Computational Cognition & Perception Lab
Research People Papers Teaching


Computational Cognition Cheat Sheets

Over the years, we've written several notes providing brief introductions to computational methods that are often useful in the study of human cognition. Many undergraduate and graduate students have told us that these notes are extremely helpful. If you're interested in these notes, please see our web page on Computational Cognition Cheat Sheets.

Selected Publications

  • German, J.S., Cui, G., Xu, C., Jacobs, R.A. (2023). Rapid runtime learning by curating small datasets of high-quality items obtained from memory. PLOS Computational Biology 19(10): e1011445. (Abstract) (PDF)

  • Sims, C.R., Lerch, R.A., Tarduno, J.A. et al. Conceptual knowledge shapes visual working memory for complex visual information. Sci Rep 12, 8088 (2022). (Abstract) (PDF)

  • Hu, R. & Jacobs, R. A. (2021). Semantic influence on visual working memory of object identity and location. Cognition, Volume 217, 2021, 104891. (Abstract) (PDF)
  • Wu M.H., Anderson, A.J., Jacobs, R.A., Raizada, R.D.S (2021). Analogy-Related Information Can be Accessed by Simple Addition and Subtraction of fMRI Activation Patterns, without Participants Performing any Analogy Task. Neurobiology of Language. (Abstract) (PDF)
  • Bates, C. J. & Jacobs, R. A. (2021). Optimal attentional allocation in the presence of capacity constraints in uncued and cued visual search. Journal of Vision, 21(5):3, 1-23. (Abstract) (PDF)

  • Wu M.H., Kleinschmidt D., Emberson L., Doko D., Edelman S., Jacobs R., Raizada R. (2020). Cortical Transformation of Stimulus Space in Order to Linearize a Linearly Inseparable Task. J Cogn Neurosci. 32(12):2342-2355. doi: 10.1162/jocn_a_01533. (Abstract) (PDF)
  • Bates, C. J. & Jacobs, R. A. (2020). Efficient data compression in perception and perceptual memory. Psychological Review, 127, 891-917. (Abstract) (PDF)
  • Bates, C.J., Sims, C.R., Jacobs, R.A. (2020). The importance of constraints on constraints Behavioral and Brain Sciences, 43, e3. (Abstract) (PDF)
  • German, J.S., Jacobs, R.A. (2020). Can machine learning account for human visual object shape similarity judgments? Vision Research, 167, 87-99. (Abstract) (PDF)

  • Bates, C. J., Lerch, R. A., Sims, C. R., & Jacobs, R. A. (2019). Adaptive allocation of human visual working memory capacity during statistical and categorical learning. Journal of Vision, 19(2):11, 1-23. (Abstract) (PDF)
  • Jacobs, R. A. & Bates, C. J. (2019). Comparing the visual representations and performance of human and deep neural networks. Current Directions in Psychological Science, 28, 34-39. (Abstract) (PDF)
  • Jacobs, R. A. & Xu, C. (2019). Can multisensory training aid visual learning? A computational investigation. Journal of Vision, 19(11):1, 1-12. (Abstract) (PDF)

  • Chen, Q., Garcea, F. E., Jacobs, R. A., & Mahon, B. Z. (2018). Abstract representations of object directed action in the left inferior parietal lobule. Cerebral Cortex, 28, 2162-2174. (Abstract) (PDF)

  • Erdogan, G. & Jacobs, R. A. (2017). Visual shape perception as Bayesian inference of 3D Object-Centered Shape Representations. Psychological Review, 124, 740-761. (Abstract) (PDF)
  • Overlan, M. C., Jacobs, R. A., & Piantadosi, S. T. (2017). Learning abstract visual concepts via probabilistic program induction in a Language of Thought. Cognition, 168, 320-334. (Abstract) (PDF)

  • Erdogan, G., Chen, Q., Garcea, F. E., Mahon, B. Z., & Jacobs, R. A. (2016). Multisensory part-based representations of objects in human lateral occipital cortex. Journal of Cognitive Neuroscience, 28, 869-881. (Abstract) (PDF)
  • Piantadosi, S. T. & Jacobs, R. A. (2016). Four problems solved by the probabilistic language of thought. Current Directions in Psychological Science, 25, 54-59. (Abstract) (PDF)

  • Erdogan, G., Yildirim, I., & Jacobs, R. A. (2015). From sensory signals to modality-independent conceptual representations: A probabilistic language of thought approach. PLoS Computational Biology, 11(11), e1004610. (Abstract) (PDF)
  • Yildirim, I. & Jacobs, R. A. (2015). Learning multisensory representations for auditory-visual transfer of sequence category knowledge: A probabilistic language of thought approach. Psychonomic Bulletin and Review, 22, 673-686. (Abstract) (PDF)

