Biology subjects

Pearson, J. M.

Publications and source records attributed to Pearson, J. M..

10 recordsLinked to original sources

bioRxiv2025

Representational Similarity and Pattern Classification of Fifteen Emotional States Induced by Movie Clips and Text Scenarios

How do different emotional states relate to each other and how are they represented in the human brain? These are important questions in the field of affective neuroscience, with profound scientific and vast clinical implications. Different theories of emotion tend to emphasize the relative importance of distinct psychological constructs, for example categorical labels (e.g., fear, joy, or sadness) versus dimensional ratings (e.g., valence and arousal), for understanding human emotions. To investigate whether categorical or dimensional constructs correspond better to patterns of brain activity associated with human affective experiences, we experimentally induced 15 emotions (spanning positive, negative, and neutral valence) in 136 participants using 150 short movie clips and 150 one- or two-sentence text scenarios, while their blood oxygenation-level dependent activity was recorded in a magnetic resonance imaging scanner. Results from our representational similarity analyses suggest participants brain activity significantly correlated with their categorical labeling of, but not their dimensional rating of, the movie clips and text scenarios. Subsequently, we were also able to decode the categorical labels of these emotional stimuli using whole-brain multi-voxel pattern classification, with important voxels found in many cortical, limbic, subcortical, cerebellar, and brainstem regions. Finally, we found similar clusters of emotions through exploratory hierarchical clustering analyses of participants categorical labeling of and brain responses to these stimuli. Taken together, these findings greatly advance our understanding of how a large set of human emotions are related to each other both in terms of the participants self-report and their brain activity. Significance StatementWe successfully decoded fifteen emotional states induced by movie clips and text scenarios based on participants brain responses (blood oxygenation-level dependent signals) to these stimuli using multi-voxel pattern classification (a supervised machine learning approach). We also found that participants brain responses correlated with their self-reported emotional experience, i.e. which emotion they felt while they were presented with these stimuli, using representative similarity analysis (an unsupervised approach). Finally, we found that these emotions are organized in very similar ways both in terms of participants categorical labeling and their brain responses to these stimuli. Together, these data-driven and computational modeling-based findings greatly advance our understanding of how a large set of emotions are organized and represented in the human brain.

neuroscience↗

bioRxiv2025

Frontal eye field neurons predict "anti-Bayesian" but not Bayesian judgments of visual stability across saccades

Expectations or prior beliefs about the world have been shown to modulate sensory processing both at the behavioral and neural levels. Bayesian models predict that such priors compensate for input uncertainty to optimize sensory judgments. Although Bayesian behavior is prevalent across sensorimotor systems, the relationship between priors and Bayesian inference is not obligatory. Priors may simply shift ones internal decision boundaries without interacting with sensory uncertainty at all. We recently showed that humans and monkeys use both Bayesian and non-Bayesian strategies when reporting judgments of visual stability across saccades, despite using priors in both cases. While they increased prior use to compensate for internal, movement-driven sensory uncertainty in a Bayesian manner, they decreased prior use when faced with external, visual image uncertainty. The latter, "anti-Bayesian" pattern was best explained by a model in which category boundaries were adjusted by the prior but susceptible to image noise. Here, we recorded neural activity in the frontal eye field (FEF), a prefrontal region important for visuosaccadic behavior, while toggling between subjects prior use for Bayesian and anti-Bayesian behavior via trial-by-trial manipulation of the two uncertainty conditions. First, we found that FEF activity signaled the priors in both conditions. The prior-related modulation of activity, however, predicted only the anti-Bayesian, categorization behavior. The results suggest that neural activity in the FEF reflects the use of a flexible decision boundary for the perception of visual stability and, more generally, that neural mechanisms for Bayesian inference and visual categorization are dissociable and distributed in the primate brain. SignificanceAppreciating a visual scene depends not only on retinal input, but also on priors about the world. Foreknowledge interacts with visual inputs to improve reactions and decisions. One way the brain combines priors and inputs is by using Bayes rule to model optimal outcomes. A simpler way is by categorizing inputs with prior-adjusted boundaries. Here, we tested how neurons in primate frontal cortex use priors: for Bayes rule, or for flexible categorization? A key feature of the study was to use a single perceptual task that was varied trial-by-trial to yield either Bayesian or categorization behaviors. We could then establish which behavior the neurons encoded. The implications extend beyond visuomotor behavior to broader neurocomputational mechanisms of prior use for cognition.

