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As the field of computational cognitive neuroscience continues to expand and generate new theories, there is a growing need for more advanced methods to test the hypothesis of brain-behavior relationships. Recent progress in Bayesian cognitive modeling has enabled the combination of neural and behavioral models into a single unifying framework. However, these approaches require manual feature extraction, and lack the capability to discover previously unknown neural features in more complex data. Consequently, this would hinder the expressiveness of the models. To address these challenges, we propose a Neurocognitive Variational Autoencoder (NCVA) to conjoin high-dimensional EEG with a cognitive model in both generative and predictive modeling analyses. Importantly, our NCVA enables both the prediction of EEG signals given behavioral data and the estimation of cognitive model parameters from EEG signals. This novel approach can allow for a more comprehensive understanding of the triplet relationship between behavior, brain activity, and cognitive processes.

When people make choices, the items they consider are often embedded in a context (of other items). How this context affects the valuation of the specific item is an important question. High-value context might make items appear less attractive because of contrast-the tendency to normalize perception of an object relative to its background-or more attractive because of assimilation-the tendency to group objects together. Alternatively, a high-value context might increase prior expectations about the item's value. Here, we investigated these possibilities. We examined how unavailable context items affect choices between two target items, as well as the willingness-to-pay for single targets. Participants viewed sets of three items for several seconds before the target(s) were highlighted. In both tasks, we found a significant assimilation-like effect where participants were more likely to choose or place a higher value on a target when it was surrounded by higher-value context. However, these context effects were only significant for participants' fastest choices. Using variants of a drift-diffusion model, we established that the unavailable context shifted participants' prior expectations towards the average values of the sets but had an inconclusive effect on their evaluations of the targets during the decision (i.e. drift rates). In summary, we find that people use context to inform their initial valuations. This can improve efficiency by allowing people to get a head start on their decision. However, it also means that the valuation of an item can change depending on the context.

As evidence mounts that the cardiac-sympathetic nervous system reacts to challenging cognitive settings, we ask if these responses are epiphenomenal companions or if there is evidence suggesting a more intertwined role of this system with cognitive function. Healthy male and female human participants performed an approach-avoidance paradigm, trading off monetary reward for painful electric shock, while we recorded simultaneous electroencephalographic and cardiac-sympathetic signals. Participants were reward sensitive but also experienced approach-avoidance "conflict" when the subjective appeal of the reward was near equivalent to the revulsion of the cost. Drift-diffusion model parameters suggested that participants managed conflict in part by integrating larger volumes of evidence into choices (wider decision boundaries). Late alpha-band (neural) dynamics were consistent with widening decision boundaries serving to combat reward sensitivity and spread attention more fairly to all dimensions of available information. Independently, wider boundaries were also associated with cardiac "contractility" (an index of sympathetically mediated positive inotropy). We also saw evidence of conflict-specific "collaboration" between the neural and cardiac-sympathetic signals. In states of high conflict, the alignment (i.e., product) of alpha dynamics and contractility were associated with a further widening of the boundary, independent of either signal's singular association. Cross-trial coherence analyses provided additional evidence that the autonomic systems controlling cardiac-sympathetics might influence the assessment of information streams during conflict by disrupting or overriding reward processing. We conclude that cardiac-sympathetic control might play a critical role, in collaboration with cognitive processes, during the approach-avoidance conflict in humans. [ABSTRACT FROM AUTHOR]

Background: 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. Methods: 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. Results: 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. Conclusions: 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

To examine the content specificity of threat-related attentional bias in health anxiety, we applied the drift-diffusion model (DDM) to investigate attentional bias toward three types of threat stimuli (general threat, illness, and symptom words). We recruited 53 undergraduates with severe health anxiety (the health anxiety group, HAG) and 53 controls (the control group, CG) to complete the dot-probe task. We calculated traditional bias scores and non-decision time (a DDM parameter) bias scores, and they were subjected to a 2 (Group: HAG, CG) × 3 (Threat: general threat, illness, and symptom words) mixed-design ANOVA. The Group × Threat interaction was non-significant on the traditional bias score. However, the results of DDM analyses showed that the Group × Threat interaction was significant; HAG had a significantly lower score for illness stimuli (a negative value) than CG, but symptom stimuli and general threat stimuli did not produce significant group differences. In conclusions, health-anxious individuals displayed attentional avoidance of illness stimuli but no attentional bias toward symptom stimuli and general threat stimuli. [ABSTRACT FROM AUTHOR]

