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

2018 ◽  
Author(s):  
Kitty K. Lui ◽  
Michael D. Nunez ◽  
Jessica M. Cassidy ◽  
Joachim Vandekerckhove ◽  
Steven C. Cramer ◽  
...  
Keyword(s):  
Decision Making ◽  
Response Time ◽  
Response Selection ◽  
Sequential Sampling ◽  
Perceptual Categorization ◽  
Accumulation Process ◽  
Time Data ◽  
Evidence Accumulation ◽  
Sequential Sampling Models ◽  
Readiness Potentials

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

scholarly journals Sequential Sampling Models Without Random Between-Trial Variability: The Racing Diffusion Model of Speeded Decision

2017 ◽  
Author(s):  
Gabriel Tillman
Keyword(s):  
Response Time ◽  
Diffusion Model ◽  
Response Times ◽  
Sequential Sampling ◽  
Good Reason ◽  
Accumulation Process ◽  
Time Data ◽  
Evidence Accumulation ◽  
Sequential Sampling Models ◽  
Sequential Sampling Model

Most current sequential sampling models have random between-trial variability in their parameters. These sources of variability make the models more complex in order to fit response time data, do not provide any further explanation to how the data were generated, and have recently been criticised for allowing infinite flexibility in the models. To explore and test the need of between-trial variability parameters we develop a simple sequential sampling model of N-choice speeded decision making: the racing diffusion model. The model makes speeded decisions from a race of evidence accumulators that integrate information in a noisy fashion within a trial. The racing diffusion does not assume that any evidence accumulation process varies between trial, and so, the model provides alternative explanations of key response time phenomena, such as fast and slow error response times relative to correct response times. Overall, our paper gives good reason to rethink including between-trial variability parameters in sequential sampling models

scholarly journals Unification of Countermanding and Perceptual Decision-making

2017 ◽  
Author(s):  
Paul G. Middlebrooks ◽  
Bram B. Zandbelt ◽  
Gordon D. Logan ◽  
Thomas J. Palmeri ◽  
Jeffrey D. Schall
Keyword(s):  
Decision Making ◽  
Response Inhibition ◽  
Sequential Sampling ◽  
Race Model ◽  
Decision Time ◽  
Perceptual Decision Making ◽  
Perceptual Decision ◽  
Evidence Accumulation ◽  
Sequential Sampling Models ◽  
Choice Tasks

Perceptual decision-making, studied using two-alternative forced-choice tasks, is explained by sequential sampling models of evidence accumulation, which correspond to the dynamics of neurons in sensorimotor structures of the brain1 2. Response inhibition, studied using stop-signal (countermanding) tasks, is explained by a race model of the initiation or canceling of a response, which correspond to the dynamics of neurons in sensorimotor structures3 4. Neither standard model accounts for performance of the other task. Sequential sampling models incorporate response initiation as an uninterrupted non-decision time parameter independent of task-related variables. The countermanding race model does not account for the choice process. Here we show with new behavioral, neural and computational results that perceptual decision making of varying difficulty can be countermanded with invariant efficiency, that single prefrontal neurons instantiate both evidence accumulation and response inhibition, and that an interactive race between two GO and one STOP stochastic accumulator fits countermanding choice behavior. Thus, perceptual decision-making and response control, previously regarded as distinct mechanisms, are actually aspects of more flexible behavior supported by a common neural and computational mechanism. The identification of this aspect of decision-making with response production clarifies the component processes of decision-making.

scholarly journals Decision making under time pressure: An independent test of sequential sampling models

1999 ◽  
Vol 27 (4) ◽  
pp. 713-725 ◽  
Author(s):  
Itiel E. Dror ◽  
Beth Basola ◽  
Jerome R. Busemeyer
Keyword(s):  
Decision Making ◽  
Time Pressure ◽  
Sequential Sampling ◽  
Sequential Sampling Models ◽  
Independent Test

scholarly journals Autocorrelation structure at rest predicts value correlates of single neurons during reward-guided choice

eLife ◽  
2016 ◽  
Vol 5 ◽  
Author(s):  
Sean E Cavanagh ◽  
Joni D Wallis ◽  
Steven W Kennerley ◽  
Laurence T Hunt
Keyword(s):  
Decision Making ◽  
Prefrontal Cortex ◽  
Orbitofrontal Cortex ◽  
Receptive Fields ◽  
Neural Mechanism ◽  
Spike Rate ◽  
Accumulation Process ◽  
Single Neurons ◽  
Evidence Accumulation ◽  
Autocorrelation Structure

Correlates of value are routinely observed in the prefrontal cortex (PFC) during reward-guided decision making. In previous work (Hunt et al., 2015), we argued that PFC correlates of chosen value are a consequence of varying rates of a dynamical evidence accumulation process. Yet within PFC, there is substantial variability in chosen value correlates across individual neurons. Here we show that this variability is explained by neurons having different temporal receptive fields of integration, indexed by examining neuronal spike rate autocorrelation structure whilst at rest. We find that neurons with protracted resting temporal receptive fields exhibit stronger chosen value correlates during choice. Within orbitofrontal cortex, these neurons also sustain coding of chosen value from choice through the delivery of reward, providing a potential neural mechanism for maintaining predictions and updating stored values during learning. These findings reveal that within PFC, variability in temporal specialisation across neurons predicts involvement in specific decision-making computations.

