scholarly journals Evidence Accumulates for Individual Attributes during Value-Based Decisions

2021 ◽  
Author(s):  
Douglas G. Lee ◽  
Todd A. Hare
Keyword(s):  
Computational Models ◽  
Response Times ◽  
Sequential Sampling ◽  
Choice Data ◽  
Attribute Importance ◽  
Snack Foods ◽  
Sequential Sampling Models ◽  
Level Information ◽  
Binary Choices ◽  
Important Evidence

When choosing between different options, we tend to consider specific attribute qualities rather than deliberating over some general sense of the objects' overall values. The importance of each attribute together with its quality will determine our preference rankings over the available alternatives. Here, we show that the relative importance of the latent attributes within food rewards reliably differs when the items are evaluated in isolation compared to when binary choices are made between them. Specifically, we used standard regression and sequential sampling models to examine six datasets in which participants evaluated, and chose between, multi-attribute snack foods. We show that models that assume that attribute importance remains constant across evaluation and choice contexts fail to reproduce fundamental patterns in the choice data and provide quantitatively worse fits to the choice outcomes, response times, and confidence reports compared to models that allow for attribute importance to vary across preference elicitation methods. Our results provide important evidence that incorporating attribute-level information into computational models helps us to better understand the cognitive processes involved in value-based decision-making.

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 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 Impaired Evidence Accumulation as a Transdiagnostic Vulnerability Factor in Psychopathology

2021 ◽  
Vol 12 ◽  
Author(s):  
Chandra Sripada ◽  
Alexander Weigard
Keyword(s):  
Computational Models ◽  
Sequential Sampling ◽  
Self Report ◽  
Evidence Accumulation ◽  
Hyperactivity Disorder ◽  
Sequential Sampling Models ◽  
Intelligence Scores ◽  
Choice Tasks ◽  
Substantial Interest ◽  
Healthy Participants

There is substantial interest in identifying biobehavioral dimensions of individual variation that cut across heterogenous disorder categories, and computational models can play a major role in advancing this goal. In this report, we focused on efficiency of evidence accumulation (EEA), a computationally characterized variable derived from sequential sampling models of choice tasks. We created an EEA factor from three behavioral tasks in the UCLA Phenomics dataset (n = 272), which includes healthy participants (n = 130) as well-participants with schizophrenia (n = 50), bipolar disorder (n = 49), and attention-deficit/hyperactivity disorder (n = 43). We found that the EEA factor was significantly reduced in all three disorders, and that it correlated with an overall severity score for psychopathology as well as self-report measures of impulsivity. Although EEA was significantly correlated with general intelligence, it remained associated with psychopathology and symptom scales even after controlling for intelligence scores. Taken together, these findings suggest EEA is a promising computationally-characterized dimension of neurocognitive variation, with diminished EEA conferring transdiagnostic vulnerability to psychopathology.

scholarly journals The Strength of Gradually Accruing Probabilistic Evidence Modulates Brain Activity during a Categorical Decision

2015 ◽  
Vol 27 (4) ◽  
pp. 705-719 ◽  
Author(s):  
Mark E. Wheeler ◽  
Sarah G. Woo ◽  
Tobin Ansel ◽  
Joshua J. Tremel ◽  
Amanda L. Collier ◽  
...  
Keyword(s):  
Brain Activity ◽  
Sequential Sampling ◽  
Brain Regions ◽  
Time On Task ◽  
Response Hand ◽  
Prior Probabilities ◽  
Parietal Lobes ◽  
Sequential Sampling Models ◽  
Binary Choices ◽  
Probabilistic Evidence

