Abstract
Eight initially novel objects with four features were learned by three participants over about 70 sessions in a variety of present-absent search tasks. This article analyzes and models trials with a single object presented for test. The features of the object were presented simultaneously, or successively at rates fast enough that the objects appeared to be simultaneous (inter-stimulus intervals were 16, 33, or 50 ms). Classification of a test object as target or foil required a conjunction of two features. When successively presented, features diagnostic for target presence could arrive first or last, and vice versa for features diagnostic for foil presence. Two results were particularly important: (1) the order in which target-diagnostic or foil-diagnostic features appeared produced large changes in accuracy and response times; (2) simultaneous feature presentation produced lower accuracy than sequential presentation with target-diagnostic features arriving first, despite the delay in such features arriving. The results required a dynamic model for perception and decision. The model has features perceived at independent times. It accumulates evidence at each moment based on the features perceived up to that time, and the diagnosticity of those features for classifying the test object as target or foil. The model also assumes that configurations of features provide evidence as processing continues: when all four features of an object are perceived the evidence points without error to the correct response. The results and modeling support the view that perceptual and decision processes operate concurrently and interactively during identification, recognition, and classification of well-learned objects, rather than in successive stages.
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Notes
Standard evidence accumulation processes are both time- and state-homogeneous. A number of newer approaches we discuss are time-inhomogeneous: the rate of progression through the states changes over time. We present a model best described as both time and state inhomogeneous, because the states include the features that have been perceived and these change as evidence is accumulated.
Due to discretization of time, and the use of integral response boundaries and steps, there are undesirable cases that arise. For example, when the response boundaries are equally spaced from the starting position, e.g., at A = +2 and B = −2, the process will always finish after an even number of steps. In order to smooth the process, we allow the walk at each epoch to stay with some probability in the same position, rather than take a step.
Parameters 2–5 must have integer values, so their posterior distributions are not normally distributed.
References
Brockdorff, N., & Lamberts, K. (2000). A feature-sampling account of the time course of old-new recognition judgments. Journal of Experimental Psychology. Learning, Memory, and Cognition, 26(1), 77–102. https://doi.org/10.1037/0278-7393.26.1.77.
Cohen, A. L., & Nosofsky, R. M. (2003). An extension of the exemplar-based random-walk model to separable-dimension stimuli. Journal of Mathematical Psychology, 47(2), 150–165. https://doi.org/10.1016/S0022-2496(02)00031-7.
Cousineau, D., & Shiffrin, R. M. (2004). Termination of a visual search with large display size effects. Spatial Vision, 17(4–5), 327–352. https://doi.org/10.1163/1568568041920104.
Cousineau, D., Donkin, C., & Dumesnil, E. (2015). Unitization of features following extended training in a visual search task. Cognitive Modeling in Perception and Memory: A Festschrift for Richard M. Shiffrin, 3–15.
Cox, G. E., & Shiffrin, R. M. (2017). A dynamic approach to recognition memory. Psychological Review, 124(6), 795–860. https://doi.org/10.1037/rev0000076.
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.
Diederich, A. (2015). Decision and choice: Sequential decision making. In International Encyclopedia of the Social & Behavioral Sciences: Second edition (pp. 906–910). Elsevier Inc. https://doi.org/10.1016/B978-0-08-097086-8.43089-8
Diederich, A. (2016). A multistage attention-switching model account for payoff effects on perceptual decision tasks with manipulated processing order. Decision, 3(2), 81–114. https://doi.org/10.1037/dec0000041.
Diederich, A., & Busemeyer, J. R. (2006). Bound-change, drift-rate-change, or two-stage-processing hypothesis. Perception & Psychophysics, 68(2), 194–207 Retrieved from https://link.springer.com/content/pdf/10.3758/BF03193669.pdf.
Diederich, A., & Colonius, H. (2020). A two-stage diffusion modeling approach to the compelled-response task. BioRxiv.
Diederich, A., & Oswald, P. (2014). Sequential sampling model for multiattribute choice alternatives with random attention time and processing order. Frontiers in Human Neuroscience, 8(September), 1–13. https://doi.org/10.3389/fnhum.2014.00697.
Ditterich, J. (2006). Evidence for time-variant decision making. European Journal of Neuroscience, 24(12), 3628–3641. https://doi.org/10.1111/j.1460-9568.2006.05221.x.
Dosher, B. A. (1984). Discriminating preexperimental (semantic) from learned (episodic) associations: A speed-accuracy study. Cognitive Psychology, 16(4), 519–555. https://doi.org/10.1016/0010-0285(84)90019-7.
Dutilh, G., Annis, J., Brown, S. D., Cassey, P., Evans, N. J., Grasman, R. P. P. P., Hawkins, G. E., Heathcote, A., Holmes, W. R., Krypotos, A. M., Kupitz, C. N., Leite, F. P., Lerche, V., Lin, Y. S., Logan, G. D., Palmeri, T. J., Starns, J. J., Trueblood, J. S., van Maanen, L., van Ravenzwaaij, D., Vandekerckhove, J., Visser, I., Voss, A., White, C. N., Wiecki, T. V., Rieskamp, J., & Donkin, C. (2019). The quality of response time data inference: A blinded, collaborative assessment of the validity of cognitive models. Psychonomic Bulletin and Review, 26(4), 1051–1069. https://doi.org/10.3758/s13423-017-1417-2.
