References
Akaike, H. (1974). A new look at the
statistical model identification. IEEE Transactions on Automatic
Control, 19(6), 716–723.
Arlot, S., & Celisse, A. (2010). A survey of cross-validation
procedures for model selection. Statistics Surveys, 4,
40–79. https://doi.org/10.1214/09-SS054
Blurton, S. P., Kesselmeier, M., & Gondan, M. (2012). Fast and
accurate calculations for cumulative first-passage time distributions in
wiener diffusion models. Journal of Mathematical Psychology,
56(6), 470–475. https://doi.org/https://doi.org/10.1016/j.jmp.2012.09.002
Broadbent, D. E. (1957). A mechanical model for human attention and
immediate memory. Psychological Review, 64(3),
205–215.
Brown, S., & Heathcote, A. (2008). The simplest complete model of
choice response time: Linear ballistic accumulation. Cognitive
Psychology, 57, 153–178.
Browne, M. W. (2000). Cross-validation methods. Journal of
Mathematical Psychology, 44(1), 108–132. https://doi.org/10.1006/jmps.1999.1279
Busemeyer, J. R., & Townsend, J. T. (1993). Decision field theory: A
dynamic–cognitive approach to decision making in an uncertain
environment. Psychological Review, 100(3), 432–459.
Busemeyer, J. R., & Wang, Y.-M. (2000). Model comparisons and model
selections based on generalization criterion methodology. Journal of
Mathematical Psychology, 44(1), 171–189. https://doi.org/10.1006/jmps.1999.1282
Cox, G. E., Palmeri, T. J., Logan, G. D., Smith, P. L., & Schall, J.
D. (2022). Salience by competitive and recurrent interactions: Bridging
neural spiking and computation in visual attention. Psychological
Review, 129(5), 1144–1182. https://doi.org/10.1037/rev0000366
Cox, G. E., Palmeri, T. J., Logan, G. D., Smith, P. L., & Schall, J.
D. (2024). Spiking, salience, and saccades: Using cognitive models to
bridge the gap between “how” and “why.” In B.
U. Forstmann & B. M. Turner (Eds.), An introduction to
model-based cognitive neuroscience (pp. 119–152). Springer
International Publishing. https://doi.org/10.1007/978-3-031-45271-0_6
Cox, G. E., & Shiffrin, R. M. (2024). Computational models of event
memory. In M. J. Kahana & A. Wagner (Eds.), Oxford handbook of
human memory. Oxford University Press.
Dennett, D. (1980). The milk of human intentionality. Behavioral and
Brain Sciences, 3(3), 428–430. https://doi.org/10.1017/s0140525x0000580x
Diederich, A. (1997). Dynamic stochastic models for decision making
under time constraints. Journal of Mathematical Psychology,
41(3), 260–274. https://doi.org/10.1006/jmps.1997.1167
Donkin, C., Brown, S., Heathcote, A., & Wagenmakers, E.-J. (2011).
Diffusion versus linear ballistic accumulation: Different models but the
same conclusions about psychological processes? Psychonomic Bulletin
& Review, 55, 140–151.
Elman, J. L. (1990). Finding structure in time. Cognitive
Science, 14(2), 179–211. https://doi.org/10.1207/s15516709cog1402_1
Garner, W. R., & Felfoldy, G. L. (1970). Integrality of stimulus
dimensions in various types of information processing. Cognitive
Psychology, 1, 225–241.
Gelman, A., Hwang, J., & Vehtari, A. (2014). Understanding
predictive information criteria for bayesian models. Statistics and
Computing, 24(6), 997–1016. https://doi.org/10.1007/s11222-013-9416-2
Gillespie, N. F., & Cox, G. E. (2024). Perception and memory for
novel auditory stimuli: Similarity, serial position, and list
homogeneity. PsyArXiv. https://doi.org/10.31234/osf.io/n294a
Gondan, M., Blurton, S. P., & Kesselmeier, M. (2014). Even faster
and even more accurate first-passage time densities and distributions
for the wiener diffusion model. Journal of Mathematical
Psychology, 60, 20–22. https://doi.org/https://doi.org/10.1016/j.jmp.2014.05.002
Grossberg, S. (1980). How does a brain build a cognitive code?
Psychological Review, 87(1), 1–51. https://doi.org/10.1037/0033-295X.87.1.1
Hanes, D. P., & Schall, J. D. (1996). Neural control of voluntary
movement initiation. Science, 274(5286), 427–430. https://doi.org/10.1126/science.274.5286.427
Harding, B., Goulet, M.-A., Jolin, S., Tremblay, C., Villeneuve, S.-P.,
& Durand, G. (2016). Systems factorial technology explained to
humans. The Quantitative Methods for Psychology,
12(1), 39–56.
Hartmann, R., & Klauer, K. C. (2021). Partial derivatives for the
first-passage time distribution in wiener diffusion models. Journal
of Mathematical Psychology, 103, 102550.
https://doi.org/https://doi.org/10.1016/j.jmp.2021.102550
Hartmann, R., & Klauer, K. C. (2023). WienR: Derivatives of the
first-passage time density and cumulative distribution function, and
random sampling from the (truncated) first-passage time
distribution. https://CRAN.R-project.org/package=WienR
Hebb, D. O. (1949). The organization of behavior: A
neuropsychological theory. Wiley.
