Perceptual decision
Why does seeing a small thing make the next one look 'large'?
Open access · cc by · source: Europe PMC
The tendency to classify an item as the opposite of the previous one seems to come from the brain shifting its internal small/large boundary, not from the eye's sensory adaptation.
Study at a glance
- Design
- Human experiment — Within-subject fMRI classification task (small vs large ring, three threshold-level sizes in m-sequence order, no trial feedback); Experiment 1 imaged V1 at high resolution, Experiment 2 imaged the whole brain and decoded latent variables of a fitted Bayesian boundary-updating model with searchlight regression.
- N
- Experiment 1: 19 adults; Experiment 2: 18 adults; 17 people took part in both, so no single pooled N is analysed.
- Population
- Healthy adults aged 20 to 30 at Seoul National University, trained on the task beforehand
- Outcome
- Repulsive bias in choices; adaptation of the V1 size-encoding signal; decoded brain signals of class boundary, inferred stimulus and decision variable, and whether their previous-stimulus-related variability predicts current choice
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Key findings
Both groups showed strong repulsive bias: after a small ring, the next ring was more often called large. The V1 size signal did adapt to the previous ring, but its adaptation-related variability did not predict current choices, which instead tracked only the current ring. The boundary-updating model reproduced the human bias closely (R-squared of 0.89 for the previous-stimulus effect), and signals of its moving boundary were found in the left inferior parietal lobe and posterior superior temporal gyrus. Unlike V1, these boundary signals' link to choice weakened when the previous stimulus was controlled, as the boundary-updating account predicts.
Methodology
Participants judged whether briefly flashed rings were small or large, using three sizes that differed by just their discrimination threshold and no trial-by-trial feedback. In Experiment 1 (19 people) the researchers imaged primary visual cortex at high resolution and read out a ring-size signal to see whether it adapted to the previous ring and whether that adaptation fed into choices. In Experiment 2 (18 people) they imaged the whole brain, fitted a Bayesian model in which the class boundary drifts toward recent rings, and searched for brain patterns tracking the model's boundary, perceived size and decision variable, testing each candidate against a long list of regressions implied by the model.
Limitations
The samples were small and largely the same people across experiments, and the boundary signals rely on latent variables estimated from a model built with several simplifying assumptions (for example exponential memory decay). The analyses are correlational (average marginal effects and model-derived regression tests), so they cannot prove the boundary signal causes the bias; stimulation studies would be needed. The model ignores effects of previous decisions, and because choices and hand responses covaried the authors could not separate choice history from motor history. fMRI's coarse resolution may also explain why no decision-variable signal appeared in prefrontal cortex.
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