Evidence Accumulation Process Research Articles

Population Representation of the Confidence in a Decision in the Lateral Intraparietal Area of the Macaque.

Confidence in a decision is the belief, prior to feedback, that one's choice is correct. In the brain, many decisions are implemented as a race between competing evidence-accumulation processes. We ask whether the neurons that represent evidence accumulation also carry information about whether the choice is correct (i.e., confidence). Monkeys performed a reaction time version of the random dot motion task. Neuropixels probes were used to record from neurons in the lateral intraparietal (LIP) area. LIP neurons with response fields that overlap the choice-target contralateral to the recording site (Tin neurons) represent the accumulation of evidence in favor of contralateral target selection. We demonstrate that shortly before a contralateral choice is reported, the population of Tin neurons contains information about the accuracy of the choice (i.e., whether the choice is correct or incorrect). This finding is unexpected because, on average, Tin neurons exhibit a level of activity before the report that is independent of reaction time and evidence strength-both strong predictors of accuracy. This apparent contradiction is resolved by examining the variability in neuronal responses across the population of Tin neurons. While on average, Tin neurons exhibit a stereotyped level of activity before a contralateral choice, many neurons depart from this average in a consistent manner. From these neurons, the accuracy of the choice can be predicted using a simple logistic decoder. The accuracy of the choice predicted from neural activity reproduces the hallmarks of confidence identified in human behavioral experiments. Therefore, neurons that represent evidence accumulation can also inform the monkey's confidence.

Neural basis of concurrent deliberation toward a choice and degree of confidence.

Decision confidence plays a key role in flexible behavior, but exactly how and when it arises in the brain remains unclear. Theoretical accounts suggest that confidence can be inferred from the same evidence accumulation process that governs choice and response time (RT), implying that a provisional confidence assessment could be updated in parallel with decision formation. We tested this using a novel RT task in nonhuman primates that measures choice and confidence with a single eye movement on every trial. Monkey behavior was well fit by a 2D bounded accumulator model instantiating parallel processing of evidence, rejecting a serial model in which choice is resolved first followed by post-decision accumulation for confidence. Neural activity in area LIP reflected concurrent accumulation, exhibiting within-trial dynamics consistent with parallel updating at near zero time lag, and significant covariation in choice and confidence signals across the population. The results demonstrate that monkeys concurrently process a single stream of evidence to arrive at a choice and level of confidence, and illuminate a candidate neural mechanism for this ability.

Bayesian confidence in optimal decisions.

The optimal way to make decisions in many circumstances is to track the difference in evidence collected in favor of the options. The drift diffusion model (DDM) implements this approach and provides an excellent account of decisions and response times. However, existing DDM-based models of confidence exhibit certain deficits, and many theories of confidence have used alternative, nonoptimal models of decisions. Motivated by the historical success of the DDM, we ask whether simple extensions to this framework might allow it to better account for confidence. Motivated by the idea that the brain will not duplicate representations of evidence, in all model variants decisions and confidence are based on the same evidence accumulation process. We compare the models to benchmark results, and successfully apply four qualitative tests concerning the relationships between confidence, evidence, and time, in a new preregistered study. Using computationally cheap expressions to model confidence on a trial-by-trial basis, we find that a subset of model variants also provide a very good to excellent account of precise quantitative effects observed in confidence data. Specifically, our results favor the hypothesis that confidence reflects the strength of accumulated evidence penalized by the time taken to reach the decision (Bayesian readout), with the penalty applied not perfectly calibrated to the specific task context. These results suggest there is no need to abandon the DDM or single accumulator models to successfully account for confidence reports. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Value-based decision-making in regular alcohol consumers following experimental manipulation of alcohol value

