Payoff Information Biases a Fast Guess Process in Perceptual Decision Making under Deadline Pressure: Evidence from Behavior, Evoked Potentials, and Quantitative Model Comparison

What mechanisms mediate prior probability effects on rapid-choice decision-making?

Rapid-choice decision-making is biased by prior probability of response alternatives. Conventionally, prior probability effects are assumed to selectively affect, response threshold, which determines the amount of evidence required to trigger a decision. However, there may also be effects on the rate at which evidence is accumulated and the time required for non-decision processes (e.g., response production). Healthy young (n = 21) and older (n = 20) adults completed a choice response-time task requiring left- or right-hand responses to imperative stimuli. Prior probability was manipulated using a warning stimulus that informed participants that a particular response was 70% likely (i.e., the imperative stimulus was either congruent or incongruent with the warning stimulus). In addition, prior probability was either fixed for blocks of trials (block-wise bias) or varied from trial-to-trial (trial-wise bias). Response time and accuracy data were analysed using the racing diffusion evidence-accumulation model to test the selective influence assumption. Response times for correct responses were slower on incongruent than congruent trials, and older adults' responses were slower, but more accurate, than young adults. Evidence-accumulation modelling favoured an effect of prior probability on both response thresholds and nondecision time. Overall, the current results cast doubt on the selective threshold influence assumption in the racing diffusion model.

Neural Mechanisms That Make Perceptual Decisions Flexible

Neural mechanisms of perceptual decision making have been extensively studied in experimental settings that mimic stable environments with repeating stimuli, fixed rules, and payoffs. In contrast, we live in an ever-changing environment and have varying goals and behavioral demands. To accommodate variability, our brain flexibly adjusts decision-making processes depending on context. Here, we review a growing body of research that explores the neural mechanisms underlying this flexibility. We highlight diverse forms of context dependency in decision making implemented through a variety of neural computations. Context-dependent neural activity is observed in a distributed network of brain structures, including posterior parietal, sensory, motor, and subcortical regions, as well as the prefrontal areas classically implicated in cognitive control. We propose that investigating the distributed network underlying flexible decisions is key to advancing our understanding and discuss a path forward for experimental and theoretical investigations.

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.

Do estimates of numerosity really adhere to Weber's law? A reexamination of two case studies

Both humans and nonhuman animals can exhibit sensitivity to the approximate number of items in a visual array or events in a sequence, and across various paradigms, uncertainty in numerosity judgments increases with the number estimated or produced. The pattern of increase is usually described as exhibiting approximate adherence to Weber’s law, such that uncertainty increases proportionally to the mean estimate, resulting in a constant coefficient of variation. Such a pattern has been proposed to be a signature characteristic of an innate “number sense.” We reexamine published behavioral data from two studies that have been cited as prototypical evidence of adherence to Weber’s law and observe that in both cases variability increases less than this account would predict, as indicated by a decreasing coefficient of variation with an increase in number. We also consider evidence from numerosity discrimination studies that show deviations from the constant coefficient of variation pattern. Though behavioral data can sometimes exhibit approximate adherence to Weber’s law, our findings suggest that such adherence is not a fixed characteristic of the mechanisms whereby humans and animals estimate numerosity. We suggest instead that the observed pattern of increase in variability with number depends on the circumstances of the task and stimuli, and reflects an adaptive ensemble of mechanisms composed to optimize performance under these circumstances.

