Belief inference for hierarchical hidden states in spatial navigation.

Uncertainty abounds in the real world, and in environments with multiple layers of unobservable hidden states, decision-making requires resolving uncertainties based on mutual inference. Focusing on a spatial navigation problem, we develop a Tiger maze task that involved simultaneously inferring the local hidden state and the global hidden state from probabilistically uncertain observation. We adopt a Bayesian computational approach by proposing a hierarchical inference model. Applying this to human task behaviour, alongside functional magnetic resonance brain imaging, allows us to separate the neural correlates associated with reinforcement and reassessment of belief in hidden states. The imaging results also suggest that different layers of uncertainty differentially involve the basal ganglia and dorsomedial prefrontal cortex, and that the regions responsible are organised along the rostral axis of these areas according to the type of inference and the level of abstraction of the hidden state, i.e. higher-order state inference involves more anterior parts.

Dissecting Bayes: Using influence measures to test normative use of probability density information derived from a sample.

Bayesian decision theory (BDT) is frequently used to model normative performance in perceptual, motor, and cognitive decision tasks where the possible outcomes of actions are associated with rewards or penalties. The resulting normative models specify how decision makers should encode and combine information about uncertainty and value-step by step-in order to maximize their expected reward. When prior, likelihood, and posterior are probabilities, the Bayesian computation requires only simple arithmetic operations: addition, etc. We focus on visual cognitive tasks where Bayesian computations are carried out not on probabilities but on (1) probability density functions and (2) these probability density functions are derived from samples. We break the BDT model into a series of computations and test human ability to carry out each of these computations in isolation. We test three necessary properties of normative use of pdf information derived from a sample-accuracy, additivity and influence. Influence measures allow us to assess how much weight each point in the sample is assigned in making decisions and allow us to compare normative use (weighting) of samples to actual, point by point. We find that human decision makers violate accuracy and additivity systematically but that the cost of failure in accuracy or additivity would be minor in common decision tasks. However, a comparison of measured influence for each sample point with normative influence measures demonstrates that the individual's use of sample information is markedly different from the predictions of BDT. We will show that the normative BDT model takes into account the geometric symmetries of the pdf while the human decision maker does not. An alternative model basing decisions on a single extreme sample point provided a better account for participants' data than the normative BDT model.

Loss, gain and choice difficulty in gambling patients: Neural and behavioural processes.

Impaired decision-making is often displayed by individuals suffering from gambling disorder (GD). Since there are a variety of different phenomena influencing decision-making, we focused in this study on the effects of GD on neural and behavioural processes related to loss aversion and choice difficulty. Behavioural responses as well as brain images of 23 patients with GD and 20 controls were recorded while they completed a mixed gambles task, where they had to decide to either accept or reject gambles with different amounts of potential gain and loss. We found no behavioural loss aversion in either group and no group differences regarding loss and gain-related choice behaviour, but there was a weaker relation between choice difficulty and decision time in patients with GD. Similarly, we observed no group differences in processing of losses or gains, but choice difficulty was weaker associated with brain activity in the right anterior insula and anterior cingulate cortex in patients with GD. Our results showed for the first time the effects of GD on neural processes related to choice difficulty. In addition, our findings on choice difficulty give new insights on the psychopathology of GD and on neural processes related to impaired decision-making in GD.

It's the biology, stupid! Proxy failures in economic decision making.

Utilitarian characterizations of economic decision making fail to capture the complex, conditional, and heterogeneous motivations underlying human behavior as shaped by the predictive, multicriterial drivers of biological regulation. Unless economic models start to acknowledge that humans have bodies and a biology with its own adaptive logic and tradeoffs, economic policies will be systematically exposed to, and systematic generators of, proxy failures.

Distinct neural markers of evidence accumulation index metacognitive processing before and after simple visual decisions.

Perceptual decision-making is affected by uncertainty arising from the reliability of incoming sensory evidence (perceptual uncertainty) and the categorization of that evidence relative to a choice boundary (categorical uncertainty). Here, we investigated how these factors impact the temporal dynamics of evidence processing during decision-making and subsequent metacognitive judgments. Participants performed a motion discrimination task while electroencephalography was recorded. We manipulated perceptual uncertainty by varying motion coherence, and categorical uncertainty by varying the angular offset of motion signals relative to a criterion. After each trial, participants rated their desire to change their mind. High uncertainty impaired perceptual and metacognitive judgments and reduced the amplitude of the centro-parietal positivity, a neural marker of evidence accumulation. Coherence and offset affected the centro-parietal positivity at different time points, suggesting that perceptual and categorical uncertainty affect decision-making in sequential stages. Moreover, the centro-parietal positivity predicted participants' metacognitive judgments: larger predecisional centro-parietal positivity amplitude was associated with less desire to change one's mind, whereas larger postdecisional centro-parietal positivity amplitude was associated with greater desire to change one's mind, but only following errors. These findings reveal a dissociation between predecisional and postdecisional evidence processing, suggesting that the CPP tracks potentially distinct cognitive processes before and after a decision.