• Drivers' decision-making time and outcomes during the yellow light are predicted. • A drift–diffusion model was adopted to characterize the decision-making process. • We examine drivers' decision-making behavior by driving simulator. • The model is validated for individual and representative drivers. • The arrival time has a significant impact on driver decision response time. The decision-making behavior of drivers during the yellow signal phase has a significant impact on intersection safety. To analyze the decision-making process, we conducted surveys on driver behavior during yellow signal phase. A drift–diffusion model was established to analyze factors associated with driver decisions. The model can accurately predict driving decision outcomes (whether to proceed through the intersection during the yellow signal phase) and the decision-making times of different drivers. Driving data were collected using a driving simulator, including 15 participants in 210 tests in seven scenarios (3150 experimental samples). Drivers with similar driving behaviors were grouped. The model was validated using both in-sample and out-of-sample data for both individual and representative drivers. It was found that the error rate of the predicted data was approximately 7 %. Different arrival times had a significant impact on decision response time. Drivers tended to make faster decisions when the arrival time was less than 2 s due to the urgency of the decision. The findings can help understand the underlying cognitive mechanisms of driver behavior during the yellow signal phase. [ABSTRACT FROM AUTHOR]

Familiar information is more likely to be accepted as true. This illusory truth effect has a tremendous negative impact on misinformation intervention. Previous studies focused on the familiarity from repeated exposure in the laboratory, ignoring preexisting familiarity with real-world misinformation. Over three studies (total N = 337), we investigated the cognitive mechanisms behind the truth biases from these two familiarity sources, and whether fact-checking can curb such biased truth perceptions. Studies 1 and 2 found robust truth effects induced by two sources of familiarity but with different cognitive processes. According to the cognitive process model, repetition-induced familiarity reduced decision prudence. Preexisting familiarity instead enhanced truth-congruent evidence accumulation. Study 3 showed that pre-exposing statements with warning flags eliminated the bias to truth induced by repetition but not that from preexisting familiarity. These repeated statements with warning labels also reduced decision caution. These findings furthered the understanding of how different sources of familiarity affect truth perceptions and undermine the intervention through different cognitive processes. [ABSTRACT FROM AUTHOR]

Teleological reasoning is the tendency for humans to see purpose and intentionality in natural phenomena when there is none. In this study, we assess three competing theories on how bias in reasoning arises by examining performance on a teleological reasoning task while measuring pupil size and response times. We replicate that humans (N = 45) are prone to accept false teleological explanations. Further, we show that errors on the teleological reasoning task are associated with slower response times, smaller baseline pupil size, and larger pupil dilations. The results are in line with the single‐process extensive integration account and directly oppose predictions from dual‐processing accounts. Lastly, by modeling responses with a drift‐diffusion model, we find that larger baseline pupil size is associated with lower decision threshold and higher drift rate, whereas larger pupil dilations are associated with higher decision threshold and lower drift rate. The results highlight the role of neural gain and the Locus Coeruleus–Norepinephrine system in modulating evidence integration and bias in reasoning. Thus, teleological reasoning and susceptibility to bias likely arise due to extensive processing rather than through fast and effortless processing. We provide evidence that teleological reasoning bias occurs due to extensive evidence integration rather than through fast, effortless processing, as predicted by dual‐process theory. Importantly, by analyzing pupil dynamics with a drift‐diffusion model, we find that baseline pupil size and pupil dilation have opposite effects on decision threshold and evidence integration, thus highlighting the role of neural gain and the Locus Coeruleus–Norepinephrine system in modulating evidence integration and decision bias. [ABSTRACT FROM AUTHOR]

Objective: Anorexia nervosa (AN) is a serious psychiatric illness associated with significant medical and psychiatric comorbidity and impairment. Theoretical models of AN and self‐report studies suggest that negative self‐evaluation (i.e., low self‐esteem) is related to the development and maintenance of AN. The goal of this study was to extend findings from self‐report methodology using a neurocognitive task that probes self‐evaluation implicitly and explicitly. Method: We compared female adolescent and adult patients with AN (n = 35) and healthy controls (HC, n = 38) on explicit (i.e., endorsement of words as self‐relevant), implicit (recall, recognition, reaction time), and composite (i.e., valence index, bias score, drift rates) indices of self‐evaluation. We applied a drift‐diffusion model to compute the drift rates, reflecting participants' decision‐making process as to whether words were self‐relevant. The association between self‐evaluation indices and eating disorder severity was examined. Results: There were significant Group × Condition interaction effects for all explicit and implicit measures (all p's ≤.01), where the AN group endorsed, recalled, and recognized more negative relative to positive words than HC. The AN group had more negative valence index and bias scores, and slower drift rate away from negative words, reflecting more negative self‐evaluation. The finding for recall was attenuated when individuals with depression were excluded. Measures of self‐evaluation bias were not related to eating disorder severity. Discussion: Using a neurocognitive approach that includes explicit and implicit indices of bias, results suggest that patients with AN have more negative self‐evaluation. Due to the cross‐sectional design, additional studies are needed to further evaluate directionality. Public Significance: Negative self‐evaluation/low self‐esteem is thought to contribute to eating disorder symptoms. Findings of this study using a neurocognitive task to probe self‐evaluation suggested that individuals with anorexia nervosa have more negative self‐evaluation, reflected by endorsing and remembering more negative (than positive) words compared to healthy controls, and doing so faster. Targeting the construct of negative self‐evaluation in treatment of AN may be warranted. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Experimental data are presented on the minimum values of energies and flux densities of electrons whose impact on the cover glasses of solar panels and reflecting elements of the thermoradiators of artificial Earth satellites produces electrostatic discharges. It is established that adding protons to the particle flux acting on the studied samples can suppress the development of discharges. A mathematical model is proposed for a qualitative interpretation of the obtained results. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

