Classic EEG motor potentials track the emergence of value-based decisions

A tutorial on fitting joint models of M/EEG and behavior to understand cognition

We present motivation and practical steps necessary to find parameter estimates of joint models of behavior and neural electrophysiological data. This tutorial is written for researchers wishing to build joint models of human behavior and scalp and intracranial electroencephalographic (EEG) or magnetoencephalographic (MEG) data, and more specifically those researchers who seek to understand human cognition. Although these techniques could easily be applied to animal models, the focus of this tutorial is on human participants. Joint modeling of M/EEG and behavior requires some knowledge of existing computational and cognitive theories, M/EEG artifact correction, M/EEG analysis techniques, cognitive modeling, and programming for statistical modeling implementation. This paper seeks to give an introduction to these techniques as they apply to estimating parameters from neurocognitive models of M/EEG and human behavior, and to evaluate model results and compare models. Due to our research and knowledge on the subject matter, our examples in this paper will focus on testing specific hypotheses in human decision-making theory. However, most of the motivation and discussion of this paper applies across many modeling procedures and applications. We provide Python (and linked R) code examples in the tutorial and appendix. Readers are encouraged to try the exercises at the end of the document.

Libet's legacy: A primer to the neuroscience of volition

The neuroscience of volition is an emerging subfield of the brain sciences, with hundreds of papers on the role of consciousness in action formation published each year. This makes the state-of-the-art in the discipline poorly accessible to newcomers and difficult to follow even for experts in the field. Here we provide a comprehensive summary of research in this field since its inception that will be useful to both groups. We also discuss important ideas that have received little coverage in the literature so far. We systematically reviewed a set of 2220 publications, with detailed consideration of almost 500 of the most relevant papers. We provide a thorough introduction to the seminal work of Benjamin Libet from the 1960s to 1980s. We also discuss common criticisms of Libet's method, including temporal introspection, the interpretation of the assumed physiological correlates of volition, and various conceptual issues. We conclude with recent advances and potential future directions in the field, highlighting modern methodological approaches to volition, as well as important recent findings.

Episodic Memory Retrieval Affects the Onset and Dynamics of Evidence Accumulation during Value-based Decisions

In neuroeconomics, there is much interest in understanding simple value-based choices where agents choose between visually presented goods, comparable to a shopping scenario in a supermarket. However, many everyday decisions are made in the physical absence of the considered goods, requiring agents to recall information about the goods from memory. Here, we asked whether and how this reliance on an internal memory representation affects the temporal dynamics of decision making on a cognitive and neural level. Participants performed a remember-and-decide task in which they made simple purchasing decisions between money offers and snack items while undergoing EEG. Snack identity was presented either visually (value trials) or had to be recalled from memory (memory trials). Behavioral data indicated comparable choice consistency across both trial types, but considerably longer RTs in memory trials. Drift-diffusion modeling suggested that this RT difference was because of longer nondecision time of decision processes as well as altered evidence accumulation dynamics (lower accumulation rate and higher decision threshold). The nondecision time effect was supported by a delayed onset of the lateralized readiness potential. These results show that both decision and nondecision processes are prolonged when participants need to resort to internal memory representations during value-based decisions.

Multiphasic value biases in fast-paced decisions

Perceptual decisions are biased toward higher-value options when overall gains can be improved. When stimuli demand immediate reactions, the neurophysiological decision process dynamically evolves through distinct phases of growing anticipation, detection, and discrimination, but how value biases are exerted through these phases remains unknown. Here, by parsing motor preparation dynamics in human electrophysiology, we uncovered a multiphasic pattern of countervailing biases operating in speeded decisions. Anticipatory preparation of higher-value actions began earlier, conferring a 'starting point' advantage at stimulus onset, but the delayed preparation of lower-value actions was steeper, conferring a value-opposed buildup-rate bias. This, in turn, was countered by a transient deflection toward the higher-value action evoked by stimulus detection. A neurally-constrained process model featuring anticipatory urgency, biased detection, and accumulation of growing stimulus-discriminating evidence, successfully captured both behavior and motor preparation dynamics. Thus, an intricate interplay of distinct biasing mechanisms serves to prioritise time-constrained perceptual decisions.

