Do incidental positive emotions induce more optimistic expectations of decision outcomes? An empirical study from the perspective of event-related potential.

INTRODUCTION: Previous research has found that incidental emotions of different valences (positive/negative/neutral) influence risky decision-making. However, the mechanism of their influence on psychological expectations of decision outcomes remains unclear. METHODS: We explored the effects of different incidental emotions on the behavioral, psychological, and electrophysiological responses of individuals in risky decision-making through a money gambling task using a one-way (emotion type: positive, negative, neutral emotions) between-subjects experimental design. RESULTS: Individuals with positive emotions had significantly greater risk-seeking rates than those with negative emotions during the decision selection phase (p < .01). In the feedback stage of decision outcomes, individuals showed stronger perceptions of uncertainty in the decision environment under gain and loss feedback compared with neutral feedback, as evidenced by a more positive P2 component (i.e., the second positive component of an event-related potential). Positive emotions produced greater than expected outcome bias than neutral emotions, as evidenced by a more negative FRN component (i.e., the feedback-related negativity component). CONCLUSION: Our results suggest that positive emotions increase individuals' psychological expectations of decision outcomes. This study provides new empirical insights to understand the influence of incidental emotions on risky decision outcome expectations.

Metacognitive evaluation of postdecisional perceptual representations.

Perceptual confidence is thought to arise from metacognitive processes that evaluate the underlying perceptual decision evidence. We investigated whether metacognitive access to perceptual evidence is constrained by the hierarchical organization of visual cortex, where high-level representations tend to be more readily available for explicit scrutiny. We found that the ability of human observers to evaluate their confidence did depend on whether they performed a high-level or low-level task on the same stimuli, but was also affected by manipulations that occurred long after the perceptual decision. Confidence in low-level perceptual decisions degraded with more time between the decision and the response cue, especially when backward masking was present. Confidence in high-level tasks was immune to backward masking and benefitted from additional time. These results can be explained by a model assuming confidence heavily relies on postdecisional internal representations of visual stimuli that degrade over time, where high-level representations are more persistent.

Biological motion perception in the theoretical framework of perceptual decision-making: An event-related potential study.

Biological motion perception plays a critical role in various decisions in daily life. Failure to decide accordingly in such a perceptual task could have life-threatening consequences. Neurophysiology and computational modeling studies suggest two processes mediating perceptual decision-making. One of these signals is associated with the accumulation of sensory evidence and the other with response selection. Recent EEG studies with humans have introduced an event-related potential called Centroparietal Positive Potential (CPP) as a neural marker aligned with the sensory evidence accumulation while effectively distinguishing it from motor-related lateralized readiness potential (LRP). The present study aims to investigate the neural mechanisms of biological motion perception in the framework of perceptual decision-making, which has been overlooked before. More specifically, we examine whether CPP would track the coherence of the biological motion stimuli and could be distinguished from the LRP signal. We recorded EEG from human participants while they performed a direction discrimination task of a point-light walker stimulus embedded in various levels of noise. Our behavioral findings revealed shorter reaction times and reduced miss rates as the coherence of the stimuli increased. In addition, CPP tracked the coherence of the biological motion stimuli with a tendency to reach a common level during the response, albeit with a later onset than the previously reported results in random-dot motion paradigms. Furthermore, CPP was distinguished from the LRP signal based on its temporal profile. Overall, our results suggest that the mechanisms underlying perceptual decision-making generalize to more complex and socially significant stimuli like biological motion.

Neural correlates of confidence during decision formation in a perceptual judgment task.

When we make a decision, we also estimate the probability that our choice is correct or accurate. This probability estimate is termed our degree of decision confidence. Recent work has reported event-related potential (ERP) correlates of confidence both during decision formation (the centro-parietal positivity component; CPP) and after a decision has been made (the error positivity component; Pe). However, there are several measurement confounds that complicate the interpretation of these findings. More recent studies that overcome these issues have so far produced conflicting results. To better characterise the ERP correlates of confidence we presented participants with a comparative brightness judgment task while recording electroencephalography. Participants judged which of two flickering squares (varying in luminance over time) was brighter on average. Participants then gave confidence ratings ranging from "surely incorrect" to "surely correct". To elicit a range of confidence ratings we manipulated both the mean luminance difference between the brighter and darker squares (relative evidence) and the overall luminance of both squares (absolute evidence). We found larger CPP amplitudes in trials with higher confidence ratings. This association was not simply a by-product of differences in relative evidence (which covaries with confidence) across trials. We did not identify postdecisional ERP correlates of confidence, except when they were artificially produced by pre-response ERP baselines. These results provide further evidence for neural correlates of processes that inform confidence judgments during decision formation.

The Utilitarian Virtual Self - Using Embodied Personalized Avatars to Investigate Moral Decision-Making in Semi-Autonomous Vehicle Dilemmas.

Embodied personalized avatars are a promising new tool to investigate moral decision-making by transposing the user into the "middle of the action" in moral dilemmas. Here, we tested whether avatar personalization and motor control could impact moral decision-making, physiological reactions and reaction times, as well as embodiment, presence and avatar perception. Seventeen participants, who had their personalized avatars created in a previous study, took part in a range of incongruent (i.e., harmful action led to better overall outcomes) and congruent (i.e., harmful action led to trivial outcomes) moral dilemmas as the drivers of a semi-autonomous car. They embodied four different avatars (counterbalanced - personalized motor control, personalized no motor control, generic motor control, generic no motor control). Overall, participants took a utilitarian approach by performing harmful actions only to maximize outcomes. We found increased physiological arousal (SCRs and heart rate) for personalized avatars compared to generic avatars, and increased SCRs in motor control conditions compared to no motor control. Participants had slower reaction times when they had motor control over their avatars, possibly hinting at more elaborate decision-making processes. Presence was also higher in motor control compared to no motor control conditions. Embodiment ratings were higher for personalized avatars, and generally, personalization and motor control were perceptually positive features. These findings highlight the utility of personalized avatars and open up a range of future research possibilities that could benefit from the affordances of this technology and simulate, more closely than ever, real-life action.

A fully spiking coupled model of a deep neural network and a recurrent attractor explains dynamics of decision making in an object recognition task.

Objective.Object recognition and making a choice regarding the recognized object is pivotal for most animals. This process in the brain contains information representation and decision making steps which both take different amount of times for different objects. While dynamics of object recognition and decision making are usually ignored in object recognition models, here we proposed a fully spiking hierarchical model, explaining the process of object recognition from information representation to making decision.Approach.Coupling a deep neural network and a recurrent attractor based decision making model beside using spike time dependent plasticity learning rules in several convolutional and pooling layers, we proposed a model which can resemble brain behaviors during an object recognition task. We also measured human choices and reaction times in a psychophysical object recognition task and used it as a reference to evaluate the model.Main results.The proposed model explains not only the probability of making a correct decision but also the time that it takes to make a decision. Importantly, neural firing rates in both feature representation and decision making levels mimic the observed patterns in animal studies (number of spikes (p-value < 10-173) and the time of the peak response (p-value < 10-31) are significantly modulated with the strength of the stimulus). Moreover, the speed-accuracy trade-off as a well-known characteristic of decision making process in the brain is also observed in the model (changing the decision bound significantly affect the reaction time (p-value < 10-59) and accuracy (p-value < 10-165)).Significance.We proposed a fully spiking deep neural network which can explain dynamics of making decision about an object in both neural and behavioral level. Results showed that there is a strong and significant correlation (r= 0.57) between the reaction time of the model and of human participants in the psychophysical object recognition task.

Cortical ß Power Reflects a Neural Implementation of Decision Boundary Collapse in Speeded Decisions.

A prominent account of decision-making assumes that information is accumulated until a fixed response threshold is crossed. However, many decisions require weighting of information appropriately against time. Collapsing response thresholds are a mathematically optimal solution to this decision problem. However, our understanding of the neurocomputational mechanisms underlying dynamic response thresholds remains significantly incomplete. To investigate this issue, we used a multistage drift-diffusion model (DDM) and also analyzed EEG ß power lateralization (BPL). The latter served as a neural proxy for decision signals. We analyzed a large dataset (n = 863; 434 females and 429 males) from a speeded flanker task and data from an independent confirmation sample (n = 119; 70 females and 49 males). We showed that a DDM with collapsing decision thresholds, a process wherein the decision boundary reduces over time, captured participants' time-dependent decision policy more accurately than a model with fixed thresholds. Previous research suggests that BPL over motor cortices reflects features of a decision signal and that its peak, coinciding with the motor response, may serve as a neural proxy for the decision threshold. We show that BPL around the response decreased with increasing RTs. Together, our findings offer compelling evidence for the existence of collapsing decision thresholds in decision-making processes.