It is increasingly clear that unconscious information impairs the performance of the corresponding action when the instruction to act is delayed. However, whether this impairment occurs at the response level or at the perceptual level remains controversial. This study used fMRI and a computational model with a pre-post design to address this elusive issue. The fMRI results showed that when the unconscious information containing strong stimulus-response associations was irrelevant to subsequent stimuli, the precuneus in the parietal lobe, which is thought to be involved in sensorimotor processing, was activated. In contrast, when the unconscious information was relevant to subsequent stimuli, regardless of the strength of the stimulus-response associations, some regions in the occipital and temporal cortices, which are thought to be involved in visual perceptual processing, were activated. In addition, the percent signal change in the regions of interest associated with motor inhibition was modulated by compatibility in the irrelevant but not in the relevant stimuli conditions. Modeling of behavioral data further supported that the irrelevant and relevant stimuli conditions involved fundamentally different mechanisms. Our finding reconciles the debate about the mechanism by which unconscious information impairs action performance and has important implications for understanding of unconscious cognition.

Background: 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. Methods: 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. Results: 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. Conclusions: 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.

Endogenous response time data are increasingly becoming available to applied researchers of economic choices. However, the usefulness of such data for preference estimation is unclear. Here, we adapt a sequential sampling model—previously validated to jointly explain subjects' choices and response times in laboratory experiments—to model users' responses to video advertisements on mobile devices in a field setting. Our estimates of utility correlate positively with out-of-sample measures of ad engagement, thus providing external validation of the value of incorporating endogenous response time information into a choice model. We then use the model estimates to assess the effectiveness of manipulating attention toward an advertisement at the beginning of a decision. Counterfactual simulations predict that making an ad "nonskippable" (requiring users to watch some portion of the ad)—as is the practice of some online platforms (e.g., YouTube)—generates only modest increases in click-through rates and revenue. This paper was accepted by David Simchi-Levi, behavioral economics and decision analysis. Funding: R. Webb work was supported by Social Sciences and Humanities Research Council [Grant 430-2019-00246]. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2023.4738. [ABSTRACT FROM AUTHOR]

Background: Emotional clarity has often been assessed with self-report measures, but efforts have also been made to measure it passively, which has advantages such as avoiding potential inaccuracy in responses stemming from social desirability bias or poor insight into emotional clarity. Response times (RTs) to emotion items administered in ecological momentary assessments (EMAs) may be an indirect indicator of emotional clarity. Another proposed indicator is the drift rate parameter, which assumes that, aside from how fast a person responds to emotion items, the measurement of emotional clarity also requires the consideration of how careful participants were in providing responses. Objective: This paper aims to examine the reliability and validity of RTs and drift rate parameters from EMA emotion items as indicators of individual differences in emotional clarity. Methods: Secondary data analysis was conducted on data from 196 adults with type 1 diabetes who completed a 2-week EMA study involving the completion of 5 to 6 surveys daily. If lower RTs and higher drift rates (from EMA emotion items) were indicators of emotional clarity, we hypothesized that greater levels (ie, higher clarity) should be associated with greater life satisfaction; lower levels of neuroticism, depression, anxiety, and diabetes distress; and fewer difficulties with emotion regulation. Because prior literature suggested emotional clarity could be valence specific, EMA items for negative affect (NA) and positive affect were examined separately. Results: Reliability of the proposed indicators of emotional clarity was acceptable with a small number of EMA prompts (ie, 4 to 7 prompts in total or 1 to 2 days of EMA surveys). Consistent with expectations, the average drift rate of NA items across multiple EMAs had expected associations with other measures, such as correlations of r=--0.27 (P<.001) with depression symptoms, r=--0.27 (P=.001) with anxiety symptoms, r=--0.15 (P=.03) with emotion regulation difficulties, and r=0.63 (P<.001) with RTs to NA items. People with a higher NA drift rate responded faster to NA emotion items, had greater subjective well-being (eg, fewer depression symptoms), and had fewer difficulties with overall emotion regulation, which are all aligned with the expectation for an emotional clarity measure. Contrary to expectations, the validities of average RTs to NA items, the drift rate of positive affect items, and RTs to positive affect items were not strongly supported by our results. Conclusions: Study findings provided initial support for the validity of NA drift rate as an indicator of emotional clarity but not for that of other RT-based clarity measures. Evidence was preliminary because the sample size was not sufficient to detect small but potentially meaningful correlations, as the sample size of the diabetes EMA study was chosen for other more primary research questions. Further research on passive emotional clarity measures is needed. [ABSTRACT FROM AUTHOR]

