Dataset Information

Value-based decision making via sequential sampling with hierarchical competition and attentional modulation.

ABSTRACT: In principle, formal dynamical models of decision making hold the potential to represent fundamental computations underpinning value-based (i.e., preferential) decisions in addition to perceptual decisions. Sequential-sampling models such as the race model and the drift-diffusion model that are grounded in simplicity, analytical tractability, and optimality remain popular, but some of their more recent counterparts have instead been designed with an aim for more feasibility as architectures to be implemented by actual neural systems. Connectionist models are proposed herein at an intermediate level of analysis that bridges mental phenomena and underlying neurophysiological mechanisms. Several such models drawing elements from the established race, drift-diffusion, feedforward-inhibition, divisive-normalization, and competing-accumulator models were tested with respect to fitting empirical data from human participants making choices between foods on the basis of hedonic value rather than a traditional perceptual attribute. Even when considering performance at emulating behavior alone, more neurally plausible models were set apart from more normative race or drift-diffusion models both quantitatively and qualitatively despite remaining parsimonious. To best capture the paradigm, a novel six-parameter computational model was formulated with features including hierarchical levels of competition via mutual inhibition as well as a static approximation of attentional modulation, which promotes "winner-take-all" processing. Moreover, a meta-analysis encompassing several related experiments validated the robustness of model-predicted trends in humans' value-based choices and concomitant reaction times. These findings have yet further implications for analysis of neurophysiological data in accordance with computational modeling, which is also discussed in this new light.

SUBMITTER: Colas JT

PROVIDER: S-EPMC5659783 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Value-based decision making via sequential sampling with hierarchical competition and attentional modulation.

Colas Jaron T JT

PloS one 20171027 10

In principle, formal dynamical models of decision making hold the potential to represent fundamental computations underpinning value-based (i.e., preferential) decisions in addition to perceptual decisions. Sequential-sampling models such as the race model and the drift-diffusion model that are grounded in simplicity, analytical tractability, and optimality remain popular, but some of their more recent counterparts have instead been designed with an aim for more feasibility as architectures to b ...[more]

PMID: 29077746

Similar Datasets

Project description:RATIONALE:During value-based decision-making, organisms make choices on the basis of reward expectations, which have been formed during prior action-outcome learning. Although it is known that neuronal manipulations of different subregions of the rat prefrontal cortex (PFC) have qualitatively different effects on behavioral tasks involving value-based decision-making, it is unclear how these regions contribute to the underlying component processes. OBJECTIVES:Assessing how different regions of the rodent PFC contribute to component processes of value-based decision-making behavior, including reward (or positive feedback) learning, punishment (or negative feedback) learning, response persistence, and exploration versus exploitation. METHODS:We performed behavioral modeling of data of rats in a probabilistic reversal learning task after pharmacological inactivation of five PFC subregions, to assess how inactivation of these different regions affected the structure of responding of animals in the task. RESULTS:Our results show reductions in reward and punishment learning after PFC subregion inactivation. The prelimbic, infralimbic, lateral orbital, and medial orbital PFC particularly contributed to punishment learning, and the prelimbic and lateral orbital PFC to reward learning. In addition, response persistence depended on the infralimbic and medial orbital PFC. As a result, pharmacological inactivation of the infralimbic and lateral orbitofrontal cortex reduced the number of reversals achieved, whereas inactivation of the prelimbic and medial orbitofrontal cortex decreased the number of rewards obtained. Finally, using simulated data, we explain discrepancies with a previous study and demonstrate complex, interacting relationships between conventional measures of probabilistic reversal learning performance, such as win-stay/lose-switch behavior, and component processes of value-based decision-making. CONCLUSIONS:Together, our data suggest that distinct components of value-based learning and decision-making are generated in medial and orbital PFC regions, displaying functional specialization and overlap, with a prominent role of large parts of the PFC in negative feedback processing.

Project description:Background: Working memory (WM) deficits and impaired decision making are among the characteristic symptoms of patients affected by attention deficit/hyperactivity disorder (ADHD). The inattention associated with the disorder is likely to be due to functional deficits of the neural networks inhibiting irrelevant sensory input. In the presence of unnecessary information, a good decisional process is impaired and ADHD patients tend to take risky decisions. This study is aimed to test the hypothesis that the level of difficulty of a WM training (WMT) is affecting the top-down modulation of the attentional processes in a probabilistic gambling task. Methods: Event-related potentials (ERP) triggered by the choice of the amount wagered in the gambling task were recorded, before and after WMT with a the dual n-back task, in young ADHD adults and matched controls. For each group of participants, randomly assigned individuals were requested to perform WMT with a fixed baseline level of difficulty. The remaining participants were trained with a performance-dependent adaptive n-level of difficulty. Results: We compared the ERP recordings before and after 20 days of WMT in each subgroup. The analysis was focused on the time windows with at least three recording sites showing differences before and after training, after Bonferroni correction ( p < 0.05 ). In ADHD, the P1 wave component was selectively affected at frontal sites and its shape was recovered close to controls' only after adaptive training. In controls, the strongest contrast was observed at parietal level with a left hemispheric dominance at latencies near 900 ms, more after baseline than after adaptive training. Conclusion: Partial restoration of early selective attentional processes in ADHD patients might occur after WMT with a high cognitive load. Modified frontal sites' activities might constitute a neural marker of this effect in a gambling task. In controls, conversely, an increase in late parietal negativity might rather be a marker of an increase in transfer effects to fluid intelligence.

Dataset Information

Value-based decision making via sequential sampling with hierarchical competition and attentional modulation.

Publications

Value-based decision making via sequential sampling with hierarchical competition and attentional modulation.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets