MD3F: Multivariate Distance Drift Diffusion Framework for High-Dimensional Datasets

High-dimensional biomedical datasets have become easier to collect in the last two decades with the advent of multi-omic and single-cell experiments. These can generate over 1000 measurements per sample or per cell. More recently, focus has been drawn toward the need for longitudinal datasets, with the appreciation that important dynamic changes occur along transitions between health and disease. Analysis of longitudinal omics data comes with many challenges, including type I error inflation and corresponding loss in power when thousands of hypothesis tests are needed. Multivariate analysis can yield approaches with higher statistical power; however, multivariate methods for longitudinal data are currently limited. We propose a multivariate distance-based drift-diffusion framework (MD3F) to tackle the need for a multivariate approach to longitudinal, high-throughput datasets. We show that MD3F can result in surprisingly simple yet valid and powerful hypothesis testing and estimation approaches using generalized linear models. Through simulation and application studies, we show that MD3F is robust and can offer a broadly applicable method for assessing multivariate dynamics in omics data.

Manipulating Prior Beliefs Causally Induces Under- and Overconfidence

Humans differ vastly in the confidence they assign to decisions. Although such under- and overconfidence relate to fundamental life outcomes, a computational account specifying the underlying mechanisms is currently lacking. We propose that prior beliefs in the ability to perform a task explain confidence differences across participants and tasks, despite similar performance. In two perceptual decision-making experiments, we show that manipulating prior beliefs about performance during training causally influences confidence in healthy adults (N = 50 each; Experiment 1: 8 men, one nonbinary; Experiment 2: 5 men) during a test phase, despite unaffected objective performance. This is true when prior beliefs are induced via manipulated comparative feedback and via manipulated training-phase difficulty. Our results were accounted for within an accumulation-to-bound model, explicitly modeling prior beliefs on the basis of earlier task exposure. Decision confidence is quantified as the probability of being correct conditional on prior beliefs, causing under- or overconfidence. We provide a fundamental mechanistic insight into the computations underlying under- and overconfidence.

A Cognitive Computational Approach to Social and Collective Decision-Making

Collective dynamics play a key role in everyday decision-making. Whether social influence promotes the spread of accurate information and ultimately results in adaptive behavior or leads to false information cascades and maladaptive social contagion strongly depends on the cognitive mechanisms underlying social interactions. Here we argue that cognitive modeling, in tandem with experiments that allow collective dynamics to emerge, can mechanistically link cognitive processes at the individual and collective levels. We illustrate the strength of this cognitive computational approach with two highly successful cognitive models that have been applied to interactive group experiments: evidence-accumulation and reinforcement-learning models. We show how these approaches make it possible to simultaneously study (a) how individual cognition drives social systems, (b) how social systems drive individual cognition, and (c) the dynamic feedback processes between the two layers.

Impact of aversive affect on neural mechanisms of categorization decisions

INTRODUCTION

Many theories contend that evidence accumulation is a critical component of decision-making. Cognitive accumulation models typically interpret two main parameters: a drift rate and decision threshold. The former is the rate of accumulation, based on the quality of evidence, and the latter is the amount of evidence required for a decision. Some studies have found neural signals that mimic evidence accumulators and can be described by the two parameters. However, few studies have related these neural parameters to experimental manipulations of sensory data or memory representations. Here, we investigated the influence of affective salience on neural accumulation parameters. High affective salience has been repeatedly shown to influence decision-making, yet its effect on neural evidence accumulation has been unexamined.

METHODS

The current study used a two-choice object categorization task of body images (feet or hands). Half the images in each category were high in affective salience because they contained highly aversive features (gore and mutilation). To study such quick categorization decisions with a relatively slow technique like functional magnetic resonance imaging, we used a gradual reveal paradigm to lengthen cognitive processing time through the gradual "unmasking" of stimuli.

RESULTS

Because the aversive features were task-irrelevant, high affective salience produced a distractor effect, slowing decision time. In visual accumulation regions of interest, high affective salience produced a longer time to peak activation. Unexpectedly, the later peak appeared to be the product of changes to both drift rate and decision threshold. The drift rate for high affective salience was shallower, and the decision threshold was greater. To our knowledge, this is the first demonstration of an experimental manipulation of sensory data or memory representations that changed the neural decision threshold.

CONCLUSION

These findings advance our knowledge of the neural mechanisms underlying affective responses in general and the influence of high affective salience on object representations and categorization decisions.

When 2 become 1: Autistic simultaneity judgements about asynchronous audiovisual speech

It has been proposed that autistic people experience a temporal distortion whereby the temporal binding window of multisensory integration is extended. Research to date has focused on autistic children so whether these differences persist into adulthood remains unknown. In addition, the possibility that the previous observations have arisen from between-group differences in response bias, rather than perceptual differences, has not been addressed. Participants completed simultaneity judgements of audiovisual speech stimuli across a range of stimulus-onset asynchronies. Response times and accuracy data were fitted to a drift-diffusion model so that the drift rate (a measure of processing efficiency) and starting point (response bias) could be estimated. In Experiment 1, we tested a sample of non-autistic adults who completed the Autism Quotient questionnaire. Autism Quotient score was not correlated with either drift rate or response bias, nor were there between-group differences when splitting based on the first and third quantiles of scores. In Experiment 2, we compared the performance of autistic with a group of non-autistic adults. There were no between-group differences in either drift rate or starting point. The results of this study do not support the previous suggestion that autistic people have an extended temporal binding window for audiovisual speech. In addition, exploratory analysis revealed that operationalising the temporal binding window in different ways influenced whether a group difference was observed, which is an important consideration for future work.

Optimal policy for uncertainty estimation concurrent with decision making

Decision making often depends on vague information that leads to uncertainty, which is a quantity contingent not on choice but on probability distributions of sensory evidence and other cognitive variables. Uncertainty may be computed in parallel and interact with decision making. Here, we adapt the classic random-dot motion direction discrimination task to allow subjects to indicate their uncertainty without having to form a decision first. The subjects' choices and reaction times for perceptual decisions and uncertainty responses are measured, respectively. We then build a value-based model in which decisions are based on optimizing value computed from a drift-diffusion process. The model accounts for key features of subjects' behavior and the variation across the individuals. It explains how the addition of the uncertainty option affects perceptual decision making. Our work establishes a value-based theoretical framework for studying uncertainty and perceptual decisions that can be readily applied in future investigations of the underlying neural mechanism.

Neurocognitive mechanisms underlying attention bias towards pain: evidence from a drift-diffusion model and event-related potentials

Although combining computational modeling with event-related potentials (ERPs) can precisely characterize neurocognitive processes involved in attention bias, it has yet to be applied in the context of pain. Here, a hierarchical drift-diffusion model (DDM) along with ERPs was used to characterize the neurocognitive mechanisms underlying attention bias towards pain. A spatial cueing paradigm was adopted, in which the locations of targets were either validly or invalidly predicted by spatial cues related to pain or non-pain signals. DDM-derived non-decision time was shorter for targets validly cued by pain signals than by non-pain signals, thus indicating speeded attention engagement towards pain; drift rate was slower for targets invalidly cued by pain signals than by non-pain signals, reflecting slower attention disengagement from pain. The facilitated engagement towards pain was partially mediated by the enhanced lateralization of cue-evoked N1 amplitudes, which relate to the bottom-up, stimulus-driven processes of detecting threatening signals. On the other hand, the retarded disengagement from pain was partially mediated by the enhanced target-evoked anterior N2 amplitudes, which relate to the top-down, goal-driven processes of conflict monitoring and behavior regulating. These results demonstrated that engagement and disengagement components of pain-related attention bias are governed by distinct neurocognitive mechanisms. However, it remains possible that the findings are not pain-specific, but rather, are related to threat or aversiveness in general. This deserves to be further examined by adding a control stimulus modality. Perspective: This study characterized the neurocognitive processes involved in attention bias towards pain through combining a hierarchical drift-diffusion model and event-related potentials. Our results revealed distinctive neurocognitive mechanisms underlying engagement and disengagement components of attention bias. Future studies are warranted to examine whether our findings are pain-specific or not.

Optimization of N-PERT Solar Cell under Atacama Desert Solar Spectrum

In the Atacama Desert, the spectral distribution of solar radiation differs from the global standard, showing very high levels of irradiation with a particularly high ultraviolet content. Additionally, the response of photovoltaic (PV) technologies is spectrally dependent, so it is necessary to consider local conditions and type of technology to optimize PV devices since solar cells are usually designed for maximum performance under standard testing conditions (STC). In this work, we determined geometrical and doping parameters to optimize the power of an n-type bifacial passivated emitter and rear totally diffused solar cell (n-PERT). Six parameters (the thicknesses of cell, emitter, and back surface field, as well as doping concentration of emitter, base, and back surface field) were used to optimize the cell under the Atacama Desert spectrum (AM 1.08) and under standard conditions (AM 1.5) through a genetic algorithm. To validate the model, the calculated performance of the n-PERT cell was compared with experimental measurements. Computed and experimental efficiencies showed a relative difference below 1% under STC conditions. Through the optimization process, we found that different geometry and doping concentrations are necessary for cells to be used in the Atacama Desert. Reducing the thickness of all layers and increasing doping can lead to a relative increment of 5.4% in the cell efficiency under AM 1.08. Finally, we show the potential effect of metallization and the viability of reducing the thicknesses of the emitter and the back surface field.

Theory of drift-enabled control in nonlocal magnon transport

Electrically injected and detected nonlocal magnon transport has emerged as a versatile method for transporting spin as well as probing the spin excitations in a magnetic insulator. We examine the role of drift currents in this phenomenon as a method for controlling the magnon propagation length. Formulating a phenomenological description, we identify the essential requirements for existence of magnon drift. Guided by this insight, we examine magnetic field gradient, asymmetric contribution to dispersion, and temperature gradient as three representative mechanisms underlying a finite magnon drift velocity, finding temperature gradient to be particularly effective.

Perceptual Decision Impairments Linked to Obsessive-Compulsive Symptoms are Substantially Driven by State-Based Effects

Computational models of decision making have identified a relationship between obsessive-compulsive symptoms (OCS), both in the general population and in patients, and impairments in perceptual evidence accumulation. Some studies have interpreted these deficits to reflect global disease traits which give rise to clusters of OCS. Such assumptions are not uncommon, even if implicit, in computational psychiatry more broadly. However, it is well established that state- and trait-symptom scores are often correlated (e.g., state and trait anxiety), and the extent to which perceptual deficits are actually explained by state-based symptoms is unclear. State-based symptoms may give rise to information processing differences in a number of ways, including the mechanistically less interesting possibility of tying up working memory and attentional resources for off-task processing. In a general population sample (N = 150), we investigated the extent to which previously identified impairments in perceptual evidence accumulation were related to trait vs stated-based OCS. In addition, we tested whether differences in working memory capacity moderated state-based impairments, such that impairments were worse in individuals with lower working memory capacity. We replicated previous work demonstrating a negative relationship between the rate of evidence accumulation and trait-based OCS when state-based symptoms were unaccounted for. When state-based effects were included in the model, they captured a significant degree of impairment while trait-based effects were attenuated, although they did not disappear completely. We did not find evidence that working memory capacity moderated the state-based effects. Our work suggests that investigating the relationship between information processing and state-based symptoms may be important more generally in computational psychiatry beyond this specific context.

Light Intensity Analysis of Photovoltaic Parameters for Perovskite Solar Cells

The number of publications on perovskite solar cells (PSCs) continues to grow exponentially. Although the efficiency of PSCs has exceeded 25.5%, not every research laboratory can reproduce this result or even pass the border of 20%. Unfortunately, it is not always clear which dominating mechanism is responsible for the performance drop. Here, a simple method of light intensity analysis of the JV parameters is developed, allowing an understanding of what the mechanisms are that appear in the solar cell and limit device performance. The developed method is supported by the drift-diffusion model and is aimed at helping in the explanation of parasitic losses from the interface or bulk recombination, series resistance, or shunt resistance in the perovskite solar cell. This method can help not only point toward the dominating of bulk or interface recombination in the devices but also determine which interface is more defective. A detailed and stepwise guidance for such a type of light intensity analysis of JV parameters is provided. The proposed method and the conclusions of this study are supported by a series of case studies, showing the effectiveness of the proposed method on real examples.

