University of Birmingham
Psychology Title

Psychology

CPL


Computational Psychology Lab

Selected publications

(see below for a full list of publications)

Patil, M. M., Heinke, D., & Zhang, F. (2025). Computational modelling reveals the influence of object similarity and proximity on visually guided movements. PeerJ, 13, p. e18953. https://doi.org/10.7717/peerj.18953

Deakin, J., Schofield, A., & Heinke, D. (2024). Support for the Time-Varying Drift Rate Model of Perceptual Discrimination in Dynamic and Static Noise Using Bayesian Model-Fitting Methodology. In Entropy (Vol. 26, Issue 8, p. 642). MDPI AG. https://doi.org/10.3390/e26080642

Wei C., Zou J., Heinke D., Liu Q. (2024) CoCoG: Controllable Visual Stimuli Generation based on Human Concept Representations, 33rd Proc. of International Joint Conference on Artificial Intelligence.

Makwana, M., Zhang, F., Heinke, D., & Song, J.-H. (2023). Continuous action with a neurobiologically inspired computational approach reveals the dynamics of selection history. PLOS Computational Biology, 19, 7, 1011283. https://doi.org/10.1371/journal.pcbi.1011283.

Heinke, D., Leonardis, A., Leek, E. C. (2022) What do deep neural networks tell us about biological vision? Vision Research, 198, 108069. https://doi.org/10.1016/j.visres.2022.108069

Leek, E. C., Leonardis, A., Heinke, D. (2022) Deep neural networks and image classification in biological vision. Vision Research, 197, 108058 https://doi.org/10.1016/j.visres.2022.108058.

Xu, S., Liu, X., Almeida, J. & Heinke, D. (2021) The contributions of the ventral and the dorsal visual streams to the automatic processing of action relations of familiar and unfamiliar object pairs. (2021). NeuroImage, (Vol. 245, p. 118629). Elsevier BV. https://doi.org/10.1016/j.neuroimage.2021.118629

Heinke, D., Wachman, P., van Zoest, W., & Leek, E. C. (2021). A failure to learn object shape geometry: Implications for convolutional neural networks as plausible models of biological vision. Vision Research, 189, 81-92. https://doi.org/10.1016/j.visres.2021.09.004

Deakin, J., Porat, L., van Zoest, W., & Heinke, D. (2021). Behavioral Research, Overt Performance. Encyclopedia of Behavioral Neuroscience, 2nd edition (pp. 197 - 203). Elsevier. https://doi.org/10.1016/b978-0-12-819641-0.00162-6

Velentza, A. M., Heinke, D., & Wyatt, J. (2020). Museum robot guides or conventional audio guides? An experimental study. Advanced Robotics , 1-10. https://doi.org/10.1080/01691864.2020.1854113

Standage, D., Areshenkoff, C. N., Nashed, J. Y., Hutchison, R. M., Hutchison, M., Heinke, D., Menon, R. S., Everling, S., & Gallivan, J. P. (2020). Dynamic Reconfiguration, Fragmentation, and Integration of Whole-Brain Modular Structure across Depths of Unconsciousness. Cerebral Cortex , 30(10), 5229-5241. https://doi.org/10.1093/cercor/bhaa085

Abu-Akel, A., Allison, C., Baron-Cohen, S., & Heinke, D. (2019). The distribution of autistic traits across the autism spectrum: evidence for discontinuous dimensional subpopulations underlying the autism continuum. Molecular Autism , 10(1). https://doi.org/10.1186/s13229-019-0275-3

Robinson, J., George, D. N., & Heinke, D. (2019). A computational implementation of a Hebbian learning network and its application to configural forms of acquired equivalence. Journal of Experimental Psychology: Animal Learning and Cognition , 45(3), 356-371. href=https://doi.org/10.1037/xan0000203

Abadi, A. K., Yahya, K., Amini, M., Friston, K., & Heinke, D. (2019). Excitatory versus inhibitory feedback in Bayesian formulations of scene construction. Journal of The Royal Society Interface , 16(154), https://doi.org/10.1098/rsif.2018.0344

Osiurak, F., & Heinke, D. (2018) Looking for Intoolligence: A unified framework for the cognitive study of human tool use and technology. American Psychologist, 73(2), 169-185. http://dx.doi.org/10.1037/amp0000162.

Narbutas, V., Lin, Y.-S., Kristan, M., & Heinke, D. (2017) Serial versus parallel search: A model comparison approach based on reaction time distributions. Visual Cognition, 1-3, 306-325. https://doi.org/10.1080/13506285.2017.1352055

Orghian, D., Smith, A., Garcia-Marques, L., & Heinke, D. (2017) Capturing spontaneous trait inference with the modified free association paradigm. Journal of Experimental Social Psychology, 73, 243-258. https://doi.org/10.1016/j.jesp.2017.07.004.

Xu, S., & Heinke, D. (2017) Implied between-object actions affect response selection without knowledge about object functionality. Visual Cognition, 1-3, 152-168. http://dx.doi.org/10.1080/13506285.2017.1330792.

