Sequential Bayesian updating as a model for human perception

Active Iterative Social Inference in Multi-Trial Signaling Games

Human behavioral choices can reveal intrinsic and extrinsic decision-influencing factors. We investigate the inference of choice priors in situations of referential ambiguity. In particular, we use the scenario of signaling games and investigate to which extent study participants profit from actively engaging in the task. Previous work has revealed that speakers are able to infer listeners' choice priors upon observing ambiguity resolution. However, it was also shown that only a small group of participants was able to strategically construct ambiguous situations to create learning opportunities. This paper sets to address how prior inference unfolds in more complex learning scenarios. In Experiment 1, we examine whether participants accumulate evidence about inferred choice priors across a series of four consecutive trials. Despite the intuitive simplicity of the task, information integration turns out to be only partially successful. Integration errors result from a variety of sources, including transitivity failure and recency bias. In Experiment 2, we investigate how the ability to actively construct learning scenarios affects the success of prior inference and whether the iterative settings improve the ability to choose utterances strategically. The results suggest that full task engagement and explicit access to the reasoning pipeline facilitates the invocation of optimal utterance choices as well as the accurate inference of listeners' choice priors.

Individual beliefs about temporal continuity explain variation of perceptual biases

Perception of magnitudes such as duration or distance is often found to be systematically biased. The biases, which result from incorporating prior knowledge in the perceptual process, can vary considerably between individuals. The variations are commonly attributed to differences in sensory precision and reliance on priors. However, another factor not considered so far is the implicit belief about how successive sensory stimuli are generated: independently from each other or with certain temporal continuity. The main types of explanatory models proposed so far-static or iterative-mirror this distinction but cannot adequately explain individual biases. Here we propose a new unifying model that explains individual variation as combination of sensory precision and beliefs about temporal continuity and predicts the experimentally found changes in biases when altering temporal continuity. Thus, according to the model, individual differences in perception depend on beliefs about how stimuli are generated in the world.

The origin of Vierordt's law: The experimental protocol matters

In 1868, Karl Vierordt discovered one type of errors in time perception-an overestimation of short duration and underestimation of long durations, known as Vierordt's law. Here we reviewed the original study in its historical context and asked whether Vierordt's law is a result of an unnatural experimental randomization protocol. Using iterative Bayesian updating, we simulated the original results with high accuracy. Importantly, the model also predicted that a slowly changing random-walk sequence produces less central tendency than a random sequence with the same durations. This was validated by a duration reproduction experiment from two sequences (random and random walk) with the same sampled distribution. The results showed that trial-wise variation influenced the magnitude of Vierordt's law. We concluded that Vierordt's law is caused by an unnatural yet widely used experimental protocol.

Variation in the "coefficient of variation": Rethinking the violation of the scalar property in time-duration judgments

The coefficient of variation (CV), also known as relative standard deviation, has been used to measure the constancy of the Weber fraction, a key signature of efficient neural coding in time perception. It has long been debated whether or not duration judgments follow Weber's law, with arguments based on examinations of the CV. However, what has been largely ignored in this debate is that the observed CVs may be modulated by temporal context and decision uncertainty, thus questioning conclusions based on this measure. Here, we used a temporal reproduction paradigm to examine the variation of the CV with two types of temporal context: full-range mixed vs. sub-range blocked intervals, separately for intervals presented in the visual and auditory modalities. We found a strong contextual modulation of both interval-duration reproductions and the observed CVs. We then applied a two-stage Bayesian model to predict those variations. Without assuming a violation of the constancy of the Weber fraction, our model successfully predicted the central-tendency effect and the variation in the CV. Our findings and modeling results indicate that both the accuracy and precision of our timing behavior are highly dependent on the temporal context and decision uncertainty. And, critically, they advise caution with using variations of the CV to reject the constancy of the Weber fraction of duration estimation.