Attentional Guidance and Match Decisions Rely on Different Template Information During Visual Search

When searching for a target object, we engage in a continuous "look-identify" cycle in which we use known features of the target to guide attention toward potential targets and then to decide whether the selected object is indeed the target. Target information in memory (the target template or attentional template) is typically characterized as having a single, fixed source. However, debate has recently emerged over whether flexibility in the target template is relational or optimal. On the basis of evidence from two experiments using college students (Ns = 30 and 70, respectively), we propose that initial guidance of attention uses a coarse relational code, but subsequent decisions use an optimal code. Our results offer a novel perspective that the precision of template information differs when guiding sensory selection and when making identity decisions during visual search.

The cognitive and perceptual correlates of ideological attitudes: a data-driven approach

Although human existence is enveloped by ideologies, remarkably little is understood about the relationships between ideological attitudes and psychological traits. Even less is known about how cognitive dispositions-individual differences in how information is perceived and processed- sculpt individuals' ideological worldviews, proclivities for extremist beliefs and resistance (or receptivity) to evidence. Using an unprecedented number of cognitive tasks (n = 37) and personality surveys (n = 22), along with data-driven analyses including drift-diffusion and Bayesian modelling, we uncovered the specific psychological signatures of political, nationalistic, religious and dogmatic beliefs. Cognitive and personality assessments consistently outperformed demographic predictors in accounting for individual differences in ideological preferences by 4 to 15-fold. Furthermore, data-driven analyses revealed that individuals' ideological attitudes mirrored their cognitive decision-making strategies. Conservatism and nationalism were related to greater caution in perceptual decision-making tasks and to reduced strategic information processing, while dogmatism was associated with slower evidence accumulation and impulsive tendencies. Religiosity was implicated in heightened agreeableness and risk perception. Extreme pro-group attitudes, including violence endorsement against outgroups, were linked to poorer working memory, slower perceptual strategies, and tendencies towards impulsivity and sensation-seeking-reflecting overlaps with the psychological profiles of conservatism and dogmatism. Cognitive and personality signatures were also generated for ideologies such as authoritarianism, system justification, social dominance orientation, patriotism and receptivity to evidence or alternative viewpoints; elucidating their underpinnings and highlighting avenues for future research. Together these findings suggest that ideological worldviews may be reflective of low-level perceptual and cognitive functions. This article is part of the theme issue 'The political brain: neurocognitive and computational mechanisms'.

Gazing at Social Interactions Between Foraging and Decision Theory

Finding the underlying principles of social attention in humans seems to be essential for the design of the interaction between natural and artificial agents. Here, we focus on the computational modeling of gaze dynamics as exhibited by humans when perceiving socially relevant multimodal information. The audio-visual landscape of social interactions is distilled into a number of multimodal patches that convey different social value, and we work under the general frame of foraging as a tradeoff between local patch exploitation and landscape exploration. We show that the spatio-temporal dynamics of gaze shifts can be parsimoniously described by Langevin-type stochastic differential equations triggering a decision equation over time. In particular, value-based patch choice and handling is reduced to a simple multi-alternative perceptual decision making that relies on a race-to-threshold between independent continuous-time perceptual evidence integrators, each integrator being associated with a patch.

Testing the drift-diffusion model

The drift-diffusion model (DDM) is a model of sequential sampling with diffusion signals, where the decision maker accumulates evidence until the process hits either an upper or lower stopping boundary and then stops and chooses the alternative that corresponds to that boundary. In perceptual tasks, the drift of the process is related to which choice is objectively correct, whereas in consumption tasks, the drift is related to the relative appeal of the alternatives. The simplest version of the DDM assumes that the stopping boundaries are constant over time. More recently, a number of papers have used nonconstant boundaries to better fit the data. This paper provides a statistical test for DDMs with general, nonconstant boundaries. As a by-product, we show that the drift and the boundary are uniquely identified. We use our condition to nonparametrically estimate the drift and the boundary and construct a test statistic based on finite samples.

Frontal eye field and caudate neurons make different contributions to reward-biased perceptual decisions

Many decisions require trade-offs between sensory evidence and internal preferences. Potential neural substrates include the frontal eye field (FEF) and caudate nucleus, but their distinct roles are not understood. Previously we showed that monkeys’ decisions on a direction-discrimination task with asymmetric rewards reflected a biased accumulate-to-bound decision process (Fan et al., 2018) that was affected by caudate microstimulation (Doi et al., 2020). Here we compared single-neuron activity in FEF and caudate to each other and to accumulate-to-bound model predictions derived from behavior. Task-dependent neural modulations were similar in both regions. However, choice-selective neurons in FEF, but not caudate, encoded behaviorally derived biases in the accumulation process. Baseline activity in both regions was sensitive to reward context, but this sensitivity was not reliably associated with behavioral biases. These results imply distinct contributions of FEF and caudate neurons to reward-biased decision-making and put experimental constraints on the neural implementation of accumulation-to-bound-like computations.

Reward Enhances Pain Discrimination in Humans

The notion that reward inhibits pain is a well-supported observation in both humans and animals, allowing suppression of pain reflexes to acquired rewarding stimuli. However, a blanket inhibition of pain by reward would also impair pain discrimination. In contrast, early counterconditioning experiments implied that reward might actually spare pain discrimination. To test this hypothesis, we investigated whether discriminative performance was enhanced or inhibited by reward. We found in adult human volunteers (N = 25) that pain-based discriminative ability is actually enhanced by reward, especially when reward is directly contingent on discriminative performance. Drift-diffusion modeling shows that this relates to an augmentation of the underlying sensory signal strength and is not merely an effect of decision bias. This enhancement of sensory-discriminative pain-information processing suggests that whereas reward can promote reward-acquiring behavior by inhibition of pain in some circumstances, it can also facilitate important discriminative information of the sensory input when necessary.