Xu, S., Humphreys, G. W., Mevorach, C., & Heinke, D. (2017) The involvement of the dorsal stream in processing implied actions between object pairs: a TMS study. Neuropsychologia, 95, 240-249.

Abu-Akel, A., Heinke, D., Gillespie, S. M., Mitchell, I. J., & Bo, S. (2015) Metacognitive impairments in schizophrenia are arrested at extreme levels of psychopathy: The cut-off effect. Journal of Abnormal Psychology, 124, 4, 1102-1109.

Strauss, S., Woodgate, P.J.W., Sami, S. A., & Heinke, D. (2015) Choice reaching with a LEGO arm robot (CoRLEGO): The motor system guides visual attention to movement-relevant information. Neural Networks, 72, 3-12. http://dx.doi.org/10.1016/j.neunet.2015.10.005

Woodgate, P.J.W., Strauss, S., Sami, S. A., & Heinke, D. (2015) Motor cortex guides selection of predictable movement targets. Behavioural Brain Research, 287, 238-246.

Xu, S., Humphreys, G. W., & Heinke, D. (2015) Implied actions between paired objects lead to affordance selection by inhibition. Journal of Experimental Psychology: Human Perception and Performance, 41(4), 1021-1036. http://dx.doi.org/10.1037/xhp0000059

Lin, Y.-S., Heinke, D. & Humphreys, G. W. (2015) Modeling visual search using three-parameter probability functions in a hierarchical Bayesian framework. Attention, Perception, & Psychophysics, 77(3), 985-1010.

Orghian, D., Garcia-Marquesa L., Uleman, J. S. , & Heinke, D. (2015) A Connectionist Model of Spontaneous Trait Inference and Spontaneous Trait Transference. Social Cognition, 33,1, 20 - 66. http://guilfordjournals.com/doi/abs/10.1521/soco.2015.33.1.20

Strauss, S. & Heinke, D. (2012) A robotics-based approach to modeling of choice reaching experiments on visual attention. Front. Psychology, 3:105. http://http://dx.doi.org/10.3389/fpsyg.2012.00105

Heinke, D. & Backhaus, A.(2011) Modeling visual search with the Selective Attention for Identification model (VS-SAIM): A novel explanation for visual search asymmetries. Cognitive Computation, 3(1), 185-205. http://www.springerlink.com/content/ql107k1623117851

Mavritsaki, E. , Heinke, D. , Allen H., Deco, G., & Humphreys, G. W. (2011) Bridging the gap between physiology and behavior: Evidence from the sSoTS model of human visual attention. Psychological Review, 118(1), 3-41.

Zhao, Y., Heinke, D., Ivanoff, J., Klein, M. K. & Humphreys, G. W. (2011) Two components in IOR: Evidence for response bias and perceptual processing delays using the SAT methodology. Attention Perception & Psychophysics, 73(7), 2143-2159. http://dx.doi.org/10.3389/10.3758/s13414-011-0181-z

Anderson, G. M., Heinke, D., & Humphreys, G. W. (2010) Featural guidance in conjunction search: The contrast between orientation and color. Journal of Experimental Psychology: Human Perception and Performance,36(5), 1108-1127.

Boehme, Ch. & Heinke, D. (2009) Modelling visual Affordances: The Selective Attention for Action Model (SAAM). In J. Mayor, N. Ruh, & K. Plunkett (Eds.), Connectionist models of behaviour and cognition II (pp. 129-140). London: World Scientific. (Proceedings of the 11th Neural Computation and Psychology Workshop (NCPW11), University of Oxford, UK, 16-18 July 2008) Draft

Heinke, D. & Mavritsaki, E. (Eds.) (2009) Computational Modelling in Behavioural Neuroscience: Closing the gap between neurophysiology and behaviour, London: Psychology Press. (preface, table of content)

Mavritsaki E., Heinke D., Humphreys G. W., & Deco G. (2006) A computational model of visual marking using an interconnected network of spiking neurons: The spiking search over time & space model (sSoTS). Journal of Physiology-Paris, 100(1/2/3), 110-124.

Heinke, D., Humphreys, G. W., & Tweed, C. L. (2006) Top-down guidance of visual search: A computational account. Visual Cognition. 14(4/5/6/7/8), 985-1005. Draft

Soto, D., Humphreys, G. W., &  Heinke, D. (2006). Dividing the mind: The necessary role of the frontal lobes in separating memory from search. NEUROPSYCHOLOGIA, 44(8), 1282-1289.

Soto, D., Humphreys, G. W., &  Heinke, D. (2006). Working memory can guide pop-out search. Vision Research, 46(6/7), 1010-1018.