  • Orhan, A. E. & Jacobs, R. A. (2014). Toward ecologically realistic theories in visual short-term memory research. Attention, Perception, and Psychophysics, 76, 1058-1070. (Abstract) (PDF)
  • Orhan, A. E., Sims, C. R., Jacobs, R. A., & Knill, D. C. (2014). The adaptive nature of visual working memory. Current Directions in Psychological Science, 23, 164-170. (Abstract) (PDF)

  • Orhan, A. E. & Jacobs, R. A. (2013). A probabilistic clustering theory of the organization of visual short-term memory. Psychological Review, 120, 297-328. (Abstract) (PDF)
  • Sims, C. R., Neth, H., Jacobs, R. A., & Gray, W. D. (2013). Melioration as rational choice: Sequential decision making in uncertain environments. Psychological Review, 120, 139-154. (Abstract) (PDF)
  • Yildirim, I. & Jacobs, R. A. (2013). Transfer of object category knowledge across visual and haptic modalities: Experimental and computational studies. Cognition, 126, 135-148. (Abstract) (PDF)

  • Evans, K. M., Jacobs, R. A., Tarduno, J. A., & Pelz, J. B. (2012). Collecting and analyzing eye-tracking data in outdoor environments. Journal of Eye Movement Research, 5(2):6, 1-19. (Abstract) (PDF)
  • Sims, C. R., Jacobs, R. A., & Knill, D. C. (2012). An ideal observer analysis of visual working memory. Psychological Review, 119, 807-830. (Abstract) (PDF)
  • Yildirim, I. & Jacobs, R. A. (2012). A rational analysis of the acquisition of multisensory representations. Cognitive Science, 36, 305-332. (Abstract) (PDF)

  • Yakushijin, R. & Jacobs, R. A. (2011). Are people successful at learning sequences of actions on a perceptual matching task? Cognitive Science, 35, 939-962. (Abstract) (PDF)
  • Sims, C. R., Jacobs, R. A., & Knill, D. C. (2011). Adaptive allocation of vision under competing task demands. Journal of Neuroscience, 31, 928-943. (Abstract) (PDF)
  • Jacobs, R. A. & Kruschke, J. K. (2011). Bayesian learning theory applied to human cognition. Wiley Interdisciplinary Reviews: Cognitive Science, 2, 8-21. (Abstract) (PDF)

  • Orhan, A. E., Michel, M. M., and Jacobs, R. A. (2010). Visual learning with reliable and unreliable features. Journal of Vision, 10(2):2, 1-15. (Abstract) (PDF)
  • Jacobs, R. A. and Shams, L. (2010). Visual learning in multisensory environments. Topics in Cognitive Science, 2, 217-225. (Abstract) (PDF)

  • Jacobs, R. A. (2009). Adaptive precision pooling of model neuron activities predicts the efficiency of human visual learning. Journal of Vision, 9(4):22, 1-15. (Abstract) (PDF)

  • Chhabra, M. and Jacobs, R. A. (2008). Learning to combine motor primitives via greedy additive regression. Journal of Machine Learning Research, 9, 1535-1558. (Abstract) (PDF)
  • Clayards, M., Tanenhaus, M. K., Aslin, R. N., and Jacobs, R. A. (2008). Perception of speech reflects optimal use of probabilistic speech cues. Cognition, 108, 804-809. (Abstract) (PDF)
  • Michel, M. M. and Jacobs, R. A. (2008). Learning optimal integration of arbitrary features in a perceptual discrimination task. Journal of Vision, 8(2):3, 1-16. (Abstract) (PDF)

  • Chhabra, M., Jacobs, R. A., and Stefanovic, D. (2007). Behavioral shaping for geometric concepts. Journal of Machine Learning Research, 8, 1835-1865. (Abstract) (PDF)
  • Ivanchenko, V. and Jacobs, R. A. (2007). Visual learning by cue-dependent and cue-invariant mechanisms. Vision Research, 47, 145-156. (Abstract) (PDF)
  • Michel, M. M. and Jacobs, R. A. (2007). Parameter learning but not structure learning: A Bayesian network model of constraints on early perceptual learning. Journal of Vision, 7(1):4, 1-18. (Abstract) (PDF)

  • Chhabra, M. and Jacobs, R.A. (2006). Near-optimal human adaptive control across different noise environments. The Journal of Neuroscience, 26, 10883-10887. (Abstract) (PDF)
  • Chhabra, M. and Jacobs, R.A. (2006). Properties of synergies arising from a theory of optimal motor behavior. Neural Computation, 18, 2320-2342. [A shorter version of this article won the "best paper" award in the area of computational models of perception and action at the 2006 Conference of the Cognitive Science Society ($1000 prize!).] (Abstract) (PDF)
  • Michel, M.M. and Jacobs, R.A. (2006). The costs of ignoring high-order correlations in populations of model neurons. Neural Computation, 18, 660-682. (Abstract) (PDF)