Perceptual change-of-mind decisions are sensitive to absolute evidence magnitude

To navigate the world safely, we often need to rapidly 'change our mind' about decisions. Current models assume that initial decisions and change-of-mind decisions draw upon common sources of sensory evidence. In two-choice scenarios, this evidence may be 'relative' or 'absolute'. For example, when judging which of two objects is the brightest, the luminance difference and luminance ratio between the two objects are sources of 'relative' evidence, which are invariant across additive and multiplicative luminance changes. Conversely, the overall luminance of the two objects combined is a source of 'absolute' evidence, which necessarily varies across symmetric luminance manipulations. Previous studies have shown that initial decisions are sensitive to both relative and absolute evidence; however, it is unknown whether change-of-mind decisions are sensitive to absolute evidence. Here, we investigated this question across two experiments. In each experiment participants indicated which of two flickering greyscale squares was brightest. Following an initial decision, the stimuli remained on screen for a brief period and participants could change their response. To investigate the effect of absolute evidence, the overall luminance of the two squares was varied whilst either the luminance difference (Experiment 1) or luminance ratio (Experiment 2) was held constant. In both experiments we found that increases in absolute evidence led to faster, less accurate initial responses and slower changes of mind. Change-of-mind accuracy decreased when the luminance difference was held constant, but remained unchanged when the luminance ratio was fixed. We show that the three existing change-of-mind models cannot account for our findings. We then fit three alternative models, previously used to account for the effect of absolute evidence on one-off decisions, to the data. A leaky competing accumulator model best accounted for the changes in behaviour across absolute evidence conditions - suggesting an important role for input-dependent leak in explaining perceptual changes of mind.

Confluence of Timing and Reward Biases in Perceptual Decision-Making Dynamics

Although the decisions of our daily lives often occur in the context of temporal and reward structures, the impact of such regularities on decision-making strategy is poorly understood. Here, to explore how temporal and reward context modulate strategy, we trained 2 male rhesus monkeys to perform a novel perceptual decision-making task with asymmetric rewards and time-varying evidence reliability. To model the choice and response time patterns, we developed a computational framework for fitting generalized drift-diffusion models, which flexibly accommodate diverse evidence accumulation strategies. We found that a dynamic urgency signal and leaky integration, in combination with two independent forms of reward biases, best capture behavior. We also tested how temporal structure influences urgency by systematically manipulating the temporal structure of sensory evidence, and found that the time course of urgency was affected by temporal context. Overall, our approach identified key components of cognitive mechanisms for incorporating temporal and reward structure into decisions.SIGNIFICANCE STATEMENT In everyday life, decisions are influenced by many factors, including reward structures and stimulus timing. While reward and timing have been characterized in isolation, ecologically valid decision-making involves a multiplicity of factors acting simultaneously. This raises questions about whether the same decision-making strategy is used when these two factors are concurrently manipulated. To address these questions, we trained rhesus monkeys to perform a novel decision-making task with both reward asymmetry and temporal uncertainty. In order to understand their strategy and hint at its neural mechanisms, we used the new generalized drift diffusion modeling framework to model both reward and timing mechanisms. We found two of each reward and timing mechanisms are necessary to explain our data.

Centroparietal activity mirrors the decision variable when tracking biased and time-varying sensory evidence

Decision-making is a fundamental human activity requiring explanation at the neurocognitive level. Current theoretical frameworks assume that, during sensory-based decision-making, the stimulus is sampled sequentially. The resulting evidence is accumulated over time as a decision variable until a threshold is reached and a response is initiated. Several neural signals, including the centroparietal positivity (CPP) measured from the human electroencephalogram (EEG), appear to display the accumulation-to-bound profile associated with the decision variable. Here, we evaluate the putative computational role of the CPP as a model-derived accumulation-to-bound signal, focussing on point-by-point correspondence between model predictions and data in order to go beyond simple summary measures like average slope. In two experiments, we explored the CPP under two manipulations (namely non-stationary evidence and probabilistic decision biases) that complement one another by targeting the shape and amplitude of accumulation respectively. We fit sequential sampling models to the behavioural data, and used the resulting parameters to simulate the decision variable, before directly comparing the simulated profile to the CPP waveform. In both experiments, model predictions deviated from our naïve expectations, yet showed similarities with the neurodynamic data, illustrating the importance of a formal modelling approach. The CPP appears to arise from brain processes that implement a decision variable (as formalised in sequential-sampling models) and may therefore inform our understanding of decision-making at both the representational and implementational levels of analysis, but at this point it is uncertain whether a single model can explain how the CPP varies across different kinds of task manipulation.