Heinke, D., & Humphreys, G. W. (2005). Selective Attention for Identification Model (SAIM): Simulating visual neglect. Computer Vision and Image Understanding, 100 (1/2), 172-197.  Article

Soto, D., Heinke, D., Humphreys, G. W., & Blanco, M. (2005). Early, involuntary top-down guidance of attention from working memory. Journal of Experimental Psychology: Human Perception and Performance, 31(2), 248-261. Draft

Heinke, D., & Humphreys, G. W. (2005). Computational Models of Visual Selective Attention: A Review. In Houghton, G., editor, Connectionist Models in Psychology, 273 - 312, Psychology Press Abstract

Heinke, D., & Humphreys, G. W. (2003). Attention, spatial representation and visual neglect: Simulating emergent attention and spatial memory in the Selective Attention for Identification Model (SAIM). Psychological Review, 110(1):29-87. Abstract Complete draft (1.3MB)

Yoon, E. Y., Heinke, D., & Humphreys, G. W. (2002). Modelling direct perceptual constraints on action selection: The Naming and Action model (NAM). Visual Cognition, 9(4/5):615-661. Abstract

Heinke, D., & Hamker, F. H. (1998). Comparing Neural Networks: A Benchmark on Growing Neural Gas, Growing Cell Structures, and Fuzzy ARTMAP. IEEE Transactions on Neural Networks, 9(6):1279-1291. Abstract

Full list of publications

Back to CPL's Homepage

Journal publications

Makwana, M., Zhang, F., Heinke, D., & Song, J.-H. (2023).
Continuous action with a neurobiologically inspired computational approach reveals the dynamics of selection history. PLOS Computational Biology, 19, 7, 1011283. https://doi.org/10.1371/journal.pcbi.1011283.
 
Heinke, D., Leonardis, A., Leek, E. C. (2022).
What do deep neural networks tell us about biological vision? Vision Research, 198, 108069. https://doi.org/10.1016/j.visres.2022.108069
 
Leek, E. C., Leonardis, A., Heinke, D. (2022).
Deep neural networks and image classification in biological vision. Vision Research, 197, 108058 https://doi.org/10.1016/j.visres.2022.108058.
 
Xu, S., Liu, X., Almeida, J. & Heinke, D. (2021).
The contributions of the ventral and the dorsal visual streams to the automatic processing of action relations of familiar and unfamiliar object pairs. (2021). NeuroImage, (Vol. 245, p. 118629). Elsevier BV. https://doi.org/10.1016/j.neuroimage.2021.118629
 
Heinke, D., Wachman, P., van Zoest, W., & Leek, E. C. (2021).
A failure to learn object shape geometry: Implications for convolutional neural networks as plausible models of biological vision. Vision Research, 189, 81-92. https://doi.org/10.1016/j.visres.2021.09.004
 
Deakin, J., Porat, L., van Zoest, W., & Heinke, D. (2021).
Behavioral Research, Overt Performance. In Reference Module in Neuroscience and Biobehavioral Psychology. Elsevier. https://doi.org/10.1016/b978-0-12-819641-0.00162-6
 
Velentza, A. M., Heinke, D., & Wyatt, J. (2020).
Museum robot guides or conventional audio guides? An experimental study. Advanced Robotics , 1-10. https://doi.org/10.1080/01691864.2020.1854113
 
Standage, D., Areshenkoff, C. N., Nashed, J. Y., Hutchison, R. M., Hutchison, M., Heinke, D., Menon, R. S., Everling, S., & Gallivan, J. P. (2020).
Dynamic Reconfiguration, Fragmentation, and Integration of Whole-Brain Modular Structure across Depths of Unconsciousness. Cerebral Cortex , 30(10), 5229-5241. https://doi.org/10.1093/cercor/bhaa085
 
Velentza, A.-M., Heinke, D., & Wyatt, J. (2019).
Human Interaction and Improving Knowledge through Collaborative Tour Guide Robots. 2019 28th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). Presented at the 2019 28th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). https://doi.org/10.1109/ro-man46459.2019.8956372
 
Abu-Akel, A., Allison, C., Baron-Cohen, S., & Heinke, D. (2019).
The distribution of autistic traits across the autism spectrum: evidence for discontinuous dimensional subpopulations underlying the autism continuum. Molecular Autism , 10(1). https://doi.org/10.1186/s13229-019-0275-3
 
Robinson, J., George, D. N., & Heinke, D. (2019).
A computational implementation of a Hebbian learning network and its application to configural forms of acquired equivalence. Journal of Experimental Psychology: Animal Learning and Cognition , 45(3), 356-371. href=https://doi.org/10.1037/xan0000203
 
Abadi, A. K., Yahya, K., Amini, M., Friston, K., & Heinke, D. (2019)
Excitatory versus inhibitory feedback in Bayesian formulations of scene construction. Journal of The Royal Society Interface , 16(154), https://doi.org/10.1098/rsif.2018.0344
 
Osiurak, F., & Heinke, D. (2018)
Looking for Intoolligence: A unified framework for the cognitive study of human tool use and technology. American Psychologist, 73(2), 169-185. http://dx.doi.org/10.1037/amp0000162.
 
