Rate Of Evidence Accumulation Research Articles

Visual Hallucinations Are Characterized by Impaired Sensory Evidence Accumulation: Insights From Hierarchical Drift Diffusion Modeling in Parkinson’s Disease

BackgroundModels of hallucinations emphasize imbalance between sensory input and top-down influences over perception, as false perceptual inference can arise when top-down predictions are afforded too much precision (certainty) relative to sensory evidence. Visual hallucinations in Parkinson’s disease (PD) are associated with lower-level visual and attentional impairments, accompanied by overactivity in higher-order association brain networks. PD therefore provides an attractive framework to explore contributions of bottom-up versus top-down disturbances in hallucinations. MethodsWe characterized sensory processing during perceptual decision making in patients with PD with (n = 20) and without (n = 25) visual hallucinations and control subjects (n = 12), by fitting a hierarchical drift diffusion model to an attentional task. The hierarchical drift diffusion model uses Bayesian estimates to decompose task performance into parameters reflecting drift rates of evidence accumulation, decision thresholds, and nondecision time. ResultsWe observed slower drift rates in patients with hallucinations, which were less sensitive to changes in task demand. In contrast, wider decision boundaries and shorter nondecision times relative to control subjects were found in patients with PD regardless of hallucinator status. Inefficient and less flexible sensory evidence accumulation emerges as a unique feature of PD hallucinators. ConclusionsWe integrate these results with evidence accumulation and predictive coding models of hallucinations, suggesting that in PD sensory evidence is less informative and may therefore be down-weighted, resulting in overreliance on top-down influences. Considering impaired drift rates as an approximation of reduced sensory precision, our findings provide a novel computational framework to specify impairments in sensory processing that contribute to development of visual hallucinations.

Paper-Based Assessment of the Effects of Aging on Response Time: A Diffusion Model Analysis.

The effects of aging on response time were examined in a paper-based lexical-decision experiment with younger (age 18–36) and older (age 64–75) adults, applying Ratcliff’s diffusion model. Using digital pens allowed the paper-based assessment of response times for single items. Age differences previously reported by Ratcliff and colleagues in computer-based experiments were partly replicated: older adults responded more conservatively than younger adults and showed a slowing of their nondecision components of RT by 53 ms. The rates of evidence accumulation (drift rate) showed no age-related differences. Participants with a higher score in a vocabulary test also had higher drift rates. The experiment demonstrates the possibility to use formal processing models with paper-based tests.

A Bayesian Perspective on Accumulation in the Magnitude System

Several theoretical and empirical work posit the existence of a common magnitude system in the brain. Such a proposal implies that manipulating stimuli in one magnitude dimension (e.g. duration in time) should interfere with the subjective estimation of another magnitude dimension (e.g. size in space). Here, we asked whether a generalized Bayesian magnitude estimation system would sample sensory evidence using a common, amodal prior. Two psychophysical experiments separately tested participants on their perception of duration, surface, and numerosity when the non-target magnitude dimensions and the rate of sensory evidence accumulation were manipulated. First, we found that duration estimation was resilient to changes in surface and numerosity, whereas lengthening (shortening) the duration yielded under- (over-) estimations of surface and numerosity. Second, the perception of surface and numerosity were affected by changes in the rate of sensory evidence accumulation, whereas duration was not. Our results suggest that a generalized magnitude system based on Bayesian computations would minimally necessitate multiple priors.

Evidence Accumulation and Change Rate Inference in Dynamic Environments.

In a constantly changing world, animals must account for environmental volatility when making decisions. To appropriately discount older, irrelevant information, they need to learn the rate at which the environment changes. We develop an ideal observer model capable of inferring the present state of the environment along with its rate of change. Key to this computation is an update of the posterior probability of all possible change point counts. This computation can be challenging, as the number of possibilities grows rapidly with time. However, we show how the computations can be simplified in the continuum limit by a moment closure approximation. The resulting low-dimensional system can be used to infer the environmental state and change rate with accuracy comparable to the ideal observer. The approximate computations can be performed by a neural network model via a rate-correlation-based plasticity rule. We thus show how optimal observers accumulate evidence in changing environments and map this computation to reduced models that perform inference using plausible neural mechanisms.

The magic of words reconsidered: Investigating the automaticity of reading color-neutral words in the Stroop task.

In 2 variants of the color-word Stroop task, we compared 5 types of color-neutral distractors-real words (e.g., HAT), pseudowords (e.g., HIX), consonant strings (e.g., HDK), symbol strings (e.g., #$%), and a row of Xs (e.g., XXX)-as well as incongruent color words (e.g., GREEN displayed in red). When participants named the color, relative to a row of Xs, words and pseudowords interfered equally and more than the consonant strings, which in turn interfered more than the symbols. In contrast, when participants identified the color by manual key-press responses, all 5 types of neutral strings produced equal color response latencies. In both tasks, the incongruent color words produced robust interference relative to the color-neutral words. Reaction time (RT) distribution analyses showed that all interference effects (relative to the row of Xs) increased across the quantiles. We interpret these results in terms of an evidence accumulation process in which the interfering distractor reduces the effective rate of evidence accumulation for the color target. We take the results to argue that the task of reading, even when triggered unintentionally, is not an invariant process driven solely by the stimulus properties, and is instead guided by the task goal. (PsycINFO Database Record

Relating accumulator model parameters and neural dynamics

Accumulator models explain decision-making as an accumulation of evidence to a response threshold. Specific model parameters are associated with specific model mechanisms, such as the time when accumulation begins, the average rate of evidence accumulation, and the threshold. These mechanisms determine both the within-trial dynamics of evidence accumulation and the predicted behavior. Cognitive modelers usually infer what mechanisms vary during decision-making by seeing what parameters vary when a model is fitted to observed behavior. The recent identification of neural activity with evidence accumulation suggests that it may be possible to directly infer what mechanisms vary from an analysis of how neural dynamics vary. However, evidence accumulation is often noisy, and noise complicates the relationship between accumulator dynamics and the underlying mechanisms leading to those dynamics. To understand what kinds of inferences can be made about decision-making mechanisms based on measures of neural dynamics, we measured simulated accumulator model dynamics while systematically varying model parameters. In some cases, decision-making mechanisms can be directly inferred from dynamics, allowing us to distinguish between models that make identical behavioral predictions. In other cases, however, different parameterized mechanisms produce surprisingly similar dynamics, limiting the inferences that can be made based on measuring dynamics alone. Analyzing neural dynamics can provide a powerful tool to resolve model mimicry at the behavioral level, but we caution against drawing inferences based solely on neural analyses. Instead, simultaneous modeling of behavior and neural dynamics provides the most powerful approach to understand decision-making and likely other aspects of cognition and perception.

Improvements in Attention and Decision-Making Following Combined Behavioral Training and Brain Stimulation.

In recent years there has been a significant commercial interest in 'brain training' - massed or spaced practice on a small set of tasks to boost cognitive performance. Recently, researchers have combined cognitive training regimes with brain stimulation to try and maximize training benefits, leading to task-specific cognitive enhancement. It remains unclear, however, whether the performance gains afforded by such regimes can transfer to untrained tasks, or how training and stimulation affect the brain's latent information processing dynamics. To examine these issues, we applied transcranial direct current stimulation (tDCS) over the prefrontal cortex while participants undertook decision-making training over several days. Anodal, relative to cathodal/sham tDCS, increased performance gains from training. Critically, these gains were reliable for both trained and untrained tasks. The benefit of anodal tDCS occurred for left, but not right, prefrontal stimulation, and was absent for stimulation delivered without concurrent training. Modeling revealed left anodal stimulation combined with training caused an increase in the brain's rate of evidence accumulation for both tasks. Thus tDCS applied during training has the potential to modulate training gains and give rise to transferable performance benefits for distinct cognitive operations through an increase in the rate at which the brain acquires information.

