Computational Behavioral Science Laboratory

We are interested in developing predictively accurate models of human behavior by applying tools from statistical machine learning. We focus on several application areas, including developing machine learning models of visual perception, decision making, aesthetic preferences, and consumer preferences. Of particular interest are adaptive methods for efficient data collection, and the use of online platforms like Qualtrics® and Prolific® for collecting behavioral data. We seek collaborations within both academia and industry. Research in vision science is currently funded by an NIH R-15 AREA Grant: NIH-R15-EY032732-01.

Vision Science

Surface segmentation and edge classification

One of the most basic problems faced by biological and artificial visual systems is how to parse images into regions corresponding to distinct surfaces. Doing so requires that one detects the edges separating different regions, and to distinguish edges arising from surface boundaries from those originating from other causes such as changes in illumination (shadows). This line of work is a collaboration with experimental neuroscientists and vision scientists at McGill University and Washington University St. Louis.

Representative Publications

DiMattina, C., Burnham, J.J., Guner, B.N., & Yerxa, H.B. (2022). Distinguishing shadows from surface boundaries using local achromatic cues. PLoS Computational Biology 18(9): e1010473. [code] [data]

DiMattina, C., & Baker, C.L., Jr. (2021). Segmenting surface boundaries using luminance cues. Scientific Reports 11: 10074.

DiMattina, C., Fox, S.A., & Lewicki, M.S. (2012). Detecting natural occlusion boundaries using local cues. Journal of Vision 12(13): 15.

Computational methods for visual experiments

Psychophysical experiments aimed at estimating the parameters of complex models can require thousands of trials, and not all observations are of equal use for model estimation and comparison. Therefore, making use of adaptive data collection in which responses to previous stimuli are used to choose new stimuli can greatly speed up experiments. Furthermore, it is of great interest to develop models of behavior which incorporate elements of biological realism to connect behavior with mechanisms.

Representative Publications

DiMattina, C., Baker, C.L. Jr. (2019). Modeling second-order boundary perception: A machine learning approach. PLoS Computational Biology 15(3): e1006829.

DiMattina, C. (2016). Comparing models of contrast gain using psychophysical experiments. Journal of Vision 16(9): 1.

DiMattina, C. (2015). Fast adaptive estimation of multidimensional psychometric functions. Journal of Vision 15(9): 5.

Visual Aesthetics

Trypophobia

In the past decade, the behavioral science community has begun systematically investigating the phenomenon that a large proportion of the general population has an aversion to patterns containing clusters of bumps and holes. This phenomenon has been termed “Trypophobia” (fear of holes), and I have been collaborating with Dr. Nate Pipitone (FGCU Psychology) to better understand the visual characteristics which explain Trypophobia.

Representative Publications

DiMattina, C., Pipitone, R.N., Renteria, M.R., & Ryan, K. (2023+). Trypophobia, skin disease, and the visual discomfort of natural texture. psyarxiv.com

Pipitone, R.N., DiMattina, C., Martin, E.R., Pavela Banai, I., Bellmore, K.N., DeAngelis, M. (2022). Evaluating the 'skin-disease-avoidance' and 'dangerous-animal' frameworks for understanding Trypophobia. Cognition & Emotion, DOI: 10.1080/02699931.2022.2071236

Pipitone, R.N., & DiMattina, C. (2020). Object clusters or spectral energy? Assessing the relative contributions of image phase and amplitude spectra to trypophobia. Frontiers in Psychology (Perception Science) 11: 1847.

Art and pattern preferences

There is an old saying that “There is no accounting for taste”. We hope to change this by making predictive models of individual artistic and design preferences, based on adaptive data collection and data mining. This can have tremendously useful commercial applications for fashion, art, architecture, and design.

Decisions and Consumer Preference

Our next adventure will be developing predictive models of consumer preferences using adaptive data collection for estimating and comparing models. We hope to apply these methods to problems of optimal personalized advertising selection for on-line shopping and social media platforms.

