (through predictive motor control processes) and retrospective awareness (through inferential sense-making processes) relative to the action execution. The proposed model integrates the findings of both conscious and unconscious explanations for both action awareness and ownership and acts as a generic computational cognitive model to explain agent behaviour through the interplay between conscious and unconscious processes. Validation of the proposed model is achieved through simulations on suitable scenarios which are covered with actions that are prepared without being conscious at any point in time, and also with the actions that agent develops prior awareness and/or retrospective awareness. Having selected an interrelated set of scenarios, a systematic approach is used to find a suitable but generic parameter value set which is used throughout all the simulations that highlights the strength of the design of this cognitive model.

The neuroscience of free will: implications for psychiatry

Belief in free will has been a mainstay in philosophy throughout history, grounded in large part in our intuitive sense that we consciously control our actions and could have done otherwise. However, psychology and psychiatry have long sought to uncover mechanistic explanations for human behavior that challenge the notion of free will. In recent years, neuroscientific discoveries have produced a model of volitional behavior that is at odds with the notion of contra-causal free will and our sense of conscious agency. Volitional behavior instead appears to have antecedents in unconscious brain activity that is localizable to specific neuroanatomical structures. Updating notions of free will in favor of a continuous model of volitional self-control provides a useful paradigm to conceptualize and study some forms of psychopathology such as addiction and impulse control disorders. Similarly, thinking of specific symptoms of schizophrenia as disorders of agency may help to elucidate mechanisms of psychosis. Beyond clinical understanding and etiological research, a neuroscientific model of volitional behavior has the potential to modernize forensic notions of responsibility and criminal punishment in order to inform public policy. Ultimately, moving away from the language of free will towards the language of volitional control may result in an enhanced understanding of the very nature of ourselves.

Characteristics of human perception and their relevance when studying information behavior

Purpose

– The purpose of this paper is to highlight findings regarding human perception in allied disciplines and to argue that information behaviour research needs to find ways to address human characteristics that imply that: first, subjects are likely to fail to recognize information that is present in an environment and potentially relevant to a task at hand; and second, subjects would not be able to report on the fact that they failed to recognize the information. The authors also discuss as to how information behaviour research can address the aforementioned challenges resulting from human movement and perception.

Design/methodology/approach

– The author draws on the literature primarily in cognitive science and psychology to highlight the findings that are most relevant to the scientific study of information behaviour, to develop a model of the information environment in which information behaviour is situated, and to critically examine how data is collected in information behaviour research. Ways to provide more comprehensive information about information behaviour are also discussed.

Findings

– The literature in cognitive science and psychology suggests that failing to notice information relevant to a task at hand may not be the exception but to be expected, and needs to be taken into account by information behaviour researchers. Popular data collection methods including questionnaires and interviews do not pick that up because subjects would not be aware of the fact which means in turn that they cannot articulate the fact either. This suggests that: first, current models of information behaviour focus too much on one side of the coin; and second, information behaviour researchers may need to complement their data collection methods with data collection methods such as gaze tracking.

Research limitations/implications

– This is a conceptual paper based on the careful analysis of relevant research primarily in cognitive science and psychology. Relating theory to practice provides a strong indication of the general validity of the findings but there may be other aspects that have not been covered as yet.

Originality/value

– The paper is unique in that it critically reviews information behaviour research from a human perception and movement point of view. There have been papers criticizing information behaviour research from a methodological point of view. This paper adds to that body of work and proposes a way forward.

Population coding of affect across stimuli, modalities and individuals

It remains unclear how the brain represents external objective sensory events alongside our internal subjective impressions of them--affect. Representational mapping of population activity evoked by complex scenes and basic tastes in humans revealed a neural code supporting a continuous axis of pleasant-to-unpleasant valence. This valence code was distinct from low-level physical and high-level object properties. Although ventral temporal and anterior insular cortices supported valence codes specific to vision and taste, both the medial and lateral orbitofrontal cortices (OFC) maintained a valence code independent of sensory origin. Furthermore, only the OFC code could classify experienced affect across participants. The entire valence spectrum was represented as a collective pattern in regional neural activity as sensory-specific and abstract codes, whereby the subjective quality of affect can be objectively quantified across stimuli, modalities and people.