Disentangling choice value and choice conflict in sequential decisions under risk

Recent years have witnessed a surge of interest in understanding the neural and cognitive dynamics that drive sequential decision making in general and foraging behavior in particular. Due to the intrinsic properties of most sequential decision-making paradigms, however, previous research in this area has suffered from the difficulty to disentangle properties of the decision related to (a) the value of switching to a new patch versus, which increases monotonically, and (b) the conflict experienced between choosing to stay or leave, which first increases but then decreases after reaching the point of indifference between staying and switching. Here, we show how the same problems arise in studies of sequential decision-making under risk, and how they can be overcome, taking as a specific example recent research on the ‘pig’ dice game. In each round of the ‘pig’ dice game, people roll a die and accumulate rewards until they either decide to proceed to the next round or lose all rewards. By combining simulation-based dissections of the task structure with two experiments, we show how an extension of the standard paradigm, together with cognitive modeling of decision-making processes, allows to disentangle properties related to either switch value or choice conflict. Our study elucidates the cognitive mechanisms of sequential decision making and underscores the importance of avoiding potential pitfalls of paradigms that are commonly used in this research area.

A large body of work has investigated how people make sequential decisions under risk. For instance, how people decide whether to continue gambling for potentially greater rewards or to cash in to avoid losing everything. Here, we identify a critical confound in this line of research, between (a) the value of switching and (b) the amount of conflict between choosing to stay or switch. Using a previously proposed paradigm (i.e., the pig dice game) as an example, we replicated behavior from a recent study and showed that switch value is highly correlated with choice conflict. By simulating behavior across hypothetical contexts, we then identified and tested novel variants of this task that allow to deconfound switch value and conflict. However, only by means of sequential sampling modeling we could conclude that it is conflict rather than switch value that drives response times in this task. Sequential sampling modeling also shows how the switch value influences other cognitive components in this task.

Contribution of the sensorimotor beta oscillations and the cortico-basal ganglia-thalamic circuitry during value-based decision making: A simultaneous EEG-fMRI investigation

In decision neuroscience, the motor system has primarily been considered to be involved in executing choice actions. However, a competing perspective suggests its engagement in the evaluation of options, traditionally considered to be performed by the brain's valuation system. Here, we investigate the role of the motor system in value-based decision making by determining the neural circuitries associated with the sensorimotor beta oscillations previously identified to encode decision options. In a simultaneous EEG-fMRI study, participants evaluated reward and risk associated with a forthcoming action. A significant sensorimotor beta desynchronization was identified prior to and independent of response. The level of beta desynchronization showed evidence of encoding the reward levels. This beta desynchronization covaried, on a trial-by-trial level, with BOLD activity in the cortico-basal ganglia-thalamic circuitry. In contrast, there was only a weak covariation within the valuation network, despite significant modulation of its BOLD activity by reward levels. These results suggest that the way in which decision variables are processed differs in the valuation network and in the cortico-basal ganglia-thalamic circuitry. We propose that sensorimotor beta oscillations indicate incentive motivational drive towards a choice action computed from the decision variables even prior to making a response, and it arises from the cortico-basal ganglia-thalamic circuitry.

Temporal dynamics of decision making: A synthesis of computational and neurophysiological approaches