Machine Learning and Data Science

Machine Learning Sometimes, a particular application requires the development of new statistical machine learning tools or methods. Furthermore, some purely statistical and mathematical problems are endemic to nearly every application, for instance model identification, selection, and comparison. Some of my PhD thesis work with Kechen Zhang touched on these issues in the specific context of fitting neural network models to the responses of sensory neurons. I am interested in working on these problems in the context of specific questions of interest to behavioral and brain sciences.

Representative Publications

DiMattina, C., & Zhang, K. (2013). Adaptive stimulus optimization for sensory systems neuroscience. Frontiers in Neural Circuits 7: 101.

DiMattina, C., & Zhang, K. (2011). Active data collection for efficient estimation and comparison of nonlinear neural models. Neural Computation 23(9): 2242-2288.

DiMattina, C., & Zhang, K. (2010). How to modify a neural network gradually without changing its input-output functionality. Neural Computation 22(1): 1-47.

Journal Articles and Invited Reviews

DiMattina, C., Burnham, J.J., Guner, B.N., & Yerxa, H.B. (2022). Distinguishing shadows from surface boundaries using local achromatic cues. PLoS Computational Biology 18(9): e1010473. [code] [data]

DiMattina, C. (2022). Luminance texture boundaries and luminance step boundaries are segmented using different mechanisms. Vision Research 190: 107968

DiMattina, C., & Baker, C.L., Jr. (2021). Segmenting surface boundaries using luminance cues. Scientific Reports 11: 10074.

DiMattina, C., Baker, C.L. Jr. (2019). Modeling second-order boundary perception: A machine learning approach. PLoS Computational Biology 15(3): e1006829.

DiMattina, C., & Zhang, K. (2017). Adaptive stimulus optimization. In: Encyclopedia of Computational Neuroscience (2nd Ed.). Springer.

DiMattina, C. (2016). Comparing models of contrast gain using psychophysical experiments. Journal of Vision 16(9): 1.

DiMattina, C. (2015). Fast adaptive estimation of multidimensional psychometric functions. Journal of Vision 15(9): 5.

DiMattina, C., & Zhang, K. (2013). Adaptive stimulus optimization for sensory systems neuroscience. Frontiers in Neural Circuits 7: 101.

DiMattina, C., Fox, S.A., & Lewicki, M.S. (2012). Detecting natural occlusion boundaries using local cues. Journal of Vision 12(13): 15.

DiMattina, C., & Zhang, K. (2011). Active data collection for efficient estimation and comparison of nonlinear neural models. Neural Computation 23(9): 2242-2288.

DiMattina, C., & Zhang, K. (2010). How to modify a neural network gradually without changing its input-output functionality. Neural Computation 22(1): 1-47.

DiMattina, C., & Zhang, K. (2008). How optimal stimuli for sensory neurons are constrained by network architecture. Neural Computation 20 (3), 668-708.

DiMattina, C., & Wang, X. (2006). Virtual vocalization stimuli for investigating neural representations of species-specific vocalizations. Journal of Neurophysiology 95(2): 1244-1262.

Kotak, V.C., DiMattina, C., & Sanes D.H. (2001). GABA_B and Trk Receptor Signaling Mediates Long-Lasting Inhibitory Synaptic Depression. Journal of Neurophysiology 86(1): 536-540.

Image Databases

Databases of shadow, texture and occlusion patches

Occlusion Databases: OSET1, OSET2. If you use OSET1 please cite DiMattina et al. (2012).

Shadow Database: SHAD. For use of SHAD or OSET2, please cite DiMattina et al. (2022).

Database of 31 Trypophobic Images

31TRY. If you use this resource please cite Pipitone & DiMattina (2020).

Database of Disease, Trypophobic and Brodatz Textures

DISTRYBDZ. If you use this resource please cite DiMattina et al. (2023+).

To view more information about Dr. DiMattina, please visit his University Directory listing.