Current studies on the impacts of indoor air quality (IAQ) focus largely on perception, 1,2 health, 3,4 and comfort 5 consequences, which have not brought appreciable improvement of IAQ in many indoor environments over the past decades. Meanwhile, increasing evidence has suggested IAQ is related to cognitive function and poor IAQ may significantly impair cognitive performance and productivity at work 6-8 or school. 9-12 The potential benefits from improved productivity, 13,14 therefore, may motivate the investment into IAQ research. Carbon dioxide (CO 2) has historically been treated as an indicator of ventilation effectiveness and not linked with severe health outcomes until concentrations reach 6500 to 18 500 ppm 15 or prolonged exposure lasting a few days. 16,17 However, growing evidence shows exposure to elevated CO 2 concentrations that are common in indoor environments (<5000 ppm) can cause various physical or psychomotor responses 18 including reductions in

Neural representations can be characterized as falling along a continuum, from distributed representations, in which neurons are responsive to many related features of the environment, to localist representations, where neurons orthogonalize activity patterns despite any input similarity. Distributed representations support powerful learning in neural network models and have been posited to exist throughout the brain, but it is unclear under what conditions humans acquire these representations and what computational advantages they may confer. In a series of behavioral experiments, we present evidence that interleaved exposure to new information facilitates the rapid formation of distributed representations in humans. As in neural network models with distributed representations, interleaved learning supports fast and automatic recognition of item relatedness, affords efficient generalization, and is especially critical for inference when learning requires statistical integration of noisy information over time. We use the data to adjudicate between several existing computational models of human memory and inference. The results demonstrate the power of interleaved learning and implicate the use of distributed representations in human inference.

Extracting shared structure across our experiences allows us to generalize our knowledge to novel contexts. How do different brain states influence this ability to generalize? Using a novel category learning paradigm, we assess the effect of both sleep and time of day on generalization that depends on the flexible integration of recent information. Counter to our expectations, we found no evidence that this form of generalization is better after a night of sleep relative to a day awake. Instead, we observed an effect of time of day, with better generalization in the morning than the evening. This effect also manifested as increased false memory for generalized information. In a nap experiment, we found that generalization did not benefit from having slept recently, suggesting a role for time of day apart from sleep. In follow-up experiments, we were unable to replicate the time of day effect for reasons that may relate to changes in category structure and task engagement. Despite this lack of consistency, we found a morning benefit for generalization when analyzing all the data from experiments with matched protocols (n = 136). We suggest that a state of lowered inhibition in the morning may facilitate spreading activation between otherwise separate memories, promoting this form of generalization.

We propose that the ability of humans to identify and create patterns led to the unique aspects of human cognition and culture as a complex emergent dynamic system consisting of the following human traits: patterning, social organization beyond that of the nuclear family that emerged with the control of fire, rudimentary set theory or categorization and spoken language that co-emerged, the ability to deal with information overload, conceptualization, imagination, abductive reasoning, invention, art, religion, mathematics and science. These traits are interrelated as they all involve the ability to flexibly manipulate information from our environments via pattern restructuring. We argue that the human mind is the emergent product of a shift from external percept-based processing to a concept and language-based form of cognition based on patterning. In this article, we describe the evolution of human cognition and culture, describing the unique patterns of human thought and how we, humans, think in terms of patterns.