Dual diffusion model for single-cell recording data from the superior colliculus in a brightness-discrimination task

Beyond discrete-choice options

While decision theories have evolved over the past five decades, their focus has largely been on choices among a limited number of discrete options, even though many real-world situations have a continuous-option space. Recently, theories have attempted to address decisions with continuous-option spaces, and several computational models have been proposed within the sequential sampling framework to explain how we make a decision in continuous-option space. This article aims to review the main attempts to understand decisions on continuous-option spaces, give an overview of applications of these types of decisions, and present puzzles to be addressed by future developments.

The olivary input to the cerebellum dissociates sensory events from movement plans

Neurons in the inferior olive are thought to anatomically organize the Purkinje cells (P-cells) of the cerebellum into computational modules, but what is computed by each module? Here, we designed a saccade task in marmosets that dissociated sensory events from motor events and then recorded the complex and simple spikes of hundreds of P-cells. We found that when a visual target was presented at a random location, the olive reported the direction of that sensory event to one group of P-cells, but not to a second group. However, just before movement onset, it reported the direction of the planned movement to both groups, even if that movement was not toward the target. At the end of the movement if the subject experienced an error but chose to withhold the corrective movement, only the first group received information about the sensory prediction error. We organized the P-cells based on the information content of their olivary input and found that in the group that received sensory information, the simple spikes were suppressed during fixation, then produced a burst before saccade onset in a direction consistent with assisting the movement. In the second group, the simple spikes were not suppressed during fixation but burst near saccade deceleration in a direction consistent with stopping the movement. Thus, the olive differentiated the P-cells based on whether they would receive sensory or motor information, and this defined their contributions to control of movements as well as holding still.

Physiological correlates of a simple saccadic-decision task to extended objects in superior colliculus

Our vision is best only in the center of our gaze, and we use saccadic eye movements to direct gaze to objects and features of interest. We make more than 180,000 saccades per day, and accurate and efficient saccades are crucial for most visuo-motor tasks. Saccades are typically studied using small point stimuli, despite the fact that most real-world visual scenes are composed of extended objects. Recent studies in humans have shown that the initiation latency of saccades is strongly dependent on the size of the target (the "size-latency effect"), perhaps reflecting a tradeoff between the cost of making a saccade to a target and the expected information gain that would result. Here, we investigate the neuronal correlates of the size-latency effect in the macaque superior colliculus. We analyzed the latency variations of saccades to different size targets within a stochastic accumulator model framework. The model predicted a steeper increase in activity for smaller targets compared to larger ones. Surprisingly, the model also predicted an increase in saccade initiation threshold for larger targets. We found that the activity of intermediate-layer SC visuomotor neurons is in close agreement with the model predictions. We also found evidence that these effects may be a consequence of the visual responses of SC neurons to targets of different sizes. These results shed new light on the sources of delay within the saccadic system, a system that we heavily depend upon in the performance of most visuo-motor tasks.

Inhibitory tagging both speeds and strengthens saccade target selection in the superior colliculus during visual search

It has been suggested that, during difficult visual search tasks involving time pressure and multiple saccades, inhibitory tagging helps to facilitate efficient saccade target selection by reducing responses to objects in the scene once they have been searched and rejected. The superior colliculus (SC) is a midbrain structure involved in target selection, and recent findings suggest an influence of inhibitory tagging on SC activity. Precisely how, and by how much, inhibitory tagging influences target selection by SC neurons, however, is unclear. The purpose of this study, therefore, was to characterize and quantify the influence of inhibitory tagging on target selection in the SC. Rhesus monkeys performed a visual search task involving time pressure and multiple saccades. Early in the fixation period between saccades in the context of this task, a subset of SC neurons reliably discriminated the stimulus selected as the next saccade goal, consistent with a role in target selection. Discrimination occurred earlier and was more robust, however, when unselected stimuli in the search array had been previously fixated on the same trial. This indicates that inhibitory tagging both speeds and strengthens saccade target selection in the SC during multisaccade search. The results provide constraints on models of target selection based on SC activity.NEW & NOTEWORTHY An important aspect of efficient behavior during difficult, time-limited visual search tasks is the efficient selection of sequential saccade targets. Inhibitory tagging, i.e., a reduction of neural activity associated with previously fixated objects, may help to facilitate such efficient selection by modulating the selection process in the superior colliculus (SC). In this study, we characterized and quantified this modulation and found that, indeed, inhibitory tagging both speeds and strengthens target selection in the SC.

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 (DDM)-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.

A spatially continuous diffusion model of visual working memory

We present results from five visual working memory (VWM) experiments in which participants were briefly shown between 2 and 6 colored squares. They were then cued to recall the color of one of the squares and they responded by choosing the color on a continuous color wheel. The experiments provided response proportions and response time (RT) measures as a function of angle for the choices. Current VWM models for this task include discrete models that assume an item is either within working memory or not and resource models that assume that memory strength varies as a function of the number of items. Because these models do not include processes that allow them to account for RT data, we implemented them within the spatially continuous diffusion model (SCDM, Ratcliff, 2018) and use the experimental data to evaluate these combined models. In the SCDM, evidence retrieved from memory is represented as a spatially continuous normal distribution and this drives the decision process until a criterion (represented as a 1-D line) is reached, which produces a decision. Noise in the accumulation process is represented by continuous Gaussian process noise over spatial position. The models that fit best from the discrete and resource-based classes converged on a common model that had a guessing component and that allowed the height of the normal memory-strength distribution to vary with number of items. The guessing component was implemented as a regular decision process driven by a flat evidence distribution, a zero-drift process. The combination of choice and RT data allows models that were not identifiable based on choice data alone to be discriminated.

A Modeling Framework to Examine Psychological Processes Underlying Ordinal Responses and Response Times of Psychometric Data

This article presents a joint modeling framework of ordinal responses and response times (RTs) for the measurement of latent traits. We integrate cognitive theories of decision-making and confidence judgments with psychometric theories to model individual-level measurement processes. The model development starts with the sequential sampling framework which assumes that when an item is presented, a respondent accumulates noisy evidence over time to respond to the item. Several cognitive and psychometric theories are reviewed and integrated, leading us to three psychometric process models with different representations of the cognitive processes underlying the measurement. We provide simulation studies that examine parameter recovery and show the relationships between latent variables and data distributions. We further test the proposed models with empirical data measuring three traits related to motivation. The results show that all three models provide reasonably good descriptions of observed response proportions and RT distributions. Also, different traits favor different process models, which implies that psychological measurement processes may have heterogeneous structures across traits. Our process of model building and examination illustrates how cognitive theories can be incorporated into psychometric model development to shed light on the measurement process, which has had little attention in traditional psychometric models.