A flexible framework for simulating and fitting generalized drift-diffusion models

The drift-diffusion model (DDM) is an important decision-making model in cognitive neuroscience. However, innovations in model form have been limited by methodological challenges. Here, we introduce the generalized drift-diffusion model (GDDM) framework for building and fitting DDM extensions, and provide a software package which implements the framework. The GDDM framework augments traditional DDM parameters through arbitrary user-defined functions. Models are solved numerically by directly solving the Fokker-Planck equation using efficient numerical methods, yielding a 100-fold or greater speedup over standard methodology. This speed allows GDDMs to be fit to data using maximum likelihood on the full response time (RT) distribution. We demonstrate fitting of GDDMs within our framework to both animal and human datasets from perceptual decision-making tasks, with better accuracy and fewer parameters than several DDMs implemented using the latest methodology, to test hypothesized decision-making mechanisms. Overall, our framework will allow for decision-making model innovation and novel experimental designs.

Decomposing loss aversion from gaze allocation and pupil dilation

Loss-averse decisions, in which one avoids losses at the expense of gains, are highly prevalent. However, the underlying mechanisms remain controversial. The prevailing account highlights a valuation bias that overweighs losses relative to gains, but an alternative view stresses a response bias to avoid choices involving potential losses. Here we couple a computational process model with eye-tracking and pupillometry to develop a physiologically grounded framework for the decision process leading to accepting or rejecting gambles with equal odds of winning and losing money. Overall, loss-averse decisions were accompanied by preferential gaze toward losses and increased pupil dilation for accepting gambles. Using our model, we found gaze allocation selectively indexed valuation bias, and pupil dilation selectively indexed response bias. Finally, we demonstrate that our computational model and physiological biomarkers can identify distinct types of loss-averse decision makers who would otherwise be indistinguishable using conventional approaches. Our study provides an integrative framework for the cognitive processes that drive loss-averse decisions and highlights the biological heterogeneity of loss aversion across individuals.

Strong Effort Manipulations Reduce Response Caution: A Preregistered Reinvention of the Ego-Depletion Paradigm

People feel tired or depleted after exerting mental effort. But even preregistered studies often fail to find effects of exerting effort on behavioral performance in the laboratory or elucidate the underlying psychology. We tested a new paradigm in four preregistered within-subjects studies (N = 686). An initial high-demand task reliably elicited very strong effort phenomenology compared with a low-demand task. Afterward, participants completed a Stroop task. We used drift-diffusion modeling to obtain the boundary (response caution) and drift-rate (information-processing speed) parameters. Bayesian analyses indicated that the high-demand manipulation reduced boundary but not drift rate. Increased effort sensations further predicted reduced boundary. However, our demand manipulation did not affect subsequent inhibition, as assessed with traditional Stroop behavioral measures and additional diffusion-model analyses for conflict tasks. Thus, effort exertion reduced response caution rather than inhibitory control, suggesting that after exerting effort, people disengage and become uninterested in exerting further effort.

Weber's Law: A Mechanistic Foundation after Two Centuries

Weber's law appears to be a universal principle describing how we discriminate between physical magnitudes. However, this law remained purely descriptive for nearly two centuries. A study by Pardo-Vazquez et al. finally provides a mechanistic explanation, revealing how both accuracy and reaction-time performance lawfully emerge during sensory discrimination tasks.

Discrete Stepping and Nonlinear Ramping Dynamics Underlie Spiking Responses of LIP Neurons during Decision-Making

Neurons in LIP exhibit ramping trial-averaged responses during decision-making. Recent work sparked debate over whether single-trial LIP spike trains are better described by discrete "stepping" or continuous "ramping" dynamics. We extended latent dynamical spike train models and used Bayesian model comparison to address this controversy. First, we incorporated non-Poisson spiking into both models and found that more neurons were better described by stepping than ramping, even when conditioned on evidence or choice. Second, we extended the ramping model to include a non-zero baseline and compressive output nonlinearity. This model accounted for roughly as many neurons as the stepping model. However, latent dynamics inferred under this model exhibited high diffusion variance for many neurons, softening the distinction between continuous and discrete dynamics. Results generalized to additional datasets, demonstrating that substantial fractions of neurons are well described by either stepping or nonlinear ramping, which may be less categorically distinct than the original labels implied.

The Neuro-Computational Architecture of Value-Based Selection in the Human Brain

Current neural models of value-based decision-making consider choices as a 2-stage process, proceeding from the "valuation" of each option under consideration to the "selection" of the best option on the basis of their subjective values. However, little is known about the computational mechanisms at play at the selection stage and its implementation in the human brain. Here, we used drift-diffusion models combined with model-based functional magnetic resonance imaging, effective connectivity, and multivariate pattern analysis to characterize the neuro-computational architecture of value-based decisions. We found that 2 key drift-diffusion computations at the selection stage, namely integration and choice readout, engage distinct brain regions, with the dorsolateral prefrontal cortex integrating a decision value signal computed in the ventromedial prefrontal cortex, and the posterior parietal cortex reading out choice outcomes. Our findings suggest that this prefronto-parietal network acts as a hub implementing behavioral selection through a distributed drift-diffusion process.

Phenotypic variability predicts decision accuracy in unicellular organisms

When deciding between different options, animals including humans face the dilemma that fast decisions tend to be erroneous, whereas accurate decisions tend to be relatively slow. Recently, it has been suggested that differences in the efficacy with which animals make a decision relate closely to individual behavioural differences. In this paper, we tested this hypothesis in a unique unicellular organism, the slime mould Physarum polycephalum. We first confirmed that slime moulds differed consistently in their exploratory behaviour from 'fast' to 'slow' explorers. Second, we showed that slow explorers made more accurate decisions than fast explorers. Third, we demonstrated that slime moulds integrated food cues in time and achieved higher accuracy when sampling time was longer. Lastly, we showed that in a competition context, fast explorers excelled when a single food source was offered, while slow explorers excelled when two food sources varying in quality were offered. Our results revealed that individual differences in accuracy were partly driven by differences in exploratory behaviour. These findings support the hypothesis that decision-making abilities are associated with behavioural types, even in unicellular organisms.

Proactive Information Sampling in Value-Based Decision-Making: Deciding When and Where to Saccade

Evidence accumulation has been the core component in recent development of perceptual and value-based decision-making theories. Most studies have focused on the evaluation of evidence between alternative options. What remains largely unknown is the process that prepares evidence: how may the decision-maker sample different sources of information sequentially, if they can only sample one source at a time? Here we propose a theoretical framework in prescribing how different sources of information should be sampled to facilitate the decision process: beliefs for different noisy sources are updated in a Bayesian manner and participants can proactively allocate resource for sampling (i.e., saccades) among different sources to maximize the information gain in such process. We show that our framework can account for human participants' actual choice and saccade behavior in a two-alternative value-based decision-making task. Moreover, our framework makes novel predictions about the empirical eye movement patterns.