How attention influences perceptual decision making: Single-trial EEG correlates of drift-diffusion model parameters

Perceptual decision making can be accounted for by drift-diffusion models, a class of decision-making models that assume a stochastic accumulation of evidence on each trial. Fitting response time and accuracy to a drift-diffusion model produces evidence accumulation rate and non-decision time parameter estimates that reflect cognitive processes. Our goal is to elucidate the effect of attention on visual decision making. In this study, we show that measures of attention obtained from simultaneous EEG recordings can explain per-trial evidence accumulation rates and perceptual preprocessing times during a visual decision making task. Models assuming linear relationships between diffusion model parameters and EEG measures as external inputs were fit in a single step in a hierarchical Bayesian framework. The EEG measures were features of the evoked potential (EP) to the onset of a masking noise and the onset of a task-relevant signal stimulus. Single-trial evoked EEG responses, P200s to the onsets of visual noise and N200s to the onsets of visual signal, explain single-trial evidence accumulation and preprocessing times. Within-trial evidence accumulation variance was not found to be influenced by attention to the signal or noise. Single-trial measures of attention lead to better out-of-sample predictions of accuracy and correct reaction time distributions for individual subjects.

Inhibition drives configural superiority of illusory Gestalt: Combined behavioral and drift–diffusion model evidence

Illusory Kanizsa figures demonstrate that a perceptually completed whole is more than the sum of its composite parts. In the current study, we explored part/whole relationships in object completion using the configural superiority effect (CSE) with illusory figures (Pomerantz & Portillo, 2011). In particular, we investigated to which extent the CSE is modulated by closure in target and distractor configurations. Our results demonstrated a typical CSE, with detection of a configural whole being more efficient than the detection of a corresponding part-level target. Moreover, the CSE was more pronounced when grouped objects were presented in distractors rather than in the target. A follow-up experiment systematically manipulated closure in whole target or, respectively, distractor configurations. The results revealed the effect of closure to be again stronger in distractor, rather than in target configurations, suggesting that closure primarily affects the inhibition of distractors, and to a lesser extent the selection of the target. In addition, a drift–diffusion model analysis of our data revealed that efficient distractor inhibition expedites the rate of evidence accumulation, with closure in distractors particularly speeding the drift toward the decision boundary. In sum, our findings demonstrate that the CSE in Kanizsa figures derives primarily from the inhibition of closed distractor objects, rather than being driven by a conspicuous target configuration. Altogether, these results support a fundamental role of inhibition in driving configural superiority effects in visual search.

Target Selection Signals Influence Perceptual Decisions by Modulating the Onset and Rate of Evidence Accumulation

Computational and neurophysiological research has highlighted neural processes that accumulate sensory evidence for perceptual decisions. These processes have been studied in the context of highly simplified perceptual discrimination paradigms in which the physical evidence appears at times and locations that are either entirely predictable or exogenously cued (e.g., by the onset of the stimulus itself). Yet, we are rarely afforded such certainty in everyday life. For example, when driving along a busy motorway, we must continually monitor the movements of surrounding vehicles for events that call for a lane change. In such scenarios, it is unknown which of the continuously present information sources will become relevant or when. Although it is well established that evidence integration provides an effective mechanism for countering the impact of noise, the question of how this mechanism is implemented in the face of uncertain evidence onsets has yet to be answered. Here, we show that when monitoring two potential sources of information for evidence occurring unpredictably in both time and space, the human brain employs discrete, early target selection signals that significantly modulate the onset and rate of neural evidence accumulation, and thereby the timing and accuracy of perceptual reports. These selection signals share many of the key characteristics of the N2pc component highlighted in the literature on visual search yet are present even in the absence of distractors and under situations of low temporal and spatial uncertainty. These data provide novel insights into how target selection supports decision making in uncertain environments.

