1Selective attention is fundamental to cognitive activity and can be deployed in 2 different ways. Non-human primate data suggests that spatial and feature-based 3 visual attention have qualitatively different effects on neural tuning, but this has 4 been challenging to assess in humans. Using multivariate decoding of MEG data, 5we tracked the effects of spatial and feature-selective attention on population-level 6 coding of novel objects. We found that spatial and feature-selective attention 7 interacted multiplicatively to enhance object representation. Moreover, the two 8 types of attention induced qualitatively different patterns of enhancement in 9 occipital cortex, and these differences were accounted for by the principles of 10 response-gain and tuning curve sharpening derived from single-unit work. A novel 11 information flow analysis further showed that stimulus representations in occipital 12 cortex were Granger-caused by coding in frontal cortices earlier in time. We find 13 that human spatial and feature-selective attention rely on qualitatively different, 14interacting, neural mechanisms. 15At any moment, there is far more information available from our senses than we can 16 possibly process at once. Accordingly, only a subset of the available information is 17 processed to a high level, making it crucial that brain can dynamically devote greatest 18 processing resources to the most relevant information. Our ability to selectively attend 19 to relevant information is remarkably flexible. For instance, we can adapt our 20 attentional state by directing our attention in space (spatial attention, e.g. attend left), 21 to a specific feature dimension (feature-selective attention, e.g. detect changes in color 22 across a scene) or based on a particular feature value along that feature dimension 23 (feature-based attention, e.g. find all the red objects), using the definitions of Chen et 24 al. (2012). Each of these types of attention can change behavior, improving 25 performance related to the attended location or feature-dimension, while decreasing 26 performance on the ignored dimension/location (Pestilli and Carrasco, 2005; Rossi and