Residual Information of Previous Decision Affects Evidence Accumulation in Current Decision

Bias in perceptual decisions can be generally defined as an effect which is controlled by factors other than the decision-relevant information (e.g., perceptual information in a perceptual task, when trials are independent). The literature on decision-making suggests two main hypotheses to account for this kind of bias: internal bias signals are derived from (a) the residual of motor signals generated to report a decision in the past, and (b) the residual of sensory information extracted from the stimulus in the past. Beside these hypotheses, this study suggests that making a decision in the past per se may bias the next decision. We demonstrate the validity of this assumption, first, by performing behavioral experiments based on the two-alternative forced-choice (TAFC) discrimination of motion direction paradigms and, then, we modified the pure drift-diffusion model (DDM) based on the accumulation-to-bound mechanism to account for the sequential effect. In both cases, the trace of the previous trial influences the current decision. Results indicate that the probability of being correct in the current decision increases if it is in line with the previously made decision even in the presence of feedback. Moreover, a modified model that keeps the previous decision information in the starting point of evidence accumulation provides a better fit to the behavioral data. Our findings suggest that the accumulated evidence in the decision-making process after crossing the bound in the previous decision can affect the parameters of information accumulation for the current decision in consecutive trials.

Optimizing sequential decisions in the drift-diffusion model

To make decisions organisms often accumulate information across multiple timescales. However, most experimental and modeling studies of decision-making focus on sequences of independent trials. On the other hand, natural environments are characterized by long temporal correlations, and evidence used to make a present choice is often relevant to future decisions. To understand decision-making under these conditions we analyze how a model ideal observer accumulates evidence to freely make choices across a sequence of correlated trials. We use principles of probabilistic inference to show that an ideal observer incorporates information obtained on one trial as an initial bias on the next. This bias decreases the time, but not the accuracy of the next decision. Furthermore, in finite sequences of trials the rate of reward is maximized when the observer deliberates longer for early decisions, but responds more quickly towards the end of the sequence. Our model also explains experimentally observed patterns in decision times and choices, thus providing a mathematically principled foundation for evidence-accumulation models of sequential decisions.

Gaze Amplifies Value in Decision Making

When making decisions, people tend to choose the option they have looked at more. An unanswered question is how attention influences the choice process: whether it amplifies the subjective value of the looked-at option or instead adds a constant, value-independent bias. To address this, we examined choice data from six eye-tracking studies ( Ns = 39, 44, 44, 36, 20, and 45, respectively) to characterize the interaction between value and gaze in the choice process. We found that the summed values of the options influenced response times in every data set and the gaze-choice correlation in most data sets, in line with an amplifying role of attention in the choice process. Our results suggest that this amplifying effect is more pronounced in tasks using large sets of familiar stimuli, compared with tasks using small sets of learned stimuli.

Accounting for Taste: A Multi-Attribute Neurocomputational Model Explains the Neural Dynamics of Choices for Self and Others

How do we make choices for others with different preferences from our own? Although neuroimaging studies implicate similar circuits in representing preferences for oneself and others, some models propose that additional corrective mechanisms come online when choices for others diverge from one's own preferences. Here we used event-related potentials (ERPs) in humans, in combination with computational modeling, to examine how social information is integrated in the time leading up to choices for oneself and others. Hungry male and female participants with unrestricted diets selected foods for themselves, a similar unrestricted eater, and a dissimilar, self-identified healthy eater. Across choices for both oneself and others, ERP value signals emerged within the same time window but differentially reflected taste and health attributes based on the recipient's preferences. Choices for the dissimilar recipient were associated with earlier activity localized to brain regions implicated in social cognition, including temporoparietal junction. Finally, response-locked analysis revealed a late ERP component specific to choices for the similar recipient, localized to the parietal lobe, that appeared to reflect differences in the response threshold based on uncertainty. A multi-attribute computational model supported the link between specific ERP components and distinct model parameters, and was not significantly improved by adding time-dependent dual processes. Model simulations suggested that longer response times previously associated with effortful correction may alternatively arise from higher choice uncertainty. Together, these results provide a parsimonious neurocomputational mechanism for social decision-making, additionally explaining divergent patterns of choice and response time data in decisions for oneself and others.SIGNIFICANCE STATEMENT How do we choose for others, particularly when they have different preferences? Whereas some studies suggest that similar neural circuits underlie decision-making for oneself and others, others argue for additional, slower perspective-taking mechanisms. Combining event-related potentials with computational modeling, we found that integration of others' preferences occurs over the same timescale as for oneself while differentially tracking recipient-relevant attributes. Although choosing for others took longer and produced differences in late-emerging neural responses, computational modeling attributed these patterns to greater response caution rather than egocentric bias correction. Computational simulations also correctly predicted when and why choosing differently for others takes longer, suggesting that a model incorporating value integration and evidence accumulation can parsimoniously account for complex patterns in social decision-making.

Sequence diversity of tubulin isotypes in regulation of the mitochondrial voltage-dependent anion channel

The microtubule protein tubulin is a heterodimer comprising α/β subunits, in which each subunit features multiple isotypes in vertebrates. For example, seven α-tubulin and eight β-tubulin isotypes in the human tubulin gene family vary mostly in the length and primary sequence of the disordered anionic carboxyl-terminal tails (CTTs). The biological reason for such sequence diversity remains a topic of vigorous enquiry. Here, we demonstrate that it may be a key feature of tubulin's role in regulation of the permeability of the mitochondrial outer membrane voltage-dependent anion channel (VDAC). Using recombinant yeast α/β-tubulin constructs with α-CTTs, β-CTTs, or both from various human tubulin isotypes, we probed their interactions with VDAC reconstituted into planar lipid bilayers. A comparative study of the blockage kinetics revealed that either α-CTTs or β-CTTs block the VDAC pore and that the efficiency of blockage by individual CTTs spans 2 orders of magnitude, depending on the CTT isotype. β-Tubulin constructs, notably β3, blocked VDAC most effectively. We quantitatively described these experimental results using a physical model that accounted only for the number and distribution of charges in the CTT, and not for the interactions between specific residues on the CTT and VDAC pore. Based on these results, we speculate that the effectiveness of VDAC regulation by tubulin depends on the predominant tubulin isotype in a cell. Consequently, the fluxes of ATP/ADP through the channel could vary significantly, depending on the isotype, thus suggesting an intriguing link between VDAC regulation and the diversity of tubulin isotypes present in vertebrates.

