CV

Tejas Savalia, PhD

Postdoc at Emory University

My research draws upon cognitive computational modeling to enable investigations into cognitive aspects of implicit and explicit learning. Broadly, I am interested in constructing neural and computationally constrained models to draw quantifiable predictions aimed at understanding the psychological processes of decision-making, learning, and memory. I use behavioral and fMRI experiments to investigate how models of cognition explain how humans learn and act upon statistical regularities in their environments.

Experience

Industry Research Experience

  • User Experience Research Intern, Google, MTV (Summer 2023)
    • Developed an end-to-end image processing pipeline to automate analysis of users’ phone grips.
    • Validated with a 24-participant study and statistically quantified method reliability.
    • Developed a toolkit to vastly improve the efficiency and scalability of conducting phone grip studies.
    • Presented the developed methodology and its validation study to an XFN audience.
  • Quantitative User Experience Research Intern, Google, NYC (Summer 2022)
    • Developed a perceptual experimental framework to measure coherence of search page UI formats.
    • Validated the developed metric using 42 MTurk studies with >20000 participants.
  • Research Intern, Tata Innovation Labs, Hyderabad (Spring 2018)

Teaching Experience

  • Decision Making Seminar Instructor, UMass Amherst (Fall 2023)
    • Designed and taught weekly seminar on decision-making in Psychology.
  • Research Methods Teaching Assistant, UMass Amherst (Fall 2022, Spring 2023, Spring 2024)
    • Led weekly tutorials and hands-on laboratory sessions teaching students how to conduct end-to-end psychology research projects using survey designs, psychometric experiments, and R scripting
  • Project Teaching Assitant Neuromatch Academy (Summer 2021)
    • Mentored teams of graduate and undergraduate students in the global summer school program through their projects on functional Magentic Resonance Imaging.
  • Statistics Teaching Assistant, UMass Amherst (Spring 2020, Fall 2020)
    • Led labs and taught students how to use R to conduct data analyses.

Current Projects

  • Disassociating neural mechanisms of affective stimuli. Analysis of neuroimaging data to assess common and distinctive neural representations of positive affective experiences in adults and adolescents.

  • Discerning the role of emotional states in statistical learning across development. Behavioral and Functional Neuroimaging experiments and computational modeling to investigate how internal affective states help or hurt implicit statistical learning in adults and adolescents.

  • Understanding neural motor directional tuning using fMRI Functional Neuroimaging experiments and computational modeling with multivariate analyses of neural tuning functions to investigate the neural correlates of motor directional planning.

Publications

Accepted and Published Articles

  1. Tejas Savalia, Rosemary Cowell, and David Huber (2024). “Leap before you look”: Conditions that promote implicit visuomotor adaptation without explicit learning. Journal of Experimental Psychology: Human Perception and Performance,

  2. Waite, Elinor E., Tejas Savalia, Andrew L. Cohen, Lauren A. Haliczer, Sarah Huffman, and Katherine L. Dixon-Gordon. “Borderline personality disorder and learning: The influences of emotional state and social versus nonsocial feedback.” Journal of Affective Disorders 363 (2024): 474-482.

  3. Tejas Savalia, Anuj Shukla, and Raju S Bapi, A Unified Framework for Cognitive Sequencing. Frontiers in Psychology, vol 7, (2016).

  4. Tejas Savalia, Andrew Lovett, Cristina Ceja, Rosemary Cowell, Cindy Xiong Bearfield, Local and Global Extrema and Recall Response Modalities Bias Position Recall in Line Charts. In Press.

In Review Articles

  1. Tejas Savalia, Xiaoya Anny Huang, Sophia Martin, Sagarika Devarayapuram Ramakrishnan, and Alexandra O. Cohen. Neural representations and functional connections underlie distinct and shared positive affect. PsyArxiv Preprint

  2. Tejas Savalia, Jeffrey Starns, and Andrew Cohen. Implicit boundaries are remembered better than non-boundaries in statistical learning (2025, In review). PsyArxiv Preprint

  3. Rohini Kumar, Tejas Savalia, David Clewett, and Alexandra O. Cohen. A dynamic affective surprise signal influences episodic memory (2025, In review). PsyArxiv Preprint.

In Prep Articles

  1. Tejas Savalia, Jeffrey Starns, Andrew Cohen. Evidence for predictive representations over associative representations in statistical learning. .

  2. Tejas Savalia, Aisling Finnegan, Jeffrey Starns, Andrew Cohen. Enhanced statistical learning via structured musical categories in serial reaction time task. .

  3. Tejas Savalia, David Hubber, Rosemary Cowell. Neural tuning functions during directional planning of visuomotor rotation .

Conference Presentations

  • Tejas Savalia, Sophia Martin, Sagarika Devarayapuram Ramakrishnan, and Alexandra O. Cohen. Brain connectivity and representations encode positive affect in dynamic experiences Upcoming Poster at SFN 2025

  • Bria Slade, Tejas Savalia, and Alexandra O. Cohen. The association between trait reward responsiveness and adolescent striatum activation. Poster at Summer Undergraduate Research Symposium, Emory University 2025

  • Katie Oshins, Michelle Sauceda, Tejas Savalia, Sophia Martin, Alexendra O. Cohen, (2025). Individual and developmental differences in emotional learning. Talk at the Undergraduate Research Symposium, Emory University

  • Tejas Savalia, Sophia Martin, Sagarika Devarayapuram Ramakrishnan, and Alexandra O. Cohen, “Whole brain decoding of positive affect. CNS, 2024”

  • Tejas Savalia, Jeffrey Starns, and Andrew Cohen, “Implicit event boundaries are remembered better in a statistical learning task. Psychonomics, 2024”

  • Tejas Savalia, Jeffrey Starns, and Andrew Cohen, “Random walk length modulates structure acquisition in modular graphs,” COSYNE, 2024

  • Tejas Savalia, Andrew Cohen, Rosemary Cowell, and David Huber. Reward changes are more disruptive than stimulus changes to implicit sequence learning of a community structure, CEMS, 2023.

  • Tejas Savalia, Rosemary Cowell and David Huber, “Leap before you look”: Conditions that promote implicit visuomotor adaptation without explicit learning. Talk at Psychonomics, 2022.

  • Tejas Savalia, Cristina Ceja, Rosemary Cowell, Cindy Xiong. “Estimating Biases in Line Charts: Effects of Shape and Response Modality”. OPAM, 2022.

  • Tejas Savalia, Cristina Ceja, Rosemary Cowell, Cindy Xiong. “Visual and Verbal Data Estimation Errors are Modulated by Chart Type”. Visual Science Society, 2022.

  • Tejas Savalia, Rosemary Cowell, David Huber, “Implicit Learning in Absence of Explicit Learning in a Visuomotor Adaptation Task.” Poster Presentation in Psychonomics, 2021.

  • Tejas Savalia, Rosemary Cowell, David Huber, ``Learning to Learn: Modeling Time Course of Visuomotor Adaptation.’’ Virtual poster presentation in Annual meeting for the Society for Mathematical Psychology, 2020. Youtube Link

  • Tejas Savalia, Rosemary Cowell and David Huber, “Learning a novel perception-action mapping: Error magnitude, speed/accuracy emphasis, and reinforcement learning.” Poster Presentation in Psychonomics, 2019.