Modeling brain dynamics and gaze behavior: Starting point bias and drift rate relate to frontal midline theta oscillations.

Frontal midline theta oscillatory dynamics have been implicated as an important neural signature of inhibitory control. However, most proactive cognitive control studies rely on behavioral tasks where individual differences are inferred through button presses. We applied computational modeling to further refine our understanding of theta dynamics in a cued anti-saccade task with gaze-contingent eye tracking. Using a drift diffusion model, increased frontal midline theta power during high-conflict, relative to low-conflict, trials predicted a more conservative style of responding through the starting point (bias). During both high- and low-conflict trials, increases in frontal midline theta also predicted improvements in response efficiency (drift rate). Regression analyses provided support for the importance of the starting point bias, which was associated with frontal midline theta over the course of the task above-and-beyond both drift rate and mean reaction time. Our findings provide a more thorough understanding of proactive gaze control by linking trial-by-trial increases of frontal midline theta to a shift in starting point bias facilitating a more neutral style of responding.

Restoring cortical control of functional movement in a human with quadriplegia.

Millions of people worldwide suffer from diseases that lead to paralysis through disruption of signal pathways between the brain and the muscles. Neuroprosthetic devices are designed to restore lost function and could be used to form an electronic 'neural bypass' to circumvent disconnected pathways in the nervous system. It has previously been shown that intracortically recorded signals can be decoded to extract information related to motion, allowing non-human primates and paralysed humans to control computers and robotic arms through imagined movements. In non-human primates, these types of signal have also been used to drive activation of chemically paralysed arm muscles. Here we show that intracortically recorded signals can be linked in real-time to muscle activation to restore movement in a paralysed human. We used a chronically implanted intracortical microelectrode array to record multiunit activity from the motor cortex in a study participant with quadriplegia from cervical spinal cord injury. We applied machine-learning algorithms to decode the neuronal activity and control activation of the participant's forearm muscles through a custom-built high-resolution neuromuscular electrical stimulation system. The system provided isolated finger movements and the participant achieved continuous cortical control of six different wrist and hand motions. Furthermore, he was able to use the system to complete functional tasks relevant to daily living. Clinical assessment showed that, when using the system, his motor impairment improved from the fifth to the sixth cervical (C5-C6) to the seventh cervical to first thoracic (C7-T1) level unilaterally, conferring on him the critical abilities to grasp, manipulate, and release objects. This is the first demonstration to our knowledge of successful control of muscle activation using intracortically recorded signals in a paralysed human. These results have significant implications in advancing neuroprosthetic technology for people worldwide living with the effects of paralysis.

Quantifying mechanisms of cognition with an experiment and modeling ecosystem.

Although there have been major strides toward uncovering the neurobehavioral mechanisms involved in cognitive functions like memory and decision making, methods for measuring behavior and accessing latent processes through computational means remain limited. To this end, we have created SUPREME (Sensing to Understanding and Prediction Realized via an Experiment and Modeling Ecosystem): a toolbox for comprehensive cognitive assessment, provided by a combination of construct-targeted tasks and corresponding computational models. SUPREME includes four tasks, each developed symbiotically with a mechanistic model, which together provide quantified assessments of perception, cognitive control, declarative memory, reward valuation, and frustrative nonreward. In this study, we provide validation analyses for each task using two sessions of data from a cohort of cognitively normal participants (N = 65). Measures of test-retest reliability (r: 0.58-0.75), stability of individual differences (ρ: 0.56-0.70), and internal consistency (α: 0.80-0.86) support the validity of our tasks. After fitting the models to data from individual subjects, we demonstrate each model's ability to capture observed patterns of behavioral results across task conditions. Our computational approaches allow us to decompose behavior into cognitively interpretable subprocesses, which we can compare both within and between participants. We discuss potential future applications of SUPREME, including clinical assessments, longitudinal tracking of cognitive functions, and insight into compensatory mechanisms.

Estimating Scale-Invariant Future in Continuous Time.