BackgroundDevaluation of alcohol leads to reductions in alcohol choice and consumption; however, the cognitive mechanisms that underpin this relationship are not well-understood. In this study we applied a computational model of value-based decision-making (VBDM) to decisions made about alcohol and alcohol-unrelated cues following experimental manipulation of alcohol value. Method: Using a pre-registered within-subject design, thirty-six regular alcohol consumers (≥14 UK units per week) completed a two-alternative forced choice task where they chose between two alcohol images (in one block) or two soft drink images (in a different block) after watching videos that emphasised the positive (alcohol value), and separately, the negative (alcohol devalue) consequences of alcohol. On each block, participants pressed a key to select the image depicting the drink they would rather consume. A drift–diffusion model (DDM) was fitted to reaction time and choice data to estimate evidence accumulation (EA) processes and response thresholds during the different blocks in each experimental condition. Findings: In the alcohol devalue condition, soft drink EA rates were significantly higher compared to alcohol EA rates (p = 0.04, d = 0.31), and compared to soft drink EA rates in the alcohol value condition (p = 0.01, d = 0.38). However, EA rates for alcoholic drinks and response thresholds (for either drink type) were unaffected by the experimental manipulation. Conclusions: In line with behavioural economic models of addiction that emphasise the important role of alternative reinforcement, experimentally manipulating alcohol value is associated with changes in the internal cognitive processes that precede soft drink choice.

Tactile shape discrimination for moving stimuli

In this study, we explored spatial-temporal dependencies and their impact on the tactile perception of moving objects. Building on previous research linking visual perception and human movement, we examined if an imputed motion mechanism operates within the tactile modality. We focused on how biological coherence between space and time, characteristic of human movement, influences tactile perception. An experiment was designed wherein participants were stimulated on their right palm with tactile patterns, either ambiguous (incongruent conditions) or non-ambiguous (congruent conditions) relative to a biological motion law (two-thirds power law) and asked to report perceived shape and associated confidence. Our findings reveal that introducing ambiguous tactile patterns (1) significantly diminishes tactile discrimination performance, implying motor features of shape recognition in vision are also observed in the tactile modality, and (2) undermines participants’ response confidence, uncovering the accessibility degree of information determining the tactile percept’s conscious representation. Analysis based on the Hierarchical Drift Diffusion Model unveiled the sensitivity of the evidence accumulation process to the stimulus’s informational ambiguity and provides insight into tactile perception as predictive dynamics for reducing uncertainty. These discoveries deepen our understanding of tactile perception mechanisms and underscore the criticality of predictions in sensory information processing.

A hybrid approach to dynamic cognitive psychometrics

Dynamic cognitive psychometrics measures mental capacities based on the way behavior unfolds over time. It does so using models of psychological processes whose validity is grounded in research from experimental psychology and the neurosciences. However, these models can sometimes have undesirable measurement properties. We propose a “hybrid” modeling approach that achieves good measurement by blending process-based and descriptive components. We demonstrate the utility of this approach in the stop-signal paradigm, in which participants make a series of speeded choices, but occasionally are required to withhold their response when a “stop signal” occurs. The stop-signal paradigm is widely used to measure response inhibition based on a modeling framework that assumes a race between processes triggered by the choice and the stop stimuli. However, the key index of inhibition, the latency of the stop process (i.e., stop-signal reaction time), is not directly observable, and is poorly estimated when the choice and the stop runners are both modeled by psychologically realistic evidence-accumulation processes. We show that using a descriptive account of the stop process, while retaining a realistic account of the choice process, simultaneously enables good measurement of both stop-signal reaction time and the psychological factors that determine choice behavior. We show that this approach, when combined with hierarchical Bayesian estimation, is effective even in a complex choice task that requires participants to perform only a relatively modest number of test trials.

Isolating objective and subjective filling-in using the drift diffusion model.

Spatial context is known to influence the behavioral sensitivity (d') and the decision criterion (c) when detecting low-contrast targets. Of interest here is the effect on the decision criterion. Polat and Sagi (2007) demonstrated that, for a Gabor target positioned between two similar co-aligned high-contrast flankers, the observers' reports of seeing the target (Hit and False Alarm) decreased with increasing target-flanker distance. This effect was more pronounced when the distance was randomized within testing blocks compared to when it was fixed. According to signal detection theory (SDT), the latter result suggests that the decision criterion is adjusted to a specific distance-dependent combination of signal (S) and noise (N) when the S and N statistics are fixed, but not when they vary across trials. However, SDT cannot differentiate between changes in the decision bias (the criterion shift) and changes introduced by variations in S and N (the signal and noise shift). To circumvent this limitation of SDT, we analyzed the reaction time (RT) data within the framework of the drift diffusion model (DDM). We performed an RT analysis of the target-flanker interactions using data from Polat and Sagi (2007) and Zomet et al. (2008; 2016). The analysis revealed a stronger dependence on flankers for faster RTs and a weaker dependence for slower RTs. The results can be explained by DDM, where an evidence accumulation process depends on the flankers via a change in the rate of the evidence (signal and noise shift) and on observers' prior knowledge via a change in the starting point (criterion shift), leading to RT-independent and RT-dependent effects, respectively. The RT-independent distance-dependent response bias is attributed to the observers' inability to learn multiple internal distributions required to accommodate the distance-dependent effects of the flankers on both the signal and noise.