As interest in the temporal dynamics of decision-making has grown, researchers have increasingly turned to computational approaches such as the drift diffusion model (DDM) to identify how cognitive processes unfold during choice. At the same time, technological advances in noninvasive neurophysiological methods such as electroencephalography and magnetoencephalography now allow researchers to map the neural time course of decision making with millisecond precision. Combining these approaches can potentially yield important new insights into how choices emerge over time. Here we review recent research on the computational and neurophysiological correlates of perceptual and value-based decision making, from DDM parameters to scalp potentials and oscillatory neural activity. Starting with motor response preparation, the most well-understood aspect of the decision process, we discuss evidence that urgency signals and shifts in baseline activation, rather than shifts in the physiological value of the choice-triggering response threshold, are responsible for adjusting response times under speeded choice scenarios. Research on the neural correlates of starting point bias suggests that prestimulus activity can predict biases in motor choice behavior. Finally, studies examining the time dynamics of evidence construction and evidence accumulation have identified signals at frontocentral and centroparietal electrodes associated respectively with these processes, emerging 300-500 ms after stimulus onset. These findings can inform psychological theories of decision-making, providing empirical support for attribute weighting in value-based choice while suggesting theoretical alternatives to dual-process accounts. Further research combining computational and neurophysiological approaches holds promise for providing greater insight into the moment-by-moment evolution of the decision process. This article is categorized under: Psychology > Reasoning and Decision Making Neuroscience > Cognition Economics > Individual Decision-Making.

The Cost of Imagined Actions in a Reward-Valuation Task

Growing evidence suggests that humans and other animals assign value to a stimulus based not only on its inherent rewarding properties, but also on the costs of the action required to obtain it, such as the cost of time. Here, we examined whether such cost also occurs for mentally simulated actions. Healthy volunteers indicated their subjective value for snack foods while the time to imagine performing the action to obtain the different stimuli was manipulated. In each trial, the picture of one food item and a home position connected through a path were displayed on a computer screen. The path could be either large or thin. Participants first rated the stimulus, and then imagined moving the mouse cursor along the path from the starting position to the food location. They reported the onset and offset of the imagined movements with a button press. Two main results emerged. First, imagery times were significantly longer for the thin than the large path. Second, participants liked significantly less the snack foods associated with the thin path (i.e., with longer imagery time), possibly because the passage of time strictly associated with action imagery discounts the value of the reward. Importantly, such effects were absent in a control group of participants who performed an identical valuation task, except that no action imagery was required. Our findings hint at the idea that imagined actions, like real actions, carry a cost that affects deeply how people assign value to the stimuli in their environment.

Cognitive and neural principles of a memory bias on preferential choices

Value-based decisions depend on different forms of memory. However, the respective roles of memory and valuation processes that give rise to these decisions are often vaguely described and have rarely been investigated jointly. In this review article, we address the problem of memory-based decision making from a neuroeconomic perspective. We first describe the neural and cognitive processes involved in decisions requiring memory processes, with a focus on episodic memory. Based on the results of a systematic research program, we then spotlight the phenomenon of the memory bias, a general preference for choice options that can be retrieved from episodic memory more successfully. Our findings indicate that failed memory recall biases neural valuation processes as indicated by altered effective connectivity between the hippocampus and ventromedial prefrontal cortex. This bias can be attributed to meta-cognitive beliefs about the relationship between subjective value and memory as well as to uncertainty aversion. After summarizing the findings, we outline potential future research endeavors to integrate the two research traditions of memory and decision making.

Advances in modeling learning and decision-making in neuroscience

An organism's survival depends on its ability to learn about its environment and to make adaptive decisions in the service of achieving the best possible outcomes in that environment. To study the neural circuits that support these functions, researchers have increasingly relied on models that formalize the computations required to carry them out. Here, we review the recent history of computational modeling of learning and decision-making, and how these models have been used to advance understanding of prefrontal cortex function. We discuss how such models have advanced from their origins in basic algorithms of updating and action selection to increasingly account for complexities in the cognitive processes required for learning and decision-making, and the representations over which they operate. We further discuss how a deeper understanding of the real-world complexities in these computations has shed light on the fundamental constraints on optimal behavior, and on the complex interactions between corticostriatal pathways to determine such behavior. The continuing and rapid development of these models holds great promise for understanding the mechanisms by which animals adapt to their environments, and what leads to maladaptive forms of learning and decision-making within clinical populations.

Timing of readiness potentials reflect a decision-making process in the human brain

Decision-making in two-alternative forced choice tasks has several underlying components including stimulus encoding, perceptual categorization, response selection, and response execution. Sequential sampling models of decision-making are based on an evidence accumulation process to a decision boundary. Animal and human studies have focused on perceptual categorization and provide evidence linking brain signals in parietal cortex to the evidence accumulation process. In this exploratory study, we use a task where the dominant contribution to response time is response selection and model the response time data with the drift-diffusion model. EEG measurement during the task show that the Readiness Potential (RP) recorded over motor areas has timing consistent with the evidence accumulation process. The duration of the RP predicts decision-making time, the duration of evidence accumulation, suggesting that the RP partly reflects an evidence accumulation process for response selection in the motor system. Thus, evidence accumulation may be a neural implementation of decision-making processes in both perceptual and motor systems. The contributions of perceptual categorization and response selection to evidence accumulation processes in decision-making tasks can be potentially evaluated by examining the timing of perceptual and motor EEG signals.

The Readiness Potential reflects planning-based expectation, not uncertainty, in the timing of action

Actions are guided by a combination of external cues, internal intentions, and stored knowledge. Self-initiated voluntary actions, produced without immediate external cues, may be preceded by a slow EEG Readiness Potential (RP) that progressively increases prior to action. The cognitive significance of this neural event is controversial. Some accounts link the RP to the fact that timing of voluntary actions is generated endogenously, without external constraints. Others link it to the unique role of a planning process, and therefore of temporal expectation, in voluntary actions. In many previous experiments, actions are unconstrained by external cues, but also potentially involve preplanning and anticipation. To separate these factors, we developed a reinforcement learning paradigm where participants learned, through trial and error, the optimal time to act. If the RP reflects freedom from external constraint, its amplitude should be greater early in learning, when participants do not yet know when to act. Conversely, if the RP reflects planning, it should be greater later on, when participants have learned, and plan in advance, the time of action. We found that RP amplitudes grew with learning, suggesting that this neural activity reflects planning and anticipation for the forthcoming action, rather than freedom from external constraint.

Representation of Perceptual Evidence in the Human Brain Assessed by Fast, Within-Trial Dynamic Stimuli

In perceptual decision making the brain extracts and accumulates decision evidence from a stimulus over time and eventually makes a decision based on the accumulated evidence. Several characteristics of this process have been observed in human electrophysiological experiments, especially an average build-up of motor-related signals supposedly reflecting accumulated evidence, when averaged across trials. Another recently established approach to investigate the representation of decision evidence in brain signals is to correlate the within-trial fluctuations of decision evidence with the measured signals. We here report results of this approach for a two-alternative forced choice reaction time experiment measured using magnetoencephalography (MEG) recordings. Our results show: (1) that decision evidence is most strongly represented in the MEG signals in three consecutive phases and (2) that posterior cingulate cortex is involved most consistently, among all brain areas, in all three of the identified phases. As most previous work on perceptual decision making in the brain has focused on parietal and motor areas, our findings therefore suggest that the role of the posterior cingulate cortex in perceptual decision making may be currently underestimated.

Goal congruency dominates reward value in accounting for behavioral and neural correlates of value-based decision-making

When choosing between options, whether menu items or career paths, we can evaluate how rewarding each one will be, or how congruent it is with our current choice goal (e.g., to point out the best option or the worst one.). Past decision-making research interpreted findings through the former lens, but in these experiments the most rewarding option was always most congruent with the task goal (choosing the best option). It is therefore unclear to what extent expected reward vs. goal congruency can account for choice value findings. To deconfound these two variables, we performed three behavioral studies and an fMRI study in which the task goal varied between identifying the best vs. the worst option. Contrary to prevailing accounts, we find that goal congruency dominates choice behavior and neural activity. We separately identify dissociable signals of expected reward. Our findings call for a reinterpretation of previous research on value-based choice.

Decision-making research has confounded the reward value of options with their goal-congruency, as the task goal was always to pick the most rewarding option. Here, authors separately asked participants to select the least rewarding of a set of options, revealing a dominant role for goal congruency.