A neural mechanism for terminating decisions

The brain makes decisions by accumulating evidence until there is enough to stop and choose. Neural mechanisms of evidence accumulation are established in association cortex, but the site and mechanism of termination are unknown. Here, we show that the superior colliculus (SC) plays a causal role in terminating decisions, and we provide evidence for a mechanism by which this occurs. We recorded simultaneously from neurons in the lateral intraparietal area (LIP) and SC while monkeys made perceptual decisions. Despite similar trial-averaged activity, we found distinct single-trial dynamics in the two areas: LIP displayed drift-diffusion dynamics and SC displayed bursting dynamics. We hypothesized that the bursts manifest a threshold mechanism applied to signals represented in LIP to terminate the decision. Consistent with this hypothesis, SC inactivation produced behavioral effects diagnostic of an impaired threshold sensor and prolonged the buildup of activity in LIP. The results reveal the transformation from deliberation to commitment.

"Reliable organisms from unreliable components" revisited: the linear drift, linear infinitesimal variance model of decision making

Diffusion models of decision making, in which successive samples of noisy evidence are accumulated to decision criteria, provide a theoretical solution to von Neumann's (1956) problem of how to increase the reliability of neural computation in the presence of noise. I introduce and evaluate a new neurally-inspired dual diffusion model, the linear drift, linear infinitesimal variance (LDLIV) model, which embodies three features often thought to characterize neural mechanisms of decision making. The accumulating evidence is intrinsically positively-valued, saturates at high intensities, and is accumulated for each alternative separately. I present explicit integral-equation predictions for the response time distribution and choice probabilities for the LDLIV model and compare its performance on two benchmark sets of data to three other models: the standard diffusion model and two dual diffusion model composed of racing Wiener processes, one between absorbing and reflecting boundaries and one with absorbing boundaries only. The LDLIV model and the standard diffusion model performed similarly to one another, although the standard diffusion model is more parsimonious, and both performed appreciably better than the other two dual diffusion models. I argue that accumulation of noisy evidence by a diffusion process and drift rate variability are both expressions of how the cognitive system solves von Neumann's problem, by aggregating noisy representations over time and over elements of a neural population. I also argue that models that do not solve von Neumann's problem do not address the main theoretical question that historically motivated research in this area.

Primate superior colliculus is engaged in abstract higher-order cognition

Categorization is a fundamental cognitive process by which the brain assigns stimuli to behaviorally meaningful groups. Investigations of visual categorization in primates have identified a hierarchy of cortical areas that are involved in the transformation of sensory information into abstract category representations. However, categorization behaviors are ubiquitous across diverse animal species, even those without a neocortex, motivating the possibility that subcortical regions may contribute to abstract cognition in primates. One candidate structure is the superior colliculus (SC), an evolutionarily conserved midbrain region that, although traditionally thought to mediate only reflexive spatial orienting, is involved in cognitive tasks that require spatial orienting. Here, we reveal a novel role of the primate SC in abstract, higher-order visual cognition. We compared neural activity in the SC and the posterior parietal cortex (PPC), a region previously shown to causally contribute to category decisions, while monkeys performed a visual categorization task in which they report their decisions with a hand movement. The SC exhibits stronger and shorter-latency category encoding than the PPC, and inactivation of the SC markedly impairs monkeys' category decisions. These results extend SC's established role in spatial orienting to abstract, non-spatial cognition.

Relating a Spiking Neural Network Model and the Diffusion Model of Decision-Making

Many models of decision making assume accumulation of evidence to threshold as a core mechanism to predict response probabilities and response times. A spiking neural network model (Wang, 2002) instantiates these mechanisms at the level of biophysically-plausible pools of neurons with excitatory and inhibitory connections, and has numerous model parameters tuned by physiological measures. The diffusion model (Ratcliff, 1978) is a cognitive model that can be fitted to a range of behaviors and conditions. We investigated how parameters of the cognitive-level diffusion model relate to the parameters of a neural-level spiking model. In each simulated "experiment", we generated "data" from the spiking neural network by factorially combining a manipulation of choice difficulty (via the input to the spiking model) and a manipulation of one of the core parameters of the spiking model. We then fitted the diffusion model to these simulated data to observe how manipulation of each core spiking model parameter mapped on to fitted drift rate, response threshold, and non-decision time. Manipulations of parameters in the spiking model related to input sensitivity, threshold, and stimulus processing time mapped on to their conceptual analogues in the diffusion model, namely drift rate, threshold, and non-decision time. Manipulations of parameters in the spiking model with no direct analogue to the diffusion model, non-stimulus-specific background input, strength of recurrent excitation, and receptor conductances, mapped on to threshold in the diffusion model. We discuss implications of these results for interpretations of fits of the diffusion model to behavioral data.

Signal neutrality, scalar property, and collapsing boundaries as consequences of a learned multi-timescale strategy

We postulate that three fundamental elements underlie a decision making process: perception of time passing, information processing in multiple timescales and reward maximisation. We build a simple reinforcement learning agent upon these principles that we train on a random dot-like task. Our results, similar to the experimental data, demonstrate three emerging signatures. (1) signal neutrality: insensitivity to the signal coherence in the interval preceding the decision. (2) Scalar property: the mean of the response times varies widely for different signal coherences, yet the shape of the distributions stays almost unchanged. (3) Collapsing boundaries: the “effective” decision-making boundary changes over time in a manner reminiscent of the theoretical optimal. Removing the perception of time or the multiple timescales from the model does not preserve the distinguishing signatures. Our results suggest an alternative explanation for signal neutrality. We propose that it is not part of motor planning. It is part of the decision-making process and emerges from information processing on multiple timescales.

