Visual object recognition is facilitated by temporal community structure

Abstract

Humans and others primates are highly attuned to temporal consistencies and regularities in their sensory environment and learn to predict such statistical structure. Moreover, in several instances, the presence of temporal structure has been found to facilitate procedural learning and to improve task performance. Here we extend these findings to visual object recognition and to presentation sequences in which mutually predictive objects form distinct clusters or "communities." Our results show that temporal community structure accelerates recognition learning and affects the order in which objects are learned ("onset of familiarity").

© 2021 Kakaei et al.; Published by Cold Spring Harbor Laboratory Press.

Figures

Figure 1.

Presentation sequence and trial structure. (A) Presentation sequences were generated as (nearly) random walks on three types of graphs, with nodes representing a distinct object and edges representing possible transitions (in both directions). A sparsely connected, modular graph generated “strongly structured” sequences with distinct community structures (left), a sparsely connected, nonmodular graph generated “weakly structured” sequences (middle), and a full connected graph generated “unstructured” or “random” sequences (right). (B) Presentation sequences consisted of 180 complex, three-dimensional objects (shown rotating for 2 sec about a randomly oriented axis in the frontal plane). Of these, 170 ± 0.04 (mean ± SEM) objects were recurring, and 9.2 ± 0.04 objects were nonrecurring. Observers categorized each object as “familiar” or “unfamiliar.” Over the four sessions of 1 wk, observers performed 24 runs and viewed 4320 presentations, with every recurring object appearing at least 250 times.

Figure 2.

Time course of recognition learning. (A) Average hit rate (recurring categorized as familiar, per window) increases with the number of presentations of a given object. (B) Average false alarm rate (nonrecurring not categorized as unfamiliar, per session) decreases with the number of presentations. (C) Average corrected performance ρ increases nearly monotonically with presentation number. It was consistently larger for strongly structured sequences (with temporal community structure) than for unstructured sequences. (D) Average criterion bias b, as a function of presentation number. Green regions indicate the transition between sessions (20%–80% of objects in previous session).

Figure 3.

Analysis of the onset of familiarity with individual objects. (A) Successive onsets of familiarity (Δn = 1) are far more likely ([**] P < 0.005) for objects within the same cluster than would be expected by chance (dashed line). For nearly successive onsets (Δn = 2) this effect was not observed. (B) Comparison of frequency of successive onsets, compared with chance level, for objects pairs either in the same cluster (outlined blue and cyan) or in different clusters (green and red), which are either adjacent (blue and green) or nonadjacent on the graph (cyan and red). Frequency is significantly elevated ([*] P < 0.05 FDR corrected) for adjacent objects in the same cluster (blue) and suppressed for nonadjacent objects in different clusters (red).