Relation between centro-parietal positivity and diffusion model parameters in both perceptual and memory-based decision making

Several studies have suggested that the centro-parietal positivity (CPP), an EEG potential occurring approximately 500 ms post-stimulus, reflects the accumulation of evidence for making a decision. Yet, most previous studies of the CPP focused exclusively on perceptual decisions with very simple stimuli. In this study, we examined how the dynamics of the CPP depended on the type of decision being made, and whether its slope was related to parameters of an accumulator model of decision making. We show initial evidence that memory- and perceptual decisions about carefully-controlled face stimuli exhibit similar dynamics, but offset by a time difference in decision onset. Importantly, the individual-trial slopes of the CPP are related to the accumulator model's drift parameter. These findings help to further understand the role of the CPP across different kinds of decisions.

The Neurodynamic Decision Variable in Human Multi-alternative Perceptual Choice

The neural dynamics underpinning binary perceptual decisions and their transformation into actions are well studied, but real-world decisions typically offer more than two response alternatives. How does decision-related evidence accumulation dynamically influence multiple action representations in humans? The heightened conservatism required in multiple compared with binary choice scenarios suggests a mechanism that compensates for increased uncertainty when multiple choices are present by suppressing baseline activity. Here, we tracked action representations using corticospinal excitability during four- and two-choice perceptual decisions and modeled them using a sequential sampling framework. We found that the predictions made by leaky competing accumulator models to accommodate multiple choices (i.e., reduced baseline activity to compensate increased uncertainty) were borne out by dynamic changes in human action representations. This suggests a direct and continuous influence of interacting evidence accumulators, each favoring a different decision alternative, on downstream corticospinal excitability during complex choice.

Value-based attentional capture affects multi-alternative decision making

Humans and other animals often violate economic principles when choosing between multiple alternatives, but the underlying neurocognitive mechanisms remain elusive. A robust finding is that adding a third option can alter the relative preference for the original alternatives, but studies disagree on whether the third option's value decreases or increases accuracy. To shed light on this controversy, we used and extended the paradigm of one study reporting a positive effect. However, our four experiments with 147 human participants and a reanalysis of the original data revealed that the positive effect is neither replicable nor reproducible. In contrast, our behavioral and eye-tracking results are best explained by assuming that the third option's value captures attention and thereby impedes accuracy. We propose a computational model that accounts for the complex interplay of value, attention, and choice. Our theory explains how choice sets and environments influence the neurocognitive processes of multi-alternative decision making.

Decisions are expedited through multiple neural adjustments spanning the sensorimotor hierarchy

When decisions are made under speed pressure, "urgency" signals elevate neural activity toward action-triggering thresholds independent of the sensory evidence, thus incurring a cost to choice accuracy. While urgency signals have been observed in brain circuits involved in preparing actions, their influence at other levels of the sensorimotor pathway remains unknown. We used a novel contrast-comparison paradigm to simultaneously trace the dynamics of sensory evidence encoding, evidence accumulation, motor preparation, and muscle activation in humans. Results indicate speed pressure impacts multiple sensorimotor levels but in crucially distinct ways. Evidence-independent urgency was applied to cortical action-preparation signals and downstream muscle activation, but not directly to upstream levels. Instead, differential sensory evidence encoding was enhanced in a way that partially countered the negative impact of motor-level urgency on accuracy, and these opposing sensory-boost and motor-urgency effects had knock-on effects on the buildup and pre-response amplitude of a motor-independent representation of cumulative evidence.

Get a grip: individual variations in grip strength are a marker of brain health

Demonstrations that grip strength has predictive power in relation to a range of health conditions-even when these are assessed decades later-has motivated claims that hand-grip dynamometry has the potential to serve as a "vital sign" for middle-aged and older adults. Central to this belief has been the assumption that grip strength is a simple measure of physical performance that provides a marker of muscle status in general, and sarcopenia in particular. It is now evident that while differences in grip strength between individuals are influenced by musculoskeletal factors, "lifespan" changes in grip strength within individuals are exquisitely sensitive to integrity of neural systems that mediate the control of coordinated movement. The close and pervasive relationships between age-related declines in maximum grip strength and expressions of cognitive dysfunction can therefore be understood in terms of the convergent functional and structural mediation of cognitive and motor processes by the human brain. In the context of aging, maximum grip strength is a discriminating measure of neurological function and brain health.