An experimental and theoretical study is carried out to control the distribution of electron beam current density in a SOLO source with a plasma cathode based on a low-pressure arc discharge with grid stabilization of the emission plasma boundary. It has been shown experimentally and numerically that using a control electrode in the form of a disk (disks) in the plasma cathode and a leading magnetic field, it is possible to change the distribution of the emission current density and, accordingly, the energy density of the electron beam on the collector. Theoretical study using the envelope method, "particle-in-cells" method and drift-diffusion models shows good agreement with experiment. [ABSTRACT FROM AUTHOR]

Self‐prioritization is a very influential modulator of human information processing. Still, little is known about the time‐frequency dynamics of the self‐prioritization network. In this EEG study, we used the familiarity‐confound free matching task to investigate the spectral dynamics of self‐prioritization and their underlying cognitive functions in a drift‐diffusion model. Participants (N = 40) repeatedly associated arbitrary geometric shapes with either "the self" or "a stranger." Behavioral results demonstrated prominent self‐prioritization effects (SPEs) in reaction time and accuracy. Remarkably, EEG cluster analysis also revealed two significant SPEs, one in delta/theta power (2–7 Hz) and one in beta power (19–29 Hz). Drift‐diffusion modeling indicated that beta activity was associated with evidence accumulation, whereas delta/theta activity was associated with response selection. The decreased beta suppression of the SPE might indicate more efficient sensorimotor processing of self‐associated stimulus–response features, whereas the increased delta/theta SPE might refer to the facilitated retrieval of self‐relevant features across a widely distributed associative self‐network. These novel oscillatory biomarkers of self‐prioritization indicate their function as an associative glue for the self‐concept. This EEG study reports time‐frequency markers of self‐prioritization in a familiarity‐confound reduced matching task, in which arbitrary geometric shapes were associated with the self or a stranger. The self‐prioritization effect was evident in behavioral indices and in two EEG clusters in delta/theta power and beta power. Going beyond previous work, these EEG clusters were linked to evidence accumulation and response caution in the drift‐diffusion model. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

As the field of computational cognitive neuroscience continues to expand and generate new theories, there is a growing need for more advanced methods to test the hypothesis of brain-behavior relationships. Recent progress in Bayesian cognitive modeling has enabled the combination of neural and behavioral models into a single unifying framework. However, these approaches require manual feature extraction, and lack the capability to discover previously unknown neural features in more complex data. Consequently, this would hinder the expressiveness of the models. To address these challenges, we propose a Neurocognitive Variational Autoencoder (NCVA) to conjoin high-dimensional EEG with a cognitive model in both generative and predictive modeling analyses. Importantly, our NCVA enables both the prediction of EEG signals given behavioral data and the estimation of cognitive model parameters from EEG signals. This novel approach can allow for a more comprehensive understanding of the triplet relationship between behavior, brain activity, and cognitive processes.

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.

Abstract 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.

How can choice, confidence, and response times be modeled simultaneously? Here, we propose the new dynamical weighted evidence and visibility (dynWEV) model, an extension of the drift-diffusion model of decision-making, to account for choices, reaction times, and confidence simultaneously. The decision process in a binary perceptual task is described as a Wiener process accumulating sensory evidence about the choice options bounded by two constant thresholds. To account for confidence judgments, we assume a period of postdecisional accumulation of sensory evidence and parallel accumulation of information about the reliability of the present stimulus. We examined model fits in two experiments, a motion discrimination task with random dot kinematograms and a postmasked orientation discrimination task. A comparison between the dynWEV model, two-stage dynamical signal detection theory, and several versions of race models of decision-making showed that only dynWEV produced acceptable fits of choices, confidence, and reaction time. This finding suggests that confidence judgments depend not only on choice evidence but also on a parallel estimate of stimulus discriminability and postdecisional accumulation of evidence. [ABSTRACT FROM AUTHOR]

Mood management theory (MMT) hypothesizes that people select entertainment content to maintain affective homeostasis. However, this hypothesis lacks a formal quantification of each affective attributes' separate impact on an individual's media content selection, as well as an integrated cognitive mechanism explaining media selection. Here we present a computational decision-making model that mathematically formalizes this affective media decision-making process. We empirically tested this formalization with the drift-diffusion model using three decision-making experiments. Contrary to MMT, all three studies showed that people prefer negatively valenced and high-arousal media content and that prevailing mood does not shape media selection as predicted by MMT. We also discovered that people are less cautious when choices have larger valence differences. Our results support the proposed mathematical formalization of affective attributes' influence on media selection, challenge core predictions drawn from MMT, and introduce a new mechanism (response caution) for media selection. [ABSTRACT FROM AUTHOR]