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.

Slower Perception Followed by Faster Lexical Decision in Longer Words: A Diffusion Model Analysis.

Effects of stimulus length on reaction times (RTs) in the lexical decision task are the topic of extensive research. While slower RTs are consistently found for longer pseudo-words, a finding coined the word length effect (WLE), some studies found no effects for words, and yet others reported faster RTs for longer words. Moreover, the WLE depends on the orthographic transparency of a language, with larger effects in more transparent orthographies. Here we investigate processes underlying the WLE in lexical decision in German-English bilinguals using a diffusion model (DM) analysis, which we compared to a linear regression approach. In the DM analysis, RT-accuracy distributions are characterized using parameters that reflect latent sub-processes, in particular evidence accumulation and decision-independent perceptual encoding, instead of typical parameters such as mean RT and accuracy. The regression approach showed a decrease in RTs with length for pseudo-words, but no length effect for words. However, DM analysis revealed that the null effect for words resulted from opposing effects of length on perceptual encoding and rate of evidence accumulation. Perceptual encoding times increased with length for words and pseudo-words, whereas the rate of evidence accumulation increased with length for real words but decreased for pseudo-words. A comparison between DM parameters in German and English suggested that orthographic transparency affects perceptual encoding, whereas effects of length on evidence accumulation are likely to reflect contextual information and the increase in available perceptual evidence with length. These opposing effects may account for the inconsistent findings on WLEs.

A new framework for modeling decisions about changing information: The Piecewise Linear Ballistic Accumulator model

In the real world, decision making processes must be able to integrate non-stationary information that changes systematically while the decision is in progress. Although theories of decision making have traditionally been applied to paradigms with stationary information, non-stationary stimuli are now of increasing theoretical interest. We use a random-dot motion paradigm along with cognitive modeling to investigate how the decision process is updated when a stimulus changes. Participants viewed a cloud of moving dots, where the motion switched directions midway through some trials, and were asked to determine the direction of motion. Behavioral results revealed a strong delay effect: after presentation of the initial motion direction there is a substantial time delay before the changed motion information is integrated into the decision process. To further investigate the underlying changes in the decision process, we developed a Piecewise Linear Ballistic Accumulator model (PLBA). The PLBA is efficient to simulate, enabling it to be fit to participant choice and response-time distribution data in a hierarchal modeling framework using a non-parametric approximate Bayesian algorithm. Consistent with behavioral results, PLBA fits confirmed the presence of a long delay between presentation and integration of new stimulus information, but did not support increased response caution in reaction to the change. We also found the decision process was not veridical, as symmetric stimulus change had an asymmetric effect on the rate of evidence accumulation. Thus, the perceptual decision process was slow to react to, and underestimated, new contrary motion information.

Feature fragments and evidence accumulation in object and face perception

Recognition and identification of objects is ubiquitous in the daily lives of human beings, yet despite its importance we still do not understand how visual information arriving at the retina is processed by the human visual cortex (and the rest of the brain) in such a fast and flexible manner. There is a large body of neurophysiological work with non-human primates showing that certain neurons in the posterior and middle regions of the inferior temporal gyrus (area TE) respond preferentially to specific features of objects. Further studies exploring the brain’s response to a large set of objects decomposed into simpler shapes led a number of researchers to suggest that part of TE was organized in column-like structures with all neurons in a column having preferential tuning for specific shapes. Assuming that object features are presented similarly in humans, the current study aimed to discover the features of objects and faces that produce the fastest and most accurate categorization responses. Participants viewed many single fragments selected from random locations in images of a set of objects and attempted to classify the fragment based on object category. Accuracy and reaction time measures were analyzed using a sequential sampling framework, specifically Drift Diffusion and Linear Ballistic Accumulator models (Brown & Heathcote, 2008; Ratcliff & Rouder, 1998). Modeling results showed that the variability in reaction times across features was accounted by the rate of perceptual evidence accumulation, which is dependent on the amount of information in the fragment. The current study represents a fundamental step toward evaluating the functional organization of the human ventral visual stream within a feature-based theoretical framework. Meeting abstract presented at VSS 2015

