Artificial intelligence powered by deep neural networks has reached a level of complexity where it can be difficult or impossible to express how a model makes its decisions. This black-box problem is especially concerning when the model makes decisions with consequences for human well-being. In response, an emerging field called explainable artificial intelligence (XAI) aims to increase the interpretability, fairness, and transparency of machine learning. In this paper, we describe how cognitive psychologists can make contributions to XAI. The human mind is also a black box, and cognitive psychologists have over one hundred and fifty years of experience modeling it through experimentation. We ought to translate the methods and rigour of cognitive psychology to the study of artificial black boxes in the service of explainability. We provide a review of XAI for psychologists, arguing that current methods possess a blind spot that can be complemented by the experimental cognitive tradition. We also provide a framework for research in XAI, highlight exemplary cases of experimentation within XAI inspired by psychological science, and provide a tutorial on experimenting with machines. We end by noting the advantages of an experimental approach and invite other psychologists to conduct research in this exciting new field.

Primate ventral and dorsal visual pathways both contain visual object representations. Dorsal regions receive direct input from magnocellular layers of LGN while ventral regions receive directly input from both magnocellular and parvocellular layers of LGN. Because magnocellular layers prefer low SF and parvocellular layers prefer mid- to high-SF components of the image, object representations in ventral and dorsal regions may differ in how they represent visual input from different spatial scales. To test this prediction, we asked observers to view blocks of images from six object categories shown in full spectrum, high SF, or low SF. We found robust object category decoding in all SF conditions as well as SF decoding in nearly all the early visual, ventral, and dorsal regions examined. Cross-SF decoding further revealed that object category representations in all regions exhibited substantial tolerance across the SF components. No difference between ventral and dorsal regions was found in their preference for the different SF components. Further comparisons revealed that, whereas differences in the SF component separated object category representations in early visual areas, such a separation was much smaller in downstream ventral and dorsal regions. In those regions, variations among the object categories played a more significant role in shaping the visual representational structures. Our findings show that ventral and dorsal regions are similar in how they represent visual input from different spatial scales and argue against a dissociation of these regions based on differential sensitivity to different SFs.

No abstract

Face recognition includes identifying a face as perceptually familiar and recollecting biographical information, or person-knowledge, associated with the face. The majority of studies examining the neural basis of face recognition have confounded these stages by comparing brain responses evoked by novel and perceptually familiar famous faces. Here, we recorded EEG in two tasks in which subjects viewed two sets of faces that were equally perceptually familiar, but which had differing levels of associated person-knowledge. Our results dissociated the effects of person-knowledge from perceptual familiarity. Faces with associated biographical information elicited a larger ~600 ms centroparietal positivity in both a passive viewing task in which subjects viewed faces without explicitly responding, and an active question-answering task in which subjects indicated whether or not they knew particular facts about the faces. In the question task only, person-knowledge was associated with a negative ERP difference over right posterior scalp over the 170–450 ms interval which appeared again at long latency (>900 ms).