neuroscience↗

bioRxiv2022

Efficient coding, channel capacity and the emergence of retinal mosaics

Among the most striking features of retinal organization is the grouping of its output neurons, the retinal ganglion cells (RGCs), into a diversity of functional types. Each of these types exhibits a mosaic-like organization of receptive fields (RFs) that tiles the retina and visual space. Previous work has shown that many features of RGC organization, including the existence of ON and OFF cell types, the structure of spatial RFs, and their relative arrangement, can be predicted on the basis of efficient coding theory. This theory posits that the nervous system is organized to maximize information in its encoding of stimuli while minimizing metabolic costs. Here, we use efficient coding theory to present a comprehensive account of mosaic organization in the case of natural videos as the retinal channel capacity--the number of simulated RGCs available for encoding--is varied. We show that mosaic density increases with channel capacity up to a series of critical points at which, surprisingly, new cell types emerge. Each successive cell type focuses on increasingly high temporal frequencies and integrates signals over large spatial areas. In addition, we show theoretically and in simulation that a transition from mosaic alignment to anti-alignment across pairs of cell types is observed with increasing output noise and decreasing input noise. Together, these results offer a unified perspective on the relationship between retinal mosaics, efficient coding, and channel capacity that can help to explain the stunning functional diversity of retinal cell types.

neuroscience↗

bioRxiv2022

Contributions of Bayesian and Discriminative Models to Active Visual Perception across Saccades

The brain interprets sensory inputs to guide behavior, but behavior disrupts sensory inputs. In primates, saccadic eye movements displace visual images on the retina and yet the brain perceives visual stability, a process called active vision. We studied whether active vision is Bayesian. Humans and monkeys reported whether an image moved during saccades. We tested whether they used prior expectations to account for sensory uncertainty in a Bayesian manner. For continuous judgments, subjects were Bayesian. For categorical judgments, they were anti-Bayesian for uncertainty due to external, image noise but Bayesian for uncertainty due to internal, motor-driven noise. A discriminative learning model explained the anti-Bayesian effect. Therefore, active vision uses both Bayesian and discriminative models depending on task requirements (continuous vs. categorical) and the source of uncertainty (image noise vs. motor-driven noise), suggesting that active perceptual mechanisms are governed by the interaction of both models.

neuroscience↗

bioRxiv2022

Between heuristics and optimality: Flexible integration of cost and evidence during information sampling

Effective decision making in an uncertain world requires balancing the benefits of acquiring relevant information with the costs of delaying choice. Optimal strategies for information sampling can be accurate but computationally expensive, whereas heuristic strategies are often computationally simple but rigid. To characterize the computations that underlie information sampling, we examined choice processes in human participants who sampled sequences of images (e.g. indoor and outdoor scenes) and attempted to infer the majority category (e.g. indoor or outdoor) under two reward conditions. We examined how behavior maps onto potential information sampling strategies. We found that choices were best described by a flexible function that lay between optimality and heuristics; integrating the magnitude of evidence favoring each category and the number of samples collected thus far. Integration of these criteria resulted in a trade-off between evidence and samples collected, in which the strength of evidence needed to stop sampling decreased linearly as the number of samples accumulated over the course of a trial. This non-optimal trade-off best accounted for choice behavior even under high reward contexts. Our results demonstrate that unlike the optimal strategy, humans are performing simple accumulations instead of computing expected values, and that unlike a simple heuristic strategy, humans are dynamically integrating multiple sources of information in lieu of using only one source. This evidence-by-costs tradeoff illustrates a computationally efficient strategy that balances competing motivations for accuracy and cost minimization.