Characterising neural signatures of successful aging: Electrophysiological correlates of preserved episodic memory in older age

While aging is associated with a gradual decline in memory, substantial preservation of function is observed in certain individuals and dissecting this heterogeneity is paramount to understanding successful aging. A cohort of elderly individuals were classified according to their level of memory preservation and administered a test of episodic memory in which participants were cued to learn or simply read each word and then to identify previously presented items in a delayed recognition phase. Mathematical modelling revealed that relatively preserved memory function was specifically linked to a faster rate of memorial evidence accumulation (drift rate). Analysis of event-related potentials at encoding revealed that high-performing elderly exhibited signals over parietal regions that discriminated between words to be learned vs. read for an additional 300-ms compared to young subjects suggesting a compensatory encoding mechanism that was absent in the low-performing group. At recognition, parietal signals associated with recollection processes discriminated previously learned words from read words in the young and high-performing old but not in low-performing old. These results reveal that successful aging is associated with specific adaptive neural markers during both encoding and retrieval.

Speed-accuracy tradeoff by a control signal with balanced excitation and inhibition.

A hallmark of flexible behavior is the brain's ability to dynamically adjust speed and accuracy in decision-making. Recent studies suggested that such adjustments modulate not only the decision threshold, but also the rate of evidence accumulation. However, the underlying neuronal-level mechanism of the rate change remains unclear. In this work, using a spiking neural network model of perceptual decision, we demonstrate that speed and accuracy of a decision process can be effectively adjusted by manipulating a top-down control signal with balanced excitation and inhibition [balanced synaptic input (BSI)]. Our model predicts that emphasizing accuracy over speed leads to reduced rate of ramping activity and reduced baseline activity of decision neurons, which have been observed recently at the level of single neurons recorded from behaving monkeys in speed-accuracy tradeoff tasks. Moreover, we found that an increased inhibitory component of BSI skews the decision time distribution and produces a pronounced exponential tail, which is commonly observed in human studies. Our findings suggest that BSI can serve as a top-down control mechanism to rapidly and parametrically trade between speed and accuracy, and such a cognitive control signal presents both when the subjects emphasize accuracy or speed in perceptual decisions.

Dynamics of postdecisional processing of confidence.

Most cognitive theories assume that confidence and choice happen simultaneously and are based on the same information. The 3 studies presented in this article instead show that confidence judgments can arise, at least in part, from a postdecisional evidence accumulation process. As a result of this process, increasing the time between making a choice and confidence judgment improves confidence resolution. This finding contradicts the notion that confidence judgments are biased by decision makers seeking confirmatory evidence. Further analysis reveals that the improved resolution is due to a reduction in confidence in incorrect responses, while confidence in correct responses remains relatively constant. These results are modeled with a sequential sampling process that allows evidence accumulation to continue after a choice is made and maps the amount of accumulated evidence onto a confidence rating. The cognitive modeling analysis reveals that the rate of evidence accumulation following a choice does slow relative to the rate preceding choice. The analysis also shows that the asymmetry between confidence in correct and incorrect choices is compatible with state-dependent decay in the accumulated evidence: Evidence consistent with the current state results in a deceleration of accumulated evidence and consequently evidence appears to have a decreasing impact on observed confidence. In contrast, evidence inconsistent with the current state results in an acceleration of accumulated evidence toward the opposite direction and consequently evidence appears to have an increasing impact on confidence. Taken together, this process-level understanding of confidence suggests a simple strategy for improving confidence accuracy: take a bit more time to make confidence judgments.

Slow down and remember to remember! A delay theory of prospective memory costs.

