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Contributed Talk Session: Tuesday, August 4, 2:00 – 3:00 pm, Skirball Theater
Poster C in Poster Session C: Wednesday, August 5, 9:30 – 11:15 am, Kimmel Center, Shorin & Rosenthal Rooms

Decomposing iconic memory decay with a joint-feature model of structured errors

Gal Vishne¹, Zoe Haynes¹, Miranda Ye², Nicholas Turk-Browne³, Michael N. Shadlen^1,4, John Morrison²; ¹Columbia University, ²Barnard College, ³Yale University, ⁴Howard Hughes Medical Institute

Presenter: Gal Vishne

Iconic memory provides a brief, high-capacity store of visual information, yet its representational structure and temporal dynamics remain poorly understood. We address this question using a continuous-report paradigm enabling precise characterization of errors over time. Subjects briefly viewed arrays of colored items and, after a variable delay, reported the location associated with a probed color. Mixture models applied to color and location errors, revealed increasing guess rates over time and, in some cases, decreasing precision. However, these models fail to account for structured errors arising from feature interactions. To address this, we developed a joint-feature probabilistic model in which items are encoded with uncertainty in both color and location. Within this framework, errors commonly interpreted as swaps emerge naturally from uncertainty in the underlying representation. The joint model provided a better account of behavior across all subjects and delays and revealed that iconic memory decay reflects two distinct mechanisms: decreasing precision and increasing probability of retrieval failure. These findings constrain the format of iconic memory representations and provide a computational framework for characterizing memory performance over time.

Topic Area: Memory, Learning & Knowledge Structures