Music-to-color associations show emotionally-mediated cross-modal correspondences (Palmer et al., 2013): people choose colors as going best with music when their emotional content matches (e.g., happy-looking colors go best with happysounding music). What musical/acoustic features underlie such correspondences? And are music-to-texture correspondences also evident? Experiments using highly-controlled melodies that varied in tonality (major/minor), note-rate (fast/medium/slow), and register (high/low) revealed systematic correspondences between musical/acoustic and colorimetric dimensions: faster, major, higher-pitched melodies were associated with more saturated, lighter, yellower colors, whereas slower, minor, lowerpitched melodies were associated with more muted, darker, bluer colors. Further experiments revealed emotion-mediated associations from music to texture, although agitated/calm and angry/not-angry emotions were stronger with textures, whereas happy/sad emotions were stronger with colors. Systematic associations were also evident between visual/spatial features of texture (e.g., Sharp/Smooth, Curved/Straight) and musical dimensions (e.g., note-rate and piano/cello timbre).

When people encode a representation of a scene, they do not necessarily represent the exact locations and orientationsof the constituent elements. Instead, people rely on preexisting inductive biases to simplify their encoding of new sceneconfigurations. We investigated people’s inductive biases in their memory for configurations of simple 2D shapes (such ascircles, triangles, etc.) using a serial reproduction paradigm (Bartlett, 1932). This paradigm establishes an iterative process inwhich information is transmitted through a chain of people (like the ”telephone” game). In our experiment, we asked peopleto memorize configurations of simple shapes (which were either generated at random or by other participants) and then askedthem to reproduce those configurations. In analyzing the final generation of reproductions, we found that people have strongpreferences for the scale of individual shapes, as well as the alignment, distance, overlap, and relative rotation between pairs ofshapes.

Visual memory can be understood as an inferential process thatcombines noisy information about the world with knowledgedrawn from experience. Biases can arise during encoding ofinformation from the outside world into internal representa-tions, or during retrieval. In this work, we use the methodof serial reproduction, in which information is passed along achain of participants who try to recreate what they observed.We apply this method to the study of visual perception in thecontext of spatial memory biases for the remembered positionof dots inside different geometric shapes. We present the re-sults of non-parametric kernel density estimation of the end re-sult of serial reproduction to model visual biases. We confirmprevious findings, and show that memory biases revealed withour method are often more intricate and complex than whathad previously been reported, suggesting that serial reproduc-tion can be effective for studying perceptual priors.

The internal representations of three dimensional objectswithin visual memory are only partially understood. Previousresearch suggests that 3D object perception is viewpoint de-pendent, and that the visual system stores viewpoint perspec-tives in a biased manner. The aim of this project was to ob-tain detailed estimates of the distributions of 3D object viewsin shared human memory. We devised a novel experimentalparadigm based on transmission chains to investigate memorybiases for the 3D orientation of objects. We found that memorytends to be biased towards orthogonal diagrammatic perspec-tives aligned with the ends of the standard basis for a set ofcommon 3D objects, and that these biases are strongest for sideviews as well as top or bottom views for a small set of bilater-ally symmetric objects. Finally, we found that views sampledfrom the modes were easier to categorize in a recognition task.