Dynamic emergence of relational structure network in human brains.

Reasoning the hidden relational structure from sequences of events is a crucial ability humans possess, which helps them to predict the future and make inferences. Besides simple statistical properties, humans also excel in learning more complex relational networks. Several brain regions are engaged in the process, yet the time-resolved neural implementation of relational structure learning and its contribution to behavior remains unknown.

Automatic and Fast Encoding of Representational Uncertainty Underlies the Distortion of Relative Frequency.

Humans do not have an accurate representation of probability information in the environment but distort it in a surprisingly stereotyped way ("probability distortion"), as shown in a wide range of judgment and decision-making tasks. Many theories hypothesize that humans automatically compensate for the uncertainty inherent in probability information ("representational uncertainty") and probability distortion is a consequence of uncertainty compensation. Here we examined whether and how the representational uncertainty of probability is quantified in the human brain and its relevance to probability distortion behavior.

The bounded rationality of probability distortion.

In decision making under risk (DMR) participants' choices are based on probability values systematically different from those that are objectively correct. Similar systematic distortions are found in tasks involving relative frequency judgments (JRF). These distortions limit performance in a wide variety of tasks and an evident question is, Why do we systematically fail in our use of probability and relative frequency information? We propose a bounded log-odds model (BLO) of probability and relative frequency distortion based on three assumptions: 1) log-odds: probability and relative frequency are mapped to an internal log-odds scale, 2) boundedness: the range of representations of probability and relative frequency are bounded and the bounds change dynamically with task, and 3) variance compensation: the mapping compensates in part for uncertainty in probability and relative frequency values.

Context Effects in the Judgment of Visual Relative-Frequency: Trial-by-Trial Adaptation and Non-linear Sequential Effect.

Humans' judgment of relative-frequency, similar to their use of probability in decision-making, is often distorted as an inverted--shape curve-small relative-frequency overestimated and large relative-frequency underestimated. Here we investigated how the judgment of relative-frequency, despite its natural reference points (0 and 1) and stereotyped distortion, may adapt to the environmental statistics. The task was to report the relative-frequency of black (or white) dots in a visual array of black and white dots.