The Spatial Leaky Competing Accumulator Model

The Leaky Competing Accumulator model (LCA) of Usher and McClelland is able to simulate the time course of perceptual decision making between an arbitrary number of stimuli. Reaction times, such as saccadic latencies, produce a typical distribution that is skewed toward longer latencies and accumulator models have shown excellent fit to these distributions. We propose a new implementation called the Spatial Leaky Competing Accumulator (SLCA), which can be used to predict the timing of subsequent fixation durations during a visual task. SLCA uses a pre-existing saliency map as input and represents accumulation neurons as a two-dimensional grid to generate predictions in visual space. The SLCA builds on several biologically motivated parameters: leakage, recurrent self-excitation, randomness and non-linearity, and we also test two implementations of lateral inhibition. A global lateral inhibition, as implemented in the original model of Usher and McClelland, is applied to all competing neurons, while a local implementation allows only inhibition of immediate neighbors. We trained and compared versions of the SLCA with both global and local lateral inhibition with use of a genetic algorithm, and compared their performance in simulating human fixation latency distribution in a foraging task. Although both implementations were able to produce a positively skewed latency distribution, only the local SLCA was able to match the human data distribution from the foraging task. Our model is discussed for its potential in models of salience and priority, and its benefits as compared to other models like the Leaky integrate and fire network.

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

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

Modeling evidence accumulation decision processes using integral equations: Urgency-gating and collapsing boundaries

Diffusion models of evidence accumulation have successfully accounted for the distributions of response times and choice probabilities from many experimental tasks, but recently their assumption that evidence is accumulated at a constant rate to constant decision boundaries has been challenged. One model assumes that decision-makers seek to optimize their performance by using decision boundaries that collapse over time. Another model assumes that evidence does not accumulate and is represented by a stationary distribution that is gated by an urgency signal to make a response. We present explicit, integral-equation expressions for the first-passage time distributions of the urgency-gating and collapsing-bounds models and use them to identify conditions under which the models are equivalent. We combine these expressions with a dynamic model of stimulus encoding that allows the effects of perceptual and decisional integration to be distinguished. We compare the resulting models to the standard diffusion model with variability in drift rates on data from three experimental paradigms in which stimulus information was either constant or changed over time. The standard diffusion model was the best model for tasks with constant stimulus information; the models with time-varying urgency or decision bounds performed similarly to the standard diffusion model on tasks with changing stimulus information. We found little support for the claim that evidence does not accumulate and attribute the good performance of the time-varying models on changing-stimulus tasks to their increased flexibility and not to their ability to account for systematic experimental effects. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Mental speed is high until age 60 as revealed by analysis of over a million participants

Response speeds in simple decision-making tasks begin to decline from early and middle adulthood. However, response times are not pure measures of mental speed but instead represent the sum of multiple processes. Here we apply a Bayesian diffusion model to extract interpretable cognitive components from raw response time data. We apply our model to cross-sectional data from 1.2 million participants to examine age differences in cognitive parameters. To efficiently parse this large dataset, we apply a Bayesian inference method for efficient parameter estimation using specialized neural networks. Our results indicate that response time slowing begins as early as age 20, but this slowing was attributable to increases in decision caution and to slower non-decisional processes, rather than to differences in mental speed. Slowing of mental speed was observed only after approximately age 60. Our research thus challenges widespread beliefs about the relationship between age and mental speed.

Neural Substrates of the Drift-Diffusion Model in Brain Disorders

Many studies on the drift-diffusion model (DDM) explain decision-making based on a unified analysis of both accuracy and response times. This review provides an in-depth account of the recent advances in DDM research which ground different DDM parameters on several brain areas, including the cortex and basal ganglia. Furthermore, we discuss the changes in DDM parameters due to structural and functional impairments in several clinical disorders, including Parkinson's disease, Attention Deficit Hyperactivity Disorder (ADHD), Autism Spectrum Disorders, Obsessive-Compulsive Disorder (OCD), and schizophrenia. This review thus uses DDM to provide a theoretical understanding of different brain disorders.

Neural mechanisms underlying the temporal control of sequential saccade planning in the frontal eye field

Sequences of saccadic eye movements are instrumental in navigating our visual environment. While neural activity has been shown to ramp up to a threshold before single saccades, the neural underpinnings of multiple saccades is unknown. To understand the neural control of saccade sequences, we recorded from the frontal eye field (FEF) of macaque monkeys while they performed a sequential saccade task. We show that the concurrent planning of two saccade plans brings forth processing bottlenecks, specifically by decreasing the growth rate and increasing the threshold of saccade-related ramping activity. The rate disruption affected both saccade plans, and a computational model, wherein activity related to the two saccade plans mutually and asymmetrically inhibited each other, predicted the behavioral and neural results observed experimentally. Borrowing from models in psychology, our results demonstrate a capacity-sharing mechanism of processing bottlenecks, wherein multiple saccade plans in a sequence compete for the processing capacity by the perturbation of the saccade-related ramping activity. Finally, we show that, in contrast to movement-related neurons, visual activity in FEF neurons is not affected by the presence of multiple saccade targets, indicating that, for perceptually simple tasks, inhibition within movement-related neurons mainly instantiates capacity sharing. Taken together, we show how psychology-inspired models of capacity sharing can be mapped onto neural responses to understand the control of rapid saccade sequences.

Setting the space for deliberation in decision-making

Decision-making models in the behavioral, cognitive, and neural sciences typically consist of forced-choice paradigms with two alternatives. While theoretically it is feasible to translate any decision situation to a sequence of binary choices, real-life decision-making is typically more complex and nonlinear, involving choices among multiple items, graded judgments, and deferments of decision-making. Here, we discuss how the complexity of real-life decision-making can be addressed using conventional decision-making models by focusing on the interactive dynamics between criteria settings and the collection of evidence. Decision-makers can engage in multi-stage, parallel decision-making by exploiting the space for deliberation, with non-binary readings of evidence available at any point in time. The interactive dynamics principally adhere to the speed-accuracy tradeoff, such that increasing the space for deliberation enables extended data collection. The setting of space for deliberation reflects a form of meta-decision-making that can, and should be, studied empirically as a value-based exercise that weighs the prior propensities, the economics of information seeking, and the potential outcomes. Importantly, the control of the space for deliberation raises a question of agency. Decision-makers may actively and explicitly set their own decision parameters, but these parameters may also be set by environmental pressures. Thus, decision-makers may be influenced-or nudged in a particular direction-by how decision problems are framed, with a sense of urgency or a binary definition of choice options. We argue that a proper understanding of these mechanisms has important practical implications toward the optimal usage of space for deliberation.

Causal role for the primate superior colliculus in the computation of evidence for perceptual decisions

Trained monkeys performed a two-choice perceptual decision-making task in which they reported the perceived orientation of a dynamic Glass pattern, before and after unilateral, reversible, inactivation of a brainstem area-the superior colliculus (SC)-involved in preparing eye movements. We found that unilateral SC inactivation produced significant decision biases and changes in reaction times consistent with a causal role for the primate SC in perceptual decision-making. Fitting signal detection theory and sequential sampling models to the data showed that SC inactivation produced a decrease in the relative evidence for contralateral decisions, as if adding a constant offset to a time-varying evidence signal for the ipsilateral choice. The results provide causal evidence for an embodied cognition model of perceptual decision-making and provide compelling evidence that the SC of primates (a brainstem structure) plays a causal role in how evidence is computed for decisions-a process usually attributed to the forebrain.

Accumulation of continuously time-varying sensory evidence constrains neural and behavioral responses in human collision threat detection

Evidence accumulation models provide a dominant account of human decision-making, and have been particularly successful at explaining behavioral and neural data in laboratory paradigms using abstract, stationary stimuli. It has been proposed, but with limited in-depth investigation so far, that similar decision-making mechanisms are involved in tasks of a more embodied nature, such as movement and locomotion, by directly accumulating externally measurable sensory quantities of which the precise, typically continuously time-varying, magnitudes are important for successful behavior. Here, we leverage collision threat detection as a task which is ecologically relevant in this sense, but which can also be rigorously observed and modelled in a laboratory setting. Conventionally, it is assumed that humans are limited in this task by a perceptual threshold on the optical expansion rate-the visual looming-of the obstacle. Using concurrent recordings of EEG and behavioral responses, we disprove this conventional assumption, and instead provide strong evidence that humans detect collision threats by accumulating the continuously time-varying visual looming signal. Generalizing existing accumulator model assumptions from stationary to time-varying sensory evidence, we show that our model accounts for previously unexplained empirical observations and full distributions of detection response. We replicate a pre-response centroparietal positivity (CPP) in scalp potentials, which has previously been found to correlate with accumulated decision evidence. In contrast with these existing findings, we show that our model is capable of predicting the onset of the CPP signature rather than its buildup, suggesting that neural evidence accumulation is implemented differently, possibly in distinct brain regions, in collision detection compared to previously studied paradigms.

Unraveling circuits of visual perception and cognition through the superior colliculus

The superior colliculus is a conserved sensorimotor structure that integrates visual and other sensory information to drive reflexive behaviors. Although the evidence for this is strong and compelling, a number of experiments reveal a role for the superior colliculus in behaviors usually associated with the cerebral cortex, such as attention and decision-making. Indeed, in addition to collicular outputs targeting brainstem regions controlling movements, the superior colliculus also has ascending projections linking it to forebrain structures including the basal ganglia and amygdala, highlighting the fact that the superior colliculus, with its vast inputs and outputs, can influence processing throughout the neuraxis. Today, modern molecular and genetic methods combined with sophisticated behavioral assessments have the potential to make significant breakthroughs in our understanding of the evolution and conservation of neuronal cell types and circuits in the superior colliculus that give rise to simple and complex behaviors.

The unknown but knowable relationship between Presaccadic Accumulation of activity and Saccade initiation

The goal of this short review is to call attention to a yawning gap of knowledge that separates two processes essential for saccade production. On the one hand, knowledge about the saccade generation circuitry within the brainstem is detailed and precise - push-pull interactions between gaze-shifting and gaze-holding processes control the time of saccade initiation, which begins when omnipause neurons are inhibited and brainstem burst neurons are excited. On the other hand, knowledge about the cortical and subcortical premotor circuitry accomplishing saccade initiation has crystalized around the concept of stochastic accumulation - the accumulating activity of saccade neurons reaching a fixed value triggers a saccade. Here is the gap: we do not know how the reaching of a threshold by premotor neurons causes the critical pause and burst of brainstem neurons that initiates saccades. Why this problem matters and how it can be addressed will be discussed. Closing the gap would unify two rich but curiously disconnected empirical and theoretical domains.

Inhibition of saccade initiation improves saccade accuracy: The role of local and remote visual distractors in the control of saccadic eye movements

When a distractor appears close to the target location, saccades are less accurate. However, the presence of a further distractor, remote from those stimuli, increases the saccade response latency and improves accuracy. Explanations for this are either that the second, remote distractor impacts directly on target selection processes or that the remote distractor merely impairs the ability to initiate a saccade and changes the time at which unaffected target selection processes are accessed. In order to tease these two explanations apart, here we examine the relationship between latency and accuracy of saccades to a target and close distractor pair while a remote distractor appears at variable distance. Accuracy improvements are found to follow a similar pattern, regardless of the presence of the remote distractor, which suggests that the effect of the remote distractor is not the result of a direct impact on the target selection process. Our findings support the proposal that a remote distractor impairs the ability to initiate a saccade, meaning the competition between target and close distractor is accessed at a later time, thus resulting in more accurate saccades.

Variable Statistical Structure of Neuronal Spike Trains in Monkey Superior Colliculus

Popular models of decision-making propose that noisy sensory evidence accumulates until reaching a bound. Behavioral evidence as well as trial-averaged ramping of neuronal activity in sensorimotor regions of the brain support this idea. However, averaging activity across trials can mask other processes, such as rapid shifts in decision commitment, calling into question the hypothesis that evidence accumulation is encoded by delay period activity of individual neurons. We mined two sets of data from experiments in four monkeys in which we recorded from superior colliculus neurons during two different decision-making tasks and a delayed saccade task. We applied second-order statistical measures and spike train simulations to determine whether spiking statistics were similar or different in the different tasks and monkeys, despite similar trial-averaged activity across tasks and monkeys. During a motion direction discrimination task, single-trial delay period activity behaved statistically consistent with accumulation. During an orientation detection task, the activity behaved superficially like accumulation, but statistically consistent with stepping. Simulations confirmed both findings. Importantly, during a simple saccade task, with similar trial-averaged activity, neither process explained spiking activity, ruling out interpretations based on differences in attention, reward, or motor planning. These results highlight the need for exploring single-trial spiking dynamics to understand cognitive processing and raise the interesting hypothesis that the superior colliculus participates in different aspects of decision-making depending on task differences.SIGNIFICANCE STATEMENT How are decisions based on sensory information transformed into actions? We report that single-trial neuronal activity dynamics in the superior colliculus of monkeys show differences in decision-making tasks depending on task idiosyncrasies and requirements and despite similar trial-averaged ramping activity. These results highlight the importance of exploring single-trial spiking dynamics to understand cognitive processing and raise the interesting hypothesis that the superior colliculus participates in different aspects of decision-making depending on task requirements.