Lessons learned from an fMRI-guided rTMS study on performance in a numerical Stroop task.

The Stroop task is a well-established tool to investigate the influence of competing visual categories on decision making. Neuroimaging as well as rTMS studies have demonstrated the involvement of parietal structures, particularly the intraparietal sulcus (IPS), in this task. Given its reliability, the numerical Stroop task was used to compare the effects of different TMS targeting approaches by Sack and colleagues (Sack AT 2009), who elegantly demonstrated the superiority of individualized fMRI targeting. We performed the present study to test whether fMRI-guided rTMS effects on numerical Stroop task performance could still be observed while using more advanced techniques that have emerged in the last decade (e.g., electrical sham, robotic coil holder system, etc.). To do so we used a traditional reaction time analysis and we performed, post-hoc, a more advanced comprehensive drift diffusion modeling approach. Fifteen participants performed the numerical Stroop task while active or sham 10 Hz rTMS was applied over the region of the right intraparietal sulcus (IPS) showing the strongest functional activation in the Incongruent > Congruent contrast. This target was determined based on individualized fMRI data collected during a separate session. Contrary to our assumption, the classical reaction time analysis did not show any superiority of active rTMS over sham, probably due to confounds such as potential cumulative rTMS effects, and the effect of practice. However, the modeling approach revealed a robust effect of rTMS on the drift rate variable, suggesting differential processing of congruent and incongruent properties in perceptual decision-making, and more generally, illustrating that more advanced computational analysis of performance can elucidate the effects of rTMS on the brain where simpler methods may not.

Decision-making under conditions of explicit risk and uncertainty in autistic and typically developing adolescents and young adults.

Adolescence has been characterized as a period of risky and possibly suboptimal decision-making, yet the development of decision-making in autistic adolescents is not well understood. To investigate decision-making in autism, we evaluated performance on 2 computerized tasks capturing decision-making under explicit risk and uncertainty in autistic and non-autistic adolescents/young adults ages 12-22 years. Participants completed the Game of Dice Task (32 IQ-matched participant pairs) to assess decision-making under explicit risk and the modified Iowa Gambling Task (35 IQ-matched pairs) to assess decision-making under uncertainty. Autistic participants overall made riskier decisions than non-autistic participants on the Game of Dice Task, and the odds of making riskier decisions varied by age and IQ. In contrast, the autistic group showed comparable levels of learning over trial blocks to the non-autistic group on the modified Iowa Gambling Task. For both tasks, younger autistic participants performed poorer than their non-autistic counterparts, while group differences diminished in older ages. This age-related pattern suggests positive development during adolescence on risk assessment and decision-making in autism but also implies differential developmental trajectories between groups. These findings also suggest differential performance by the risk type, with additional complex influences of IQ and fluid cognition, which warrants further investigations.

Forming cognitive maps for abstract spaces: the roles of the human hippocampus and orbitofrontal cortex.

How does the human brain construct cognitive maps for decision-making and inference? Here, we conduct an fMRI study on a navigation task in multidimensional abstract spaces. Using a deep neural network model, we assess learning levels and categorized paths into exploration and exploitation stages. Univariate analyses show higher activation in the bilateral hippocampus and lateral prefrontal cortex during exploration, positively associated with learning level and response accuracy. Conversely, the bilateral orbitofrontal cortex (OFC) and retrosplenial cortex show higher activation during exploitation, negatively associated with learning level and response accuracy. Representational similarity analysis show that the hippocampus, entorhinal cortex, and OFC more accurately represent destinations in exploitation than exploration stages. These findings highlight the collaboration between the medial temporal lobe and prefrontal cortex in learning abstract space structures. The hippocampus may be involved in spatial memory formation and representation, while the OFC integrates sensory information for decision-making in multidimensional abstract spaces.

Heuristics in risky decision-making relate to preferential representation of information.

When making choices, individuals differ from one another, as well as from normativity, in how they weigh different types of information. One explanation for this relates to idiosyncratic preferences in what information individuals represent when evaluating choice options. Here, we test this explanation with a simple risky-decision making task, combined with magnetoencephalography (MEG). We examine the relationship between individual differences in behavioral markers of information weighting and neural representation of stimuli pertinent to incorporating that information. We find that the extent to which individuals (N = 19) behaviorally weight probability versus reward information is related to how preferentially they neurally represent stimuli most informative for making probability and reward comparisons. These results are further validated in an additional behavioral experiment (N = 88) that measures stimulus representation as the latency of perceptual detection following priming. Overall, the results suggest that differences in the information individuals consider during choice relate to their risk-taking tendencies.