Dendritic Integration of Sensory Evidence in Perceptual Decision-Making

Perceptual decisions require the accumulation of sensory information to a response criterion. Most accounts of how the brain performs this process of temporal integration have focused on evolving patterns of spiking activity. We report that subthreshold changes in membrane voltage can represent accumulating evidence before a choice. αβ core Kenyon cells (αβ_c KCs) in the mushroom bodies of fruit flies integrate odor-evoked synaptic inputs to action potential threshold at timescales matching the speed of olfactory discrimination. The forkhead box P transcription factor (FoxP) sets neuronal integration and behavioral decision times by controlling the abundance of the voltage-gated potassium channel Shal (K_V4) in αβ_c KC dendrites. αβ_c KCs thus tailor, through a particular constellation of biophysical properties, the generic process of synaptic integration to the demands of sequential sampling.

Loss Aversion Reflects Information Accumulation, Not Bias: A Drift-Diffusion Model Study

Defined as increased sensitivity to losses, loss aversion is often conceptualized as a cognitive bias. However, findings that loss aversion has an attentional or emotional regulation component suggest that it may instead reflect differences in information processing. To distinguish these alternatives, we applied the drift-diffusion model (DDM) to choice and response time (RT) data in a card gambling task with unknown risk distributions. Loss aversion was measured separately for each participant. Dividing the participants into terciles based on loss aversion estimates, we found that the most loss-averse group showed a significantly lower drift rate than the other two groups, indicating overall slower uptake of information. In contrast, neither the starting bias nor the threshold separation (barrier) varied by group, suggesting that decision thresholds are not affected by loss aversion. These results shed new light on the cognitive mechanisms underlying loss aversion, consistent with an account based on information accumulation.

The Attentional Drift Diffusion Model of Simple Perceptual Decision-Making

Perceptual decisions requiring the comparison of spatially distributed stimuli that are fixated sequentially might be influenced by fluctuations in visual attention. We used two psychophysical tasks with human subjects to investigate the extent to which visual attention influences simple perceptual choices, and to test the extent to which the attentional Drift Diffusion Model (aDDM) provides a good computational description of how attention affects the underlying decision processes. We find evidence for sizable attentional choice biases and that the aDDM provides a reasonable quantitative description of the relationship between fluctuations in visual attention, choices and reaction times. We also find that exogenous manipulations of attention induce choice biases consistent with the predictions of the model.

Unbounded evidence accumulation characterizes subjective visual vertical forced-choice perceptual choice and confidence

Humans can subjectively yet quantitatively assess choice confidence based on perceptual precision even when a perceptual decision is made without an immediate reward or feedback. However, surprisingly little is known about choice confidence. Here we investigate the dynamics of choice confidence by merging two parallel conceptual frameworks of decision making, signal detection theory and sequential analyses (i.e., drift-diffusion modeling). Specifically, to capture end-point statistics of binary choice and confidence, we built on a previous study that defined choice confidence in terms of psychophysics derived from signal detection theory. At the same time, we augmented this mathematical model to include accumulator dynamics of a drift-diffusion model to characterize the time dependence of the choice behaviors in a standard forced-choice paradigm in which stimulus duration is controlled by the operator. Human subjects performed a subjective visual vertical task, simultaneously reporting binary orientation choice and probabilistic confidence. Both binary choice and confidence experimental data displayed statistics and dynamics consistent with both signal detection theory and evidence accumulation, respectively. Specifically, the computational simulations showed that the unbounded evidence accumulator model fits the confidence data better than the classical bounded model, while bounded and unbounded models were indistinguishable for binary choice data. These results suggest that the brain can utilize mechanisms consistent with signal detection theory-especially when judging confidence without time pressure.NEW & NOTEWORTHY We found that choice confidence data show dynamics consistent with evidence accumulation for a forced-choice subjective visual vertical task. We also found that the evidence accumulation appeared unbounded when judging confidence, which suggests that the brain utilizes mechanisms consistent with signal detection theory to determine choice confidence.

Individual Confidence-Weighting and Group Decision-Making

Group-living species frequently pool individual information so as to reach consensus decisions such as when and where to move, or whether a predator is present. Such opinion-pooling has been demonstrated empirically, and theoretical models have been proposed to explain why group decisions are more reliable than individual decisions. Behavioural ecology theory frequently assumes that all individuals have equal decision-making abilities, but decision theory relaxes this assumption and has been tested in human groups. We summarise relevant theory and argue for its applicability to collective animal decisions. We consider selective pressure on confidence-weighting in groups of related and unrelated individuals. We also consider which species and behaviours may provide evidence of confidence-weighting, paying particular attention to the sophisticated vocal communication of cooperative breeders.

From faces to prosocial behavior: cues, tools, and mechanisms

In this review we ask how looking at people's faces can influence prosocial behaviors towards them. Components of this process have often been studied by disparate literatures: one focused on perception and judgment of faces, using both psychological and neuroscience approaches; and a second focused on actual social behaviors, as studied in behavioral economics and decision science. Bridging these disciplines requires a more mechanistic account of how processing of particular face attributes or features influences social judgments and behaviors. Here we review these two lines of research, and suggest that combining some of their methodological tools can provide the bridging mechanistic explanations.