Choice history biases subsequent evidence accumulation

Perceptual choices depend not only on the current sensory input but also on the behavioral context, such as the history of one's own choices. Yet, it remains unknown how such history signals shape the dynamics of later decision formation. In models of decision formation, it is commonly assumed that choice history shifts the starting point of accumulation toward the bound reflecting the previous choice. We here present results that challenge this idea. We fit bounded-accumulation decision models to human perceptual choice data, and estimated bias parameters that depended on observers' previous choices. Across multiple task protocols and sensory modalities, individual history biases in overt behavior were consistently explained by a history-dependent change in the evidence accumulation, rather than in its starting point. Choice history signals thus seem to bias the interpretation of current sensory input, akin to shifting endogenous attention toward (or away from) the previously selected interpretation.

Choice perseveration in value-based decision making: The impact of inter-trial interval and mood

In a series of decisions, people tend to show choice perseveration, that is, they repeat their choices. This choice perseveration is assumed to emerge due to residual activity from the previous decision. Here, we use a computational model with attractor dynamics to describe this process and to predict how choice perseveration can be modulated. We derive two qualitative predictions: Choice perseveration should decrease under longer (vs. shorter) inter-trial intervals and positive (vs. negative) mood. We test these predictions in a dynamic decision task where we modulate decisions across trials via sequentially manipulated reward options. Our findings replicate our previous study in showing choice perseveration in value-based decision making. Furthermore, choice perseveration decreased with increasing inter-trial interval as predicted by the model. However, we did not find clear evidence supporting mood effects on choice perseveration. We discuss how integrating decision process dynamics by the means of applying the neural attractor model can increase our understanding of the evolution of decision outcomes and therefore complement the psychophysical perspective on decision making.

Visuomotor Correlates of Conflict Expectation in the Context of Motor Decisions

Many behaviors require choosing between conflicting options competing against each other in visuomotor areas. Such choices can benefit from top-down control processes engaging frontal areas in advance of conflict when it is anticipated. Yet, very little is known about how this proactive control system shapes the visuomotor competition. Here, we used electroencephalography in human subjects (male and female) to identify the visual and motor correlates of conflict expectation in a version of the Eriksen Flanker task that required left or right responses according to the direction of a central target arrow surrounded by congruent or incongruent (conflicting) flankers. Visual conflict was either highly expected (it occurred in 80% of trials; mostly incongruent blocks) or very unlikely (20% of trials; mostly congruent blocks). We evaluated selective attention in the visual cortex by recording target- and flanker-related steady-state visual-evoked potentials (SSVEPs) and probed action selection by measuring response-locked potentials (RLPs) in the motor cortex. Conflict expectation enhanced accuracy in incongruent trials, but this improvement occurred at the cost of speed in congruent trials. Intriguingly, this behavioral adjustment occurred while visuomotor activity was less finely tuned: target-related SSVEPs were smaller while flanker-related SSVEPs were higher in mostly incongruent blocks than in mostly congruent blocks, and incongruent trials were associated with larger RLPs in the ipsilateral (nonselected) motor cortex. Hence, our data suggest that conflict expectation recruits control processes that augment the tolerance for inappropriate visuomotor activations (rather than processes that downregulate their amplitude), allowing for overflow activity to occur without having it turn into the selection of an incorrect response.SIGNIFICANCE STATEMENT Motor choices made in front of discordant visual information are more accurate when conflict can be anticipated, probably due to the engagement of top-down control from frontal areas. How this control system modulates activity within visual and motor areas is unknown. Here, we show that, when control processes are recruited in anticipation of conflict, as evidenced by higher midfrontal theta activity, visuomotor activity is less finely tuned: visual processing of the goal-relevant location was reduced and the motor cortex displayed more inappropriate activations, compared with when conflict was unlikely. We argue that conflict expectation is associated with an expansion of the distance-to-selection threshold, improving accuracy while the need for online control of visuomotor activity is reduced.