Distributed coding of evidence accumulation across the mouse brain using microcircuits with a diversity of timescales.

The gradual accumulation of noisy evidence for or against options is the main step in the perceptual decision-making process. Using brain-wide electrophysiological recording in mice (Steinmetz et al., 2019), we examined neural correlates of evidence accumulation across brain areas. We demonstrated that the neurons with Drift-Diffusion-Model-like firing rate activity (i.e., evidence-sensitive ramping firing rate) were distributed across the brain. Exploring the underlying neural mechanism of evidence accumulation for the DDM-like neurons revealed different accumulation mechanisms (i.e. single and race) both within and across the brain areas. Our findings support the hypothesis that evidence accumulation is happening through multiple integration mechanisms in the brain. We further explored the timescale of the integration process in the single and race accumulator models. The results demonstrated that the accumulator microcircuits within each brain area had distinct properties in terms of their integration timescale, which were organized hierarchically across the brain. These findings support the existence of evidence accumulation over multiple timescales. Besides the variability of integration timescale across the brain, a heterogeneity of timescales was observed within each brain area as well. We demonstrated that this variability reflected the diversity of microcircuit parameters, such that accumulators with longer integration timescales had higher recurrent excitation strength.Significance StatementIn this paper we characterized the perceptual decision-making process across the mouse brain. Our findings shed more light on the decision-making process by analyzing the brain-wide electrophysiological recording dataset. This paper contains a comprehensive analysis to characterize different aspects of the evidence accumulation process, including the distribution of accumulator-like neurons, the timescale of information integration, accumulation architecture, and the relationship between accumulators' timescale and their integration circuit properties.

Risky decisions are influenced by individual attributes as a function of risk preference

It has long been assumed in economic theory that multi-attribute decisions involving several attributes or dimensions – such as probabilities and amounts of money to be earned during risky choices – are resolved by first combining the attributes of each option to form an overall expected value and then comparing the expected values of the alternative options, using a unique evidence accumulation process. A plausible alternative would be performing independent comparisons between the individual attributes and then integrating the results of the comparisons afterwards. Here, we devise a novel method to disambiguate between these types of models, by orthogonally manipulating the expected value of choice options and the relative salience of their attributes. Our results, based on behavioral measures and drift-diffusion models, provide evidence in favor of the framework where information about individual attributes independently impacts deliberation. This suggests that risky decisions are resolved by running in parallel multiple comparisons between the separate attributes – possibly alongside an additional comparison of expected value. This result stands in contrast with the assumption of standard economic theory that choices require a unique comparison of expected values and suggests that at the cognitive level, decision processes might be more distributed than commonly assumed. Beyond our planned analyses, we also discovered that attribute salience affects people of different risk preference type in different ways: risk-averse participants seem to focus more on probability, except when monetary amount is particularly high; risk-neutral/seeking participants, in contrast, seem to focus more on monetary amount, except when probability is particularly low.

Competing neural representations of choice shape evidence accumulation in humans.

Making adaptive choices in dynamic environments requires flexible decision policies. Previously, we showed how shifts in outcome contingency change the evidence accumulation process that determines decision policies. Using in silico experiments to generate predictions, here we show how the cortico-basal ganglia-thalamic (CBGT) circuits can feasibly implement shifts in decision policies. When action contingencies change, dopaminergic plasticity redirects the balance of power, both within and between action representations, to divert the flow of evidence from one option to another. When competition between action representations is highest, the rate of evidence accumulation is the lowest. This prediction was validated in in vivo experiments on human participants, using fMRI, which showed that (1) evoked hemodynamic responses can reliably predict trial-wise choices and (2) competition between action representations, measured using a classifier model, tracked with changes in the rate of evidence accumulation. These results paint a holistic picture of how CBGT circuits manage and adapt the evidence accumulation process in mammals.

