scholarly journals Attention as a source of variability in decision-making: Accounting for overall-value effects with diffusion models

2020 ◽  
Author(s):  
Blair Shevlin ◽  
Ian Krajbich
Keyword(s):  
Decision Making ◽  
Response Times ◽  
Sequential Sampling ◽  
Diffusion Models ◽  
Tracking Data ◽  
Sequential Sampling Models ◽  
Relative Value ◽  
Data Generating Process ◽  
Multiplicative Relationship ◽  
Value Effect

Research has demonstrated that value-based decisions depend not only on the relative value difference between options, but also on their overall value. In particular, response times tend to decrease as the overall value of the options increase. Standard sequential sampling models such as the diffusion model can account for this fact by assuming that decision thresholds or noise vary with overall value. Alternatively, attention-based models that incorporate eye-tracking data accommodate this overall-value effect directly as a consequence of the multiplicative relationship between attention and value magnitude. Using non-attentional diffusion models fit to data simulated with an attention-based model, we find that parameters related to decision thresholds or noise vary as a function of overall value, even though these were not features of the data generating process. We find additional evidence for misidentified parameters in a similar analysis using empirical data. Our results indicate that neglecting attentional effects can lead to mistaken conclusions about which decision parameters are sensitive to overall value.

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.

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

Reinforcement-Based Decision Making in Corticostriatal Circuits: Mutual Constraints by Neurocomputational and Diffusion Models

2012 ◽  
Vol 24 (5) ◽  
pp. 1186-1229 ◽  
Author(s):  
Roger Ratcliff ◽  
Michael J. Frank
Keyword(s):  
Decision Making ◽  
Diffusion Model ◽  
Response Times ◽  
Simulated Data ◽  
Diffusion Models ◽  
Choice Data ◽  
Interactive Decision Making ◽  
Corticostriatal Circuits ◽  
Nondecision Time

In this letter, we examine the computational mechanisms of reinforce-ment-based decision making. We bridge the gap across multiple levels of analysis, from neural models of corticostriatal circuits—the basal ganglia (BG) model (Frank, 2005 , 2006 ) to simpler but mathematically tractable diffusion models of two-choice decision making. Specifically, we generated simulated data from the BG model and fit the diffusion model (Ratcliff, 1978 ) to it. The standard diffusion model fits underestimated response times under conditions of high response and reinforcement conflict. Follow-up fits showed good fits to the data both by increasing nondecision time and by raising decision thresholds as a function of conflict and by allowing this threshold to collapse with time. This profile captures the role and dynamics of the subthalamic nucleus in BG circuitry, and as such, parametric modulations of projection strengths from this nucleus were associated with parametric increases in decision boundary and its modulation by conflict. We then present data from a human reinforcement learning experiment involving decisions with low- and high-reinforcement conflict. Again, the standard model failed to fit the data, but we found that two variants similar to those that fit the BG model data fit the experimental data, thereby providing a convergence of theoretical accounts of complex interactive decision-making mechanisms consistent with available data. This work also demonstrates how to make modest modifications to diffusion models to summarize core computations of the BG model. The result is a better fit and understanding of reinforcement-based choice data than that which would have occurred with either model alone.

scholarly journals Equal evidence perceptual tasks suggest key role for interactive competition in decision-making

2019 ◽  
Author(s):  
Ryan Kirkpatrick ◽  
Brandon Turner ◽  
Per B. Sederberg
Keyword(s):  
Decision Making ◽  
Response Times ◽  
Sequential Sampling ◽  
The Past ◽  
Human Decision ◽  
Complex Decision Making ◽  
Complex Decision ◽  
The Difference ◽  
Best Fit ◽  
Made In

The dynamics of decision-making have been widely studied over the past several decades through the lens of an overarching theory called sequential sampling theory (SST). Within SST, choices are represented as accumulators, each of which races toward a decision boundary by drawing stochastic samples of evidence through time. Although progress has been made in understanding how decisionsare made within the SST framework, considerable debate centers on whether the accumulators exhibit dependency during the evidence accumulation process; namely whether accumulators are independent, fully dependent, or partially dependent. To evaluate which type of dependency is the most plausible representation of human decision-making, we applied a novel twist on two classic perceptual tasks; namely, in addition to the classic paradigm (i.e., the unequal-evidence conditions), we used stimuli that provided different magnitudes of equal-evidence (i.e., the equal-evidence conditions). In equal-evidence conditions, response times systematically decreased with increases in the magnitude of evidence, whereas in unequal evidence conditions, response times systematically increased as the difference in evidence between the two alternatives decreased. We designed a spectrum of models that ranged from independent accumulation to fully dependent accumulation, while also examining the effects of within-trial and between-trial variability. We then fit the set of models to our two experiments and found that models instantiating the principles of partial dependency provided the best fit to the data. Our results further suggest that mechanisms inducing partial dependency, such as lateral inhibition, are beneficial for understanding complex decision-making dynamics, even when the task is relatively simple.

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 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 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.

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