Accumulation Of Evidence Research Articles

Arkypallidal neurons in the external globus pallidus can mediate inhibitory control by altering competition in the striatum

Reactive inhibitory control is crucial for survival. Traditionally, this control in mammals was attributed solely to the hyperdirect pathway, with cortical control signals flowing unidirectionally from the subthalamic nucleus (STN) to basal ganglia output regions. Yet recent findings have put this model into question, suggesting that the STN is assisted in stopping actions through ascending control signals to the striatum mediated by the external globus pallidus (GPe). Here, we investigate this suggestion by harnessing a biologically constrained spiking model of the cortico-basal ganglia-thalamic (CBGT) circuit that includes pallidostriatal pathways originating from arkypallidal neurons. Through a series of experiments probing the interaction between three critical inhibitory nodes (the STN, arkypallidal cells, and indirect pathway spiny projection neurons), we find that the GPe acts as a critical mediator of both ascending and descending inhibitory signals in the CBGT circuit. In particular, pallidostriatal pathways regulate this process by weakening the direct pathway dominance of the evidence accumulation process driving decisions, which increases the relative suppressive influence of the indirect pathway on basal ganglia output. These findings delineate how pallidostriatal pathways can facilitate action cancellation by managing the bidirectional flow of information within CBGT circuits.

A dynamical systems approach to optimal foraging

Foraging for resources in an environment is a fundamental activity that must be addressed by any biological agent. Modelling this phenomenon in simulations can enhance our understanding of the characteristics of natural intelligence. In this work, we present a novel approach to model foraging in-silico using a continuous coupled dynamical system. The dynamical system is composed of three differential equations, representing the position of the agent, the agent’s control policy, and the environmental resource dynamics. Crucially, the control policy is implemented as a parameterized differential equation which allows the control policy to adapt in order to solve the foraging task. Using this setup, we show that when these dynamics are coupled and the controller parameters are optimized to maximize the rate of reward collected, adaptive foraging emerges in the agent. We further show that the internal dynamics of the controller, as a surrogate brain model, closely resemble the dynamics of the evidence accumulation mechanism, which may be used by certain neurons of the dorsal anterior cingulate cortex region in non-human primates, for deciding when to migrate from one patch to another. We show that by modulating the resource growth rates of the environment, the emergent behaviour of the artificial agent agrees with the predictions of the optimal foraging theory. Finally, we demonstrate how the framework can be extended to stochastic and multi-agent settings.

Real-world aging and deactivation of catalytic diesel particulate filters in heavy-duty vehicles

Low-temperature soot oxidation in catalytic diesel particulate filters (DPF) is important for maintaining high efficiency of heavy-duty vehicles. This can be achieved by coating DPFs with an oxidation catalyst. In this work, catalytic DPFs have been collected from real-world operating heavy-duty vehicles for assessment of their catalytic activity and subsequent characterization. Testing of catalytic activity revealed the diminishing nitric oxide (NO) oxidation of the aged catalysts. The apparent reaction rates showed that the number of available catalytic sites decreased with mileage explaining the loss in activity. Characterization of the samples showed a decreasing surface area as well as an accumulation of metals and poisonous elements. An important finding from SEM-EDS analysis is the evident accumulation of phosphorus and sulfur in the washcoat in the absence of other ash-related elements, potentially explaining the decreased activity.

Endogenous Attention Affects Decision-related Neural Activity but Not Afferent Visual Responses.

Endogenous shifts of spatial attention toward an upcoming stimulus are associated with improvements in behavioral responses to the stimulus, preparatory retinotopic shifts in alpha power, and changes in ERPs. Although attentional modulation of several early sensory ERPs is well established, there is still debate about under what circumstances attention affects the earliest cortical visual evoked response-the C1 ERP component-which is putatively generated from afferent input into primary visual cortex. Moreover, the effects of spatial attention on the recently discovered ERP signature of evidence accumulation-the central parietal positivity (CPP)-have not been fully characterized. The present study assessed the effect of spatial attention on the C1 and CPP components through a spatially cued contrast discrimination task using stimuli that were specifically designed to produce large-amplitude C1 responses and that varied in sensory evidence strength to characterize the CPP. Participants responded according to which of two checkerboard stimuli had greater contrast following an 80% valid cue toward the upper or lower visual field. Prestimulus alpha power changed topographically based on the cue, suggesting participants shifted attention to prepare for the upcoming stimuli. Despite these attentional shifts in alpha power and the fact that the stimuli reliably elicited C1 responses several times greater than many prior studies, there was no evidence of an attention effect on the C1. The CPP, however, showed a clear increase in build-up rate on valid trials. Our findings suggest that endogenous attention may not affect the early C1 ERP component but may improve behavior at a decision stage, as reflected in brain signals related to evidence accumulation (the CPP).

