Optimal policy for attention-modulated decisions explains human fixation behavior

  1. Anthony Injoon Jang
  2. Ravi Sharma
  3. Jan Drugowitsch  Is a corresponding author
  1. Harvard Medical School, United States
  2. UC San Diego School of Medicine, United States

Abstract

Traditional accumulation-to-bound decision-making models assume that all choice options are processed with equal attention. In real life decisions, however, humans alternate their visual fixation between individual items to efficiently gather relevant information (Yang et al., 2016). These fixations also causally affect one's choices, biasing them toward the longer-fixated item (Krajbich et al., 2010). We derive a normative decision-making model in which attention enhances the reliability of information, consistent with neurophysiological findings (Cohen and Maunsell, 2009). Furthermore, our model actively controls fixation changes to optimize information gathering. We show that the optimal model reproduces fixation-related choice biases seen in humans and provides a Bayesian computational rationale for this phenomenon. This insight led to additional predictions that we could confirm in human data. Finally, by varying the relative cognitive advantage conferred by attention, we show that decision performance is benefited by a balanced spread of resources between the attended and unattended items.

Data availability

The human behavioral data and code are available through an open source license at https://github.com/DrugowitschLab/Optimal-policy-attention-modulated-decisions

Article and author information

Author details

  1. Anthony Injoon Jang

    Department of Neurobiology, Harvard Medical School, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
  2. Ravi Sharma

    Department of Family Medicine and Public Health, UC San Diego School of Medicine, San Diego, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Jan Drugowitsch

    Department of Neurobiology, Harvard Medical School, Boston, United States
    For correspondence
    jan_drugowitsch@hms.harvard.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7846-0408

Funding

National Institute of Mental Health (R01MH115554)

  • Jan Drugowitsch

James S. McDonnell Foundation (220020462)

  • Jan Drugowitsch

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Ethics

Human subjects: Human behavioral data were obtained from previously published work from the California Institute of Technology (Krajbich et al., 2010). Caltech's Human Subjects Internal Review Board approved the experiment. Written informed consent was obtained from all participants.

Copyright

© 2021, Jang et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 2,259
    views
  • 371
    downloads
  • 35
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Anthony Injoon Jang
  2. Ravi Sharma
  3. Jan Drugowitsch
(2021)
Optimal policy for attention-modulated decisions explains human fixation behavior
eLife 10:e63436.
https://doi.org/10.7554/eLife.63436

Share this article

https://doi.org/10.7554/eLife.63436

Further reading

    1. Neuroscience
    Kaspar E Vogt, Ashwinikumar Kulkarni ... Robert W Greene
    Research Article

    Sleep loss increases AMPA-synaptic strength and number in the neocortex. However, this is only part of the synaptic sleep loss response. We report an increased AMPA/NMDA EPSC ratio in frontal-cortical pyramidal neurons of layers 2–3. Silent synapses are absent, decreasing the plastic potential to convert silent NMDA to active AMPA synapses. These sleep loss changes are recovered by sleep. Sleep genes are enriched for synaptic shaping cellular components controlling glutamate synapse phenotype, overlap with autism risk genes, and are primarily observed in excitatory pyramidal neurons projecting intra-telencephalically. These genes are enriched with genes controlled by the transcription factor, MEF2c, and its repressor, HDAC4. Sleep genes can thus provide a framework within which motor learning and training occur mediated by the sleep-dependent oscillation of glutamate-synaptic phenotypes.

    1. Neuroscience
    Hans Auer, Donna Gift Cabalo ... Jessica Royer
    Research Article

    The amygdala is a subcortical region in the mesiotemporal lobe that plays a key role in emotional and sensory functions. Conventional neuroimaging experiments treat this structure as a single, uniform entity, but there is ample histological evidence for subregional heterogeneity in microstructure and function. The current study characterized subregional structure-function coupling in the human amygdala, integrating post-mortem histology and in vivo MRI at ultra-high fields. Core to our work was a novel neuroinformatics approach that leveraged multiscale texture analysis as well as non-linear dimensionality reduction techniques to identify salient dimensions of microstructural variation in a 3D post-mortem histological reconstruction of the human amygdala. We observed two axes of subregional variation in this region, describing inferior-superior as well as mediolateral trends in microstructural differentiation that in part recapitulated established atlases of amygdala subnuclei. Translating our approach to in vivo MRI data acquired at 7 Tesla, we could demonstrate the generalizability of these spatial trends across 10 healthy adults. We then cross-referenced microstructural axes with functional blood-oxygen-level dependent (BOLD) signal analysis obtained during task-free conditions, and revealed a close association of structural axes with macroscale functional network embedding, notably the temporo-limbic, default mode, and sensory-motor networks. Our novel multiscale approach consolidates descriptions of amygdala anatomy and function obtained from histological and in vivo imaging techniques.