Inequity aversion is an important factor in fairness behavior. Previous work suggests that children show more cross-cultural variation in their willingness to reject allocations that would give them more rewards than their partner--advantageous inequity--as opposed to allocations that would give them less than their partner--disadvantageous inequity. However, as past work has relied solely on children's decisions to accept or reject these offers, the algorithms underlying this pattern of variation remain unclear. Here, we explore the computational signatures of inequity aversion by applying a computational model of decision-making to data from children (N = 807) who played the Inequity Game across seven societies. Specifically, we used drift-diffusion models to formally distinguish evaluative processing (i.e., the computation of the subjective value of accepting or rejecting inequity) from alternative factors such as decision speed and response strategies. Our results suggest that variation in the development of inequity aversion across societies is best accounted for by variation in the drift rate--the direction and strength of the evaluative preference. Our findings underscore the utility of looking beyond decision data to better understand behavioral diversity. [ABSTRACT FROM AUTHOR]

Knowledge without awareness, or implicit knowledge, influences a variety of behaviors. It is unknown however, whether implicit knowledge of statistical structure informs visual perceptual decisions or whether explicit knowledge of statistical probabilities is required. Here, we measured visual decision-making performance using a novel task in which humans reported the orientation of two differently colored translational Glass patterns; each color associated with different orientation probabilities. The task design allowed us to assess participants’ ability to learn and use a general orientation prior as well as a color specific feature prior. Classifying decision-makers based on a questionnaire revealed that both implicit and explicit learners implemented a general orientation bias by adjusting the starting point of evidence accumulation in the drift diffusion model framework. Explicit learners additionally adjusted the drift rate offset. When subjects implemented a stimulus specific bias, they did so by adjusting primarily the drift rate offset. We conclude that humans can learn priors implicitly for perceptual decision-making and depending on awareness implement the priors using different mechanisms.

The processes accompanying the formation of a contracted (columnar) high-current nanosecond electric discharge in subcentimeter gaps filled with nitrogen are numerically investigated in this work. The space between two flat electrodes is considered in the case when a potential difference of 25 kV is instantly established between them. The voltage is applied for a time interval of 200 ns and then instantly removed. The characteristics of the nonthermal and thermal stages of electric-discharge development are studied, namely: the formation and growth of a streamer, the closure of a discharge gap by a streamer, the formation of a plasma channel, secondary ionization waves, and an increase in current density and temperature in the axial region of the channel. After switching off the electric field, the gas-dynamic processes associated with the discharge thermal effect on a neutral gas are investigated. Gas-dynamic processes are determined by the propagation of shock waves and rarefaction waves in the radial direction with respect to the axis of symmetry. The quantitative values of both the electric field (electron density and intensity) and gas-dynamic parameters (temperature, pressure, and gas velocity) are determined. [ABSTRACT FROM AUTHOR]

We theoretically analyzed the detailed carrier transport process based on the drift-diffusion model in the InGaAs/InP modified Uni-Traveling-Carrier Photodiode (MUTC-PD) under high optical input power conditions. A high-speed MUTC-PD design was simulated in depth using the commercial simulation software APSYS. The complex interplay between photo-electron and hole transport processes was quantitatively analyzed. The slowdown of hole transit time due to E field reduction in the undoped InGaAs absorber layer dominated the response speed of MUTC-PDs at a high optical power level. The optimized MUTC-PD design has a relatively strong dependence on optical power level. Based on an optimized design, an O–E conversion responsivity around 0.15 A/W and the intrinsic 3 dB bandwidth of 172 GHz were demonstrated when the input optical power density reached 20 mW/μm2. Our simulation analysis results presented here can be utilized for designing broadband MUTC-PDs in future sub-Terahertz free-space data link applications. [ABSTRACT FROM AUTHOR]

A series of heterostructure designs of low-voltage InP homothyristors have been investigated using numerical simulation methods. The design with a space charge layer formed in the p-base region of the n‒p‒n transistor part was considered as the base one. The dynamic characteristics and processes that determine the rate of transition to the on state are investigated. It is shown that as the p-base thickness increases from 1 to 2.6 μm, the maximum on-state currents increase from 70 to 90 A, while the minimum turn-on transition time is 11 ns at a maximum blocking voltage of 55 V. It is shown that the operation efficiency in the on state is determined by the residual voltage. Residual voltage decreases with a decrease in the thickness of the p-base. [ABSTRACT FROM AUTHOR]

The current–voltage characteristic curves of a perovskite solar cell (PSC) show a hysteretic effect. The effect is quantified as a hysteresis index (HI), which is a measure of the degree of hysteresis. The higher the value of the HI, the higher is the hysteretic effect. HI is determined by varying the thickness of the perovskite layer while keeping the interfacial layer constant and vice versa. It is observed that the value of the HI increases from 0.10 to 0.57 with increasing the thickness of the perovskite layer from 100 to 400 nm and saturates beyond 400 nm. In contrast, the value of HI is observed to remain constant at 0.57 over the entire variation of interfacial thickness. The observations are correlated with the number of trap‐states and ionic effects, inherently present in the perovskite and interface layers. The saturation of hysteretic effects beyond 400 nm is proposed to be due to the effective saturation of the screening effect produced by the mobile ions. The work highlights the combined role of trap‐states and ionic effects in controlling the hysteresis and provides an insight to optimize the PSCs to minimize the hysteretic effect, leading to a reliable power conversion efficiency. [ABSTRACT FROM AUTHOR]