Neural segregation and integration of sensory, decision, and action processes during object categorization

Neural and computational evidence suggests that perceptual decisions depend on an evidence accumulation process. The gradual reveal fMRI method, which prolongs a decision to match the slow temporal resolution of fMRI measurements, has classified dorsal visual stream regions as “Action” (alternatively, “Moment of Recognition” or “Commitment”) and ventral visual stream regions as “Accumulator.” Previous gradual reveal fMRI studies, however, only tested actions that were in response to decisions and, thus, related to evidence accumulation. To fully dissociate the contribution of sensory, decision, and motor components to Action and Accumulator regions in the dorsal and ventral visual streams, we extended the gradual reveal paradigm to also include responses made to cues where no decision was necessary. We found that the lateral occipital cortex in the ventral visual stream showed a highly selective Accumulator profile, whereas regions in the fusiform gyrus were influenced by action generation. Dorsal visual stream regions showed strikingly similar profiles as classical motor regions and also as regions of the salience network. These results suggest that the dorsal and ventral visual streams may appear highly segregated because they include a small number of regions that are highly selective for Accumulator or Action. However, the streams may be more integrated than previously thought and this integration may be accomplished by regions with graded responses that are less selective (i.e., more distributed).

Balancing true and false detection of intermittent sensory targets by adjusting the inputs to the evidence accumulation process.

Decisions about noisy stimuli are widely understood to be made by accumulating evidence up to a decision bound that can be adjusted according to task demands. However, relatively little is known about how such mechanisms operate in continuous monitoring contexts requiring intermittent target detection. Here, we examined neural decision processes underlying detection of 1 s coherence targets within continuous random dot motion, and how they are adjusted across contexts with weak, strong, or randomly mixed weak/strong targets. Our prediction was that decision bounds would be set lower when weak targets are more prevalent. Behavioural hit and false alarm rate patterns were consistent with this, and were well captured by a bound-adjustable leaky accumulator model. However, beta-band EEG signatures of motor preparation contradicted this, instead indicating lower bounds in the strong-target context. We thus tested two alternative models in which decision-bound dynamics were constrained directly by beta measurements, respectively, featuring leaky accumulation with adjustable leak, and non-leaky accumulation of evidence referenced to an adjustable sensory-level criterion. We found that the latter model best explained both behaviour and neural dynamics, highlighting novel means of decision policy regulation and the value of neurally informed modelling.

Enhanced decision-making in nicotine dependent individuals who abstain: A computational analysis using Hierarchical Drift Diffusion Modeling

BackgroundVariability in decision-making capacity and reward responsiveness may underlie differences in the ability to abstain from smoking. Computational modeling of choice behavior, as with the Hierarchical Drift Diffusion Model (HDDM), can help dissociate reward responsiveness from underlying components of decision-making. Here we used the HDDM to identify which decision-making or reward-related parameters, extracted from data acquired in a reward processing task, contributed to the ability of people who smoke that are not seeking treatment to abstain from cigarettes during a laboratory task. Methods80 adults who smoke cigarettes completed the Probabilistic Reward Task (PRT) - a signal detection task with a differential reinforcement schedule - following smoking as usual, and the Relapse Analogue Task (RAT) - a task in which participants could earn money for delaying smoking up to 50min - after a period of overnight abstinence. Two cohorts were defined by the RAT; those who waited either 0-min (n=36) or the full 50-min (n=44) before smoking. ResultsPRT signal detection metrics indicated all subjects learned the task contingencies, with no differences in response bias or discriminability between the two groups. However, HDDM analyses indicated faster drift rates in 50-min vs. 0-min waiters. ConclusionsRelative to those who did not abstain, computational modeling indicated that people who abstained from smoking for 50min showed faster evidence accumulation during reward-based decision-making. These results highlight the importance of decision-making mechanisms to smoking abstinence, and suggest that focusing on the evidence accumulation process may yield new targets for treatment.