A new model of decision processing in instrumental learning tasks

Learning and decision-making are interactive processes, yet cognitive modeling of error-driven learning and decision-making have largely evolved separately. Recently, evidence accumulation models (EAMs) of decision-making and reinforcement learning (RL) models of error-driven learning have been combined into joint RL-EAMs that can in principle address these interactions. However, we show that the most commonly used combination, based on the diffusion decision model (DDM) for binary choice, consistently fails to capture crucial aspects of response times observed during reinforcement learning. We propose a new RL-EAM based on an advantage racing diffusion (ARD) framework for choices among two or more options that not only addresses this problem but captures stimulus difficulty, speed-accuracy trade-off, and stimulus-response-mapping reversal effects. The RL-ARD avoids fundamental limitations imposed by the DDM on addressing effects of absolute values of choices, as well as extensions beyond binary choice, and provides a computationally tractable basis for wider applications.

Inhibition of return: An information processing theory of its natures and significance

Inhibition of return (IOR) is an inhibitory aftereffect of visuospatial orienting, typically resulting in slower responses to targets presented in an area that has been recently attended. Since its discovery, myriad research has sought to explain the causes and effects underlying this phenomenon. Here, we briefly summarize the history of the phenomenon, and describe the early work supporting the functional significance of IOR as a foraging facilitator. We then shine a light on the discordance in the literature with respect to mechanism-in particular the lack of theoretical constructs that can consistently explain innumerable dissociations. We then describe three diagnostics (central arrow targets, locus of slack logic and the psychological refractory period, and performance in speed-accuracy space) used to support our theory that there are two forms of inhibition of return-the form which is manifest being contingent upon the activation state of the reflexive oculomotor system. The input form, which operates to decrease the salience of inputs, is generated when the reflexive oculomotor system is suppressed; the output form, which operates to bias responding, is generated when the reflexive oculomotor system is not suppressed. Then, we subject a published data set, wherein inhibitory effects had been generated while the reflexive oculomotor system was either active or suppressed, to diffusion modelling. As we hypothesized, based on the aforementioned theory, the effects of the two forms of IOR were best accounted for by different drift diffusion parameters. The paper ends with a variety of suggestions for further research.

Mechanisms of competitive selection: A canonical neural circuit framework

Competitive selection, the transformation of multiple competing sensory inputs and internal states into a unitary choice, is a fundamental component of animal behavior. Selection behaviors have been studied under several intersecting umbrellas including decision-making, action selection, perceptual categorization, and attentional selection. Neural correlates of these behaviors and computational models have been investigated extensively. However, specific, identifiable neural circuit mechanisms underlying the implementation of selection remain elusive. Here, we employ a first principles approach to map competitive selection explicitly onto neural circuit elements. We decompose selection into six computational primitives, identify demands that their execution places on neural circuit design, and propose a canonical neural circuit framework. The resulting framework has several links to neural literature, indicating its biological feasibility, and has several common elements with prominent computational models, suggesting its generality. We propose that this framework can help catalyze experimental discovery of the neural circuit underpinnings of competitive selection.

Smooth Pursuit and Saccades after Sport-Related Concussion

Smooth pursuit eye movements (SPEMs) and saccadic eye movements are both commonly impaired following sport-related concussion (SRC). Typical oculomotor assessments measure individual eye movements in a series of restrictive tests designed to isolate features such as response times. These measures lack ecological validity for athletes because athletes are adept at simple tasks designed for the general population. Yet, because eye movement metrics are sensitive and well-characterized neuroanatomically, it would be valuable to test whether athletes exhibit abnormal eye movements with more challenging tasks. To address this gap in knowledge, we collected eye-tracking data during a sport-like task to gain insight on gaze behavior during active self-motion. SPEMs and saccadic eye movements were recorded during a sport-like visual task within 24-48 h following SRC. Thirty-six Division I student-athletes were divided into SRC and control (CON) groups. All participants completed two blocks of the Wii Fit© soccer heading game (WF) while wearing a monocular infrared eye tracker. Eye movement classification systems quantified saccadic amplitude (SA), velocity (SV), and count (SC); as well as SPEM velocity (SPV) and amplitude (SPA). Separate Mann-Whitney U tests evaluated SPA and SC and found no significant effects (SPA, p = 0.11; SC, p = 0.10). A multi-variate analysis of variance (MANOVA) for remaining variables revealed SPV was significantly greater in CON (p < 0.05), but the SRC group had greater SA and SV (p < 0.05). These findings suggest that during a sport-like task, to maintain foveation SRC subjects used larger amplitude, faster saccades, but exhibited slower SPEMs. Measuring oculomotor function during ecologically valid, sport-like tasks may serve as a concussion biomarker and provide insights into eye movement control after SRC.

ChaRTr: An R toolbox for modeling choices and response times in decision-making tasks

BACKGROUND

Decision-making is the process of choosing and performing actions in response to sensory cues to achieve behavioral goals. Many mathematical models have been developed to describe the choice behavior and response time (RT) distributions of observers performing decision-making tasks. However, relatively few researchers use these models because it demands expertise in various numerical, statistical, and software techniques.

NEW METHOD

We present a toolbox - Choices and Response Times in R, or ChaRTr - that provides the user the ability to implement and test a wide variety of decision-making models ranging from classic through to modern versions of the diffusion decision model, to models with urgency signals, or collapsing boundaries.

RESULTS

In three different case studies, we demonstrate how ChaRTr can be used to effortlessly discriminate between multiple models of decision-making behavior. We also provide guidance on how to extend the toolbox to incorporate future developments in decision-making models.

COMPARISON WITH EXISTING METHOD(S)

Existing software packages surmounted some of the numerical issues but have often focused on the classical decision-making model, the diffusion decision model. Recent models that posit roles for urgency, time-varying decision thresholds, noise in various aspects of the decision-formation process or low pass filtering of sensory evidence have proven to be challenging to incorporate in a coherent software framework that permits quantitative evaluation among these competing classes of decision-making models.

CONCLUSION

ChaRTr can be used to make insightful statements about the cognitive processes underlying observed decision-making behavior and ultimately for deeper insights into decision mechanisms.

Accumulators, Neurons, and Response Time

The marriage of cognitive neurophysiology and mathematical psychology to understand decision-making has been exceptionally productive. This interdisciplinary area is based on the proposition that particular neurons or circuits instantiate the accumulation of evidence specified by mathematical models of sequential sampling and stochastic accumulation. This linking proposition has earned widespread endorsement. Here, a brief survey of the history of the proposition precedes a review of multiple conundrums and paradoxes concerning the accuracy, precision, and transparency of that linking proposition. Correctly establishing how abstract models of decision-making are instantiated by particular neural circuits would represent a remarkable accomplishment in mapping mind to brain. Failing would reveal challenging limits for cognitive neuroscience. This is such a vigorous area of research because so much is at stake.