Within this paper, a total optoelectronic simulation of a PIN photodiode structure was presented. The microlens structure has been introduced on the top of the PIN photodiode to compensate the low sensitivity level of the sensor. Finite-Difference Time-Domain (FDTD) method has been used to estimate the optical generation inside the active device. Optical simulation was combined to the electric device simulation stemmed from the drift-diffusion model (DDM) that describe the charge carrier transport in the PIN photodiode. The suggested algorithm provides the time and space distribution of the principals parameters as carriers' concentration, electrostatic potential and current density. Furthermore, external quantum efficiency and sensitivity of the PIN photodiode are estimated and compared with the solution obtained from SILVACO-TCAD simulator. [ABSTRACT FROM AUTHOR]

Processing speed is a crucial ability that changes over the course of the lifespan. Training interventions on processing speed have shown promising effects and have been associated with improved cognitive functioning. While training-related changes in processing speed are often studied using reaction times (RTs) and error rates, these measures provide limited insight into the mechanisms underlying changes during training. The drift-diffusion model provides estimates of the cognitive processes underlying speeded decision tasks, such as the rate of evidence accumulation (drift rate), response strategies (boundary separation), as well as time for other processes such as stimulus encoding and motor response (non-decision time). In the current study, we analyzed existing data of an extensive multi-session training intervention (von Bastian & Oberauer, 2013) to disentangle changes in drift rate, boundary separation, and non-decision time during training of different speeded choice-RT tasks. During this training intervention, 30 participants performed 20 training sessions over the course of four weeks, completing three tasks each session: a face-matching, a pattern-matching, and a digit-matching task. Our results show that processing speed training increased drift rates throughout training. Boundary separation and non-decision time decreased mostly during the initial parts of training. This pattern of prolonged training-related changes in rate of evidence accumulation as well as early changes in response strategy and non-decision processes was observed across all three tasks. Future research should investigate how these training-related changes relate to improvements in cognitive functioning more broadly. [ABSTRACT FROM AUTHOR]

We continue our effort in Li et al. (2024) to develop a stabilizer-free weak Galerkin finite element method (SFWG-FEM) for the two-dimensional unsteady-state drift-diffusion (DD) model. The transient state equation includes both the first derivative convection and the second derivative diffusion terms, and it couples with a Poisson potential equation. The piecewise P k (k ≥ 1) polynomials elements in the interior and on the boundaries are utilized for both electron concentration and electric potential functions. The vector function of [ P j (K) ] 2 (j ≥ j 0) for the discrete weak gradient space is used on each element K. The main difficulty is the treatment of the non-linearity and coupling of the system. Based on an introduced Ritz projection and two L 2 projection operators, optimal error estimates of O (Δ t + h k + 1) in the L 2 norm and O (Δ t + h k) in the energy-like norm are derived. Numerical experiments are presented to illustrate the theoretical analysis. • Optimal estimate of stabilizer-free WG method for drift-diffusion model is derived. • The method is flexible while reducing complexity in analyzing and programming. • A Ritz projection that can be applied to estimate other problems is introduced. [ABSTRACT FROM AUTHOR]

Abstract A field effect transistor having the active area made of monolayer graphene is simulated by a drift-diffusion model coupled with the Poisson equation. The adopted geometry, already proposed in (Nastasi and Romano in IEEE Trans. Electron. Devices 68:4729–4734, 2021, https://doi.org/10.1109/TED.2021.3096492 ), gives a good current-ON/current-OFF ratio as it is evident in the simulations. In this paper, we compare the numerical simulations of the standard (non-degenerate) drift-diffusion model, that includes the Einstein diffusion coefficient, with the degenerate case.

Perovskite solar cell (PSC) consisting textile-based electrodes has been created novel features for future energy supply. Recently, Cs0.05(FA0.85MA0.15)0.95Pb(I0.85Br0.15)3 perovskite has appeared as a favourable candidate for flexible perovskite solar cells. However, a confined performance was acquired when the conventional Spiro-OMeTAD were used as hole transport layer (HTL) material because of suboptimal valance band offset (ΔEV) between Perovskite/HTL layers. In this study, a theoretical drift–diffusion​ modelling is used by finite volume method (FVM) to investigate performance of textile perovskite solar cell with double hole transport layers (HTLs) with graded band gap energies. Copper thiocyanate (CuSCN) and Nickel Oxide (NiO) are replaced as double HTLs, which dramatically enhanced the performance due to optimal upward step by step valance band offsets (ΔEV) between Perovskite/Textile Composite Electrode layers. It is shown that the decreasing of the metal–semiconductor schottky barrier height of electrodes leads to significant increase in fill factor (FF). Also, it is revealed that the enriching of perovskite carrier diffusion length by equipoising of carrier capture cross section, carrier total defect density and carrier thermal velocity substantially promote the overall device efficiency. The short-circuit current (Jsc) of 20.65 mA/cm 2, open-circuit voltage (Voc) of 1.19 V, fill factor (FF) of 73.57%, and power conversion efficiency (PCE) of 18.20% have been obtained by optimization of electrodes properties and perovskite layers. Remarkably, the advantageous of low cost, flexible, and chemical stable conducive textile composite, extends new opportunities in the commercial expansion of highly efficient photovoltaic textile in optoelectronic industry field.

