The gambler’s fallacy has been a notorious showcase of humanirrationality in probabilistic reasoning. Recent studies suggestthe neural basis of this fallacy might have originated from thepredictive learning by neuron populations over the latent tem-poral structures of random sequences, particularly due to thestatistics of pattern times and the precedence odds betweenpatterns. Here we present a biologically-motivated minimalneural network model with only eight neurons. Through unsu-pervised training, the model naturally develops a bias towardalternation patterns over repetition patterns, even when bothpatterns are equally likely presented to the model. Our analysessuggest that the way the neocortex integrates information overtime makes the neuron populations not only sensitive to thefrequency signals but also relational structures embedded overtime. Moreover, we offer an explanation for how higher-levelcognitive biases may have an early start at the level of sensoryprocessing.

Numerical tasks with Arabic numerals involve the integration of internal and external information and the interaction between perception and cognition. 2-digit number comparison task was selected to study these integration and interaction processes. To compare the magnitudes of two 2digit Arabic numerals, we can (1) compare them digitbydigit sequentially, (2) compare corresponding digits in parallel, or (3) encode them as an integrated representation and compare the whole numerical values. Previous studies showed that 2- digit comparison was holistic when target numerals were compared with a standard held in memory. In our experiment target numerals and standards were presented on the same external display at the same time. Instead of a holistic comparison, we found that 2-digit comparison was a combination of sequential and parallel comparisons. The implications of this discrepancy were discussed in terms of the interplay between perception and cognition.

Dissociations between explicit and implicit memory tests, between recollective and automatic retrieval processes, and between memorial states of awareness of past events all suggest that human memory is not a unitary faculty. Memory dissociations reflect the complex relationship between consciousness and memory. To understand such a complex relationship, any single level of analysis is not enough and may be misleading. A multi-level analysis was proposed. One of the most serious problems with the process-dissociation procedure is its failure to separate process level of analysis and memorial awareness level of analysis. One experiment was reported to support the above arguments.

A cornerstone of human statistical learning is the ability to extract abstract regularities from sequential events. Here we present a unique method to derive the generating functions for the waiting time of sequential patterns, then compare these functions with the neural mechanisms for learning sequential structures. We show that the way the neocortex integrates information over time bears a striking resemblance to the way these normative functions operate. They both operate by organizing combinatorial objects into meaningful groups then compressing the representations by discarding irrelevant information. As a result, discrete-time signals are converted into frequency signals, and similarity-based structures are converted into abstract relational structures. Our analyses not only reveal surprisingly rich statistical structures embedded in the seemingly random sequences, but also offer an explanation for how higher-order cognitive biases may have emerged as a consequence of temporal integration.