Locus coeruleus integrity is related to an exploitation-based decision-making bias in older adulthood.

Optimal decision-making balances exploration for new information against exploitation of known rewards, a process mediated by the locus coeruleus and its norepinephrine projections. We predicted that an exploitation-bias that emerges in older adulthood would be associated with lower microstructural integrity of the locus coeruleus. Leveraging in vivo histological methods from quantitative MRI-magnetic transfer saturation-we provide evidence that older age is associated with lower locus coeruleus integrity. Critically, we demonstrate that an exploitation bias in older adulthood, assessed with a foraging task, is sensitive and specific to lower locus coeruleus integrity. Because the locus coeruleus is uniquely vulnerable to Alzheimer's disease pathology, our findings suggest that aging, and a presymptomatic trajectory of Alzheimer's related decline, may fundamentally alter decision-making abilities in later life.

Graded decisions in the human brain.

Decision-makers objectively commit to a definitive choice, yet at the subjective level, human decisions appear to be associated with a degree of uncertainty. Whether decisions are definitive (i.e., concluding in all-or-none choices), or whether the underlying representations are graded, remains unclear. To answer this question, we recorded intracranial neural signals directly from the brain while human subjects made perceptual decisions. The recordings revealed that broadband gamma activity reflecting each individual's decision-making process, ramped up gradually while being graded by the accumulated decision evidence. Crucially, this grading effect persisted throughout the decision process without ever reaching a definite bound at the time of choice. This effect was most prominent in the parietal cortex, a brain region traditionally implicated in decision-making. These results provide neural evidence for a graded decision process in humans and an analog framework for flexible choice behavior.

Learning to Choose: Behavioral Dynamics Underlying the Initial Acquisition of Decision-Making.

Current theories of decision-making propose that decisions arise through competition between choice options. Computational models of the decision process estimate how quickly information about choice options is integrated and how much information is needed to trigger a choice. Experiments using this approach typically report data from well-trained participants. As such, we do not know how the decision process evolves as a decision-making task is learned for the first time. To address this gap, we used a behavioral design separating learning the value of choice options from learning to make choices. We trained male rats to respond to single visual stimuli with different reward values. Then, we trained them to make choices between pairs of stimuli. Initially, the rats responded more slowly when presented with choices. However, as they gained experience in making choices, this slowing reduced. Response slowing on choice trials persisted throughout the testing period. We found that it was specifically associated with increased exponential variability when the rats chose the higher value stimulus. Additionally, our analysis using drift diffusion modeling revealed that the rats required less information to make choices over time. These reductions in the decision threshold occurred after just a single session of choice learning. These findings provide new insights into the learning process of decision-making tasks. They suggest that the value of choice options and the ability to make choices are learned separately and that experience plays a crucial role in improving decision-making performance.

Group vocal composition and decision-making during roost finding in Spix's disk-winged bats.

Theoretical work suggests that having many informed individuals within social groups can promote efficient resource location. However, it may also give rise to group fragmentation if members fail to reach consensus on their direction of movement. In this study, we investigate whether the number of informed individuals, exemplified by bats emitting calls from different roosts, influences group cohesion in Spix's disk-winged bats (Thyroptera tricolor). Additionally, we explore the role of signal reliability, quantified through signalling rates, in group consensus on where to roost. These bats use contact calls to announce the location of a roost site and recruit conspecifics. The groups they form exhibit high levels of cohesion and consist of both vocal and non-vocal bats, with vocal behaviour being consistent over time. Our findings revealed that an increase in the number of roosts broadcasting calls is strongly associated with the likelihood of groups fragmenting among multiple roosts. Additionally, we found that a majority of group members enter the roost with higher calling rates. This phenomenon can mitigate the risk of group fragmentation, as bats emitting more calls may contribute to greater group consensus on roosting locations, thereby reducing the likelihood of individuals separating and enhancing overall group cohesion. Our results highlight the potential costs of having too many information producers for group coordination, despite their established role in finding critical resources. This article is part of the theme issue 'The power of sound: unravelling how acoustic communication shapes group dynamics'.

Visual social information use in collective foraging.

Collective dynamics emerge from individual-level decisions, yet we still poorly understand the link between individual-level decision-making processes and collective outcomes in realistic physical systems. Using collective foraging to study the key trade-off between personal and social information use, we present a mechanistic, spatially-explicit agent-based model that combines individual-level evidence accumulation of personal and (visual) social cues with particle-based movement. Under idealized conditions without physical constraints, our mechanistic framework reproduces findings from established probabilistic models, but explains how individual-level decision processes generate collective outcomes in a bottom-up way. In clustered environments, groups performed best if agents reacted strongly to social information, while in uniform environments, individualistic search was most beneficial. Incorporating different real-world physical and perceptual constraints profoundly shaped collective performance, and could even buffer maladaptive herding by facilitating self-organized exploration. Our study uncovers the mechanisms linking individual cognition to collective outcomes in human and animal foraging and paves the way for decentralized robotic applications.