Houpt, J. W., Blaha, L. M., McIntire, J. P., Havig, P. R., &
Townsend, J. T. (2014). Systems factorial technology with
R. Behavior Research Methods, 46,
307–330.
Kruschke, J. K. (2009). Highlighting: A canonical experiment. In
Psychology of learning and motivation (Vol. 51, pp. 153–185).
Elsevier.
Kruschke, J. K. (2011). Models of attentional learning. In E. M. Pothos
& A. J. Wills (Eds.), Formal approaches in categorization
(pp. 120–152). Cambridge University Press.
Landauer, T. K., & Dumais, S. T. (1997). A solution to Plato’s problem: The latent semantic analysis
theory of acquisition, induction, and representation of knowledge.
Psychological Review, 104(2), 211–240.
Mackintosh, N. J. (1975). A theory of attention: Variations in the
associability of stimuli with reinforcement. Psychological
Review, 82(4), 276–298.
Marr, D. (1982). Vision: A computational investigation into the
human representation and processing of visual information. W.H.
Freeman.
McClelland, J. L., McNaughton, B. L., & O’Reilly, R. C. (1995). Why
there are complementary learning systems in the hippocampus and
neocortex: Insights from the successes and failures of connectionist
models of learning and memory. Psychological Review,
102(3), 419–457.
Medin, D. L., Altom, M. W., Edelson, S. M., & Freko, D. (1982).
Correlated symptoms and simulated medical classification. Journal of
Experimental Psychology: Learning, Memory, and Cognition,
8(1), 37–50. https://www.proquest.com/scholarly-journals/correlated-symptoms-simulated-medical/docview/614362118/se-2
Murdock, B. B. (1982). A theory for the storage and retrieval of item
and associative information. Psychological Review,
89(3), 609–626.
Neisser, U. (1967). Cognitive psychology.
Appleton-Century-Crofts.
Nosofsky, R. M. (1986). Attention, similarity, and the
identification-categorization relationship. Journal of Experimental
Psychology: General, 115(1), 39–57.
Nosofsky, R. M. (1992). Similarity scaling and cognitive process models.
Annual Review of Psychology, 43, 25–53.
Nosofsky, R. M., Cox, G. E., Cao, R., & Shiffrin, R. M. (2014). An
exemplar-familiarity model predicts short-term and long-term probe
recognition across diverse forms of memory search. Journal of
Experimental Psychology: Learning, Memory, and Cognition,
40(6), 1524–1539.
Nosofsky, R. M., Little, D. R., Donkin, C., & Fific, M. (2011).
Short-term memory scanning viewed as exemplar-based categorization.
Psychological Review, 118(2), 280–315.
Nosofsky, R. M., & Palmeri, T. J. (1997). An exemplar-based random
walk model of speeded classification. Psychological Review,
104(2), 266–300.
O’Connor, C., & Weatherall, J. O. (2018). Scientific polarization.
European Journal for Philosophy of Science, 8(3),
855–875. https://doi.org/10.1007/s13194-018-0213-9
Philiastides, M. G., Ratcliff, R., & Sajda, P. (2006). Neural
representation of task difficulty and decision making during perceptual
categorization: A timing diagram. Journal of Neuroscience,
26(35), 8965–8975. https://doi.org/10.1523/JNEUROSCI.1655-06.2006
Piironen, J., & Vehtari, A. (2017). Comparison of bayesian
predictive methods for model selection. Statistics and
Computing, 27(3), 711–735. https://doi.org/10.1007/s11222-016-9649-y
Posner, M. I., & Keele, S. W. (1968). On the genesis of abstract
ideas. Journal of Experimental Psychology, 77,
353–363. https://doi.org/10.1037/h0025953
Purcell, B. A., Heitz, R. P., Cohen, J. Y., Schall, J. D., Logan, G. D.,
& Palmeri, T. J. (2010). Neurally constrained modeling of perceptual
decision making. Psychological Review, 117(4),
1113–1143. https://doi.org/10.1037/a0020311
Purcell, B. A., Schall, J. D., Logan, G. D., & Palmeri, T. J.
(2012). From salience to saccades: Multiple-alternative gated stochastic
accumulator model of visual search. Journal of Neuroscience,
32(10), 3433–3446. https://doi.org/10.1523/JNEUROSCI.4622-11.2012
Raab, D. H. (1962). Statistical facilitation of simple reaction times.
Transactions of the New York Academy of Sciences, 24(5
Series II), 574–590.
Rae, B., Heathcote, A., Donkin, C., Averell, L., & Brown, S. (2014).
The hare and the tortoise: Emphasizing speed can change the evidence
used to make decisions. Journal of Experimental Psychology:
Learning, Memory, and Cognition, 40(5), 1226–1243.
Raftery, A. E. (1995). Bayesian model selection in social research.
Sociological Methodology, 25, 111–163. https://doi.org/10.2307/271063
Ratcliff, R. (1978). A theory of memory retrieval. Psychological
Review, 85(2), 59–108.
Ratcliff, R., & Rouder, J. N. (1998). Modeling response times for
two-choice decisions. Psychological Science, 9(5),
347–356.
Rescorla, R. A., & Wagner, A. R. (1972). A theory of
Pavlovian conditioning: Variations in the effectiveness of
reinforcement and nonreinforcement. In A. H. Black & W. F. Prokasy
(Eds.), Classical conditioning II: Current research and
theory (pp. 64–99). Appleton–Century–Crofts.
Rogers, T. T., & McClelland, J. L. (2004). Semantic cognition: A
parallel distributed processing approach. MIT Press.
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning
internal representations by error propagation. In D. E. Rumelhart &
J. L. McClelland (Eds.), Parallel distributed processing: Vol.
I. The MIT Press.
Schall, J. D. (2004). On building a bridge between brain and behavior.
Annual Review of Psychology, 55(1), 23–50. https://doi.org/10.1146/annurev.psych.55.090902.141907
Schwarz, G. (1978). Estimating the dimension of a model. The Annals
of Statistics, 6(2), 461–464.
Shadlen, M. N., & Newsome, W. T. (2001). Neural basis of a
perceptual decision in the parietal cortex (area LIP) of the rhesus
monkey. Journal of Neurophysiology, 86(4), 1916–1936.
https://doi.org/10.1152/jn.2001.86.4.1916
Shepard, R. N. (1962a). The analysis of proximities:
Multidimensional scaling with an unknown distance function.
I. Psychometrika, 27(2), 125–140.
https://doi.org/https://doi.org/10.1007/BF02289630
Shepard, R. N. (1962b). The analysis of proximities:
Multidimensional scaling with an unknown distance function.
II. Psychometrika, 27(3), 219–246.
https://doi.org/https://doi.org/10.1007/BF02289621
Singmann, H., Kellen, D., Cox, G. E., Chandramouli, S. H., Davis-Stober,
C. P., Dunn, J. C., Gronau, Q. F., Kalish, M. L., McMullin, S. D.,
Navarro, D. J., & Shiffrin, R. M. (2022). Statistics in the service
of science: Don’t let the tail wag the dog. Computational Brain
& Behavior.
Smith, E. R., & Conrey, F. R. (2007). Agent-based modeling: A new
approach for theory building in social psychology. Personality and
Social Psychology Review, 11(1), 87–104. https://doi.org/10.1177/1088868306294789
Smith, P. L. (2000). Stochastic dynamic models of response time and
accuracy: A foundational primer. Journal of Mathematical
Psychology, 44(3), 408–463.
Stone, M. (1977). An asymptotic equivalence of choice of model by
cross-validation and Akaike’s criterion.
Journal of the Royal Statistical Society. Series B
(Methodological), 39(1), 44–47.
Teller, D. Y. (1984). Linking propositions. Vision Research,
24(10), 1233–1246. https://doi.org/10.1016/0042-6989(84)90178-0
Teodorescu, A. R., & Usher, M. (2013). Disentangling decision
models: From independence to competition. Psychological Review,
120(1), 1–38.
Tillman, G., Van Zandt, T., & Logan, G. D. (2020). Sequential
sampling models without random between-trial variability: The racing
diffusion model of speeded decision making. Psychonomic Bulletin
& Review, 27(5), 911–936. https://doi.org/10.3758/s13423-020-01719-6
Townsend, J. T., & Nozawa, G. (1995). Spatio-temporal properties of
elementary perception: An investigation of parallel, serial, and
coactive theories. Journal of Mathematical Psychology,
39, 321–359.
Trueblood, J. S., Holmes, W. R., Seegmiller, A. C., Douds, J., Compton,
M., Szentirmai, E., Woodruff, M., Huang, W., Stratton, C., &
Eichbaum, Q. (2018). The impact of speed and bias on the cognitive
processes of experts and novices in medical image decision-making.
Cognitive Research: Principles and Implications, 3,
1–14.
Tuerlincx, F. (2004). The efficient computation of the cumulative
distribution and probability density functions in the diffusion model.
Behavior Research Methods, Instruments, & Computers,
36(4), 702–716.
Turner, B. M., Forstmann, B. U., Wagenmakers, E.-J., Brown, S. D.,
Sederberg, P. B., & Steyvers, M. (2013). A bayesian framework for
simultaneously modeling neural and behavioral data. NeuroImage,
72, 193–206. https://doi.org/10.1016/j.neuroimage.2013.01.048
Usher, M., & McClelland, J. L. (2001). The time course of perceptual
choice: The leaky, competing accumulator model. Psychological
Review, 108(3), 550–592.
Vehtari, A., & Lampinen, J. (2002). Bayesian model assessment and
comparison using cross-validation predictive densities. Neural
Computation, 14(10), 2439–2468. https://doi.org/10.1162/08997660260293292
Widrow, B., & Hoff, M. E. (1960). Adaptive switching circuits.
IRE WESCON Convention Record, 96–104.
Zucchini, W. (2000). An introduction to model selection. Journal of
Mathematical Psychology, 44(1), 41–61. https://doi.org/10.1006/jmps.1999.1276