Psychometrics of drift-diffusion model parameters derived from the Eriksen flanker task: Reliability and validity in two independent samples.

The flanker task is a widely used measure of cognitive control abilities. Drift-diffusion modeling of flanker task behavior can yield separable parameters of cognitive control-related subprocesses, but the parameters' psychometrics are not well-established. We examined the reliability and validity of four behavioral measures: (1) raw accuracy, (2) reaction time (RT) interference, (3) NIH Toolbox flanker score, and (4) two drift-diffusion model (DDM) parameters-drift rate and boundary separation-capturing evidence accumulation efficiency and speed-accuracy trade-off, respectively. Participants from two independent studies - one cross-sectional (N = 381) and one with three timepoints (N = 83) - completed the flanker task while electroencephalography data were collected. Across both studies, drift rate and boundary separation demonstrated comparable split-half and test-retest reliability to accuracy, RT interference, and NIH Toolbox flanker score, but better incremental convergent validity with psychophysiological measures (i.e., the error-related negativity; ERN) and neuropsychological measures of cognitive control than the other behavioral indices. Greater drift rate (i.e., faster and more accurate responses) to congruent and incongruent stimuli, and smaller boundary separation to incongruent stimuli were related to 1) larger ERN amplitudes (in both studies) and 2) faster and more accurate inhibition and set-shifting over and above raw accuracy, reaction time, and NIH Toolbox flanker scores (in Study 1). Computational models, such as DDM, can parse behavioral performance into subprocesses that exhibit comparable reliability to other scoring approaches, but more meaningful relationships with other measures of cognitive control. The application of these computational models may be applied to existing data and enhance the identification of cognitive control deficits in psychiatric disorders.

Direct observation of the neural computations underlying a single decision

Neurobiological investigations of perceptual decision-making have furnished the first glimpse of a flexible cognitive process at the level of single neurons. Neurons in the parietal and prefrontal cortex are thought to represent the accumulation of noisy evidence, acquired over time, leading to a decision. Neural recordings averaged over many decisions have provided support for the deterministic rise in activity to a termination bound. Critically, it is the unobserved stochastic component that is thought to confer variability in both choice and decision time. Here, we elucidate this drift-diffusion signal on individual decisions. We recorded simultaneously from hundreds of neurons in the lateral intraparietal cortex of monkeys while they made decisions about the direction of random dot motion. We show that a single scalar quantity, derived from the weighted sum of the population activity, represents a combination of deterministic drift and stochastic diffusion. Moreover, we provide direct support for the hypothesis that this drift-diffusion signal approximates the quantity responsible for the variability in choice and reaction times. The population-derived signals rely on a small subset of neurons with response fields that overlap the choice targets. These neurons represent the integral of noisy evidence. Another subset of direction-selective neurons with response fields that overlap the motion stimulus appear to represent the integrand. This parsimonious architecture would escape detection by state-space analyses, absent a clear hypothesis.

Direct observation of the neural computations underlying a single decision.

Neurobiological investigations of perceptual decision-making have furnished the first glimpse of a flexible cognitive process at the level of single neurons. Neurons in the parietal and prefrontal cortex are thought to represent the accumulation of noisy evidence, acquired over time, leading to a decision. Neural recordings averaged over many decisions have provided support for the deterministic rise in activity to a termination bound. Critically, it is the unobserved stochastic component that is thought to confer variability in both choice and decision time. Here, we elucidate this drift-diffusion signal on individual decisions. We recorded simultaneously from hundreds of neurons in the lateral intraparietal cortex of monkeys while they made decisions about the direction of random dot motion. We show that a single scalar quantity, derived from the weighted sum of the population activity, represents a combination of deterministic drift and stochastic diffusion. Moreover, we provide direct support for the hypothesis that this drift-diffusion signal approximates the quantity responsible for the variability in choice and reaction times. The population-derived signals rely on a small subset of neurons with response fields that overlap the choice targets. These neurons represent the integral of noisy evidence. Another subset of direction-selective neurons with response fields that overlap the motion stimulus appear to represent the integrand. This parsimonious architecture would escape detection by state-space analyses, absent a clear hypothesis.