A Bayesian Reformulation of the Extended Drift-Diffusion Model in Perceptual Decision Making

Perceptual decision making can be described as a process of accumulating evidence to a bound which has been formalized within drift-diffusion models (DDMs). Recently, an equivalent Bayesian model has been proposed. In contrast to standard DDMs, this Bayesian model directly links information in the stimulus to the decision process. Here, we extend this Bayesian model further and allow inter-trial variability of two parameters following the extended version of the DDM. We derive parameter distributions for the Bayesian model and show that they lead to predictions that are qualitatively equivalent to those made by the extended drift-diffusion model (eDDM). Further, we demonstrate the usefulness of the extended Bayesian model (eBM) for the analysis of concrete behavioral data. Specifically, using Bayesian model selection, we find evidence that including additional inter-trial parameter variability provides for a better model, when the model is constrained by trial-wise stimulus features. This result is remarkable because it was derived using just 200 trials per condition, which is typically thought to be insufficient for identifying variability parameters in DDMs. In sum, we present a Bayesian analysis, which provides for a novel and promising analysis of perceptual decision making experiments.

Perceptual Decision-Making: Picking the Low-Hanging Fruit?

How do we decide what we perceive? Obviously, we base our decisions on sensory evidence. However, a new and surprising study by Hagura et al. shows that our perceptual decisions are also biased by the action costs that are associated with our decisions.

The Computational and Neural Basis of Rhythmic Timing in Medial Premotor Cortex

The neural underpinnings of rhythmic behavior, including music and dance, have been studied using the synchronization-continuation task (SCT), where subjects initially tap in synchrony with an isochronous metronome and then keep tapping at a similar rate via an internal beat mechanism. Here, we provide behavioral and neural evidence that supports a resetting drift-diffusion model (DDM) during SCT. Behaviorally, we show the model replicates the linear relation between the mean and standard-deviation of the intervals produced by monkeys in SCT. We then show that neural populations in the medial premotor cortex (MPC) contain an accurate trial-by-trial representation of elapsed-time between taps. Interestingly, the autocorrelation structure of the elapsed-time representation is consistent with a DDM. These results indicate that MPC has an orderly representation of time with features characteristic of concatenated DDMs and that this population signal can be used to orchestrate the rhythmic structure of the internally timed elements of SCT.SIGNIFICANCE STATEMENT The present study used behavioral data, ensemble recordings from medial premotor cortex (MPC) in macaque monkeys, and computational modeling, to establish evidence in favor of a class of drift-diffusion models of rhythmic timing during a synchronization-continuation tapping task (SCT). The linear relation between the mean and standard-deviation of the intervals produced by monkeys in SCT is replicated by the model. Populations of MPC cells faithfully represent the elapsed time between taps, and there is significant trial-by-trial relation between decoded times and the timing behavior of the monkeys. Notably, the neural decoding properties, including its autocorrelation structure are consistent with a set of drift-diffusion models that are arranged sequentially and that are resetting in each SCT tap.

Patients with Parkinson's Disease Show Impaired Use of Priors in Conditions of Sensory Uncertainty

Perceptual decisions arise after considering the available sensory evidence [1]. When sensory information is unreliable, a good strategy is to rely on previous experience in similar situations to guide decisions [2-6]. It is well known that patients with Parkinson's disease (PD) are impaired at value-based decision-making [7-11]. How patients combine past experience and sensory information to make perceptual decisions is unknown. We developed a novel, perceptual decision-making task and manipulated the statistics of the sensory stimuli presented to patients with PD and healthy participants to determine the influence of past experience on decision-making. We show that patients with PD are impaired at combining previously learned information with current sensory information to guide decisions. We modeled the results using the drift-diffusion model (DDM) and found that the impairment corresponds to a failure in adjusting the amount of sensory evidence needed to make a decision. Our modeling results also show that two complementary mechanisms operate to implement a bias when two sets of priors are learned concurrently. Asymmetric decision threshold adjustments, as reflected by changes in the starting point of evidence accumulation, are responsible for a general choice bias, whereas the adjustment of a dynamic bias that develops over the course of a trial, as reflected by a drift-rate offset, provides the stimulus-specific component of the prior. A proper interplay between these two processes is required to implement a bias based on concurrent, stimulus-specific priors in decision-making. We show here that patients with PD are impaired in these across-trial decision threshold adjustments.

Magnitude Estimation with Noisy Integrators Linked by an Adaptive Reference

Judgments of physical stimuli show characteristic biases; relatively small stimuli are overestimated whereas relatively large stimuli are underestimated (regression effect). Such biases likely result from a strategy that seeks to minimize errors given noisy estimates about stimuli that itself are drawn from a distribution, i.e., the statistics of the environment. While being conceptually well described, it is unclear how such a strategy could be implemented neurally. The present paper aims toward answering this question. A theoretical approach is introduced that describes magnitude estimation as two successive stages of noisy (neural) integration. Both stages are linked by a reference memory that is updated with every new stimulus. The model reproduces the behavioral characteristics of magnitude estimation and makes several experimentally testable predictions. Moreover, the model identifies the regression effect as a means of minimizing estimation errors and explains how this optimality strategy depends on the subject's discrimination abilities and on the stimulus statistics. The latter influence predicts another property of magnitude estimation, the so-called range effect. Beyond being successful in describing decision-making, the present work suggests that noisy integration may also be important in processing magnitudes.

Different decision deficits impair response inhibition in progressive supranuclear palsy and Parkinson's disease

Progressive supranuclear palsy and Parkinson's disease have distinct underlying neuropathology, but both diseases affect cognitive function in addition to causing a movement disorder. They impair response inhibition and may lead to impulsivity, which can occur even in the presence of profound akinesia and rigidity. The current study examined the mechanisms of cognitive impairments underlying disinhibition, using horizontal saccadic latencies that obviate the impact of limb slowness on executing response decisions. Nineteen patients with clinically diagnosed progressive supranuclear palsy (Richardson's syndrome), 24 patients with clinically diagnosed Parkinson's disease and 26 healthy control subjects completed a saccadic Go/No-Go task with a head-mounted infrared saccadometer. Participants were cued on each trial to make a pro-saccade to a horizontal target or withhold their responses. Both patient groups had impaired behavioural performance, with more commission errors than controls. Mean saccadic latencies were similar between all three groups. We analysed behavioural responses as a binary decision between Go and No-Go choices. By using Bayesian parameter estimation, we fitted a hierarchical drift-diffusion model to individual participants' single trial data. The model decomposes saccadic latencies into parameters for the decision process: decision boundary, drift rate of accumulation, decision bias, and non-decision time. In a leave-one-out three-way classification analysis, the model parameters provided better discrimination between patients and controls than raw behavioural measures. Furthermore, the model revealed disease-specific deficits in the Go/No-Go decision process. Both patient groups had slower drift rate of accumulation, and shorter non-decision time than controls. But patients with progressive supranuclear palsy were strongly biased towards a pro-saccade decision boundary compared to Parkinson's patients and controls. This indicates a prepotency of responding in combination with a reduction in further accumulation of evidence, which provides a parsimonious explanation for the apparently paradoxical combination of disinhibition and severe akinesia. The combination of the well-tolerated oculomotor paradigm and the sensitivity of the model-based analysis provides a valuable approach for interrogating decision-making processes in neurodegenerative disorders. The mechanistic differences underlying participants' poor performance were not observable from classical analysis of behavioural data, but were clearly revealed by modelling. These differences provide a rational basis on which to develop and assess new therapeutic strategies for cognition and behaviour in these disorders.