Natural learners must compute an estimate of future outcomes that follow from a stimulus in continuous time. Widely used reinforcement learning algorithms discretize continuous time and estimate either transition functions from one step to the next (model-based algorithms) or a scalar value of exponentially discounted future reward using the Bellman equation (model-free algorithms). An important drawback of model-based algorithms is that computational cost grows linearly with the amount of time to be simulated. An important drawback of model-free algorithms is the need to select a timescale required for exponential discounting. We present a computational mechanism, developed based on work in psychology and neuroscience, for computing a scale-invariant timeline of future outcomes. This mechanism efficiently computes an estimate of inputs as a function of future time on a logarithmically compressed scale and can be used to generate a scale-invariant power-law-discounted estimate of expected future reward. The representation of future time retains information about what will happen when. The entire timeline can be constructed in a single parallel operation that generates concrete behavioral and neural predictions. This computational mechanism could be incorporated into future reinforcement learning algorithms.

A context-change account of temporal distinctiveness.

The distinctiveness effect refers to the finding that items that stand out from other items in a learning set are more likely to be remembered later. Traditionally, distinctiveness has been defined based on item features; specifically, an item is deemed to be distinctive if its features are different from the features of other to-be-learned items. We propose that distinctiveness can be redefined based on context change-distinctive items are those with features that deviate from the others in the current temporal context, a recency-weighted running average of experience-and that this context change modulates learning. We test this account with two novel experiments and introduce a formal mathematical model that instantiates our proposed theory. In the experiments, participants studied lists of words, with each word appearing on one of two background colors. Within each list, each color was used for 50% of the words, but the sequence of the colors was controlled so that runs of the same color for that list were common in Experiment 1 and common, rare, or random in Experiment 2. In both experiments, participants' source memory for background color was enhanced for items where the color changed, especially if the change occurred after a stable run without color changes. Conversely, source memory was not significantly better for nonchanges after runs of alternating colors with each item. This pattern is inconsistent with theories of learning based on prediction error, but is consistent with our context-change account.

Human hippocampus represents space and time during retrieval of real-world memories.

Memory stretches over a lifetime. In controlled laboratory settings, the hippocampus and other medial temporal lobe brain structures have been shown to represent space and time on the scale of meters and seconds. It remains unclear whether the hippocampus also represents space and time over the longer scales necessary for human episodic memory. We recorded neural activity while participants relived their own experiences, cued by photographs taken with a custom lifelogging device. We found that the left anterior hippocampus represents space and time for a month of remembered events occurring over distances of up to 30 km. Although previous studies have identified similar drifts in representational similarity across space or time over the relatively brief time scales (seconds to minutes) that characterize individual episodic memories, our results provide compelling evidence that a similar pattern of spatiotemporal organization also exists for organizing distinct memories that are distant in space and time. These results further support the emerging view that the anterior, as opposed to posterior, hippocampus integrates distinct experiences, thereby providing a scaffold for encoding and retrieval of autobiographical memories on the scale of our lives.

Trial-level information for individual faces in the fusiform face area depends on subsequent memory.

Previous research has shown that face-sensitive brain regions, such as the fusiform face area (FFA) and anterior inferior temporal lobe (aIT), not only respond selectively to face stimuli, but also respond uniquely to individual faces. A common factor in the existing literature is that face stimuli in these experiments are highly familiar to participants, usually by design. We set out to investigate to what extent familiarity correlates with the emergence of face-specific information in face-sensitive regions by testing novel faces with only a single repetition. Our results, consistent with a familiarity hypothesis, demonstrate that the FFA and aIT show face-specific information only when participants demonstrate subsequent memory for those faces. Functionally-defined regions that are not believed to process faces holistically showed no face-specific information, regardless of subsequent memory. To our knowledge, this is the first demonstration of face-specific information in face-sensitive regions for stimuli that were not highly familiar. These results contribute to our understanding of how individuating information comes to be represented in face-sensitive regions and suggest that this process can take place even after a single repetition of a particular face.

Spinal Cord Stimulation (SCS) and Functional Magnetic Resonance Imaging (fMRI): Modulation of Cortical Connectivity With Therapeutic SCS.