Prior probability biases perceptual choices by modulating the accumulation rate, rather than the baseline, of decision evidence

Abstract The prior probability of an upcoming stimulus has been shown to influence the formation of perceptual decisions. Computationally these effects have typically been attributed to changes in the starting point (i.e. baseline) of evidence accumulation in sequential sampling models. More recently, it has also been proposed that prior probability might additionally lead to changes in the rate of evidence accumulation. Here, we introduce a neurally-informed behavioural modelling approach to understand whether prior probability influences the starting point, the rate of evidence accumulation or both. To this end, we employ a well-established visual object categorization task for which two neural components underpinning participants’ choices have been characterised using single-trial analysis of the electroencephalogram. These components are reliable measures of trial-by-trial variability in the quality of the relevant decision evidence, which we use to constrain the estimation of a hierarchical drift diffusion model of perceptual choice. We find that, unlike previous computational accounts, constraining the model with the endogenous variability in the relevant decision evidence, results in prior probability effects being explained primarily by changes in the rate of evidence accumulation rather than changes in the starting point or a combination of both. Ultimately, our neurally-informed modelling approach helps disambiguate the mechanistic effect of prior probability on perceptual decision formation, suggesting that prior probability biases primarily the interpretation of sensory evidence towards the most likely stimulus.

Benign polymorphisms in the BRCA genes with linkage disequilibrium is associated with cancer characteristics.

Germline pathogenic mutation of the BRCA gene increases the prevalence of breast cancer. Reports on the benign variants of BRCA genes are limited. However, the definition of these variants might be altered with the accumulation of clinical evidence. Therefore, in the present study, we focused on benign single nucleotide polymorphisms (SNPs) of BRCA genes. Linkage disequilibrium was calculated from whole genome sequencing of the BRCA genes obtained from 500 healthy controls and 49 breast cancer patients. Sanger sequencing was used to confirm the mutation. The linkage disequilibrium was noted for seven and three SNPs in the BRCA1 and BRCA2 genes, respectively. Breast cancer with BRCA1/2 linkage disequilibrium was not correlated with a personal history of benign diseases or family history of cancer. Nevertheless, breast cancer with BRCA1 linkage disequilibrium was correlated with high tumor-infiltrating lymphocytes and positive extensive intraductal components. The patients with BRCA1 linkage disequilibrium tended to have worse disease-specific survival. Cancers with BRCA2 linkage disequilibrium are associated with a lower ratio of grade III cancer. Moreover, patients with BRCA2 linkage disequilibrium tended to have better overall survival. In conclusion, linkage disequilibrium from benign SNPs of the BRCA genes potentially affects cancer characteristics.

Value construction through sequential sampling explains serial dependencies in decision making.

Many decisions are expressed as a preference for one item over another. When these items are familiar, it is often assumed that the decision maker assigns a value to each of the items and chooses the item with the highest value. These values may be imperfectly recalled, but are assumed to be stable over the course of an interview or psychological experiment. Choices that are inconsistent with a stated valuation are thought to occur because of unspecified noise that corrupts the neural representation of value. Assuming that the noise is uncorrelated over time, the pattern of choices and response times in value-based decisions are modeled within the framework of Bounded Evidence Accumulation (BEA), similar to that used in perceptual decision-making. In BEA, noisy evidence samples accumulate over time until the accumulated evidence for one of the options reaches a threshold. Here, we argue that the assumption of temporally uncorrelated noise, while reasonable for perceptual decisions, is not reasonable for value-based decisions. Subjective values depend on the internal state of the decision maker, including their desires, needs, priorities, attentional state, and goals. These internal states may change over time, or undergo revaluation, as will the subjective values. We reasoned that these hypothetical value changes should be detectable in the pattern of choices made over a sequence of decisions. We reanalyzed data from a well-studied task in which participants were presented with pairs of snacks and asked to choose the one they preferred. Using a novel algorithm ( Reval ), we show that the subjective value of the items changes significantly during a short experimental session (about 1 hour). Values derived with Reval explain choice and response time better than explicitly stated values. They also better explain the BOLD signal in the ventromedial prefrontal cortex, known to represent the value of decision alternatives. Revaluation is also observed in a BEA model in which successive evidence samples are not assumed to be independent. We argue that revaluation is a consequence of the process by which values are constructed during deliberation to resolve preference choices.