Neural correlates of economic value and valuation context: an event-related potential study

The value of environmental cues and internal states is continuously evaluated by the human brain, and it is this subjective value that largely guides decision making. The present study aimed to investigate the initial value attribution process, specifically the spatiotemporal activation patterns associated with values and valuation context, using electroencephalographic event-related potentials (ERPs). Participants completed a stimulus rating task in which everyday household items marketed up to a price of £4 were evaluated with respect to their desirability or material properties. The subjective values of items were evaluated as willingness to pay (WTP) in a Becker-DeGroot-Marschak auction. On the basis of the individual's subjective WTP values, the stimuli were divided into high- and low-value items. Source dipole modeling was applied to estimate the cortical sources underlying ERP components modulated by subjective values (high vs. low WTP) and the evaluation condition (value-relevant vs. value-irrelevant judgments). Low-WTP items and value-relevant judgments both led to a more pronounced N2 visual evoked potential at right frontal scalp electrodes. Source activity in right anterior insula and left orbitofrontal cortex was larger for low vs. high WTP at ∼200 ms. At a similar latency, source activity in right anterior insula and right parahippocampal gyrus was larger for value-relevant vs. value-irrelevant judgments. A stronger response for low- than high-value items in anterior insula and orbitofrontal cortex appears to reflect aversion to low-valued item acquisition, which in an auction experiment would be perceived as a relative loss. This initial low-value bias occurs automatically irrespective of the valuation context. NEW & NOTEWORTHY We demonstrate the spatiotemporal characteristics of the brain valuation process using event-related potentials and willingness to pay as a measure of subjective value. The N2 component resolves values of objects with a bias toward low-value items. The value-related changes of the N2 component are part of an automatic valuation process.

Avoidant Responses to Interpersonal Provocation Are Associated with Increased Amygdala and Decreased Mentalizing Network Activity

When intentionally pushed or insulted, one can either flee from the provoker or retaliate. The implementation of such fight-or-flight decisions is a central aspect in the genesis and evolution of aggression episodes, yet it is usually investigated only indirectly or in nonsocial situations. In the present fMRI study, we aimed to distinguish brain regions associated with aggressive and avoidant responses to interpersonal provocation in humans. Participants (thirty-six healthy young women) could either avoid or face a highly (HP) and a lowly (LP) provoking opponent in a competitive reaction time task: the fight-or-escape (FOE) paradigm. Subjects avoided the HP more often, but retaliated when facing her. Moreover, they chose to fight the HP more quickly, and showed increased heart rate (HR) right before confronting her. Orbitofrontal cortex (OFC) and sensorimotor cortex were more active when participants decided to fight, whereas the mentalizing network was engaged when deciding to avoid. Importantly, avoiding the HP relative to the LP was associated with both higher activation in the right basolateral amygdala and lower relative activity in several mentalizing regions [e.g., medial and inferior frontal gyrus (IFG), temporal-parietal junction (TPJ)]. These results suggest that avoidant responses to provocation might result from heightened threat anticipation and are associated with reduced perspective taking. Furthermore, our study helps to reconcile conflicting findings on the role of the mentalizing network, the amygdala, and the OFC in aggression.

Of monkeys and men: Impatience in perceptual decision-making

For decades sequential sampling models have successfully accounted for human and monkey decision-making, relying on the standard assumption that decision makers maintain a pre-set decision standard throughout the decision process. Based on the theoretical argument of reward rate maximization, some authors have recently suggested that decision makers become increasingly impatient as time passes and therefore lower their decision standard. Indeed, a number of studies show that computational models with an impatience component provide a good fit to human and monkey decision behavior. However, many of these studies lack quantitative model comparisons and systematic manipulations of rewards. Moreover, the often-cited evidence from single-cell recordings is not unequivocal and complimentary data from human subjects is largely missing. We conclude that, despite some enthusiastic calls for the abandonment of the standard model, the idea of an impatience component has yet to be fully established; we suggest a number of recently developed tools that will help bring the debate to a conclusive settlement.