animal behavior and cognition↗

bioRxiv2021

Deep Generative Analysis for Task-Based Functional MRI Experiments

While functional magnetic resonance imaging (fMRI) remains one of the most widespread and important methods in basic and clinical neuroscience, the data it produces--time series of brain volumes--continue to pose daunting analysis challenges. The current standard ("mass univariate") approach involves constructing a matrix of task regressors, fitting a separate general linear model at each volume pixel ("voxel"), computing test statistics for each model, and correcting for false positives post hoc using bootstrap or other resampling methods. Despite its simplicity, this approach has enjoyed great success over the last two decades due to: 1) its ability to produce effect maps highlighting brain regions whose activity significantly correlates with a given variable of interest; and 2) its modeling of experimental effects as separable and thus easily interpretable. However, this approach suffers from several well-known drawbacks, namely: inaccurate assumptions of linearity and noise Gaussianity; a limited ability to capture individual effects and variability; and difficulties in performing proper statistical testing secondary to independently fitting voxels. In this work, we adopt a different approach, modeling entire volumes directly in a manner that increases model flexibility while preserving interpretability. Specifically, we use a generalized additive model (GAM) in which the effects of each regressor remain separable, the product of a spatial map produced by a variational autoencoder and a (potentially nonlinear) gain modeled by a covariate-specific Gaussian Process. The result is a model that yields group-level effect maps comparable or superior to the ones obtained with standard fMRI analysis software while also producing single-subject effect maps capturing individual differences. This suggests that generative models with a decomposable structure might offer a more flexible alternative for the analysis of task-based fMRI data.

neuroscience↗

bioRxiv2021

The optimal spatial arrangement of ON and OFF receptive fields

Many sensory systems utilize parallel ON and OFF pathways that signal stimulus increments and decrements, respectively. These pathways consist of ensembles or grids of ON and OFF detectors spanning sensory space. Yet encoding by opponent pathways raises a question: How should grids of ON and OFF detectors be arranged to optimally encode natural stimuli? We investigated this question using a model of the retina guided by efficient coding theory. Specifically, we optimized spatial receptive fields and contrast response functions to encode natural images given noise and constrained firing rates. We find that the optimal arrangement of ON and OFF receptive fields exhibits a transition between aligned and anti-aligned grids. The preferred phase depends on detector noise and the statistical structure of the natural stimuli. These results reveal that noise and stimulus statistics produce qualitative shifts in neural coding strategies and provide novel theoretical predictions for the configuration of opponent pathways in the nervous system. Significance StatementAcross a wide variety of species, cells in the retina specialized for signaling either increases (ON) or decreases (OFF) in light represent one of the most basic building blocks of visual computation. These cells coordinate to form mosaics, with each cell responsible for a small, minimally-overlapping portion of visual space, but the ways in which these mosaics could be spatially coordinated with each other are relatively unknown. Here, we show how efficient coding theory, which hypothesizes that the nervous system minimizes the amount of redundant information it encodes, can predict the relative spatial arrangement of ON and OFF mosaics. The most information-efficient arrangements are determined both by levels of noise in the system and the statistics of natural images.

neuroscience↗

bioRxiv2021

improv: A flexible software platform for adaptive neuroscience experiments

Current neuroscience research is often limited to testing predetermined hypotheses and post hoc analysis of already collected data. Adaptive experimental designs, in which modeling drives ongoing data collection and selects experimental manipulations, offer a promising alternative. Still, tight integration between models and data collection requires coordinating diverse hardware configurations and complex computations under real-time constraints. Here, we introduce improv, a software platform that allows users to fully integrate custom modeling, analysis, and visualization with data collection and experimental control. We demonstrate both in silico and in vivo how improv enables more efficient experimental designs for discovery and validation across various model organisms and data types. Improv can orchestrate custom real-time behavioral analyses, rapid functional typing of neural responses from large populations via calcium microscopy, and optimal visual stimulus selection. We incorporate real-time machine learning methods for dimension reduction and predictive modeling of latent neural and behavioral features. Finally, we demonstrate how improv can perform model-driven interactive imaging and simultaneous optogenetic photostimulation of visually responsive neurons in the larval zebrafish brain expressing GCaMP6s and the red-shifted opsin rsChRmine. Together, these results demonstrate the power of improv to integrate modeling with data collection and experimental control to achieve next-generation adaptive experiments.