Rat movements reflect internal decision dynamics in an evidence accumulation task.

Perceptual decision-making involves multiple cognitive processes, including accumulation of sensory evidence, planning, and executing a motor action. How these processes are intertwined is unclear; some models assume that decision-related processes precede motor execution, whereas others propose that movements reflecting on-going decision processes occur before commitment to a choice. Here we combine and apply two complementary methods to study the relationship between decision processes and the movements leading up to a choice. The first is a free response pulse-based evidence accumulation task, in which stimuli continue until choice is reported and the second is a motion-based drift diffusion model (mDDM), in which movement variables from video pose estimation constrain decision parameters on a trial-by-trial basis. We find the mDDM provides a better model fit to rats' decisions in the free response accumulation task than traditional DDM models. Interestingly, on each trial we observed a period of time, prior to choice, that was characterized by head immobility. The length of this period was positively correlated with the rats' decision bounds and stimuli presented during this period had the greatest impact on choice. Together these results support a model in which internal decision dynamics are reflected in movements and demonstrate that inclusion of movement parameters improves the performance of diffusion-to-bound decision models.

Emotion-Driven Moral Evaluation: A Mechanistic Study Based on the Drift-Diffusion Model.

Moral evaluation is identified as the first stage in the theory of moral judgment, and academics believe that it may align with the social intuitionist model. This study aims to prove that the model's emotional dominance hypothesis applies to moral evaluation by presenting a computational decision-making model that mathematically formalizes this emotional dominance decision-making process. We also compared different types of valence evaluation tasks to test the emotional priority hypothesis. We used a convenience sampling method to randomly recruit 30 enrolled college students. The drift-diffusion model was employed to analyze reaction times for words with various emotional and moral valences Additionally, we designed different valence evaluation tasks based on the response relevance hypothesis and evaluated the processing order through reaction time comparisons. The analysis revealed that the emotional mechanism of immoral evaluation differs from moral evaluation. An increase in emotional valence accelerates the speed of evidence accumulation (v) for moral evaluation (M = 1.21, 0.2% < 0 < 99.8%) but lowers decision caution (a) in immoral evaluation (M = -0.64, 96.1% < 0 < 3.9%). In contrast, moral valence does not have a significant influence on evaluation processes (v, M = -0.28, 72.1% < 0 < 27.9%; a, M = -0.32, 79.3% < 0 < 20.7%). Furthermore, We found no significant difference in reaction times between moral and immoral words in the emotional evaluation task (F(1,29) = 0.55, p = 0.464, partial η2 = 0.02), but a significant difference existed in the moral evaluation task (F(1,29) = 17.99, p < 0.001, partial η2 = 0.38), indicating that the tendency of relatively fast immoral evaluation in emotional evaluation tasks may be caused by emotional priority. Our findings support the intuitive model's emotional dominance hypothesis and introduce a new emotional mechanism into moral evaluation. This study clarifies the distinct emotional processes in moral and immoral evaluations, fills a gap in the research on moral evaluation, and offers insights into human decision-making in moral contexts.

Chromium Selectively Accumulates in the Rat Hippocampus after 90 Days of Exposure to Cr(VI) in Drinking Water and Induces Age- and Sex-Dependent Metal Dyshomeostasis.