The Interplay of Hippocampus and Ventromedial Prefrontal Cortex in Memory-Based Decision Making

Episodic memory and value-based decision making are two central and intensively studied research domains in cognitive neuroscience, but we are just beginning to understand how they interact to enable memory-based decisions. The two brain regions that have been associated with episodic memory and value-based decision making are the hippocampus and the ventromedial prefrontal cortex, respectively. In this review article, we first give an overview of these brain–behavior associations and then focus on the mechanisms of potential interactions between the hippocampus and ventromedial prefrontal cortex that have been proposed and tested in recent neuroimaging studies. Based on those possible interactions, we discuss several directions for future research on the neural and cognitive foundations of memory-based decision making.

Will I Regret It? Anticipated Negative Emotions Modulate Choices in Moral Dilemmas

We tested if post-decisional emotions of regret, guilt, shame, anger, and disgust can account for individuals’ choices in moral dilemmas depicting the choice of letting some people die (non-utilitarian option) or sacrificing one person to save them (utilitarian option). We collected participants’ choices and post-decisional emotional ratings for each option using Footbridge-type dilemmas, in which the sacrifice of one person is the means to save more people, and Trolley-type dilemmas, in which the sacrifice is only a side effect. Moreover, we computed the EEG Readiness Potential to test if the neural activity related to the last phase of decision-making was related to the emotional conflict. Participants reported generally stronger emotions for the utilitarian as compared to the non-utilitarian options, with the exception of anger and regret, which in Trolley-type dilemmas were stronger for the non-utilitarian option. Moreover, participants tended to choose the option that minimized the intensity of negative emotions, irrespective of dilemma type. No significant relationship between emotions and the amplitude of the Readiness Potential emerged. It is possible that anticipated post-decisional emotions play a role in earlier stages of decision-making.

Primary motor cortex contributes to the implementation of implicit value-based rules during motor decisions

In the present study, we investigated the functional contribution of the human primary motor cortex (M1) to motor decisions. Continuous theta burst stimulation (cTBS) was used to alter M1 activity while participants performed a decision-making task in which the reward associated with the subjects' responses (right hand finger movements) depended on explicit and implicit value-based rules. Subjects performed the task over two consecutive days and cTBS occurred in the middle of Day 2, once the subjects were just about to implement implicit rules, in addition to the explicit instructions, to choose their responses, as evident in the control group (cTBS over the right somatosensory cortex). Interestingly, cTBS over the left M1 prevented subjects from implementing the implicit value-based rule while its implementation was enhanced in the group receiving cTBS over the right M1. Hence, cTBS had opposite effects depending on whether it was applied on the contralateral or ipsilateral M1. The use of the explicit value-based rule was unaffected by cTBS in the three groups of subject. Overall, the present study provides evidence for a functional contribution of M1 to the implementation of freshly acquired implicit rules, possibly through its involvement in a cortico-subcortical network controlling value-based motor decisions.

Is There Neural Evidence for an Evidence Accumulation Process in Memory Decisions?

Models of evidence accumulation have been very successful at describing human decision making behavior. Recent years have also seen the first reports of neural correlates of this accumulation process. However, these studies have mostly focused on perceptual decision making tasks, ignoring the role of additional cognitive processes like memory retrieval that are crucial in real-world decisions. In this study, we tried to find a neural signature of evidence accumulation during a recognition memory task. To do this, we applied a method we have successfully used to localize evidence accumulation in scalp EEG during a perceptual decision making task. This time, however, we applied it to intracranial EEG recordings, which provide a much higher spatial resolution. We identified several brain areas where activity ramps up over time, but these neural patterns do not appear to be modulated by behavioral variables such as the amount of available evidence or response time. This casts doubt on the idea of evidence accumulation as a general decision-making mechanism underlying different types of decisions.