Event-based prospective memory (PM) requires a deferred action to be performed when a target event is encountered in the future. Individuals are often slower to perform a concurrent ongoing task when they have PM task requirements relative to performing the ongoing task in isolation. Theories differ in their detailed interpretations of this PM cost, but all assume that the PM task shares limited-capacity resources with the ongoing task. In what was interpreted as support of this core assumption, diffusion model fits reported by Boywitt and Rummel (2012) and Horn, Bayen, and Smith (2011) indicated that PM demands reduced the rate of accumulation of evidence about ongoing task choices. We revaluate this support by fitting both the diffusion and linear ballistic accumulator (Brown & Heathcote, 2008) models to these same data sets and 2 new data sets better suited to model fitting. There was little effect of PM demands on evidence accumulation rates, but PM demands consistently increased the evidence required for ongoing task response selection (response thresholds). A further analysis of data reported by Lourenço, White, and Maylor (2013) found that participants differentially adjusted their response thresholds to slow responses associated with stimuli potentially containing PM targets. These findings are consistent with a delay theory account of costs, which contends that individuals slow ongoing task responses to allow more time for PM response selection to occur. Our results call for a fundamental reevaluation of current capacity-sharing theories of PM cost that until now have dominated the PM literature.

Deciding on race: A diffusion model analysis of race-categorisation

It has long been known that a person’s race can affect their decisions about people of another race; an observation that clearly taps into some deep societal issues. However, in order to behave differently in response to someone else’s race, you must first categorise that person as other-race. The current study investigates the process of race-categorisation. Two groups of participants, Asian and Caucasian, rapidly classified facial images that varied from strongly Asian, through racially intermediate, to strongly Caucasian. In agreement with previous findings, there was a difference in category boundary between the two groups. Asian participants more frequently judged intermediate images as Caucasian and vice versa. We fitted a decision model, the Ratcliff diffusion model, to our two choice reaction time data. This model provides an account of the processes thought to underlie binary choice decisions. Within its architecture it has two components that could reasonably lead to a difference in race category boundary, these being evidence accumulation rate and a priori bias. The latter is the expectation or prior belief that a participant brings to the task, whilst the former indexes sensitivity to race-dependent perceptual cues. Whilst we find no good evidence for a difference in a priori bias between our two groups, we do find evidence for a difference in evidence accumulation rate. Our Asian participants were more sensitive to Caucasian cues within the images than were our Caucasian participants (and vice versa). These results support the idea that differences in perceptual sensitivity to race-defining visual characteristics drive differences in race categorisation. We propose that our findings fit with a wider view in which perceptual adaptation plays a central role in the visual processing of own and other race.

Individual differences in attention influence perceptual decision making.

Sequential sampling decision-making models have been successful in accounting for reaction time (RT) and accuracy data in two-alternative forced choice tasks. These models have been used to describe the behavior of populations of participants, and explanatory structures have been proposed to account for between individual variability in model parameters. In this study we show that individual differences in behavior from a novel perceptual decision making task can be attributed to (1) differences in evidence accumulation rates, (2) differences in variability of evidence accumulation within trials, and (3) differences in non-decision times across individuals. Using electroencephalography (EEG), we demonstrate that these differences in cognitive variables, in turn, can be explained by attentional differences as measured by phase-locking of steady-state visual evoked potential (SSVEP) responses to the signal and noise components of the visual stimulus. Parameters of a cognitive model (a diffusion model) were obtained from accuracy and RT distributions and related to phase-locking indices (PLIs) of SSVEPs with a single step in a hierarchical Bayesian framework. Participants who were able to suppress the SSVEP response to visual noise in high frequency bands were able to accumulate correct evidence faster and had shorter non-decision times (preprocessing or motor response times), leading to more accurate responses and faster response times. We show that the combination of cognitive modeling and neural data in a hierarchical Bayesian framework relates physiological processes to the cognitive processes of participants, and that a model with a new (out-of-sample) participant's neural data can predict that participant's behavior more accurately than models without physiological data.