Predeliberation activity in prefrontal cortex and striatum and the prediction of subsequent value judgment

Rational, value-based decision-making mandates selecting the option with highest subjective expected value after appropriate deliberation. We examined activity in the dorsolateral prefrontal cortex (DLPFC) and striatum of monkeys deciding between smaller, immediate rewards and larger, delayed ones. We previously found neurons that modulated their activity in this task according to the animal's choice, while it deliberated (choice neurons). Here we found neurons whose spiking activities were predictive of the spatial location of the selected target (spatial-bias neurons) or the size of the chosen reward (reward-bias neurons) before the onset of the cue presenting the decision-alternatives, and thus before rational deliberation could begin. Their predictive power increased as the values the animals associated with the two decision alternatives became more similar. The ventral striatum (VS) preferentially contained spatial-bias neurons; the caudate nucleus (CD) preferentially contained choice neurons. In contrast, the DLPFC contained significant numbers of all three neuron types, but choice neurons were not preferentially also bias neurons of either kind there, nor were spatial-bias neurons preferentially also choice neurons, and vice versa. We suggest a simple winner-take-all (WTA) circuit model to account for the dissociation of choice and bias neurons. The model reproduced our results and made additional predictions that were borne out empirically. Our data are compatible with the hypothesis that the DLPFC and striatum harbor dissociated neural populations that represent choices and predeliberation biases that are combined after cue onset; the bias neurons have a weaker effect on the ultimate decision than the choice neurons, so their influence is progressively apparent for trials where the values associated with the decision alternatives are increasingly similar.

He who is well prepared has half won the battle: an FMRI study of task preparation

The neural mechanism underlying preparation for tasks that vary in difficulty has not been explored. This functional magnetic resonance imaging study manipulated task difficulty by varying the working memory (WM) load of the n-back task. Each n-back task block was preceded by a preparation period involving a screen that indicated the level of difficulty of the upcoming task. Consistent with previous work, activation in some brain regions depended on WM load in the task. These regions were used as regions of interest for the univariate and multivariate (classification) analyses of preparation periods. The findings were that the patterns of brain activation during task preparation contain information about the upcoming task difficulty. (1) A support vector machine classifier was able to decode the n-back task difficulty from the patterns of brain activation during task preparation. Those individuals whose activation patterns for anticipated 1- versus 2- versus 3-back conditions were classified with higher accuracy showed better behavioral performance on the task, suggesting that task performance depends on task preparation. (2) Left inferior frontal gyrus, intraparietal sulcus, and anterior cingulate cortex parametrically decreased activation as anticipated task difficulty increased. Taken together, these results suggest dynamic involvement of the WM network not only during WM task performance, but also during task preparation.

Cognitive-motor brain-machine interfaces

Brain-machine interfaces (BMIs) open new horizons for the treatment of paralyzed persons, giving hope for the artificial restoration of lost physiological functions. Whereas BMI development has mainly focused on motor rehabilitation, recent studies have suggested that higher cognitive functions can also be deciphered from brain activity, bypassing low level planning and execution functions, and replacing them by computer-controlled effectors. This review describes the new generation of cognitive-motor BMIs, focusing on three BMI types: By outlining recent progress in developing these BMI types, we aim to provide a unified view of contemporary research towards the replacement of behavioral outputs of cognitive processes by direct interaction with the brain.