Orghian, D., Smith, A., Garcia-Marques, L., & Heinke, D. (2017)
Capturing spontaneous trait inference with the modified free association paradigm. Journal of Experimental Social Psychology, 73, 243-258. https://doi.org/10.1016/j.jesp.2017.07.004.
 
Xu, S., & Heinke, D. (2017)
Implied between-object actions affect response selection without knowledge about object functionality. Visual Cognition, 1-3, 152-168. http://dx.doi.org/10.1080/13506285.2017.1330792.
 
Narbutas, V., Lin, Y.-S., Kristan, M., & Heinke, D. (2017)
Serial versus parallel search: A model comparison approach based on reaction time distributions. Visual Cognition, 1-3, 306-325. https://doi.org/10.1080/13506285.2017.1352055.
 
Xu, S., Humphreys, G. W., Mevorach, C., & Heinke, D. (2017)
The involvement of the dorsal stream in processing implied actions between object pairs: a TMS study. Neuropsychologia, 95, 240-249.
 
Fard, F. S., Hollensen, P., Heinke, D.,& Trappenberg, T. P. (2015)
Modeling human target reaching with an adaptive observer implemented with dynamic neural fields. Neural Networks, 72, 13-30. http://dx.doi.org/10.1016/j.neunet.2015.10.003
.
 
Strauss, S., Woodgate, P.J.W., Sami, S. A., & Heinke, D. (2015)
Choice reaching with a LEGO arm robot (CoRLEGO): The motor system guides visual attention to movement-relevant information. Neural Networks, 72, 3-12. http://dx.doi.org/10.1016/j.neunet.2015.10.005
 
Abu-Akel, A., Heinke, D., Gillespie, S. M., Mitchell, I. J., & Bo, S. (2015)
Metacognitive impairments in schizophrenia are arrested at extreme levels of psychopathy: The cut-off effect. Journal of Abnormal Psychology, 124, 4, 1102-1109.
 
Woodgate, P.J.W., Strauss, S., Sami, S. A., & Heinke, D. (2015)
Motor cortex guides selection of predictable movement targets. Behavioural Brain Research, 287, 238-246.
 
Xu, S., Humphreys, G. W., & Heinke, D. (2015)
Implied actions between paired objects lead to affordance selection by inhibition. Journal of Experimental Psychology: Human Perception and Performance, 41(4), 1021-1036. http://dx.doi.org/10.1037/xhp0000059
 
Lin, Y.-S., Heinke, D. & Humphreys, G. W. (2015)
Modeling visual search using three-parameter probability functions in a hierarchical Bayesian framework. Attention, Perception, & Psychophysics, 77(3), 985-1010.
 
Orghian, D., Garcia-Marquesa L., Uleman, J. S. , & Heinke, D. (2015)
A Connectionist Model of Spontaneous Trait Inference and Spontaneous Trait Transference. Social Cognition, 33,1, 20 - 66.
 
Zhao, Y. & Heinke, D. (2014)
What causes IOR? Attention or perception? - Manipulating cue and target luminance in either blocked or mixed condition. Vision Research, 105:37�46.
 
Heinke, D., Carslaw, G. & Christian J. (2013)
An Agent-based Simulation of Destigmatization (DSIM): Introducing a Contact Theory and Self-fulfilling Prophecy Approach. Journal of Artificial Societies and Social Simulation, 16(4), 10.
 
Anderson, G.M, Heinke, D. & Humphreys, G. W. (2013)
Top-down guidance of eye movements in conjunction search. Vision Research, 79, 36-46. DOI
 
Strauss, S. & Heinke, D. (2012)
A robotics-based approach to modeling of choice reaching experiments on visual attention. Front. Psychology, 3:105. doi: 10.3389/fpsyg.2012.00105
 
Zhao, Y., Humphreys, G. W., Heinke, D. (2012)
A Biased-Competition Approach to Spatial Cuing: Combining Empirical Studies and Computational Modelling. Visual Cognition, 20(2), 170-210.DOI
 
Anderson, G.M, Heinke, D. & Humphreys, G. W. (2012)
Bottom-up Guidance to Grouped Items in Conjunction Search: Evidence for Color Grouping. Vision Research, 52(1), 88-96. DOI
 
Zhao, Y., Heinke, D., Ivanoff, J., Klein, M. K. & Humphreys, G. W. (2011)
Two components in IOR: Evidence for response bias and perceptual processing delays using the SAT methodology. Attention Perception & Psychophysics, 73(7), 2143-2159. DOI: 10.3758/s13414-011-0181-z
 
Heinke, D. & Backhaus, A.(2011)
Modeling visual search with the Selective Attention for Identification model (VS-SAIM): A novel explanation for visual search asymmetries. Cognitive Computation, 3(1), 185-205. Open Access
 
Anderson, G. M., Heinke, D., & Humphreys, G. W. (2011)
Differential time course of implicit and explicit cueing by colour and orientation in visual search Visual Cognition, 19(2), 258-288.
 
Mavritsaki, E. , Heinke, D. , Allen H., Deco, G., & Humphreys, G. W. (2011)
Bridging the gap between physiology and behavior: Evidence from the sSoTS model of human visual attention. Psychological Review, 118(1), 3-41.
 
Anderson, G. M., Heinke, D., & Humphreys, G. W. (2010)
Featural guidance in conjunction search: The contrast between orientation and color. Journal of Experimental Psychology: Human Perception and Performance,36(5), 1108-1127
 
Mavritsaki, E., Heinke, D., Deco, & G. , Humphreys, G. W. (2009)
Simulating posterior parietal damage in a biologically plausible framework: Neuropsychological tests of the sSoTS model. Cognitive Neuropsychology, 26(4), 343-390.
 
Deco, G., & Heinke, D. (2007)
Attention and Spatial Resolution: A theoretical and experimental study of visual search in hierarchical patterns. Perception, 36(3), 335-354
 
Mavritsaki, E., Heinke, D., Humphreys, G. W., & Deco, G. (2007)
Suppressive effects in visual search: A neurocomputational analysis of preview search. Neurocomputing, 70(10-12), Sp. Iss. SI, 1925-1931.
 
Mavritsaki E., Heinke D., Humphreys G. W., & Deco G. (2006)
A computational model of visual marking using an interconnected network of spiking neurons: The spiking search over time & space model (sSoTS). Journal of Physiology-Paris, 100(1/2/3), 110-124.
 
Heinke, D., Humphreys, G. W., & Tweed, C. L. (2006)
Top-down guidance of visual search: A computational account. Visual Cognition. 14(4/5/6/7/8), 985-1005. Draft
 
Soto, D., Humphreys, G. W., &  Heinke, D. (2006).
Dividing the mind: The necessary role of the frontal lobes in separating memory from search. NEUROPSYCHOLOGIA, 44(8), 1282-1289.
 
Soto, D., Humphreys, G. W., &  Heinke, D. (2006).
Working memory can guide pop-out search. Vision Research, 46(6/7), 1010-1018.
 
Heinke, D., & Humphreys, G. W. (2005).
Selective Attention for Identification Model (SAIM): Simulating visual neglect. Computer Vision and Image Understanding, 100 (1/2), 172-197.  Article
 
Soto, D., Heinke, D., Humphreys, G. W., & Blanco, M. (2005).
Early, involuntary top-down guidance of attention from working memory.
Journal of Experimental Psychology: Human Perception and Performance, 31(2), 248-261. Draft
 
Heinke, D., & Humphreys, G. W. (2003).
Attention, spatial representation and visual neglect: Simulating emergent attention and spatial memory in the Selective Attention for Identification Model (SAIM).
Psychological Review, 110(1):29-87. Abstract Complete draft (1.3MB)
 
Heinke, D., Deco, G., Zihl, J., & Humphreys, G. W. (2002).
A Computational Neuroscience Account of Visual Neglect. Neurocomputing, 44:811-816. Abstract Paper
Heinke, D., Humphreys, G. W., & diVirgilo, G. (2002).
Modeling visual search experiments: Selective Attention for Identification Model (SAIM).
Neurocomputing, 44:817-822. Abstract Paper
 
Humphreys, G. W., Riddoch, M. J., Nys, G., & Heinke, D. (2002).
Unconscious, transient binding by time: Neuropsychological evidence from anti-extinction.
Cognitive Neuropsychology, 19(4):361-380. Abstract
 
Olivers, N. L. C., Humphreys, G. W., Heinke, D., & Cooper, A. C. G. (2002).
Prioritization in visual search: Visual marking is not dependent on a mnemonic search.
Perception & Psychophysics, 64(4), 540-560.Abstract
 
Yoon, E. Y., Heinke, D., & Humphreys, G. W. (2002).
Modelling direct perceptual constraints on action selection: The Naming and Action model (NAM). Visual Cognition, 9(4/5):615-661. Abstract
 
Heinke, D., & Hamker, F. H. (1998).
Comparing Neural Networks: A Benchmark on Growing Neural Gas, Growing Cell Structures, and Fuzzy ARTMAP. IEEE Transactions on Neural Networks, 9(6):1279-1291. Abstract

Humphreys, G. W., & Heinke, D. (1998).
Spatial representation and selection in the brain: Neuropsychological and computational constraints. Visual Cognition, 5(1/2):9-47.

Heinke, D., & Humphreys, G. W. (in preparation).
Attentional Repulsion and Attraction Effects in Localisation.
 

Top of page

Authored Books

Heinke, D. (1998).
Selbstorganisation einer zeitlichen Objektrepräsentation (Selforganisation of a temporal object representation).
Logos Verlag Berlin. PhD-Thesis, University of Ilmenau.

Top of page

Edited Books

Heinke, D. & Mavritsaki, E. (Eds.) (2009).
Computational Modelling in Behavioural Neuroscience: Closing the gap between neurophysiology and behaviour, London: Psychology Press. (preface, table of content)
 
Heinke, D., Humphreys, G. W., & Olson, A., editors (1998).
Connectionist Models in Cognitive Neuroscience- The 5th Neural Computation and Psychology Workshop.
Springer Verlag, University of Birmingham, England.

Top of page

Conference proceedings - refereed

Boehme, Ch. & Heinke, D. (2011).
A Neurodynamical Approach to Visual Affordances: The Selective Attention for Action Model (SAAM) In R. Wang & F. Gu (Eds.), Advances in Cognitive Neurodynamics (II) (pp. 501-505). Springer-Verlag. (Proc. of the of the Second International Conference on Cognitive Neurodynamics - 2009)
 
Heinke, D., Mavritsaki, E., Backhaus, A., & Kreyling, M. (2009).
The selective attention for identification model (SAIM): A framework for closing the gap between behaviour and neurological level. In Heinke, D. & Mavritsaki, E. (Eds.) (2009) Computational Modelling in Behavioural Neuroscience: Closing the gap between neurophysiology and behaviour, London: Psychology Press.
 
Humphreys, G. W., Mavritsaki, E., Allen, H., Heinke, D. & Deco, G. (2009).
Application of neural level model to human visual search: Modelling the whole system behaviour, neurophysiological break down and neural signal response. In Heinke, D. & Mavritsaki, E. (Eds.) (2009) Computational Modelling in Behavioural Neuroscience: Closing the gap between neurophysiology and behaviour, London: Psychology Press.
 
Heinke, D. (2009).
Computational modelling in behavioural neuroscience: Methodologies and Approaches - Minutes of discussions at the workshop in Birmingham, UK in May 2007. In Heinke, D. & Mavritsaki, E. (Eds.) (2009) Computational Modelling in Behavioural Neuroscience: Closing the gap between neurophysiology and behaviour, London: Psychology Press.
 
Boehme, Ch. & Heinke, D. (2009).
Where Do We Grasp Objects? - An Experimental Verification of the Selective Attention for Action Model (SAAM). In L. Paletta & J. K. Tsotsos (Eds.), Attention in cognitive systems: International workshop on attention in cognitive systems (pp. 41-53). Springer-Verlag. (Proc. of the WAPCV-2008 Fira, Santorini, Greece, May 12, 2008) Draft
 
Boehme, Ch. & Heinke, D. (2009).
Modelling visual Affordances: The Selective Attention for Action Model (SAAM). In J. Mayor, N. Ruh, & K. Plunkett (Eds.), Connectionist models of behaviour and cognition II (pp. 129-140). London: World Scientific. (Proceedings of the 11th Neural Computation and Psychology Workshop (NCPW11), University of Oxford, UK, 16-18 July 2008) Draft
 
Heinke, D., Backhaus, A., Sun, Y. & Humphreys, G.W. (2008).
The Selective Attention for Identification model (SAIM): Simulating Visual Search in Natural Colour Images. In Paletta, L. and Rome, E. (eds.) Attention in Cognitive Systems, 143-172, Springer LNAI 4840 Draft
 
Backhaus, A., Heinke, D., & Humphreys, G. W. (2005).
Contextual Learning in the Selective Attention for Identification model (CL-SAIM): Modeling contextual cueing in visual search tasks. In Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cvpr'05) - Workshops - Volume 03 (June 20 - 26, 2005). CVPR. IEEE Computer Society, Washington, DC.  Draft
 
 
Heinke, D., Sun, Y., & Humphreys, G. W. (2005).
Modeling Grouping Through Interactions Between Top-Down and Bottom-Up Processes: The Grouping and Selective Attention for Identification Model (G-SAIM).  In Paletta, L., Tsotsos, J. K., Rome, E., Humphreys, G. W. editors: Attention and Performance in Computational Vision, 2nd International Workshop, WAPCV 2004, Pragues, Czech Republic, May 15, 2004, pages 148-158, Springer, ISBN 3-540-24421-2.   Draft
 
Heinke, D., Humphreys, G. W., & Tweed, C. L.(2004).
Modelling visual search: Evolving the Selection Attention for Identification model (SAIM).
In Bowman, H. and Labiouse, A., editors, The 8th Neural Computation and Psychology Workshop (NCPW8): Connectionist Models of Cognition and Perception II, pages 168-177, University of Kent, England, World Scientific.Draft
 
Heinke, D. & Humphreys, G. W. (1999).
Modelling Emergent Attentional Properties.
In Heinke, D., Humphreys, G., and Olson, A., editors, Connectionist Models in Cognitive Neuroscience - The 5th Neural Computation and Psychology Workshop, pages 240-251, University of Birmingham, England, Springer Verlag. Draft
 
Heinke, D. & Humphreys, G. W. (1997).
SAIM: A Model of Visual Attention and Neglect.
In Proc. of the 7th International Conference on Artificial Neural Networks-ICANN'97, pages 913-918, Lausanne, Switzerland. Springer Verlag. Draft
 
Humphreys, G. W., & Heinke, D. (1997).
Representing and attending to visual space: A computational perspective on neuropsychological problems. In 4th Neural Computation and Psychology Workshop Connectionist Representations: Theory and Practice, pages 99-112, University of London, England.
 
Böhme, H.-J., Gross, H.-M., Brauman, U.-D., Heinke, D., Pomierski, T., & Brakensiek, A.
(1996).
Active-Vision zur Selbstorganisation von Verhalten in senso-motorischen Systemen.
In Mertsching, B., editor, Aktives Sehen in technischen und biologischen Systemen, pages 60-66. infix.
 
Gross, H.-M., Boehme, H.-J., Braumann, U.-D., Heinke, D., & Pomierski, T. (1996).
Corticale Architekturprinzipien zur Selbstorganisation eines verhaltensbasierten Objektverständnisses bei der Farbszenenanlyse (Namos-Teilprojekt D).
In Proc. des BMBF-Statusseminars ''Neuroinformatik und Künstliche Intelligenz'', pages 276-283.
 
Böhme, H.-J., Braumann, U.-D., Heinke, D., & Gross, H.-M. (1995).
A Neural Network Architecture for Scene Interpretation.
In Proc. of 3. Annual SNN Symposium on Neural Networks, pages 173-176, Nijmegen, The Netherlands. Springer Verlag.

Gross, H.-M., Heinke, D., Böhme, H.-J., Braumann, U.-D., & Pomierski, T. (1995).
A Behaviour-oriented Approach to an Implicit ''Object-understanding'' in Visual Attention.
In Proc. of IEEE-Intern. Conference on Neural Networks - ICNN'95, pages 395-419.
 
Gross, H.-M., Boehme, H.-J., Heinke, D., Pomierski, T., & Möller, R. (1994a).
Self-Organizing a Behaviour-Oriented Interpretation of Objects in Active-Vision.
In Proc. of International Conference on Artificial Neural Network - ICANN'94, pages 58-61, Sorrento, Italy. Springer Verlag.

Gross, H.-M., Heinke, D., Boehme, H.-J., & Pomierski, T. (1994b).
A Behaviour-oriented Approach to an Object-understanding Visual Attention.
In Proc. of 39. International Scientific Collogium, pages 95-103, University of Ilmenau, Germany.

Heinke, D., & Gross, H.-M. (1994).
A Selforganizing Neural Network Architecture for Visual Attention.
In Proc. of 39. International Scientific Collogium, pages 124-131, University of Ilmenau, Germany.
 
Heinke, D. & Gross, H.-M. (1993).
A Simple Selforganizing Neural Network Archiecture for Selective Visual Attention.
In Proc. of the International Conference on Artificial Neural Network - ICANN'93, pages 63-66, Amsterdam, The Netherlands. Springer Verlag.
 
Giannakopoulos, F., Heinke, D., & Best, J. (1991).
A network-model for binocular interaction in the thalamo-cortical feedback-system.
In Kohonen, T., Mäkisara, K., and Simula, O., editors, Artificial Neural Networks, pages 1723-1726. Elsevier Science Publishers.
 
Heinke, D., Giannakopoulos, F., & Best, J. (1990).
The thalamo-cortical feedback-system: A nonlinear network-model for binocular interaction.
In Eckmiller, R., Hartmann, G., & Hauske, G., editors, Parallel Processing in Neural Systems and Computers, pages 113-116. Elsevier Science Publishers B.V.


Top of page

Reviews

 
Heinke, D., & Humphreys, G. W. (2005).
Computational Models of Visual Selective Attention: A Review.
In Houghton, G., editor, Connectionist Models in Psychology, 273 - 312, Psychology Press Abstract
 
Heinke, D. (2004).
Objects and attention, Perception, 33(6):761-762.

Heinke, D. (2000).
A Dynamical System Theory Approach to Cognitive Neuroscience. Book Review on: Neural Organization, M. A. Arbib, P. Erdi & J. Szentagothai.
Behavioural and Brain Science, 23(4):543-566.
 
Humphreys, G. W., Heinke, D., & Yoon, E.. Y. (2006).
Cognitive Neuropsychology and Computational Modelling: The Contribution of Computational Neuroscience to Understanding the Mind. In  C. Senior,  T. Russell &  M. S. Gazzaniga, editors, Methods in Mind, pp. 71-102, The MIT Press. Draft
 
Olivers, C. N. L., Heinke, D., Humphreys, G. W., & Müller, H. (1999).
Close interactions between 'when' and 'where' in saccade target selection: Multiple saliency and distractor effects. Comment on: A model of saccade generation based on parallel processing and competitve inhibition, J.M. Findlay & R. Walker.
Behavioural and Brain Science, 22(4):693-694.


Top of page

Abstracts

 
Strauss S and Heinke D (2011).
A robotics-based approach to modelling choice reaching tasks. Front. Comput. Neurosci. Conference Abstract: BC11 : Computational Neuroscience & Neurotechnology Bernstein Conference & Neurex Annual Meeting 2011. doi: 10.3389/conf.fncom.2011.53.00100
 
Zhao, Y., Heinke D., & Humphreys, G. W. (2007).
Effects of cue luminance on facilitation and inhibition in a spatial cueing paradigm. Proc. of the European society for cognitive psychology (ESCOP). Marseille, France, 29. August – 1. September, 2007.
 
Anderson, G. M., Heinke, D. & Humphreys, G. W.  (2006)
Top-down modulation in inefficient search:  Evidence of differences between orientation and colour cuing.
Perception, 35, S163b, 2006. (ECVP 2006).
 
Anderson, G. M., Heinke, D., & Humphreys, G. W. (2006)
Top-down modulation in inefficient search: Evidence of differences between orientation and colour cuing.
Proc. of 60th meeting of The Experimental Psychology Group, EPS2006, Birmingham, UK, April 10-12, 2006, p. 62.
 
Backhaus, A., & Heinke, D.  (2006).
Simulating asymmetries and similarity effects in visual search with SAIM (Selective Attention for Identification model).
Proceedings of the Experimental Psychology Society (EPS)'06, Birmingham, United Kingdom, April 10-11, 2006, p. 33
 
Soto, D., Humphreys, G. W. & Heinke, D. (2006).
Dividing the mind: The necessary role of the frontal lobes in separating memory from search. Proc. of the Experimental Psychology Society (EPS)'06, Birmingham, United Kingdom, April 10-11, 2006, p. 21.
 
Heinke, D. (2003).
Modeling visual search experiments: A new version of the Selective Attention for Identification Model (SAIM). Proceedings of the Symposium on Visual Search and Selective Attention, Holzhausen am Ammersee, Germany, June 6 - 10 Slides

Heinke, D. (2003).
Selective attention for identification model (SAIM): Computational modelling of visual extinction and neglect, Proceedings of the 26th European Conference on visual perception . Paris, France, September 1 - 5, p.18

Heinke, D., & Humphreys, G. W. (2003).
Modeling visual search experiments: A new version of the Selective Attention for Identification Model (SAIM), Proceedings of the 8th Neural Computation and Psychology Workshop. Kent, United Kingdom, August 27 - 30, p. 10

Heinke, D., di Virgilio, G., & Humphreys, G. W. (2001).
Selective attention for identification model (SAIM): Modelling data from visual search experiments. Proceedings of the 12th Congress of the European Society for Cognitive Psychology (ESCOP)'01, Edinburgh, United Kingdom, September, 2001, p.20.

Heinke, D., & Humphreys, G. W. (1998a).
Computational Modelling of Attention and Disorders in the Human Visual System.
Proceedings of the 10th Congress of the European Society for Cognitive Psychology (ESCOP), Jerusalem, Israel, September 13 - 17, 1998, p. 172.

Heinke, D. and Humphreys, G. W. (1998b).
Modelling emergent attentional processes.
Proceedings of the Experimental Psychology Society (EPS)'98, York, United Kingdom, July 2-4, 1998, p. 19

Humphreys, G. W., & Heinke, D. (2001).
Modelling attention in a quasi-modular interactive system: The SAIM model.
Proceedings of the Experimental Psychology Society (EPS)'01, Manchester, United Kingdom, July, 2001, p.20

Humphreys, G. W., Riddoch, M. J., Nys, G., & Heinke, D. (2002).
Unconscious temporal binding by time: Evidence from anti-extinction.
Neurocase, Proceedings of the British Neuropsychological Society (BNS) autumn meeting, 8:253

Yoon, E. Y., Heinke, D., & Humphreys, G. W. (2001).
Modeling direct perceptual constraints on action selection: The and Action model (NAM).
Proceedings of the 12th Congress of the European Society for Cognitive Psychology (ESCOP)'01, Edinburgh, Scottland, September, 2001, p.30.
 
Gross, H.-M., Heinke, D., Boehme, H.-J., Braumann, U.-D., & Pomierski, T. (1995).
A Behaviour-oriented Approach to an Implicit ''Object-Understanding'' in Visual Attention.
In Proc. of International Conference on Neural Networks - ICNN'95, Perth, Australia.


Top of page

Others

Hamker, F., & Heinke, D. (1997).
Implementation and Comparison of Growing Neural Gas, Growing Cell Structures and Fuzzy Artmap (Report 1/97).
Schriftenreihe des Fachgebietes Neuroinformatik der TU Ilmenau, ISSN 0945-7518. Fachgebiet Neuroinformatik, University of Ilmenau.


Top of page
Back to CLP-homepage

 

 

Computational Psychology Lab, School of Psychology, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK. Tel: +44 (0)121 41 44920. Fax: 0121 41 44897.
Email: d.g.heinke.bham.ac.uk
LegalPrivacyAccessibilityContact us