Multiple timescales of learning indicated by changes in evidence-accumulation processes during perceptual decision-making

Evidence accumulation models have enabled strong advances in our understanding of decision-making, yet their application to examining learning has not been common. Using data from participants completing a dynamic random dot-motion direction discrimination task across four days, we characterized alterations in two components of perceptual decision-making (Drift Diffusion Model drift rate and response boundary). Continuous-time learning models were applied to characterize trajectories of performance change, with different models allowing for varying dynamics. The best-fitting model included drift rate changing as a continuous, exponential function of cumulative trial number. In contrast, response boundary changed within each daily session, but in an independent manner across daily sessions. Our results highlight two different processes underlying the pattern of behavior observed across the entire learning trajectory, one involving a continuous tuning of perceptual sensitivity, and another more variable process describing participants’ threshold of when enough evidence is present to act.

Prospective memory decision control: A computational model of context effects on prospective memory.

Prospective memory (PM) tasks require remembering to perform a deferred action and can be associated with predictable contexts. We present a theory and computational model, prospective memory decision control (PMDC), of the cognitive processes by which context supports PM. Under control conditions, participants completed lexical decisions. Under PM conditions, participants had the additional PM task of responding to letter strings containing certain syllables. Stimuli were presented in one of two colors, with color potentially changing after each set of four trials. A pretrial colored fixation was presented before each set. Under control and PM standard conditions, fixation color was meaningless. Under PM context conditions, fixation color indicated whether a PM target could occur within the next set. We replicated prior findings of higher PM accuracy for context compared to standard conditions, and the expected variation in PM costs (slowed lexical decisions) as a function of context relevance. PMDC, which formalizes PM as a process of evidence accumulation among ongoing and PM task responses, accounted for the impact of context on PM costs and accuracy via proactive and reactive cognitive control. Increased ongoing task thresholds and decreased PM thresholds in relevant contexts indicated proactive control. With context provision, PM accumulation rates on PM trials increased, as did inhibition of accumulation to competing responses, indicating reactive control. Although an observed capacity-sharing effect explained some portion of PM costs, we found no evidence that participants redirected more capacity from the ongoing to the PM task when contextually cued to relevant contexts. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Recovery From Nicotine Addiction: A Diffusion Model Decomposition of Value-Based Decision-Making in Current Smokers and Ex-smokers.

A considerable number of people successfully give up tobacco smoking. In nicotine-dependent individuals, tobacco choice is determined by greater expected drug value; however, less is known about the underlying mechanisms through which people quit smoking. This study aimed to explore whether computational parameters of value-based decision-making (VBDM) characterize recovery from nicotine addiction. Using a preregistered, between-subject design, current daily smokers (n = 51) and ex-smokers who used to smoke daily (n = 51) were recruited from the local community. Participants completed a two-alternative forced choice task in which they chose between either two tobacco-related images (in one block) or tobacco-unrelated images (in a different block). During each trial, participants pressed a computer key to select the image they rated most positively during a previous task block. To estimate evidence accumulation (EA) processes and response thresholds during the different blocks, a drift-diffusion model was fitted to the reaction time and error data. Ex-smokers had significantly higher response thresholds when making tobacco-related decisions (p = .01, d = 0.45) compared to current smokers, although there were no significant group differences during tobacco-unrelated decisions. Furthermore, there were no significant group differences in EA rates when making tobacco or tobacco-unrelated decisions. Greater cautiousness when making value-based decisions about tobacco-related cues characterized recovery from nicotine addiction. The number of people dependent on nicotine has decreased steadily during the past decade; however, the mechanisms that underlie recovery are currently less well understood. The present study applied advances in the measurement of value-based choice. The aim was to explore whether the internal processes that underpin VBDM discriminate current daily tobacco smokers from ex-tobacco smokers who used to smoke daily. Findings revealed that recovery from nicotine addiction was characterized by higher response thresholds when making value-based decisions about tobacco-related cues; this may serve as a novel target for treatment interventions that focus on helping people to stop smoking.

Reconstructing a disambiguation sequence that forms perceptual memory of multistable displays via reverse correlation method: Bias onset perception but gently.