Imaging volition: what the brain can tell us about the will

The question of how we can voluntarily control our behaviour dates back to the beginnings of scientific psychology. Currently, there are two empirical research disciplines tackling human volition: cognitive neuroscience and social psychology. To date, there is little interaction between the two disciplines in terms of the investigation of human volition. The aim of the current article is to highlight recent brain imaging work on human volition and to relate social psychological concepts of volition to the functional neuroanatomy of intentional action. A host of studies indicate that the medial prefrontal cortex plays a crucial role in voluntary action. Accordingly, we postulate that social psychological concepts of volition can be investigated using neuroimaging techniques, and propose that by developing a social cognitive neuroscience of human volition, we may gain a deeper understanding of this fascinating and complex aspect of the human mind.

Analyzing the information distribution in the fMRI measurements by estimating the degree of locality

In this study, we propose a new method for analyzing and representing the distribution of discriminative information for data acquired via functional Magnetic Resonance Imaging (fMRI). For this purpose, we form a spatially local mesh with varying size, around each voxel, called the seed voxel. The relationship among each seed voxel and its neighbors is estimated using a linear regression model by minimizing the square error. Then, we estimate the optimal mesh size that represents the connections among each seed voxel and its surroundings by minimizing Akaike's Final Prediction Error (FPE) with respect to the mesh size. The degree of locality is represented by the optimum mesh size. Our results indicate that the local mesh size with the highest discriminative power varies across individual participants. The proposed method was tested on an fMRI study consisting of item recognition (IR) and judgment of recency (JOR) tasks. For each participant, the estimated arc weights of each local mesh with different mesh size are used to classify the type of memory judgment (i.e.IR or JOR). Classification accuracy for each participant was derived using k-Nearest Neighbor (k-NN) method. The results indicate that the proposed local mesh model with optimal mesh size can successfully represent discriminative information for neuroimaging data.

Recent developments in multivariate pattern analysis for functional MRI

Multivariate pattern analysis (MVPA) is a recently-developed approach for functional magnetic resonance imaging (fMRI) data analyses. Compared with the traditional univariate methods, MVPA is more sensitive to subtle changes in multivariate patterns in fMRI data. In this review, we introduce several significant advances in MVPA applications and summarize various combinations of algorithms and parameters in different problem settings. The limitations of MVPA and some critical questions that need to be addressed in future research are also discussed.

Movement-related neuromagnetic fields and performances of single trial classifications

In order to clarify whether neurophysiological profiles affect the performance of brain machine interfaces (BMI), we examined the relationships between amplitudes of movement-related cortical fields (MRCFs) and decoding performances during movement. Neuromagnetic activities were recorded in nine healthy participants during three types of unilateral upper limb movements. The movement types were inferred by a support vector machine. The amplitude of MRCF components, motor field (MF), movement-evoked field I (MEFI), and movement-evoked field II (MEFII) were compared with the decoding accuracies in all participants. Decoding accuracies at the latencies of MF, MEFI, and MEFII surpassed the chance level in all participants. In particular, accuracies at MEFI and MEFII were significantly higher in comparison with that of MF. The amplitudes and decoding accuracies were strongly correlated (MF, r(s)=0.90; MEFI, r(s)=0.90; and MEFII, r(s)=0.87). Our results show that the variation of MRCF components among participants reflects decoding performance. Neurophysiological profiles may serve as a predictor of individual BMI performance and assist in the improvement of general BMI performance.

Distributed representations in memory: insights from functional brain imaging

Forging new memories for facts and events, holding critical details in mind on a moment-to-moment basis, and retrieving knowledge in the service of current goals all depend on a complex interplay between neural ensembles throughout the brain. Over the past decade, researchers have increasingly utilized powerful analytical tools (e.g., multivoxel pattern analysis) to decode the information represented within distributed functional magnetic resonance imaging activity patterns. In this review, we discuss how these methods can sensitively index neural representations of perceptual and semantic content and how leverage on the engagement of distributed representations provides unique insights into distinct aspects of memory-guided behavior. We emphasize that, in addition to characterizing the contents of memories, analyses of distributed patterns shed light on the processes that influence how information is encoded, maintained, or retrieved, and thus inform memory theory. We conclude by highlighting open questions about memory that can be addressed through distributed pattern analyses.