Evidence integration in model-based tree search

Research on the dynamics of reward-based, goal-directed decision making has largely focused on simple choice, where participants decide among a set of unitary, mutually exclusive options. Recent work suggests that the deliberation process underlying simple choice can be understood in terms of evidence integration: Noisy evidence in favor of each option accrues over time, until the evidence in favor of one option is significantly greater than the rest. However, real-life decisions often involve not one, but several steps of action, requiring a consideration of cumulative rewards and a sensitivity to recursive decision structure. We present results from two experiments that leveraged techniques previously applied to simple choice to shed light on the deliberation process underlying multistep choice. We interpret the results from these experiments in terms of a new computational model, which extends the evidence accumulation perspective to multiple steps of action.

Reward Pays the Cost of Noise Reduction in Motor and Cognitive Control

Speed-accuracy trade-off is an intensively studied law governing almost all behavioral tasks across species. Here we show that motivation by reward breaks this law, by simultaneously invigorating movement and improving response precision. We devised a model to explain this paradoxical effect of reward by considering a new factor: the cost of control. Exerting control to improve response precision might itself come at a cost--a cost to attenuate a proportion of intrinsic neural noise. Applying a noise-reduction cost to optimal motor control predicted that reward can increase both velocity and accuracy. Similarly, application to decision-making predicted that reward reduces reaction times and errors in cognitive control. We used a novel saccadic distraction task to quantify the speed and accuracy of both movements and decisions under varying reward. Both faster speeds and smaller errors were observed with higher incentives, with the results best fitted by a model including a precision cost. Recent theories consider dopamine to be a key neuromodulator in mediating motivational effects of reward. We therefore examined how Parkinson's disease (PD), a condition associated with dopamine depletion, alters the effects of reward. Individuals with PD showed reduced reward sensitivity in their speed and accuracy, consistent in our model with higher noise-control costs. Including a cost of control over noise explains how reward may allow apparent performance limits to be surpassed. On this view, the pattern of reduced reward sensitivity in PD patients can specifically be accounted for by a higher cost for controlling noise.

Maximally informative foraging by Caenorhabditis elegans

Animals have evolved intricate search strategies to find new sources of food. Here, we analyze a complex food seeking behavior in the nematode Caenorhabditis elegans (C. elegans) to derive a general theory describing different searches. We show that C. elegans, like many other animals, uses a multi-stage search for food, where they initially explore a small area intensively ('local search') before switching to explore a much larger area ('global search'). We demonstrate that these search strategies as well as the transition between them can be quantitatively explained by a maximally informative search strategy, where the searcher seeks to continuously maximize information about the target. Although performing maximally informative search is computationally demanding, we show that a drift-diffusion model can approximate it successfully with just three neurons. Our study reveals how the maximally informative search strategy can be implemented and adopted to different search conditions.

Asymmetrical integration of sensory information during mating decisions in grasshoppers

Decision-making processes, like all traits of an organism, are shaped by evolution; they thus carry a signature of the selection pressures associated with choice behaviors. The way sexual communication signals are integrated during courtship likely reflects the costs and benefits associated with mate choice. Here, we study the evaluation of male song by females during acoustic courtship in grasshoppers. Using playback experiments and computational modeling we find that information of different valence (attractive vs. nonattractive) is weighted asymmetrically: while information associated with nonattractive features has large weight, attractive features add little to the decision to mate. Accordingly, nonattractive features effectively veto female responses. Because attractive features have so little weight, the model suggests that female responses are frequently driven by integration noise. Asymmetrical weighting of negative and positive information may reflect the fitness costs associated with mating with a nonattractive over an attractive singer, which are also highly asymmetrical. In addition, nonattractive cues tend to be more salient and therefore more reliable. Hence, information provided by them should be weighted more heavily. Our findings suggest that characterizing the integration of sensory information during a natural behavior has the potential to provide valuable insights into the selective pressures shaping decision-making during evolution.

Dissociable mechanisms of speed-accuracy tradeoff during visual perceptual learning are revealed by a hierarchical drift-diffusion model

Two phenomena are commonly observed in decision-making. First, there is a speed-accuracy tradeoff (SAT) such that decisions are slower and more accurate when instructions emphasize accuracy over speed, and vice versa. Second, decision performance improves with practice, as a task is learnt. The SAT and learning effects have been explained under a well-established evidence-accumulation framework for decision-making, which suggests that evidence supporting each choice is accumulated over time, and a decision is committed to when the accumulated evidence reaches a decision boundary. This framework suggests that changing the decision boundary creates the tradeoff between decision speed and accuracy, while increasing the rate of accumulation leads to more accurate and faster decisions after learning. However, recent studies challenged the view that SAT and learning are associated with changes in distinct, single decision parameters. Further, the influence of speed-accuracy instructions over the course of learning remains largely unknown. Here, we used a hierarchical drift-diffusion model to examine the SAT during learning of a coherent motion discrimination task across multiple training sessions, and a transfer test session. The influence of speed-accuracy instructions was robust over training and generalized across untrained stimulus features. Emphasizing decision accuracy rather than speed was associated with increased boundary separation, drift rate and non-decision time at the beginning of training. However, after training, an emphasis on decision accuracy was only associated with increased boundary separation. In addition, faster and more accurate decisions after learning were due to a gradual decrease in boundary separation and an increase in drift rate. The results suggest that speed-accuracy instructions and learning differentially shape decision-making processes at different time scales.