  • Aslin, R.N., Battaglia, P.W., and Jacobs, R.A. (2004). Depth-dependent contrast gain-control. Vision Research, 44, 685-693. (Abstract) (PDF)
  • Battaglia, P.W., Jacobs, R.A., and Aslin, R.N. (2004). Depth-dependent blur adaptation. Vision Research, 44, 113-117. (Abstract) (PDF)

  • Atkins, J.E., Jacobs, R.A., and Knill, D.C. (2003). Experience-dependent visual cue recalibration based on discrepancies between visual and haptic percepts. Vision Research, 43, 2603-2613. (Abstract) (PDF)
  • Battaglia, P.W., Jacobs, R.A, and Aslin, R.N. (2003). Bayesian integration of visual and auditory signals for spatial localization. Journal of the Optical Society of America A, 20, 1391-1397. (Abstract) (PDF)
  • Dominguez, M. and Jacobs, R.A. (2003). Developmental constraints aid the acquisition of binocular disparity sensitivities. Neural Computation, 15, 161-182. (Abstract) (PDF)
  • Ivanchenko, V. and Jacobs, R.A. (2003). A developmental approach aids motor learning. Neural Computation, 15, 2051-2065. (Abstract) (PDF)
  • Jacobs, R.A. and Dominguez, M. (2003). Visual development and the acquisition of motion velocity sensitivities. Neural Computation, 15, 761-781. (Abstract) (PDF)

  • Fine, I. and Jacobs, R.A. (2002). Comparing perceptual learning across tasks: A review. Journal of Vision, 2, 190-203. (Abstract) (PDF)
  • Jacobs, R.A. (2002). What determines visual cue reliability? Trends in Cognitive Sciences, 6, 345-350. (Abstract) (PDF)
  • Jacobs, R.A., Jiang, W., and Tanner, M.A. (2002). Factorial hidden Markov models and the generalized backfitting algorithm. Neural Computation, 14, 2415-2437. (Abstract) (PDF)
  • Triesch, J., Ballard, D.H., and Jacobs, R.A. (2002). Fast temporal dynamics of visual cue integration. Perception, 31, 421-434. (Abstract) (PDF)

  • Atkins, J.E., Fiser, J., and Jacobs, R.A. (2001). Experience-dependent visual cue integration based on consistencies between visual and haptic percepts. Vision Research, 41, 449-461. (Abstract) (PDF)

  • Fine, I. and Jacobs, R.A. (2000). Perceptual learning for a pattern discrimination task. Vision Research, 40, 3209-3230. (Abstract) (PDF)
  • Meegan, D.V., Aslin, R.N., and Jacobs, R.A. (2000). Motor timing learned without motor training. Nature Neuroscience, 3, 860-862. (Abstract) (PDF)

  • Fine, I. and Jacobs, R.A. (1999). Modeling the combination of motion, stereo, and vergence angle cues to visual depth. Neural Computation, 11, 1297-1330. (Abstract) (PDF)
  • Jacobs, R.A. (1999). Computational studies of the development of functionally specialized neural modules. Trends in Cognitive Sciences, 3, 31-38. (Abstract) (PDF)
  • Jacobs, R.A. (1999). Optimal integration of texture and motion cues to depth. Vision Research, 39, 3621-3629. (Abstract) (PDF)
  • Jacobs, R.A. and Fine, I. (1999). Experience-dependent integration of texture and motion cues to depth. Vision Research, 39, 4062-4075. (Abstract) (PDF)

  • Jacobs, R.A. (1997). Nature, nurture, and the development of functional specializations: A computational approach. Psychonomic Bulletin and Review, 4, 299-309. (Abstract) (PDF)
  • Jacobs, R.A. (1997). Bias/Variance analyses of mixtures-of-experts architectures. Neural Computation, 9, 369-383. (Abstract)

  • Peng, F., Jacobs, R.A., and Tanner, M.A. (1996). Bayesian inference in mixtures-of-experts and hierarchical mixtures-of-experts models with an application to speech recognition. Journal of the American Statistical Association, 91, 953-960. (Abstract) (PDF)

  • Jacobs, R.A. and Kosslyn, S.M. (1994). Encoding shape and spatial relations: The role of receptive field size in coordinating complementary representations. Cognitive Science, 18, 361-386. (Abstract) (PDF)
  • Jordan, M.I. and Jacobs, R.A. (1994). Hierarchical mixtures of experts and the EM algorithm. Neural Computation, 6, 181-214. (Abstract) (PDF)

  • Jacobs, R.A., Jordan, M.I., and Barto, A.G. (1991). Task decomposition through competition in a modular connectionist architecture: The what and where vision tasks. Cognitive Science, 15, 219-250. (Abstract) (PDF)
  • Jacobs, R.A., Jordan, M.I., Nowlan, S.J., and Hinton, G.E. (1991). Adaptive mixtures of local experts. Neural Computation, 3, 79-87. (Abstract) (PDF)

  • Jacobs, R.A. (1988). Increased rates of convergence through learning rate adaptation. Neural Networks, 1, 295-307. (Abstract) (PDF)

This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.