neuroscience↗

bioRxiv2020

Distinct brain mechanisms linked to evidence accumulation and crime-type bias in juror decisions

Efforts to explain complex human decisions have focused on competing theories emphasizing utility and narrative mechanisms. These are difficult to distinguish using behavior alone. Both narrative and utility theories have been proposed to explain juror decisions, which are among the most consequential complex decisions made in a modern society. Here, we asked jury-eligible male and female subjects to rate the strength of a series of criminal cases while recording the resulting patterns of brain activation. We compared patterns of brain activation associated with evidence accumulation to patterns of brain activation derived from a large neuroimaging database to look for signatures of the cognitive processes associated with different models of juror decision making. Evidence accumulation correlated with multiple narrative processes, including reading and recall. Of the cognitive processes traditionally viewed as components of utility, activation patterns associated with uncertainty, but not value, were more active with stronger evidence. Independent of utility and narrative, activations linked to reasoning and relational logic also correlated with increasing evidence. Hierarchical modeling of cognitive processes associated with evidence accumulation supported a more prominent role for narrative in weighing evidence in complex decisions. However, utility processes were also associated with evidence accumulation. These complementary findings support an emerging view that integrates utility and narrative processes in complex decisions. Significance StatementThe last decade has seen a sharply increased interest in narrative as a central cognitive process in human decision making and as an important factor in the evolution of human societies. However, the roles of narrative versus utility models of decision making remain hotly debated. While available models frequently produce similar behavioral predictions, they rely on different cognitive processes and so their roles can be separated using the right neural tests. Here, we use brain imaging during mock juror decisions to show that cognitive processes associated with narrative, and to a lesser extent utility, were engaged while subjects evaluated evidence. These results are consistent with interactions between narrative and utility processes during complex decision making.

neuroscience↗

bioRxiv2019

Monkeys and Humans Implement Causal Inference to Simultaneously Localize Auditory and Visual Stimuli

The environment is sampled by multiple senses, which are woven together to produce a unified perceptual state. However, optimally unifying such signals requires assigning particular signals to the same or different underlying objects or events. Many prior studies (especially in animals) have assumed fusion of cross-modal information, whereas recent work in humans has begun to probe the appropriateness of this assumption. Here we present results from a novel behavioral task in which both monkeys and humans localized visual and auditory stimuli and reported their perceived sources through saccadic eye movements. When the locations of visual and auditory stimuli were widely separated, subjects made two saccades, while when the two stimuli were presented at the same location they made only a single saccade. Intermediate levels of separation produced mixed response patterns: a single saccade to an intermediate position on some trials or separate saccades to both locations on others. The distribution of responses was well described by a hierarchical causal inference model that accurately predicted both the explicit \"same vs. different\" source judgements as well as biases in localization of the source(s) under each of these conditions. The results from this task are broadly consistent with prior work in humans across a wide variety of analogous tasks, extending the study of multisensory causal inference to non-human primates and to a natural behavioral task with both a categorical assay of the number of perceived sources and a continuous report of the perceived position of the stimuli.\n\nAuthor SummaryWe experience the world through multiple sensory systems, which interact to shape perception. To do so, the brain must first determine which pieces of sensory input arise from the same source and which have nothing to do with one another. To probe how the brain accomplishes this causal inference, we developed a naturalistic paradigm that provides a behavioral report both of the number of perceived stimuli and their locations. We tested performance on this task in both humans and monkeys, and we found that both species perform causal inference in a similar manner. By providing this cross-species comparison at the behavioral level, our paradigm lays the groundwork for future experiments using neuronal recording techniques that may be impractical or impossible in human subjects.

neuroscience↗