Early evidence affects later decisions: Why evidence accumulation is required to explain response time data

2014 ◽  
Author(s):  
Jasper Winkel ◽  
Max C. Keuken ◽  
Leendert van Maanen ◽  
Eric-Jan Wagenmakers ◽  
Birte U. Forstmann
Keyword(s):  
Response Time ◽  
Response Time Data ◽  
Time Data ◽  
Evidence Accumulation ◽  
Early Evidence

Modeling Moral Decision Making With Sequential Sampling Models

2012 ◽  
Author(s):  
Nicolas A. J. Berkowitsch ◽  
Joerg Rieskamp ◽  
Benjamin Scheibehenne
Keyword(s):  
Decision Making ◽  
Sequential Sampling ◽  
Moral Decision ◽  
Moral Decision Making ◽  
Sequential Sampling Models

scholarly journals Evidence accumulation account of human operators’ decisions in intermittent control during inverted pendulum balancing

2018 ◽  
Author(s):  
Arkady Zgonnikov ◽  
Gustav Markkula
Keyword(s):  
Decision Making ◽  
Inverted Pendulum ◽  
Experimental Studies ◽  
Intermittent Control ◽  
Accumulation Process ◽  
Activation Patterns ◽  
Control Error ◽  
Evidence Accumulation ◽  
Human Operators ◽  
Control Activation

Human operators often employ intermittent, discontinuous control strategies in a variety of tasks. A typical intermittent controller monitors control error and generates corrective action when the deviation of the controlled system from the desired state becomes too large to ignore. Most contemporary models of human intermittent control employ simple, threshold-based trigger mechanism to model the process of control activation. However, recent experimental studies demonstrate that the control activation patterns produced by human operators do not support threshold-based models, and provide evidence for more complex activation mechanisms. In this paper, we investigate whether intermittent control activation in humans can be modeled as a decision-making process. We utilize an established drift-diffusion model, which treats decision making as an evidence accumulation process, and study it in simple numerical simulations. We demonstrate that this model robustly replicates the control activation patterns (distributions of control error at movement onset) produced by human operators in previously conducted experiments on virtual inverted pendulum balancing. Our results provide support to the hypothesis that intermittent control activation in human operators can be treated as an evidence accumulation process.

scholarly journals Evidence Accumulation Models: Current Limitations and Future Directions

2019 ◽  
Author(s):  
Nathan J. Evans ◽  
Eric-Jan Wagenmakers
Keyword(s):  
Decision Making ◽  
Response Time ◽  
Cognitive Processes ◽  
Measurement Tool ◽  
Decision Making Process ◽  
Choice Response ◽  
Experimental Conditions ◽  
Evidence Accumulation ◽  
Choice Response Time ◽  
Response Time Distributions

Evidence accumulation models (EAMs) have been the dominant models of speeded decision-making for several decades. These models propose that evidence accumulates for decision alternatives at some rate, until the evidence for one alternative reaches some threshold that triggers a decision. As a theory, EAMs have provided an accurate account of the choice response time distributions in a range of decision-making tasks, and as a measurement tool, EAMs have provided direct insight into how cognitive processes differ between groups and experimental conditions, resulting in EAMs becoming the standard paradigm of speeded decision-making. However, we argue that there are several limitations to how EAMs are currently tested and applied, which have begun to limit their value as a standard paradigm. Specifically, we believe that a theoretical plateau has been reached for the level of explanation that EAMs can provide about the decision-making process, and that applications of EAMs have started to become restrictive and of limited value. We provide several recommendations for how researchers can help to overcome these limitations. As a theory, we believe that EAMs can provide further value through being constrained by sources of data beyond the standard choice response time distributions, being extended to the entire decision-making process from encoding to responding, and having the random sources of variability replaced by systematic sources of variability. As a measurement tool, we believe that EAMs can provide further value through being a default method of inference for cognitive psychology in place of mean response time and choice, and being applied to a broader range of empirical questions that better capture individual differences in cognitive processes.

scholarly journals An LBA account of decisions in the multiple object tracking task

2019 ◽  
Author(s):  
Reilly James Innes ◽  
Caroline Kuhne
Keyword(s):  
Decision Making ◽  
Object Tracking ◽  
Response Times ◽  
Sequential Sampling ◽  
Multiple Object Tracking ◽  
Tracking Task ◽  
Everyday Functioning ◽  
Multiple Object ◽  
Sequential Sampling Models ◽  
Linear Ballistic Accumulator

Decision making is a vital aspect of our everyday functioning, from simple perceptual demands to more complex and meaningful decisions. The strategy adopted to make such decisions is often viewed as balancing elements of speed and caution, i.e. making fast or careful decisions. Using sequential sampling models to analyse decision making data can allow us to tease apart strategic differences, such as being more or less cautious, from processing differences, which would otherwise be indistinguishable in behavioural data. Our study used a multiple object tracking task where student participants and a highly skilled military group were compared on their ability to track several items at once. Using a mathematical model of decision making (the linear ballistic accumulator), we show the underpinnings of how two groups differ in performance. Results showed a large difference between the groups on accuracy, with the RAAF group outperforming students. An interaction effect was observed between groups and level of difficulty in response times, where RAAF response times slowed at a greater rate than the student group as difficulty increased. Model results indicated that the RAAF personnel were more cautious in their decisions than students, and had faster processing in some conditions. Our study shows the strength of sequential sampling models, as well as providing a first attempt at fitting a sequential sampling model to data from a multiple object tracking task.

Sign in / Sign up

Export Citation Format

Share Document