INTRODUCTION: The neurophysiological basis of pain relief due to spinal cord stimulation (SCS) and the related cortical processing of sensory information are not completely understood. The aim of this study was to use resting state functional magnetic resonance imaging (rs-fMRI) to detect changes in cortical networks and cortical processing related to the stimulator-induced pain relief. METHODS: Ten patients with complex regional pain syndrome (CRPS) or neuropathic leg pain underwent thoracic epidural spinal cord stimulator implantation. Stimulation parameters associated with "optimal" pain reduction were evaluated prior to imaging studies. Rs-fMRI was obtained on a 3 Tesla, Philips Achieva MRI. Rs-fMRI was performed with stimulator off (300TRs) and stimulator at optimum (Opt, 300 TRs) pain relief settings. Seed-based analysis of the resting state functional connectivity was conducted using seeds in regions established as participating in pain networks or in the default mode network (DMN) in addition to the network analysis. NCUT (normalized cut) parcellation was used to generate 98 cortical and subcortical regions of interest in order to expand our analysis of changes in functional connections to the entire brain. We corrected for multiple comparisons by limiting the false discovery rate to 5%. RESULTS: Significant differences in resting state connectivity between SCS off and optimal state were seen between several regions related to pain perception, including the left frontal insula, right primary and secondary somatosensory cortices, as well as in regions involved in the DMN, such as the precuneus. In examining changes in connectivity across the entire brain, we found decreased connection strength between somatosensory and limbic areas and increased connection strength between somatosensory and DMN with optimal SCS resulting in pain relief. This suggests that pain relief from SCS may be reducing negative emotional processing associated with pain, allowing somatosensory areas to become more integrated into default mode activity. CONCLUSION: SCS reduces the affective component of pain resulting in optimal pain relief. Study shows a decreased connectivity between somatosensory and limbic areas associated with optimal pain relief due to SCS.

A temporal context repetition effect in rats during a novel object recognition memory task.

Recent research in humans has used formal models of temporal context, broadly defined as a lingering representation of recent experience, to explain a wide array of recall and recognition memory phenomena. One difficulty in extending this work to studies of experimental animals has been the challenge of developing a task to test temporal context effects on performance in rodents. The current study presents results from a novel object recognition memory paradigm that was adapted from a task used in humans and demonstrates a temporal context repetition effect in rats. Specifically, the findings indicate that repeating the first two objects from a once-encountered sequence of three objects incidentally cues memory for the third object, even in its absence. These results reveal that temporal context influences item memory in rats similar to the manner in which it influences memory in humans and also highlight a new task for future studies of temporal context in experimental animals.

Power shifts track serial position and modulate encoding in human episodic memory.

The first events in a series exert a powerful influence on cognition and behavior in both humans and animals. This is known as the law of primacy. Here, we analyze the neural correlates of primacy in humans by analyzing electrocorticographic recordings in 84 neurosurgical patients as they studied and subsequently recalled lists of common words. We found that spectral power in the gamma frequency band (28-100 Hz) was elevated at the start of the list and gradually subsided, whereas lower frequency (2-8 Hz) delta and theta band power exhibited the opposite trend. This gradual shift in the power spectrum was found across a widespread network of brain regions. The degree to which the subsequent memory effect was modulated by list (serial) position was most pronounced in medial temporal lobe structures. These results suggest that globally increased gamma and decreased delta-theta spectral powers reflect a brain state that predisposes medial temporal lobe structures to enhance the encoding and maintenance of early list items.

Do We Really Become Smarter When Our Fluid-Intelligence Test Scores Improve?

Recent reports of training-induced gains on fluid intelligence tests have fueled an explosion of interest in cognitive training-now a billion-dollar industry. The interpretation of these results is questionable because score gains can be dominated by factors that play marginal roles in the scores themselves, and because intelligence gain is not the only possible explanation for the observed control-adjusted far transfer across tasks. Here we present novel evidence that the test score gains used to measure the efficacy of cognitive training may reflect strategy refinement instead of intelligence gains. A novel scanpath analysis of eye movement data from 35 participants solving Raven's Advanced Progressive Matrices on two separate sessions indicated that one-third of the variance of score gains could be attributed to test-taking strategy alone, as revealed by characteristic changes in eye-fixation patterns. When the strategic contaminant was partialled out, the residual score gains were no longer significant. These results are compatible with established theories of skill acquisition suggesting that procedural knowledge tacitly acquired during training can later be utilized at posttest. Our novel method and result both underline a reason to be wary of purported intelligence gains, but also provide a way forward for testing for them in the future.

Brain rhythms in mental time travel.

The memory theorist Endel Tulving referred to the ability to search through one's memories, and revisit events and episodes from one's past, as mental time travel. This process involves the reactivation of past mental states reflecting the perceptual and conceptual characteristics of the original experience. Widely distributed neural circuitry is engaged in the service of memory search, and the dynamics of these circuits are reflected in rhythmic oscillatory signals at widespread frequencies, recorded both in the local field around neurons and more globally at the scalp. Retrieved-context theory provides a theoretical bridge between the behavioral phenomena exhibited by participants in memory search tasks, and the neural signals reflecting the dynamics of the underlying circuitry. Computational models based on this theory make broad predictions regarding the representational structure of neural activity recorded during these tasks. In recent work, researchers have used multivariate analytic techniques on topographic patterns of oscillatory neural activity to confirm critical predictions of retrieved-context theory. We review the cognitive theory motivating this recent work, and the analytic techniques being developed to create integrated neural-behavioral models of human memory search.

