Evidence Accumulates for Individual Attributes during Value-Based Decisions

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.

Impaired Evidence Accumulation as a Transdiagnostic Vulnerability Factor in Psychopathology

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.

A sequential sampling account of semantic relatedness decisions

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.

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

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.

Impaired Evidence Accumulation as a Transdiagnostic Vulnerability Factor in Psychopathology

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.

Serial integration of sensory evidence for perceptual decisions and oculomotor responses

Perceptual decisions often require the integration of noisy sensory evidence over time. This process is formalized with sequential sampling models, where evidence is accumulated up to a decision threshold before a choice is made. Although classical accounts grounded in cognitive psychology tend to consider the process of decision formation and the preparation of the motor response as occurring serially, neurophysiological studies have proposed that decision formation and response preparation occur in parallel and are inseparable (Cisek, 2007; Shadlen et al., 2008). To address this serial vs. parallel debate, we developed a behavioural, reverse correlation protocol, in which the stimuli that influence perceptual decisions can be distinguished from the stimuli that influence motor responses. We show that the temporal integration windows supporting these two processes are distinct and largely non-overlapping, suggesting that they proceed in a serial or cascaded fashion.