Hexavalent chromium (Cr[VI]) is a widespread environmental pollutant in air and water that is primarily attributed to industrial pollution. The current maximum contaminant levels (MCLs) for drinking water from the World Health Organization and the U.S. Environmental Protection Agency (0.05 and 0.1 mg/L, respectively) were set based on contact dermatitis and warrant further toxicological investigation. While Cr(VI) is neurotoxic and accumulates in the brain, most animal studies only report whole-brain Cr, leaving large knowledge gaps. Few studies consider differences between ages or sexes, and fewer consider essential metal dyshomeostasis. We sought to investigate where Cr accumulates in the brain, considering sex and age differences, following a 90-day drinking water exposure to current MCLs. Here, we report Cr levels in six brain regions of rats exposed to drinking water Cr(VI). We observed Cr only accumulated in the hippocampus, and only in older females. We further assessed changes to essential metals in the hippocampus, observing opposite effects across sexes and between young rats compared to older rats. In sum, our data indicate drinking water Cr(VI) selectively targeted the hippocampus, with geriatric females accumulating the most Cr, and induced significant essential metal dyshomeostasis even in tissues lacking evident Cr accumulation.

COVID-19 vaccine evidence monitoring assisted by artificial Intelligence: An emergency system implemented by the Public Health Agency of Canada to capture and describe the trajectory of evolving pandemic vaccine literature

BackgroundThe COVID-19 pandemic resulted in a rapid accumulation of novel vaccine research evidence. As a means to monitor this evidence, the Public Health Agency of Canada (PHAC) created the Evidence eXtraction Team for Research Analysis (EXTRA), which contributed to situational awareness in Canada through a bibliographic repository used to support decision-making by the National Advisory Committee on Immunization. We describe the process by which this literature was identified and catalogued, and provide an overview of characteristics in the identified literature. MethodsTo expedite the process, PHAC leveraged an artificial intelligence (AI) tool to assist in the screening and selection of relevant articles. Literature search results were initially screened by AI, then manually reviewed for relevance. Relevant articles were tagged using controlled vocabulary and stored in a bibliographic repository. This repository was analyzed to identify trends in vaccine research over time according to several key characteristics. ResultsAs of December 31, 2023, EXTRA’s repository contained 19,050 articles relevant to PHAC’s immunization mandate. The majority of these articles (63.9 %) were identified between August 2021 and January 2023, with an average of 20 relevant articles added daily during this period. Nearly 14,000 articles reported on mRNA vaccines. Safety outcomes were most frequently reported (n = 8,289), followed by immunogenicity (n = 7,269) and efficacy/effectiveness (n = 3,246). COVID-19 vaccine literature output started to decrease in mid-2023, two years after the initial dramatic increase in mid-2021. ConclusionsThis hybrid (AI and human) approach was critical for PHAC situational awareness and the development of timely vaccine guidance in Canada during the COVID-19 pandemic. Given the volume of data and analyses required, the AI-augmented processes made this massive undertaking manageable. Analysis of COVID-19 vaccine research patterns supports projections of research volume, type, and rate that will help predict resourcing and information needs to plan future emergency vaccine guidance activities.

How bad becomes good: A neurocomputational model of affect-informed choice.

People often draw on their current affective experience to inform their decisions, yet little is known about the underlying mechanisms of this process. Understanding them has important implications for many big questions in both the affective and decision sciences. Do the same neural circuits that generate affect generate value? What differentiates people who have greater contextual flexibility in their reliance on affect? Do affective choices invoke processes that are distinct from less affective choices? To investigate these questions, we developed a neurocomputational model of affect-informed choice, in which people convert subjective affect into context-sensitive decision value through a process of weighted evidence accumulation. We then tested model predictions by recording electroencephalography and facial electromyography during a novel affective choice paradigm in a sample of racially diverse undergraduate participants (data collected in 2018-2019). In addition to validating our model, we found that generation of affective responses occurs earlier than, and is neurally distinct from, valuation of that affect. Moreover, individual differences in contextual flexibility of affective weighting correlated only with later valuation processes, not earlier affect generation processes. Our results have important theoretical implications for emotion, emotion regulation, and decision making. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Neural basis of concurrent deliberation toward a choice and degree of confidence.

Decision confidence plays a key role in flexible behavior and (meta)cognition, but its underlying neural mechanisms remain elusive. To uncover the latent dynamics of confidence formation at the level of population activity, we designed a decision task for nonhuman primates that measures choice, reaction time, and confidence with a single eye movement on every trial. Monkey behavior was well fit by a bounded accumulator model instantiating parallel processing of evidence, rejecting a serial model in which the choice is resolved first followed by post-decision accumulation for confidence. Neurons in area LIP reflected concurrent accumulation, exhibiting covariation of choice and confidence signals across the population, and within-trial dynamics consistent with parallel updating at near-zero time lag. The results demonstrate that monkeys can process a single stream of evidence in service of two computational goals simultaneously-a categorical decision and associated level of confidence-and illuminate a candidate neural substrate for this ability.