Most value-based decision-making (VBDM) tasks instruct people to make value judgements about stimuli using wording relating to consumption, however in some contexts this may be inappropriate. This study aims to explore whether variations of trial wording capture a common construct of value. This is a pre-registered experimental study with a within-subject design. Fifty-nine participants completed a two-alternative forced-choice task where they chose between two food images. Participants completed three blocks of trials: one asked which they would rather consume (standard wording), one asked which image they liked more, and one asked them to recall which image they rated higher during a previous block. We fitted the EZ drift-diffusion model to the reaction time and choice data to estimate evidence accumulation (EA) processes during the different blocks. There was a highly significant main effect of trial difficulty, but this was not modified by trial wording (F = 2.00, p = .11, ηp2 = .03, BF10 = .05). We also found highly significant positive correlations between the EA rates across task blocks (rs > .44, ps < .001). The findings provide initial validation of alternative wording in VBDM tasks that can be used in contexts where it may be undesirable to ask participants to make consummatory judgements.

Adolescent girls are a high-risk stratum for the emergence of depression. Previous research has established that depression is associated with blunted responses to rewards. Research using Drift Diffusion Model (DDM) has found that deficits in accumulating reward-based evidence characterize adult depression. However, little is known about how reduced reward sensitivity is reflected in the computational processes involved in reward-based decision-making in late childhood and early adolescent depression. One hundred and sixty-six 8- to 14-year-old girls completed a probabilistic reward-based decision-making task. Participants were instructed to identify which one of two similar visual stimuli were presented, and correct responses were rewarded with unequal probabilities. Analysis using hierarchical DDM quantified rate of evidence accumulation (i.e., drift rate) and starting point. Depression severity was measured using the Children's Depression Inventory. Across all participants, there was a higher drift rate, indicating faster evidence accumulation, for the more frequently rewarded than the less frequently rewarded decision. In addition, the starting point of the evidence accumulation was closer to the more frequently rewarded decision, indicating a starting point bias. Higher depression severity was associated with a slower drift rate for both types of decisions. Higher depression severity was associated with a smaller starting point bias towards the more frequently rewarded decision. The current study uses computational modeling to reveal that late childhood and early adolescent girls with greater depression demonstrate impairments in the reward-related evidence accumulation process. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

• Mice rely on their vision to chase prey, avoid predators, find refuge, and choose mates. • We acquired thousands of responses from adult mice using an automated visual discrimination task. • Choices and response times (RT) can be described with an information-accumulation model. • Changes in the experimental conditions were mirrored by changes in model parameters. • Our approach is useful to study attention, discrimination, and learning in adult mice. Response time (RT) distributions are histograms of the observed RTs for discriminative choices, comprising a rich source of empirical information to study perceptual processes. The drift–diffusion model (DDM), a mathematical formulation predicting decision tasks, reproduces the RT distributions, contributing to our understanding of these processes from a theoretical perspective. Notably, although the mouse is a popular model system for studying brain function and behavior, little is known about mouse perceptual RT distributions, and their description from an information-accumulation perspective. We combined an automated visual discrimination task with pharmacological micro-infusions of targeted brain regions to acquire thousands of responses from freely-moving adult mice. Both choices and escape latencies showed a strong dependency on stimulus discriminability. By applying a DDM fit to our experimental data, we found that the rate of incoming evidence (drift rate) increased with stimulus contrast but was reversibly impaired when inactivating the primary visual cortex (V1). Other brain regions involved in the decision-making process, the posterior parietal cortex (PPC) and the frontal orienting fields (FOF), also influenced relevant parameters from the DDM. The large number of empirical observations that we collected for this study allowed us to achieve accurate convergence for the model fit. Therefore, changes in the experimental conditions were mirrored by changes in model parameters, suggesting the participation of relevant brain areas in the decision-making process. This approach could help interpret future studies involving attention, discrimination, and learning in adult mice. [ABSTRACT FROM AUTHOR]