Slowing of Movements in Healthy Aging as a Rational Economic Response to an Elevated Effort Landscape.

Why do we move slower as we grow older? The reward circuits of the brain, which tend to invigorate movements, decline with aging, raising the possibility that reduced vigor is due to the diminishing value that our brain assigns to movements. However, as we grow older, it also becomes more effortful to make movements. Is age-related slowing principally a consequence of increased effort costs from the muscles, or reduced valuation of reward by the brain? Here, we first quantified the cost of reaching via metabolic energy expenditure in human participants (male and female), and found that older adults consumed more energy than the young at a given speed. Thus, movements are objectively more costly for older adults. Next, we observed that when reward increased, older adults, like the young, responded by initiating their movements earlier. Yet, unlike the young, they were unwilling to increase their movement speed. Was their reluctance to reach quicker for rewards due to the increased effort costs, or because they ascribed less value to the movement? Motivated by a mathematical model, we next made the young experience a component of aging by making their movements more effortful. Now the young responded to reward by reacting faster but chose not to increase their movement speed. This suggests that slower movements in older adults are partly driven by an adaptive response to an elevated effort landscape. Moving slower may be a rational economic response the brain is making to mitigate the elevated effort costs that accompany aging.

Synaptic wiring motifs in posterior parietal cortex support decision-making.

The posterior parietal cortex exhibits choice-selective activity during perceptual decision-making tasks1-10. However, it is not known how this selective activity arises from the underlying synaptic connectivity. Here we combined virtual-reality behaviour, two-photon calcium imaging, high-throughput electron microscopy and circuit modelling to analyse how synaptic connectivity between neurons in the posterior parietal cortex relates to their selective activity. We found that excitatory pyramidal neurons preferentially target inhibitory interneurons with the same selectivity. In turn, inhibitory interneurons preferentially target pyramidal neurons with opposite selectivity, forming an opponent inhibition motif. This motif was present even between neurons with activity peaks in different task epochs. We developed neural-circuit models of the computations performed by these motifs, and found that opponent inhibition between neural populations with opposite selectivity amplifies selective inputs, thereby improving the encoding of trial-type information. The models also predict that opponent inhibition between neurons with activity peaks in different task epochs contributes to creating choice-specific sequential activity. These results provide evidence for how synaptic connectivity in cortical circuits supports a learned decision-making task.

Visuo-frontal interactions during social learning in freely moving macaques.

Social interactions represent a ubiquitous aspect of our everyday life that we acquire by interpreting and responding to visual cues from conspecifics1. However, despite the general acceptance of this view, how visual information is used to guide the decision to cooperate is unknown. Here, we wirelessly recorded the spiking activity of populations of neurons in the visual and prefrontal cortex in conjunction with wireless recordings of oculomotor events while freely moving macaques engaged in social cooperation. As animals learned to cooperate, visual and executive areas refined the representation of social variables, such as the conspecific or reward, by distributing socially relevant information among neurons in each area. Decoding population activity showed that viewing social cues influences the decision to cooperate. Learning social events increased coordinated spiking between visual and prefrontal cortical neurons, which was associated with improved accuracy of neural populations to encode social cues and the decision to cooperate. These results indicate that the visual-frontal cortical network prioritizes relevant sensory information to facilitate learning social interactions while freely moving macaques interact in a naturalistic environment.

An fMRI Dataset on Social Reward Processing and Decision Making in Younger and Older Adults.

Behavioural and neuroimaging research has shown that older adults are less sensitive to financial losses compared to younger adults. Yet relatively less is known about age-related differences in social decisions and social reward processing. As part of a pilot study, we collected behavioural and functional magnetic resonance imaging (fMRI) data from 50 participants (Younger: N = 26, ages 18-34 years; Older: N = 24, ages 63-80 years) who completed three tasks in the scanner: an economic trust game as the investor with three partners (computer, stranger, friend) as the investee; a card-guessing task with monetary gains and losses shared with three partners (computer, stranger, friend); and an ultimatum game as responder to three anonymous proposers (computer, age-similar adults, age-dissimilar adults). We also collected B0 field maps and high-resolution structural images (T1-weighted and T2-weighted images). These data could be reused to answer questions about moment-to-moment variability in fMRI signal, representational similarity between tasks, and brain structure.