Auditory decision-making deficits after permanent noise-induced hearing loss.

Loud noise exposure is one of the leading causes of permanent hearing loss. Individuals with noise-induced hearing loss (NIHL) suffer from speech comprehension deficits and experience impairments to cognitive functions such as attention and decision-making. Here, we tested whether a specific sensory deficit, NIHL, can directly impair auditory cognitive function. Gerbils were trained to perform an auditory decision-making task that involves discriminating between slow and fast presentation rates of amplitude-modulated (AM) noise. Decision-making task performance was assessed across pre-versus post-NIHL sessions within the same gerbils. A single exposure session (2 hours) to loud broadband noise (120 dB SPL) produced permanent NIHL with elevated threshold shifts in auditory brainstem responses (ABRs). Following NIHL, decision-making task performance was tested at sensation levels comparable to those prior to noise exposure in all animals. Our findings demonstrate NIHL diminished perceptual acuity, reduced attentional focus, altered choice bias, and slowed down evidence accumulation speed. Finally, video-tracking analysis of motor behavior during task performance demonstrates that NIHL can impact sensory-guided decision-based motor execution. Together, these results suggest that NIHL impairs the sensory, cognitive, and motor factors that support auditory decision-making.

The subthalamic nucleus contributes causally to perceptual decision-making in monkeys.

The subthalamic nucleus (STN) plays critical roles in the motor and cognitive function of the basal ganglia (BG), but the exact nature of these roles is not fully understood, especially in the context of decision-making based on uncertain evidence. Guided by theoretical predictions of specific STN contributions, we used single-unit recording and electrical microstimulation in the STN of healthy monkeys to assess its causal, computational roles in visual-saccadic decisions based on noisy evidence. The recordings identified subpopulations of STN neurons with distinct task-related activity patterns that related to different theoretically predicted functions. Microstimulation caused changes in behavioral choices and response times that reflected multiple contributions to an 'accumulate-to-bound'-like decision process, including modulation of decision bounds and evidence accumulation, and to non-perceptual processes. These results provide new insights into the multiple ways that the STN can support higher brain function.

Individual differences in belief updating and phasic arousal are related to psychosis proneness

Many decisions entail the updating of beliefs about the state of the environment by accumulating noisy sensory evidence. This form of probabilistic reasoning may go awry in psychosis. Computational theory shows that optimal belief updating in environments subject to hidden changes in their state requires a dynamic modulation of the evidence accumulation process. Recent empirical findings implicate transient responses of pupil-linked central arousal systems to individual evidence samples in this modulation. Here, we analyzed behavior and pupil responses during evidence accumulation in a changing environment in a community sample of human participants. We also assessed their subclinical psychotic experiences (psychosis proneness). Participants most prone to psychosis showed overall less flexible belief updating profiles, with diminished behavioral impact of evidence samples occurring late during decision formation. These same individuals also exhibited overall smaller pupil responses and less reliable pupil encoding of computational variables governing the dynamic belief updating. Our findings provide insights into the cognitive and physiological bases of psychosis proneness and open paths to unraveling the pathophysiology of psychotic disorders.

The subthalamic nucleus contributes causally to perceptual decision-making in monkeys

The subthalamic nucleus (STN) plays critical roles in the motor and cognitive function of the basal ganglia (BG), but the exact nature of these roles is not fully understood, especially in the context of decision-making based on uncertain evidence. Guided by theoretical predictions of specific STN contributions, we used single-unit recording and electrical microstimulation in the STN of healthy monkeys to assess its causal, computational roles in visual-saccadic decisions based on noisy evidence. The recordings identified subpopulations of STN neurons with distinct task-related activity patterns that related to different theoretically predicted functions. Microstimulation caused changes in behavioral choices and response times that reflected multiple contributions to an ‘accumulate-to-bound’-like decision process, including modulation of decision bounds and evidence accumulation, and to non-perceptual processes. These results provide new insights into the multiple ways that the STN can support higher brain function.