The Fox and the Grapes-How Physical Constraints Affect Value Based Decision Making

One fundamental question in decision making research is how humans compute the values that guide their decisions. Recent studies showed that people assign higher value to goods that are closer to them, even when physical proximity should be irrelevant for the decision from a normative perspective. This phenomenon, however, seems reasonable from an evolutionary perspective. Most foraging decisions of animals involve the trade-off between the value that can be obtained and the associated effort of obtaining. Anticipated effort for physically obtaining a good could therefore affect the subjective value of this good. In this experiment, we test this hypothesis by letting participants state their subjective value for snack food while the effort that would be incurred when reaching for it was manipulated. Even though reaching was not required in the experiment, we find that willingness to pay was significantly lower when subjects wore heavy wristbands on their arms. Thus, when reaching was more difficult, items were perceived as less valuable. Importantly, this was only the case when items were physically in front of the participants but not when items were presented as text on a computer screen. Our results suggest automatic interactions of motor and valuation processes which are unexplored to this date and may account for irrational decisions that occur when reward is particularly easy to reach.

Lateralized readiness potentials reveal properties of a neural mechanism for implementing a decision threshold

Many perceptual decision making models posit that participants accumulate noisy evidence over time to improve the accuracy of their decisions, and that in free response tasks, participants respond when the accumulated evidence reaches a decision threshold. Research on the neural correlates of these models' components focuses primarily on evidence accumulation. Far less attention has been paid to the neural correlates of decision thresholds, reflecting the final commitment to a decision. Inspired by a model of bistable neural activity that implements a decision threshold, we reinterpret human lateralized readiness potentials (LRPs) as reflecting the crossing of a decision threshold. Interestingly, this threshold crossing preserves signatures of a drift-diffusion process of evidence accumulation that feeds in to the threshold mechanism. We show that, as our model predicts, LRP amplitudes and growth rates recorded while participants performed a motion discrimination task correlate with individual differences in behaviorally-estimated prior beliefs, decision thresholds and evidence accumulation rates. As such LRPs provide a useful measure to test dynamical models of both evidence accumulation and decision commitment processes non-invasively.

Deciding not to decide: computational and neural evidence for hidden behavior in sequential choice

Understanding the cognitive and neural processes that underlie human decision making requires the successful prediction of how, but also of when, people choose. Sequential sampling models (SSMs) have greatly advanced the decision sciences by assuming decisions to emerge from a bounded evidence accumulation process so that response times (RTs) become predictable. Here, we demonstrate a difficulty of SSMs that occurs when people are not forced to respond at once but are allowed to sample information sequentially: The decision maker might decide to delay the choice and terminate the accumulation process temporarily, a scenario not accounted for by the standard SSM approach. We developed several SSMs for predicting RTs from two independent samples of an electroencephalography (EEG) and a functional magnetic resonance imaging (fMRI) study. In these studies, participants bought or rejected fictitious stocks based on sequentially presented cues and were free to respond at any time. Standard SSM implementations did not describe RT distributions adequately. However, by adding a mechanism for postponing decisions to the model we obtained an accurate fit to the data. Time-frequency analysis of EEG data revealed alternating states of de- and increasing oscillatory power in beta-band frequencies (14–30 Hz), indicating that responses were repeatedly prepared and inhibited and thus lending further support for the existence of a decision not to decide. Finally, the extended model accounted for the results of an adapted version of our paradigm in which participants had to press a button for sampling more information. Our results show how computational modeling of decisions and RTs support a deeper understanding of the hidden dynamics in cognition.

When decisions are made under uncertainty, we often decide not to choose immediately but to search for more information that reduces the uncertainty. In most psychological experiments, however, participants are forced to choose at once and cognitive models do not account for the possibility of deliberately delaying decisions. By modeling RT distributions in a sequential choice paradigm, we demonstrate that people decide not to decide when given the opportunity to sample more information. Importantly, this explicit decision to wait is distinguishable from an implicit delay in ongoing decisions as it actively inhibits this ongoing process. We then looked at EEG spectral power in the beta-band (frequencies from 14 to 30 Hz), which is known to reflect both the preparation and inhibition of responses. The obtained pattern is consistent with our proposal that participants repeatedly alternated between considering and postponing their decision in the sequential task. In an additional behavioral experiment, we show that our model also predicts RTs of the decision to sample more information. Hence, our combination of cognitive modeling and EEG provides converging evidence for the existence of a decision that is usually not directly observable.