Decomposing spatiotemporal brain patterns into topographic latent sources.

This paper extends earlier work on spatial modeling of fMRI data to the temporal domain, providing a framework for analyzing high temporal resolution brain imaging modalities such as electroencapholography (EEG). The central idea is to decompose brain imaging data into a covariate-dependent superposition of functions defined over continuous time and space (what we refer to as topographic latent sources). The continuous formulation allows us to parametrically model spatiotemporally localized activations. To make group-level inferences, we elaborate the model hierarchically by sharing sources across subjects. We describe a variational algorithm for parameter estimation that scales efficiently to large data sets. Applied to three EEG data sets, we find that the model produces good predictive performance and reproduces a number of classic findings. Our results suggest that topographic latent sources serve as an effective hypothesis space for interpreting spatiotemporal brain imaging data.

Scene representations in parahippocampal cortex depend on temporal context.

Human perception is supported by regions of ventral visual cortex that become active when specific types of information appear in the environment. This coupling has led to a common assumption in cognitive neuroscience that stimulus-evoked activity in these regions only reflects information about the current stimulus. Here we challenge this assumption for how scenes are represented in a scene-selective region of parahippocampal cortex. This region treated two identical scenes as more similar when they were preceded in time by the same stimuli compared to when they were preceded by different stimuli. These findings suggest that parahippocampal cortex embeds scenes in their temporal context to determine what they represent. By integrating the past and present, such representations may support the encoding and navigation of complex environments.

A Bayesian framework for simultaneously modeling neural and behavioral data.

Scientists who study cognition infer underlying processes either by observing behavior (e.g., response times, percentage correct) or by observing neural activity (e.g., the BOLD response). These two types of observations have traditionally supported two separate lines of study. The first is led by cognitive modelers, who rely on behavior alone to support their computational theories. The second is led by cognitive neuroimagers, who rely on statistical models to link patterns of neural activity to experimental manipulations, often without any attempt to make a direct connection to an explicit computational theory. Here we present a flexible Bayesian framework for combining neural and cognitive models. Joining neuroimaging and computational modeling in a single hierarchical framework allows the neural data to influence the parameters of the cognitive model and allows behavioral data, even in the absence of neural data, to constrain the neural model. Critically, our Bayesian approach can reveal interactions between behavioral and neural parameters, and hence between neural activity and cognitive mechanisms. We demonstrate the utility of our approach with applications to simulated fMRI data with a recognition model and to diffusion-weighted imaging data with a response time model of perceptual choice.

Foraging for thought: an inhibition-of-return-like effect resulting from directing attention within working memory.

Perceptual processing of a target stimulus may be inhibited if its location has just been cued, a phenomenon of spatial attention known as inhibition of return (IOR). In the research reported here, we demonstrated a striking effect, wherein items that have just been the focus of reflective attention (internal attention to an active representation) also are inhibited. Participants saw two items, followed by a cue to think back to (i.e., refresh, or direct reflective attention toward) one item, and then had to identify either the refreshed item, the unrefreshed item, or a novel item. Responses were significantly slower for refreshed items than for unrefreshed items, although refreshed items were better remembered on a later memory test. Control experiments in which we replaced the refresh event with a second presentation of one of the words did not show similar effects. These results suggest that reflective attention can produce an inhibition effect for attended items that may be analogous to IOR effects in perceptual attention.

Foundations of a temporal RL.