The photocurrent spectra (PCS)of the ITO (indium tin oxide)/PEDOT:PSS(poly(3,4-ethylenedioxythiophene)-oly(styrenesulfonate))/P3HT:PCBM(poly(3-hexylthiophene-2,5-diyl):[6,6]-phenyl-C61-butyric acid methyl ester)/Al organic photovoltaic devices(OPVs) with six different thicknesses of P3HT:PCBM layer, ranging from 80 to 345 nm,were measured under monochromatic visible light for the reverse voltage range from - 4 to 0 V .The measured PCS showed a good correlation with the P3HT:PCBM absorption spectrum for thinner devices, while for thicker OPVs a pronounced red shift in PCS was observed. As this PCS behavior is usually explained by the surface recombination effects at electrode contacts, a drift–diffusion model (DDM), which includes charge carrier surface recombination and thermal injection on the anode and cathode through boundary conditions, was used for the PSC simulations. The experimentally obtained normalized PCS were very well reproduced by the DDM when absorption and charge carrier photogeneration in P3HT:PCBM thin films were assumed to follow the Beer-Lambert law, and surface recombination at electrode contacts was negligible. Further, the effect of surface recombination on normalized PCS was analyzed in detail by reducing the surface recombination velocity (SRV) in the DDM calculations for either majority or minority charge carriers at one of the electrodes. Also, the DDM calculations were conducted for the cases of electron dominated, balanced, and hole dominated transport in the OPVs active layer. It was concluded that only the reduction of SRV for majority carriers at the electrode which extracts faster carriers affects the normalized PCS. For balanced hole and electron transport the surface recombination had no influence on the normalized PCS. [ABSTRACT FROM AUTHOR]

The quasineutral limit and the mixed layer problem of the bipolar drift-diffusion model for semiconductors with different mobilities are studied in one dimensional space in this paper. For the general smooth doping profile, the general initial data and the different mobilities of electrons and holes, the quasineutral limit is proven by the matched asymptotic expansion method of singular perturbation problem and the energy method. [ABSTRACT FROM AUTHOR]

Individuals with methamphetamine use disorder (MUD) commonly exhibit socially problematic behaviours. Investigating the prosocial decision-making of individuals with MUD could enable a better understanding of their impaired social functioning and help improve their social relationships. We conducted two studies to examine the performance of individuals with MUD and healthy controls on a modified dictator game. In Study 1, 55 male individuals with MUD and 34 healthy male participants made a series of choices between two pairs of monetary prizes for themselves and for others. In Study 2, 62 male individuals with MUD and 31 healthy male participants made the same choices as in Study 1 after a brief exposure to methamphetamine-related pictures. In both studies, we consistently found a context dependency of decreased prosociality in individuals with MUD. That is, individuals with MUD made fewer prosocial choices than healthy individuals in disadvantageous contexts (but not advantageous contexts). The results of the computational model suggested that the lower prosociality of individuals with MUD in disadvantageous contexts could be attributed to the lower weight placed on others' benefits. Moreover, when exposed to methamphetamine-related pictures, individuals with MUD showed less caution and slower encoding/motor speed than healthy individuals, and individuals with MUD with a longer history of methamphetamine use tended to respond less cautiously. Our findings provide evidence that in disadvantageous contexts, individuals with MUD show reduced prosociality and less consideration of others' benefits. Identifying the origin of the alterations in prosocial decision-making has implications for diagnosis and treatment. [ABSTRACT FROM AUTHOR]

Intentional inhibition, the ability to voluntarily inhibit or suspend an action preparation, is closely related to self-control. It is widely believed that subliminal stimuli can also activate action preparation, but whether intentional inhibition is enhanced or disrupted with greater subliminal action preparation remains unclear. In this study, participants voluntarily decided whether or not to perform the action in the scenario with subliminal action preparation, and the strength of the action preparation was manipulated by a precueing procedure. The results, based on behavioural measures and drift-diffusion models, showed that intentional inhibition enhanced with increasing subliminal action preparation, suggesting that as subliminal action preparation increases, people are more inclined to make inhibitory decisions. This study provides evidence for a framework in which strong subliminal action preparation induces enhanced cognitive monitoring., Competing Interests: Declaration of conflicting interestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Background and Hypothesis: Individuals with schizophrenia (SZ) and bipolar disorder (BD) show disruptions in self-referential gaze perception-a social perceptual process related to symptoms and functioning. However, our current mechanistic understanding of these dysfunctions and relationships is imprecise., Study Design: The present study used mathematical modeling to uncover cognitive processes driving gaze perception abnormalities in SZ and BD, and how they relate to cognition, symptoms, and social functioning. We modeled the behavior of 28 SZ, 38 BD, and 34 controls (HC) in a self-referential gaze perception task using drift-diffusion models parameterized to index key cognitive components: drift rate (evidence accumulation efficiency), drift bias (perceptual bias), start point (expectation bias), threshold separation (response caution), and nondecision time (encoding/motor processes)., Study Results: Results revealed that aberrant gaze perception in SZ and BD was driven by less efficient evidence accumulation, perceptual biases predisposing self-referential responses, and greater caution (SZ only). Across SZ and HC, poorer social functioning was related to greater expectation biases. Within SZ, perceptual and expectancy biases were associated with hallucination and delusion severity, respectively., Conclusions: These findings indicate that diminished evidence accumulation and perceptual biases may underlie altered gaze perception in patients and that SZ may engage in compensatory cautiousness, sacrificing response speed to preserve accuracy. Moreover, biases at the belief and perceptual levels may relate to symptoms and functioning. Computational modeling can, therefore, be used to achieve a more nuanced, cognitive process-level understanding of the mechanisms of social cognitive difficulties, including gaze perception, in individuals with SZ and BD., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)