A Role for the Superior Colliculus in Decision Criteria

Simple decisions arise from the evaluation of sensory evidence. But decisions are determined by more than just evidence. Individuals establish internal decision criteria that influence how they respond. Where or how decision criteria are established in the brain remains poorly understood. Here, we show that neuronal activity in the superior colliculus (SC) predicts changes in decision criteria. Using a novel "Yes-No" task that isolates changes in decision criterion from changes in decision sensitivity, and computing neuronal measures of sensitivity and criterion, we find that SC neuronal activity correlates with the decision criterion regardless of the location of the choice report. We also show that electrical manipulation of activity within the SC produces changes in decisions consistent with changes in decision criteria and are largely independent of the choice report location. Our correlational and causal results together provide strong evidence that SC activity signals the position of a decision criterion. VIDEO ABSTRACT.

Time-varying decision boundaries: insights from optimality analysis

The most widely used account of decision-making proposes that people choose between alternatives by accumulating evidence in favor of each alternative until this evidence reaches a decision boundary. It is frequently assumed that this decision boundary stays constant during a decision, depending on the evidence collected but not on time. Recent experimental and theoretical work has challenged this assumption, showing that constant decision boundaries are, in some circumstances, sub-optimal. We introduce a theoretical model that facilitates identification of the optimal decision boundaries under a wide range of conditions. Time-varying optimal decision boundaries for our model are a result only of uncertainty over the difficulty of each trial and do not require decision deadlines or costs associated with collecting evidence, as assumed by previous authors. Furthermore, the shape of optimal decision boundaries depends on the difficulties of different decisions. When some trials are very difficult, optimal boundaries decrease with time, but for tasks that only include a mixture of easy and medium difficulty trials, the optimal boundaries increase or stay constant. We also show how this simple model can be extended to more complex decision-making tasks such as when people have unequal priors or when they can choose to opt out of decisions. The theoretical model presented here provides an important framework to understand how, why, and whether decision boundaries should change over time in experiments on decision-making.

Residual Information of Previous Decision Affects Evidence Accumulation in Current Decision

Bias in perceptual decisions can be generally defined as an effect which is controlled by factors other than the decision-relevant information (e.g., perceptual information in a perceptual task, when trials are independent). The literature on decision-making suggests two main hypotheses to account for this kind of bias: internal bias signals are derived from (a) the residual of motor signals generated to report a decision in the past, and (b) the residual of sensory information extracted from the stimulus in the past. Beside these hypotheses, this study suggests that making a decision in the past per se may bias the next decision. We demonstrate the validity of this assumption, first, by performing behavioral experiments based on the two-alternative forced-choice (TAFC) discrimination of motion direction paradigms and, then, we modified the pure drift-diffusion model (DDM) based on the accumulation-to-bound mechanism to account for the sequential effect. In both cases, the trace of the previous trial influences the current decision. Results indicate that the probability of being correct in the current decision increases if it is in line with the previously made decision even in the presence of feedback. Moreover, a modified model that keeps the previous decision information in the starting point of evidence accumulation provides a better fit to the behavioral data. Our findings suggest that the accumulated evidence in the decision-making process after crossing the bound in the previous decision can affect the parameters of information accumulation for the current decision in consecutive trials.

Reconciling age-related changes in behavioural and neural indices of human perceptual decision-making

Ageing impacts on decision-making behaviour across a range of cognitive tasks and scenarios. Computational modelling has proved valuable in providing mechanistic interpretations of these age-related differences; however, the extent to which model parameter differences accurately reflect changes to the underlying neural computations remains unclear. Here, we report that age-related effects on neural signatures of decision formation are inconsistent with behavioural fits derived from a prominent accumulation-to-bound model. Most notably, model-predicted bound differences were absent neurophysiologically. However, constraining the model to match the decision-predictive elements of the brain signals provided more parsimonious fits to behaviour and generated predictions regarding the neural data that were empirically validated. These included a task-dependent slowing of evidence accumulation among older adults and reduced between-trial accumulation rate variability, which was linked to enhanced attentional engagement. Our findings highlight how combining neurophysiological measurements with computational modelling can yield unique insights into group differences in neural decision mechanisms.

Saccade latency delays in young apolipoprotein E (APOE) epsilon 4 carriers

The apolipoprotein E (APOE) epsilon 4 isoform has been associated with a significantly greater risk of developing late onset Alzheimer's disease (AD). However, the negative effects of APOE-ε4 allele on cognitive function vary across the lifespan: reduced memory and executive function have been found in older individuals but, paradoxically, young APOE-ε4 carriers perform better on cognitive tests and show higher neural efficiency. This study aimed to assess the association between APOE genotype and saccade latency using a prosaccade and antisaccade task in young individuals (N = 97, age: 17-35 years). Results showed that prosaccade latency was significantly delayed in a group of ε4 carriers in comparison to non-carriers, which was due to a lower rate of signal accumulation rather than a change in the criterion threshold. In contrast, there was no significant genotype difference for antisaccade latency in this young cohort. These results indicate that prosaccade latency may be useful in establishing the APOE behavioural phenotype, which could ultimately assist with distinguishing between normal and pathological aging.

Detectability of stop-signal determines magnitude of deceleration in saccade planning

An inhibitory control is exerted when the context in which a movement has been planned changes abruptly making the impending movement inappropriate. Neurons in the frontal eye field and superior colliculus steadily increase activity before a saccadic eye movement, but cease the rise below a threshold when an impending saccade is withheld in response to an unexpected stop-signal. This type of neural modulation has been majorly considered as an outcome of a race between preparatory and inhibitory processes ramping up to reach a decision criterion. An alternative model claims that the rate of saccade planning is diminished exclusively when the stop-signal is detected within a stipulated period. However, due to a dearth of empirical evidence in support of the latter model, it remains unclear how the detectability of the stop-signal influences saccade inhibition. In our study, human participants selected a visual target to look at by discriminating a go-cue. Infrequently they cancelled saccade and reported whether they saw the stop-signal. The go-cue and stop-signal both were embedded in a stream of irrelevant stimuli presented in rapid succession. Participants exhibited difficulty in detection of the stop-signal when presented almost immediately after the go-cue. We found a robust relationship between the detectability of the stop-signal and the odds of saccade inhibition. Saccade latency increased exponentially with the maximum time available for processing the stop-signal before gaze shifted. A model in which the stop-signal onset spontaneously decelerated progressive saccade planning with the magnitude proportional to its detectability accounted for the data.