Recent advances in neuroscience and psychology show that the brain has access to timelines of both the past and the future. Spiking across populations of neurons in many regions of the mammalian brain maintains a robust temporal memory, a neural timeline of the recent past. Behavioral results demonstrate that people can estimate an extended temporal model of the future, suggesting that the neural timeline of the past could extend through the present into the future. This paper presents a mathematical framework for learning and expressing relationships between events in continuous time. We assume that the brain has access to a temporal memory in the form of the real Laplace transform of the recent past. Hebbian associations with a diversity of synaptic time scales are formed between the past and the present that record the temporal relationships between events. Knowing the temporal relationships between the past and the present allows one to predict relationships between the present and the future, thus constructing an extended temporal prediction for the future. Both memory for the past and the predicted future are represented as the real Laplace transform, expressed as the firing rate over populations of neurons indexed by different rate constants s. The diversity of synaptic timescales allows for a temporal record over the much larger time scale of trial history. In this framework, temporal credit assignment can be assessed via a Laplace temporal difference. The Laplace temporal difference compares the future that actually follows a stimulus to the future predicted just before the stimulus was observed. This computational framework makes a number of specific neurophysiological predictions and, taken together, could provide the basis for a future iteration of RL that incorporates temporal memory as a fundamental building block.

The successor representation and temporal context.

The successor representation was introduced into reinforcement learning by Dayan ( 1993 ) as a means of facilitating generalization between states with similar successors. Although reinforcement learning in general has been used extensively as a model of psychological and neural processes, the psychological validity of the successor representation has yet to be explored. An interesting possibility is that the successor representation can be used not only for reinforcement learning but for episodic learning as well. Our main contribution is to show that a variant of the temporal context model (TCM; Howard & Kahana, 2002 ), an influential model of episodic memory, can be understood as directly estimating the successor representation using the temporal difference learning algorithm (Sutton & Barto, 1998 ). This insight leads to a generalization of TCM and new experimental predictions. In addition to casting a new normative light on TCM, this equivalence suggests a previously unexplored point of contact between different learning systems.

Transparency, replicability, and discovery in cognitive aging research: A computational modeling approach.

Healthy aging is associated with deficits in performance on episodic memory tasks. Popular verbal theories of the mechanisms underlying this decrement have primarily focused on inferred changes in associative memory. However, performance on any task is the result of interactions between different neurocognitive mechanisms, such as perceptuomotor, memory, and decision-making processes. As a result, age-related differences in performance could arise from multiple processes, which could lead to incomplete or incorrect conclusions about the sources of aging effects. In addition, standard statistical comparisons of group-level summary statistics, such as mean accuracy, may not provide sufficient information to allow detailed mechanistic explanations of age-related change. We argue that these and other drawbacks of relying exclusively on verbal theories can hamper replicability, transparency, and scientific progress in aging research and psychological science more generally, and that computational modeling is a tool that can address many of these limitations. Computational models make mathematically transparent claims about how latent processes give rise to observed behavior and decompose an individual's performance into model parameters governing hypothesized mechanisms. In this work, we present a short memory task designed for and analyzed with mechanistic model-based approaches. We provide an example of a computational model and fit the model to data from young and older adults with hierarchical Bayesian techniques in order to (a) detect differences in latent cognitive processes between young and older adults (as well as individual participants), (b) quantitatively compare models to assess different processes that could underlie performance, and (c) simulate data to make predictions for future experiments based on model mechanisms. We argue that computational modeling is a powerful tool to examine age differences in latent processes, make theories more transparent, and facilitate discovery in cognitive aging research. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Intraobject and extraobject memory binding across early development.

The ability to bind, or link, different aspects of an experience in memory undergoes protracted development across childhood. Most studies of memory binding development have assessed extraobject binding between an object and some external element such as another object, whereas little work has examined the development of intraobject binding, such as between shape and color features within the same object. In this work, we investigate the development of intra- and extraobject memory binding in five-year-olds, eight-year-olds, and young adults with a memory interference paradigm. Between two experiments, we manipulate whether stimuli are presented as coherent objects (Experiment 1: n5-year-olds = 32, 19 males, 13 females; n8-year-olds = 30, 15 males, 15 females; nadults = 30, 15 males, 15 females), requiring intraobject binding between shape and color features, or as spatially separated features (Experiment 2: n5-year-olds = 24, 16 males, 8 females; n8-year-olds = 41, 19 males, 22 females; nadults = 31, 13 males, 18 females), requiring extraobject binding. To estimate the contributions of different binding structures to performance, we present a novel computational model that mathematically instantiates the memory binding, forgetting, and retrieval processes we hypothesize to underlie performance on the task. The results provide evidence of substantial developmental improvements in both intraobject and extraobject binding of shape and color features between 5 and 8 years of age, as well as stronger intraobject compared with extraobject binding of features in all age groups. These findings provide key insights into memory binding across early development. (PsycInfo Database Record (c) 2022 APA, all rights reserved).