When multistable displays are presented intermittently with long blank intervals, their onset perception is determined by perceptual memory of multistable displays. We investigated when and how it is formed using a reverse correlation method and bistable kinetic depth effect displays. Each experimental block consisted of interleaved fully ambiguous probe and exogenously disambiguated prime displays. The purpose of the former was to "read out" the perceptual memory, whereas the latter contained purely random disambiguation sequences that were presented at the beginning of the prime display, throughout the entire presentation, or at the beginning and the end of the presentation. For each experiment and condition, we selected a subset of trials with disambiguation sequences that led to a change in perception of either the prime itself (sequences that modified perception) or the following fully ambiguous probe (sequences that modified perceptual memory). We estimated average disambiguation sequences for each participant using additive linear models. We found that an optimal sequence started at the onset with a moderate disambiguation against the previously dominant state (dominant perception for the previous probe) that gradually reduced until the display is fully ambiguous. We also show that the same sequence leads to an altered perception of the prime, indicating that perception and perceptual memory form at the same time. We suggest that perceptual memory is a consequence of an earlier evidence accumulation process and is informative about how the visual system treated ambiguity in the past rather than how it anticipates an uncertain future.

A process model account of the role of dopamine in intertemporal choice.

Theoretical accounts disagree on the role of dopamine in intertemporal choice and assume that dopamine either promotes delay of gratification by increasing the preference for larger rewards or that dopamine reduces patience by enhancing the sensitivity to waiting costs. Here, we reconcile these conflicting accounts by providing empirical support for a novel process model according to which dopamine contributes to two dissociable components of the decision process, evidence accumulation and starting bias. We re-analyzed a previously published data set where intertemporal decisions were made either under the D2 antagonist amisulpride or under placebo by fitting a hierarchical drift diffusion model that distinguishes between dopaminergic effects on the speed of evidence accumulation and the starting point of the accumulation process. Blocking dopaminergic neurotransmission not only strengthened the sensitivity to whether a reward is perceived as worth the delay costs during evidence accumulation (drift rate) but also attenuated the impact of waiting costs on the starting point of the evidence accumulation process (bias). In contrast, re-analyzing data from a D1 agonist study provided no evidence for a causal involvement of D1R activation in intertemporal choices. Taken together, our findings support a novel, process-based account of the role of dopamine for cost-benefit decision making, highlight the potential benefits of process-informed analyses, and advance our understanding of dopaminergic contributions to decision making.

A unified theory of discrete and continuous responding.

Understanding the cognitive processes underlying choice requires theories that can disentangle the representation of stimuli from the processes that map these representations onto observed responses. We develop a dynamic theory of how stimuli are mapped onto discrete (choice) and onto continuous response scales. It proposes that the mapping from a stimulus to an internal representation and then to an evidence accumulation process is accomplished using multiple reference points or "anchors." Evidence is accumulated until a threshold amount for a particular response is obtained, with the relative balance of support for each anchor at that time determining the response. We tested this multiple anchored accumulation theory (MAAT) using the results of two experiments requiring discrete or continuous responses to line length and color stimuli. We manipulated the number of options for discrete responses, the number of different stimuli, and the similarity among them, and compared the outcomes to continuous response conditions. We show that MAAT accounts for several key phenomena: more accurate, faster, and more skewed distributions of responses near the ends of a response scale; lower accuracy and slower responses as the number of discrete choice options increases; and longer response times and lower accuracy when alternative responses are more similar to the target response. Our empirical and modeling results suggest that discrete and continuous response tasks can share a common evidence representation, and that the decision process is sensitive to the perceived similarity among the response options. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

How celebrity status and gaze direction in ads drive visual attention to shape consumer decisions

AbstractMarketers have long used celebrity endorsement to help viewers build strong brand‐product associations. Celebrity endorsements increase brand awareness and recall, but how celebrity endorsements shape the decision process remains unclear. A wealth of research indicates that people tend to follow someone else's gaze, yet its effects in advertisements have been largely unexplored. We recruited 77 participants to investigate the effect of celebrities and gaze‐cueing in advertisements on both gaze behavior and binary choices. We combined computational modeling with eye‐tracking and pupillometry to identify which internal components of processing are affected by celebrity endorsement. We found that gaze‐cueing and celebrity endorsement influence gaze allocation and option selection. Further, results from computational modeling, eye‐tracking, and pupillometry revealed that the effect of celebrity endorsement on decisions can be explained as an offset in the starting point of an evidence accumulation process as well as changes in the rate of accumulation, thereby biasing choice.