It Pays to Prepare: Human Motor Preparation Depends on the Relative Value of Potential Response Options

Alternative motor responses can be prepared in parallel. Here, we used electroencephalography (EEG) to test whether the parallel preparation of alternative response options is modulated by their relative value. Participants performed a choice response task with three potential actions: isometric contraction of the left, the right, or both wrists. An imperative stimulus (IS) appeared after a warning cue, such that the initiation time of a required action was predictable, but the specific action was not. To encourage advanced preparation, the target was presented 200 ms prior to the IS, and only correct responses initiated within ±100 ms of the IS were rewarded. At baseline, all targets were equally rewarded and probable. Then, responses with one hand were made more valuable, either by increasing the probability that the left or right target would be required (Exp. 1; n = 31) or by increasing the reward magnitude of one target (Exp. 2, n = 36). We measured reaction times, movement vigor, and an EEG correlate of action preparation (value-based lateralized readiness potential) prior to target presentation. Participants responded earlier to more frequent and more highly rewarded targets, and movements to highly rewarded targets were more vigorous. The EEG was more negative over the hemisphere contralateral to the more repeated/rewarded hand, implying an increased neural preparation of more valuable actions. Thus, changing the value of alternative response options can lead to greater preparation of actions associated with more valuable outcomes. This preparation asymmetry likely contributes to behavioral biases that are typically observed toward repeated or rewarded targets.

Dynamic Interplay of Value and Sensory Information in High-Speed Decision Making

In dynamic environments, split-second sensorimotor decisions must be prioritized according to potential payoffs to maximize overall rewards. The impact of relative value on deliberative perceptual judgments has been examined extensively [1-6], but relatively little is known about value-biasing mechanisms in the common situation where physical evidence is strong but the time to act is severely limited. In prominent decision models, a noisy but statistically stationary representation of sensory evidence is integrated over time to an action-triggering bound, and value-biases are affected by starting the integrator closer to the more valuable bound. Here, we show significant departures from this account for humans making rapid sensory-instructed action choices. Behavior was best explained by a simple model in which the evidence representation-and hence, rate of accumulation-is itself biased by value and is non-stationary, increasing over the short decision time frame. Because the value bias initially dominates, the model uniquely predicts a dynamic "turn-around" effect on low-value cues, where the accumulator first launches toward the incorrect action but is then re-routed to the correct one. This was clearly exhibited in electrophysiological signals reflecting motor preparation and evidence accumulation. Finally, we construct an extended model that implements this dynamic effect through plausible sensory neural response modulations and demonstrate the correspondence between decision signal dynamics simulated from a behavioral fit of that model and the empirical decision signals. Our findings suggest that value and sensory information can exert simultaneous and dynamically countervailing influences on the trajectory of the accumulation-to-bound process, driving rapid, sensory-guided actions.

Neural Signature of Value-Based Sensorimotor Prioritization in Humans

In situations in which impending sensory events demand fast action choices, we must be ready to prioritize higher-value courses of action to avoid missed opportunities. When such a situation first presents itself, stimulus-action contingencies and their relative value must be encoded to establish a value-biased state of preparation for an impending sensorimotor decision. Here, we sought to identify neurophysiological signatures of such processes in the human brain (both female and male). We devised a task requiring fast action choices based on the discrimination of a simple visual cue in which the differently valued sensory alternatives were presented 750-800 ms before as peripheral "targets" that specified the stimulus-action mapping for the upcoming decision. In response to the targets, we identified a discrete, transient, spatially selective signal in the event-related potential (ERP), which scaled with relative value and strongly predicted the degree of behavioral bias in the upcoming decision both across and within subjects. This signal is not compatible with any hitherto known ERP signature of spatial selection and also bears novel distinctions with respect to characterizations of value-sensitive, spatially selective activity found in sensorimotor areas of nonhuman primates. Specifically, a series of follow-up experiments revealed that the signal was reliably invoked regardless of response laterality, response modality, sensory feature, and reward valence. It was absent, however, when the response deadline was relaxed and the strategic need for biasing removed. Therefore, more than passively representing value or salience, the signal appears to play a versatile and active role in adaptive sensorimotor prioritization.SIGNIFICANCE STATEMENT In many situations such as fast-moving sports, we must be ready to act fast in response to sensory events and, in our preparation, prioritize courses of action that lead to greater rewards. Although behavioral effects of value biases in sensorimotor decision making have been widely studied, little is known about the neural processes that set these biases in place beforehand. Here, we report the discovery of a transient, spatially selective neural signal in humans that encodes the relative value of competing decision alternatives and strongly predicts behavioral value biases in decisions made ∼500 ms later. Follow-up manipulations of value differential, reward valence, response modality, sensory features, and time constraints establish that the signal reflects an active, feature- and effector-general preparatory mechanism for value-based prioritization.