Prefrontal tDCS modulates risk-taking in male violent offenders.

Detrimental decision-making is a major problem among violent offenders. Non-invasive brain stimulation offers a promising method to directly influence decision-making and has already been shown to modulate risk-taking in non-violent controls. We hypothesize that anodal transcranial direct current stimulation (tDCS) over the right dorsolateral prefrontal cortex beneficially modulates the neural and behavioral correlates of risk-taking in a sample of violent offenders. We expect offenders to show more risky decision-making than non-violent controls and that prefrontal tDCS will induce stronger changes in the offender group. In the current study, 22 male violent offenders and 24 male non-violent controls took part in a randomized double-blind sham-controlled cross-over study applying tDCS over the right dorsolateral prefrontal cortex. Subsequently, participants performed the Balloon Analogue Risk Task (BART) during functional magnetic resonance imaging (fMRI). Violent offenders showed significantly less optimal decision-making compared to non-violent controls. Active tDCS increased prefrontal activity and improved decision-making only in violent offenders but not in the control group. Also, in offenders only, prefrontal tDCS influenced functional connectivity between the stimulated area and other brain regions such as the thalamus. These results suggest baseline dependent effects of tDCS and pave the way for treatment options of disadvantageous decision-making behavior in this population.

Cingulate microstimulation induces negative decision-making via reduced top-down influence on primate fronto-cingulo-striatal network.

The dorsolateral prefrontal cortex (dlPFC) is crucial for regulation of emotion that is known to aid prevention of depression. The broader fronto-cingulo-striatal (FCS) network, including cognitive dlPFC and limbic cingulo-striatal regions, has been associated with a negative evaluation bias often seen in depression. The mechanism by which dlPFC regulates the limbic system remains largely unclear. Here we have successfully induced a negative bias in decision-making in female primates performing a conflict decision-making task, by directly microstimulating the subgenual cingulate cortex while simultaneously recording FCS local field potentials (LFPs). The artificially induced negative bias in decision-making was associated with a significant decrease in functional connectivity from cognitive to limbic FCS regions, represented by a reduction in Granger causality in beta-range LFPs from the dlPFC to the other regions. The loss of top-down directional influence from cognitive to limbic regions, we suggest, could underlie negative biases in decision-making as observed in depressive states.

The neurocomputational link between defensive cardiac states and approach-avoidance arbitration under threat.

Avoidance, a hallmark of anxiety-related psychopathology, often comes at a cost; avoiding threat may forgo the possibility of a reward. Theories predict that optimal approach-avoidance arbitration depends on threat-induced psychophysiological states, like freezing-related bradycardia. Here we used model-based fMRI analyses to investigate whether and how bradycardia states are linked to the neurocomputational underpinnings of approach-avoidance arbitration under varying reward and threat magnitudes. We show that bradycardia states are associated with increased threat-induced avoidance and more pronounced reward-threat value comparison (i.e., a stronger tendency to approach vs. avoid when expected reward outweighs threat). An amygdala-striatal-prefrontal circuit supports approach-avoidance arbitration under threat, with specific involvement of the amygdala and dorsal anterior cingulate (dACC) in integrating reward-threat value and bradycardia states. These findings highlight the role of human freezing states in value-based decision making, relevant for optimal threat coping. They point to a specific role for amygdala/dACC in state-value integration under threat.

Interpersonal heart rate synchrony predicts effective information processing in a naturalistic group decision-making task.

Groups often outperform individuals in problem-solving. Nevertheless, failure to critically evaluate ideas risks suboptimal outcomes through so-called groupthink. Prior studies have shown that people who hold shared goals, perspectives, or understanding of the environment show similar patterns of brain activity, which itself can be enhanced by consensus-building discussions. Whether shared arousal alone can predict collective decision-making outcomes, however, remains unknown. To address this gap, we computed interpersonal heart rate synchrony, a peripheral index of shared arousal associated with joint attention, empathic accuracy, and group cohesion, in 44 groups (n = 204) performing a collective decision-making task. The task required critical examination of all available information to override inferior, default options and make the right choice. Using multidimensional recurrence quantification analysis (MdRQA) and machine learning, we found that heart rate synchrony predicted the probability of groups reaching the correct consensus decision with >70% cross-validation accuracy-significantly higher than that predicted by the duration of discussions, subjective assessment of team function or baseline heart rates alone. We propose that heart rate synchrony during group discussion provides a biomarker of interpersonal engagement that facilitates adaptive learning and effective information sharing during collective decision-making.

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.

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.