Sequential neuronal processing of number values, abstract decision, and action in the primate prefrontal cortex.

Decision-making requires processing of sensory information, comparing the gathered evidence to make a judgment, and performing the action to communicate it. How neuronal representations transform during this cascade of representations remains a matter of debate. Here, we studied the succession of neuronal representations in the primate prefrontal cortex (PFC). We trained monkeys to judge whether a pair of sequentially presented displays had the same number of items. We used a combination of single neuron and population-level analyses and discovered a sequential transformation of represented information with trial progression. While numerical values were initially represented with high precision and in conjunction with detailed information such as order, the decision was encoded in a low-dimensional subspace of neural activity. This decision encoding was invariant to both retrospective numerical values and prospective motor plans, representing only the binary judgment of "same number" versus "different number," thus facilitating the generalization of decisions to novel number pairs. We conclude that this transformation of neuronal codes within the prefrontal cortex supports cognitive flexibility and generalizability of decisions to new conditions.

Putting a human in the loop: Increasing uptake, but decreasing accuracy of automated decision-making.

Automated decision-making gains traction, prompting discussions on regulation with calls for human oversight. Understanding how human involvement affects the acceptance of algorithmic recommendations and the accuracy of resulting decisions is vital. In an online experiment (N = 292), for a prediction task, participants choose a recommendation stemming either from an algorithm or another participant. In a between-subject design, we varied if the prediction was delegated completely or if the recommendation could be adjusted. 66% of times, participants preferred to delegate the decision to an algorithm over an equally accurate human. The preference for an algorithm increased by 7 percentage points if participants could monitor and adjust the recommendations. Participants followed algorithmic recommendations more closely. Importantly, they were less likely to intervene with the least accurate recommendations. Hence, in our experiment the human-in-the-loop design increases the uptake but decreases the accuracy of the decisions.

Neuroscience: Therapy modulates decision-making in Parkinson's disease.

There is mounting evidence that decision-making can be affected by treatment in Parkinson's disease. A new study shows that dopamine and deep brain stimulation, two mainstay treatments of Parkinson's, differently affect how patients make decisions weighing rewards against effort costs.

Dopamine activity in the nigrostriatal pathway alters cue-induced risky choice patterns in female rats.

Deficits in cost/benefit decision making is a critical risk factor for gambling disorder. Reward-paired cues may play an important role, as these stimuli can enhance risk preference in rats. Despite extensive research implicating the dorsal striatum in the compulsive aspects of addiction, the role of nigrostriatal dopaminergic activity in cue-induced risk preference remains unclear, particularly in females. Accordingly, we examined the effects of manipulating the dopaminergic nigrostriatal pathway on cue-induced risky choice in female rats. TH:Cre rats were trained on the cued version of the rat Gambling Task. This task was designed such that maximal reward is attained by avoiding the high-risk, high-reward options and instead favouring the options associated with lower per-trial gains, as they feature less frequent and shorter time-out penalties. Adding reward-paired audiovisual cues to the task leads to greater risky choice on average. To assess the role of the nigrostriatal pathway, a viral vector carrying either Cre-dependent inhibitory or excitatory DREADD was infused into the substantia nigra. Rats then received clozapine-N-oxide either during task acquisition or after a stable performance baseline was reached. Inhibition of this pathway accelerated the development of risk preference in early sessions and increased risky choice during performance, but long-term inhibition actually improved decision making. Activation of this pathway had minimal effects. These results provide evidence for the involvement of the dopaminergic nigrostriatal pathway in cue-induced risk preference in females, therefore shedding light on its role in cost/benefit decision-making deficits and expanding our knowledge of the female dopaminergic system.

Prefrontal signals precede striatal signals for biased credit assignment in motivational learning biases.

Actions are biased by the outcomes they can produce: Humans are more likely to show action under reward prospect, but hold back under punishment prospect. Such motivational biases derive not only from biased response selection, but also from biased learning: humans tend to attribute rewards to their own actions, but are reluctant to attribute punishments to having held back. The neural origin of these biases is unclear. Specifically, it remains open whether motivational biases arise primarily from the architecture of subcortical regions or also reflect cortical influences, the latter being typically associated with increased behavioral flexibility and control beyond stereotyped behaviors. Simultaneous EEG-fMRI allowed us to track which regions encoded biased prediction errors in which order. Biased prediction errors occurred in cortical regions (dorsal anterior and posterior cingulate cortices) before subcortical regions (striatum). These results highlight that biased learning is not a mere feature of the basal ganglia, but arises through prefrontal cortical contributions, revealing motivational biases to be a potentially flexible, sophisticated mechanism.