Mice optimize timed decisions about probabilistic outcomes under deadlines

Optimal performance in temporal decisions requires the integration of timing uncertainty with environmental statistics such as probability or cost functions. Reward maximization under response deadlines constitutes one of the most stringent examples of these problems. The current study investigated whether and how mice can optimize their timing behavior in a complex experimental setting under a response deadline in which reward maximization required the integration of timing uncertainty with a geometrically increasing probability/decreasing cost function. Mice optimized their performance under seconds-long response deadlines when the underlying function was reward probability but approached this level of performance when the underlying function was reward cost, only under the assumption of logarithmically scaled subjective costs. The same subjects were then tested in a timed response inhibition task characterized by response rules that conflicted with the initial task, not responding earlier than a schedule as opposed to not missing the deadline. Irrespective of original test groups, mice optimized the timing of their inhibitory control in the second experiment. These results provide strong support for the ubiquity of optimal temporal risk assessment in mice.

Response repetition biases in human perceptual decisions are explained by activity decay in competitive attractor models

Animals and humans have a tendency to repeat recent choices, a phenomenon known as choice hysteresis. The mechanism for this choice bias remains unclear. Using an established, biophysically informed model of a competitive attractor network for decision making, we found that decaying tail activity from the previous trial caused choice hysteresis, especially during difficult trials, and accurately predicted human perceptual choices. In the model, choice variability could be directionally altered through amplification or dampening of post-trial activity decay through simulated depolarizing or hyperpolarizing network stimulation. An analogous intervention using transcranial direct current stimulation (tDCS) over left dorsolateral prefrontal cortex (dlPFC) yielded a close match between model predictions and experimental results: net soma depolarizing currents increased choice hysteresis, while hyperpolarizing currents suppressed it. Residual activity in competitive attractor networks within dlPFC may thus give rise to biases in perceptual choices, which can be directionally controlled through non-invasive brain stimulation.

The effects of payoff manipulations on temporal bisection performance

There is growing evidence that alterations in reward rates modify timing behavior demonstrating the role of motivational factors in interval timing behavior. This study aimed to investigate the effects of manipulations of rewards and penalties on temporal bisection performance in humans. Participants were trained to classify experienced time intervals as short or long based on the reference durations. Two groups of participants were tested under three different bias conditions in which either the relative reward magnitude or penalty associated with correct or incorrect categorizations of short and long reference durations was manipulated. Participants adapted their choice behavior (i.e., psychometric functions shifted) based on these payoff manipulations in directions predicted by reward maximization. The signal detection theory-based analysis of the data revealed that payoff contingencies affected the response bias parameter (B″) without altering participants' sensitivity (A') to temporal distances. Finally, the response time (RT) analysis showed that short categorization RTs increased, whereas long categorization RTs decreased as a function of stimulus durations. However, overall RTs did not exhibit any modulation in response to payoff manipulations. Taken together, this study provides additional support for the effects of motivational variables on temporal decision-making.

Neural Mechanisms of Post-error Adjustments of Decision Policy in Parietal Cortex

Humans often slow down after mistakes (post-error slowing [PES]), but the neural mechanism and adaptive role of PES remain controversial. We studied changes in the neural mechanisms of decision making after errors in humans and monkeys that performed a motion direction discrimination task. We found that PES is mediated by two factors: a reduction in sensitivity to sensory information and an increase in the decision bound. Both effects are implemented through dynamic changes in the decision-making process. Neuronal responses in the monkey lateral intraparietal area revealed that bound changes are implemented by decreasing an evidence-independent urgency signal. They also revealed a reduction in the rate of evidence accumulation, reflecting reduced sensitivity. These changes in the bound and sensitivity provide a quantitative account of choices and response times. We suggest that PES reflects an adaptive increase of decision bound in anticipation of maladaptive reductions in sensitivity to incoming evidence.