# Mechanisms of multi-attribute decision-making

> **NIH NIH R01** · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · 2024 · $721,370

## Abstract

When making a complex decision, we often consider multiple dimensions, such as costs and qualities, that vary
among choice options. Evaluating important attributes of a given option is critical for optimal choice behavior,
and poor decision-making can result from an inability to properly weigh attributes, as is commonly observed in
psychiatric disorders. These deficits are accompanied by alterations in the structure and function of the
orbitofrontal cortex (OFC), an area critical for value-based decision-making. However, the underlying neural
mechanisms and how they are disrupted remain unclear, and this limits our ability to map decision-making
deficits to neural computation. The long-term goal of this proposal is to understand how the brain uses
information to make optimal decisions, and our specific objective is to develop a comprehensive model of
information processing in OFC during multi-attribute choices. To do this, we will use a multi-modal approach to
evaluate different frameworks of decision formation. A neuroeconomics view posits that the values of different
attributes are combined to compute an overall, or integrated value, and comparisons are made in the space of
these option values. In contrast, other evidence suggests that direct competition between attributes, perhaps
mediated by visual attention, is an important part of the decision process. Arbitrating between these models is
critical to advancing theoretical frameworks that can link decision-making deficits to disordered neural
computations, but a key challenge is that the steps of decision formation occur rapidly and internally, making
them difficult to observe or otherwise measure. Here, we address this by combining a novel multi-attribute choice
task with large-scale neural recording and population analyses necessary to reveal within-trial dynamics of
otherwise covert decision-making processes. In Aim 1, we will assess how OFC codes individual attributes during
multi-attribute decisions, and how this relates to classically reported integrated value signals. Next, we will
assess how attention to attributes alters OFC coding, value computation, and subsequent decisions (Aim 2).
Finally, in Aim 3, we propose a novel computational model of multi-attribute decisions that can determine the
extent to which choices are driven by the relative values of attributes versus integrated options. Our model will
also reveal latent variables that evolve during decision formation, which we will map on to neural responses. In
doing so, we aim to localize specific choice processes to unique neural circuits, and also demonstrate the
biological relevance of the model and its conclusions. Together, these studies leverage our combined expertise
in non-human primate behavior, computational analysis, and modeling to define the neural underpinnings of
multi-attribute choice in OFC. If successful, our results will not only refine the theoretical frameworks that guide
decision neuroscience, but will also...

## Key facts

- **NIH application ID:** 10932265
- **Project number:** 5R01MH134845-02
- **Recipient organization:** ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
- **Principal Investigator:** Erin L Rich
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $721,370
- **Award type:** 5
- **Project period:** 2023-09-20 → 2025-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10932265

## Citation

> US National Institutes of Health, RePORTER application 10932265, Mechanisms of multi-attribute decision-making (5R01MH134845-02). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10932265. Licensed CC0.

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