Psychophysical reverse correlation reflects both sensory and decision-making processes

Goal-directed behavior depends on both sensory mechanisms that gather information from the outside world and decision-making mechanisms that select appropriate behavior based on that sensory information. Psychophysical reverse correlation is commonly used to quantify how fluctuations of sensory stimuli influence behavior and is generally believed to uncover the spatiotemporal weighting functions of sensory processes. Here we show that reverse correlations also reflect decision-making processes and can deviate significantly from the true sensory filters. Specifically, changes of decision bound and mechanisms of evidence integration systematically alter psychophysical reverse correlations. Similarly, trial-to-trial variability of sensory and motor delays and decision times causes systematic distortions in psychophysical kernels that should not be attributed to sensory mechanisms. We show that ignoring details of the decision-making process results in misinterpretation of reverse correlations, but proper use of these details turns reverse correlation into a powerful method for studying both sensory and decision-making mechanisms.

Causal contribution and dynamical encoding in the striatum during evidence accumulation

A broad range of decision-making processes involve gradual accumulation of evidence over time, but the neural circuits responsible for this computation are not yet established. Recent data indicate that cortical regions that are prominently associated with accumulating evidence, such as the posterior parietal cortex and the frontal orienting fields, may not be directly involved in this computation. Which, then, are the regions involved? Regions that are directly involved in evidence accumulation should directly influence the accumulation-based decision-making behavior, have a graded neural encoding of accumulated evidence and contribute throughout the accumulation process. Here, we investigated the role of the anterior dorsal striatum (ADS) in a rodent auditory evidence accumulation task using a combination of behavioral, pharmacological, optogenetic, electrophysiological and computational approaches. We find that the ADS is the first brain region known to satisfy the three criteria. Thus, the ADS may be the first identified node in the network responsible for evidence accumulation.

Paradoxical Decision-Making: A Framework for Understanding Cognition in Parkinson's Disease

People with Parkinson's disease (PD) show impaired decision-making when sensory and memory information must be combined. This recently identified impairment results from an inability to accumulate the proper amount of information needed to make a decision and appears to be independent of dopamine tone and reinforcement learning mechanisms. Although considerable work focuses on PD and decisions involving risk and reward, in this Opinion article we propose that the emerging findings in perceptual decision-making highlight the multisystem nature of PD, and that unraveling the neuronal circuits underlying perceptual decision-making impairment may help in understanding other cognitive impairments in people with PD. We also discuss how a decision-making framework may be extended to gain insights into mechanisms of motor impairments in PD.

Neural mechanisms of speed-accuracy tradeoff of visual search: saccade vigor, the origin of targeting errors, and comparison of the superior colliculus and frontal eye field

Balancing the speed-accuracy tradeoff (SAT) is necessary for successful behavior. Using a visual search task with interleaved cues emphasizing speed or accuracy, we recently reported diverse contributions of frontal eye field (FEF) neurons instantiating salience evidence and response preparation. Here, we report replication of visual search SAT performance in two macaque monkeys, new information about variation of saccade dynamics with SAT, extension of the neurophysiological investigation to describe processes in the superior colliculus (SC), and a description of the origin of search errors in this task. Saccade vigor varied idiosyncratically across SAT conditions and monkeys but tended to decrease with response time. As observed in the FEF, speed-accuracy tradeoff was accomplished through several distinct adjustments in the superior colliculus. In "Accurate" relative to "Fast" trials, visually responsive neurons in SC as in FEF had lower baseline firing rates and later target selection. The magnitude of these adjustments in SC was indistinguishable from that in FEF. Search errors occurred when visual salience neurons in the FEF and the SC treated distractors as targets, even in the Accurate condition. Unlike FEF, the magnitude of visual responses in the SC did not vary across SAT conditions. Also unlike FEF, the activity of SC movement neurons when saccades were initiated was equivalent in Fast and Accurate trials. Saccade-related neural activity in SC, but not FEF, varied with saccade peak velocity. These results extend our understanding of the cortical and subcortical contributions to SAT. NEW & NOTEWORTHY Neurophysiological mechanisms of speed-accuracy tradeoff (SAT) have only recently been investigated. This article reports the first replication of SAT performance in nonhuman primates, the first report of variation of saccade dynamics with SAT, the first description of superior colliculus contributions to SAT, and the first description of the origin of errors during SAT. These results inform and constrain new models of distributed decision making.

Antagonistic Interactions Between Microsaccades and Evidence Accumulation Processes During Decision Formation

Despite their small size, microsaccades can impede stimulus detections if executed at inopportune times. Although it has been shown that microsaccades evoke both inhibitory and excitatory responses across different visual regions, their impact on the higher-level neural decision processes that bridge sensory responses to action selection has yet to be examined. Here, we show that when human observers monitor stimuli for subtle feature changes, the occurrence of microsaccades long after (up to 800 ms) change onset predicts slower reaction times and this is accounted for by momentary suppression of neural signals at each key stage of decision formation: visual evidence encoding, evidence accumulation, and motor preparation. Our data further reveal that, independent of the timing of the change events, the onset of neural decision formation coincides with a systematic inhibition of microsaccade production, persisting until the perceptual report is executed. Our combined behavioral and neural measures highlight antagonistic interactions between microsaccade occurrence and evidence accumulation during visual decision-making tasks.SIGNIFICANCE STATEMENT When fixating on a location in space, we frequently make tiny eye movements called microsaccades. In the present study, we show that these microsaccades impede our ability to make perceptual decisions about visual stimuli and this impediment specifically occurs via the disruption of several processing levels of the sensorimotor network: the encoding of visual evidence itself, the accumulation of visual evidence toward a response, and effector-selective motor preparation. Furthermore, we show that the production of microsaccades is inhibited during the perceptual decision, possibly as a counteractive measure to mitigate their negative effect on behavior in this context. The combined behavioral and neural measures used in this study provide strong and novel evidence for the interaction of fixational eye movements and the perceptual decision-making process.

How the Midbrain Helps Us Decide

In an elegant synthesis of behavior, modeling, and neurophysiology, Crapse et al. (2018) show that neurons of the superior colliculus influence choice behavior by encoding choice criterion, a quantitative measure of decision bias integral to signal detection theory.

The elimination of positive priming with increasing prime duration reflects a transition from perceptual fluency to disfluency rather than bias against primed words

With immediate repetition priming of forced choice perceptual identification, short prime durations produce positive priming (i.e., priming the target leads to higher accuracy, while priming the foil leads to lower accuracy). Many theories explain positive priming following short duration primes as reflecting increased perceptual fluency for the primed target (i.e., decreased identification latency). However, most studies only examine either accuracy or response times, rather than considering the joint constraints of response times and accuracy to properly address the role of decision biases and response caution. This is a critical oversight because several theories propose that the transition to negative priming following a long duration prime reflects a decision strategy to compensate for the effect of increased perceptual fluency. In contrast, the nROUSE model of Huber and O'Reilly (2003) explains this transition as reflecting perceptual habituation, and thus a change to perceptual disfluency. We confirmed this prediction by applying a sequential sampling model (the diffusion race model) to accuracy and response time distributions from a new single item same-different version of the priming task. In this way, we measured strategic biases and perceptual fluency in each condition for each subject. The nROUSE model was only applied to accuracy from the original forced-choice version of the priming task. This application of nROUSE produced separate predictions for each subject regarding the degree of fluency and disfluency in each condition, and these predictions were confirmed by the drift rate parameters (i.e., fluency) from the response time model in contrast to the threshold parameters (i.e., bias).

Directional Limits on Motion Transparency Assessed Through Colour-Motion Binding

Motion-defined transparency is the perception of two or more distinct moving surfaces at the same retinal location. We explored the limits of motion transparency using superimposed surfaces of randomly positioned dots defined by differences in motion direction and colour. In one experiment, dots were red or green and we varied the proportion of dots of a single colour that moved in a single direction ('colour-motion coherence') and measured the threshold direction difference for discriminating between two directions. When colour-motion coherences were high (e.g., 90% of red dots moving in one direction), a smaller direction difference was required to correctly bind colour with direction than at low coherences. In another experiment, we varied the direction difference between the surfaces and measured the threshold colour-motion coherence required to discriminate between them. Generally, colour-motion coherence thresholds decreased with increasing direction differences, stabilising at direction differences around 45°. Different stimulus durations were compared, and thresholds were higher at the shortest (150 ms) compared with the longest (1,000 ms) duration. These results highlight different yet interrelated aspects of the task and the fundamental limits of the mechanisms involved: the resolution of narrowly separated directions in motion processing and the local sampling of dot colours from each surface.

Frontal Eye Field Inactivation Diminishes Superior Colliculus Activity, But Delayed Saccadic Accumulation Governs Reaction Time Increases

Stochastic accumulator models provide a comprehensive framework for how neural activity could produce behavior. Neural activity within the frontal eye fields (FEFs) and intermediate layers of the superior colliculus (iSC) support such models for saccade initiation by relating variations in saccade reaction time (SRT) to variations in such parameters as baseline, rate of accumulation of activity, and threshold. Here, by recording iSC activity during reversible cryogenic inactivation of the FEF in four male nonhuman primates, we causally tested which parameter(s) best explains concomitant increases in SRT. While FEF inactivation decreased all aspects of ipsilesional iSC activity, decreases in accumulation rate and threshold poorly predicted accompanying increases in SRT. Instead, SRT increases best correlated with delays in the onset of saccade-related accumulation. We conclude that FEF signals govern the onset of saccade-related accumulation within the iSC, and that the onset of accumulation is a relevant parameter for stochastic accumulation models of saccade initiation.SIGNIFICANCE STATEMENT The superior colliculus (SC) and frontal eye fields (FEFs) are two of the best-studied areas in the primate brain. Surprisingly, little is known about what happens in the SC when the FEF is temporarily inactivated. Here, we show that temporary FEF inactivation decreases all aspects of functionally related activity in the SC. This combination of techniques also enabled us to relate changes in SC activity to concomitant increases in saccadic reaction time (SRT). Although stochastic accumulator models relate SRT increases to reduced rates of accumulation or increases in threshold, such changes were not observed in the SC. Instead, FEF inactivation delayed the onset of saccade-related accumulation, emphasizing the importance of this parameter in biologically plausible models of saccade initiation.

Overcoming indecision by changing the decision boundary

The dominant theoretical framework for decision making asserts that people make decisions by integrating noisy evidence to a threshold. It has recently been shown that in many ecologically realistic situations, decreasing the decision boundary maximizes the reward available from decisions. However, empirical support for decreasing boundaries in humans is scant. To investigate this problem, we used an ideal observer model to identify the conditions under which participants should change their decision boundaries with time to maximize reward rate. We conducted 6 expanded-judgment experiments that precisely matched the assumptions of this theoretical model. In this paradigm, participants could sample noisy, binary evidence presented sequentially. Blocks of trials were fixed in duration, and each trial was an independent reward opportunity. Participants therefore had to trade off speed (getting as many rewards as possible) against accuracy (sampling more evidence). Having access to the actual evidence samples experienced by participants enabled us to infer the slope of the decision boundary. We found that participants indeed modulated the slope of the decision boundary in the direction predicted by the ideal observer model, although we also observed systematic deviations from optimality. Participants using suboptimal boundaries do so in a robust manner, so that any error in their boundary setting is relatively inexpensive. The use of a normative model provides insight into what variable(s) human decision makers are trying to optimize. Furthermore, this normative model allowed us to choose diagnostic experiments and in doing so we present clear evidence for time-varying boundaries. (PsycINFO Database Record

Probing the Characteristics of Colour-Motion Binding and Its Dependence on Persistent Surface Segregation

Identifying the spatial and temporal characteristics of visual feature binding is a remaining challenge in the science of perception. Within the feature-binding literature, disparate findings have suggested the existence of more than one feature-binding mechanism with differing temporal resolutions. For example, one surprising result is that temporal alternations between two different feature pairings of colour and motion (e.g., orange dots moving left with blue dots moving right) support accurate conjunction discrimination at alternation frequencies of around 10 Hz and greater. However, at lower alternation frequencies around 5 Hz, conjunction discrimination falls to chance. To further investigate this effect, we present two experiments that probe the stimulus characteristics that facilitate or impede feature binding. Using novel manipulations of random dot kinematograms, we identify that facilitating surface representations through temporal integration can enable accurate conjunction discrimination at both intermediate and high alternation frequencies. We also offer a neurally plausible evidence accumulator model to describe these results, removing the need to suggest multiple binding mechanisms acting at different timescales. In effect, we propose a single, flexible binding process, whereby the relatively low temporal resolution for binding features can be circumvented by extracting them from rapidly formed and persistent surface representations.