The evolution of neural activity during a perceptual decision is well characterized by the evidence parameter in sequential sampling models. However, it is not known whether accumulating signals in human neuroimaging are related to the integration of evidence. Our aim was to determine whether activity accumulates in a nonperceptual task by identifying brain regions tracking the strength of probabilistic evidence. fMRI was used to measure whole-brain activity as choices were informed by integrating a series of learned prior probabilities. Participants first learned the predictive relationship between a set of shape stimuli and one of two choices. During scanned testing, they made binary choices informed by the sum of the predictive strengths of individual shapes. Sequences of shapes adhered to three distinct rates of evidence (RoEs): rapid, gradual, and switch. We predicted that activity in regions informing the decision would modulate as a function of RoE prior to the choice. Activity in some regions, including premotor areas, changed as a function of RoE and response hand, indicating a role in forming an intention to respond. Regions in occipital, temporal, and parietal lobes modulated as a function of RoE only, suggesting a preresponse stage of evidence processing. In all of these regions, activity was greatest on rapid trials and least on switch trials, which is consistent with an accumulation-to-boundary account. In contrast, activity in a set of frontal and parietal regions was greatest on switch and least on rapid trials, which is consistent with an effort or time-on-task account.

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 Impaired Evidence Accumulation as a Transdiagnostic Vulnerability Factor in Psychopathology

2020 ◽  
Author(s):  
Chandra Sripada ◽  
Alexander Samuel Weigard
Keyword(s):  
Computational Models ◽  
Sequential Sampling ◽  
Self Report ◽  
Evidence Accumulation ◽  
Hyperactivity Disorder ◽  
Sequential Sampling Models ◽  
Intelligence Scores ◽  
Choice Tasks ◽  
Substantial Interest ◽  
Healthy Participants

There is substantial interest in identifying biobehavioral dimensions of individual variation that cut across heterogenous disorder categories, and computational models can play a major role in advancing this goal. In this report, we focused on efficiency of evidence accumulation (EEA), a computationally characterized variable derived from sequential sampling models of choice tasks. We created an EEA factor from three behavioral tasks in the UCLA Phenomics dataset (n=272), which includes healthy participants (n=130) as well participants with schizophrenia (n=50), bipolar disorder (n=49), and attention-deficit/hyperactivity disorder (n=43). We found that the EEA factor was significantly reduced in all three disorders, and that it correlated with an overall severity score for psychopathology as well as self-report measures of impulsivity. Although EEA was significantly correlated with general intelligence, it remained associated with psychopathology and symptom scales even after controlling for intelligence scores. Taken together, these findings suggest EEA is a promising computationally-characterized dimension of neurocognitive variation, with diminished EEA conferring transdiagnostic vulnerability to psychopathology.

scholarly journals A sequential sampling account of semantic relatedness decisions

2021 ◽  
Author(s):  
Peter Maximilian Kraemer ◽  
Dirk U. Wulff ◽  
Sebastian Gluth
Keyword(s):  
Semantic Memory ◽  
Memory Retrieval ◽  
Response Times ◽  
Sequential Sampling ◽  
Strong Support ◽  
Race Model ◽  
Semantic Relatedness ◽  
Data Set ◽  
Memory Research ◽  
Sequential Sampling Models

Semantic memory research often draws on decisions about the semantic relatedness of concepts. These decisions depend on cognitive processes of memory retrieval and choice formation. However, most previous research focused on memory retrieval but neglected the decision aspects. Here we propose the sequential sampling framework to account for choices and response times in semantic relatedness decisions. We focus on three popular sequential sampling models, the Race model, the Leaky Competing Accumulator model (LCA) and the Drift Diffusion Model (DDM). Using model simulations, we investigate if and how these models account for two empirical benchmarks: the relatedness effect, denoting faster "related" than "unrelated" decisions when judging the relatedness of word pairs; and an inverted-U shaped relationship between response time and the relatedness strength of word pairs. Our simulations show that the LCA and DDM, but not the Race model, can reproduce both effects. Furthermore, the LCA predicts a novel phenomenon: the inverted relatedness effect for weakly related word pairs. Reanalyzing a publicly available data set, we obtained credible evidence of such an inverted relatedness effect. These results provide strong support for sequential sampling models -- and in particular the LCA -- as a viable computational account of semantic relatedness decisions and suggest an important role for decision-related processes in (semantic) memory tasks.

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