Double dissociation of dopamine and subthalamic nucleus stimulation on effortful cost/benefit decision making.

Deep brain stimulation (DBS) and dopaminergic therapy (DA) are common interventions for Parkinson's disease (PD). Both treatments typically improve patient outcomes, and both can have adverse side effects on decision making (e.g., impulsivity).1,2 Nevertheless, they are thought to act via different mechanisms within basal ganglia circuits.3 Here, we developed and formally evaluated their dissociable predictions within a single cost/benefit effort-based decision-making task. In the same patients, we manipulated DA medication status and subthalamic nucleus (STN) DBS status within and across sessions. Using a series of descriptive and computational modeling analyses of participant choices and their dynamics, we confirm a double dissociation: DA medication asymmetrically altered participants' sensitivities to benefits vs. effort costs of alternative choices (boosting the sensitivity to benefits while simultaneously lowering sensitivity to costs); whereas STN DBS lowered the decision threshold of such choices. To our knowledge, this is the first study to show, using a common modeling framework, a dissociation of DA and DBS within the same participants. As such, this work offers a comprehensive account for how different mechanisms impact decision making, and how impulsive behavior (present in DA-treated patients with PD and DBS patients) may emerge from separate physiological mechanisms.

Dual-process modeling of sequential decision making in the balloon analogue risk task.

People are often faced with repeated risky decisions that involve uncertainty. In sequential risk-taking tasks, like the Balloon Analogue Risk Task (BART), the underlying decision process is not yet fully understood. Dual-process theory proposes that human cognition involves two main families of processes, often referred to as System 1 (fast and automatic) and System 2 (slow and conscious). We cross models of the BART with different architectures of the two systems to yield a pool of computational dual-process models that are evaluated on multiple performance measures (e.g., parameter identifiability, model recovery, and predictive accuracy). Results show that the best-performing model configuration assumes the two systems are competitively connected, an evaluation process based on the Scaled Target Learning model of the BART, and an assessment rate that incorporates sensitivity to the trial number, pumping opportunity, and bias to engage in System 1. Findings also shed light on how modeling choices and response times in a dual-process framework can benefit our understanding of sequential risk-taking behavior.

Distributional reinforcement learning in prefrontal cortex.

The prefrontal cortex is crucial for learning and decision-making. Classic reinforcement learning (RL) theories center on learning the expectation of potential rewarding outcomes and explain a wealth of neural data in the prefrontal cortex. Distributional RL, on the other hand, learns the full distribution of rewarding outcomes and better explains dopamine responses. In the present study, we show that distributional RL also better explains macaque anterior cingulate cortex neuronal responses, suggesting that it is a common mechanism for reward-guided learning.

Insight into differing decision-making strategies that underlie cognitively effort-based decision making using computational modeling in rats.

RATIONALE: The rat cognitive effort task (rCET), a rodent model of cognitive rather than physical effort, requires animals to choose between an easy or hard visuospatial discrimination, with a correct hard choice more highly rewarded. Like in humans, there is stable individual variation in choice behavior. In previous reports, animals were divided into two groups-workers and slackers-based on their mean preference for the harder option. Although these groups differed in their response to pharmacological challenges, the rationale for using this criterion for grouping was not robust. METHODS: We collated experimental data from multiple cohorts of male and female rats performing the rCET and used a model-based framework combining drift diffusion modeling with cluster analysis to identify the decision-making processes underlying variation in choice behavior. RESULTS: We verified that workers and slackers are statistically different groups but also found distinct intra-group profiles. These subgroups exhibited dissociable performance during the attentional phase, linked to distinct decision-making profiles during choice. Reanalysis of previous pharmacology data using this model-based framework showed that serotonergic drug effects were explained by changes in decision boundaries and non-decision times, while scopolamine's effects were driven by changes in decision starting points and rates of evidence accumulation. CONCLUSIONS: Modeling revealed the decision-making processes that are associated with cognitive effort costs, and how these differ across individuals. Reanalysis of drug data provided insight into the mechanisms through which different neurotransmitter systems impact cognitively effortful attention and decision-making processes, with relevance to multiple psychiatric disorders.