Eriksen, B. A., & Eriksen, C. W. (1974). Effects of noise letters upon the identification of a target letter in a nonsearch task. Perception & Psychophysics, 16(1), 143–149. https://doi.org/10.3758/BF03203267.
Evans, N. J., Tillman, G., & Wagenmakers, E. J. (2020). Systematic and random sources of variability in perceptual decision-making: Comment on Ratcliff, Voskuilen, and McKoon (2018). Psychological Review, 127(5), 932–944. https://doi.org/10.1037/rev0000192.
Fifić, M., Nosofsky, R. M., & Townsend, J. T. (2008a). Information-processing architectures in multidimensional classification: A validation test of the systems factorial technology. Journal of Experimental Psychology: Human Perception and Performance, 34(2), 356–375. https://doi.org/10.1037/0096-1523.34.2.356.
Fifić, M., Townsend, J. T., & Eidels, A. (2008b). Studying visual search using systems factorial methodology with target–distractor similarity as the factor. Perception & Psychophysics, 70(4), 583–603.
Friedman, J., Brown, S., & Finkbeiner, M. (2013). Linking cognitive and reaching trajectories via intermittent movement control. Journal of Mathematical Psychology, 57(3–4), 140–151. https://doi.org/10.1016/j.jmp.2013.06.005.
Garner, W. R. (1976). Interaction of stimulus dimensions in concept and choice processes. Cognitive Psychology, 8(1), 98–123. https://doi.org/10.1016/0010-0285(76)90006-2.
Goulet, M. A., & Cousineau, D. (2021). The cognitive architecture of processes responsible to assess similarity and clarity in a comparison task. Acta Psychologica, 212, 103207. https://doi.org/10.1016/j.actpsy.2020.103207.
Gronlund, S. D., & Ratcliff, R. (1989). Time course of item and associative information: Implications for global memory models. Journal of Experimental Psychology. Learning, Memory, and Cognition, 15(5), 846–858. https://doi.org/10.1037/0278-7393.15.5.846.
Hawkins, G., & Heathcote, A. (2020). Racing against the clock: evidence-based vs. time-based decisions. https://doi.org/10.31234/osf.io/m4uh7.
Heit, E., Brockdorff, N., & Lamberts, K. (2003). Adaptive changes of response criterion in recognition memory. Psychonomic Bulletin and Review, 10(3), 718–723. https://doi.org/10.3758/BF03196537.
Heitz, R. P. (2014). The speed-accuracy tradeoff: History, physiology, methodology, and behavior. Frontiers in Neuroscience, (8 JUN), 1–19. https://doi.org/10.3389/fnins.2014.00150.
Holmes, W. R., Trueblood, J. S., & Heathcote, A. (2016). A new framework for modeling decisions about changing information: The piecewise linear ballistic accumulator model. Cognitive Psychology, 85, 1–29. https://doi.org/10.1016/j.cogpsych.2015.11.002.
Krueger, L. E. (1978). A theory of perceptual matching. Psychological Review, 85(4), 278–304. https://doi.org/10.1037/0033-295X.85.4.278.
Lamberts, K. (1995). Categorization under time pressure. Journal of Experimental Psychology: General, 124(2), 161–180. https://doi.org/10.1037/0096-3445.124.2.161.
Lamberts, K. (2000). Information-accumulation theory of speeded categorization. Psychological Review, 107(2), 227–260. https://doi.org/10.1037/0033-295X.107.2.227.
Lamberts, K., & Freeman, R. P. J. (1999a). Building object representations from parts: Tests of a stochastic sampling model. Journal of Experimental Psychology: Human Perception and Performance, 25(4), 904–926. https://doi.org/10.1037/0096-1523.25.4.904.
Lamberts, K., & Freeman, R. P. J. (1999b). Categorization of briefly presented objects. Psychological Research, 62, 107–117.
Lamberts, K., Brockdorff, N., & Heit, E. (2002). Perceptual processes in matching and recognition of complex pictures. Journal of Experimental Psychology: Human Perception and Performance, 28(5), 1176–1191. https://doi.org/10.1037/0096-1523.28.5.1176.
Mallik, P. R., Allen, P. A., Lien, M. C., Jardin, E., Houston, M. L., Houston, J. R., & Jurosic, B. K. (2021). An electrophysiological study of aging and perceptual letter-matching. Experimental Aging Research, 47(1), 92–108. https://doi.org/10.1080/0361073X.2020.1848329.
Nordfang, M., & Wolfe, J. M. (2014). Guided search for triple conjunctions. Attention, Perception, & Psychophysics, 76(6), 1535–1559. https://doi.org/10.3758/s13414-014-0715-2.
Nosofsky, R. M. (1986). Attention, similarity, and the identification–categorization relationship. Journal of Experimental Psychology: General, 115(1), 39–57. https://doi.org/10.1037/0096-3445.115.1.39.
Nosofsky, R. M., & Palmeri, T. J. (1997). An exemplar-based random walk model of speeded classification. Psychological Review, 104(2), 266–300. https://doi.org/10.1037/0033-295X.104.2.266.
Pelli, D. G., Farell, B., & Moore, D. C. (2003). The remarkable inefficiency of word recognition. Nature, 423(6941), 752–756. https://doi.org/10.1038/nature01516.
Pomerantz, J. R., Sager, L. C., & Stoever, R. J. (1977). Perception of wholes and of their component parts: Some configural superiority effects. Journal of Experimental Psychology: Human Perception and Performance, 3(3), 422–435. https://doi.org/10.1037/0096-1523.3.3.422.
Proctor, R. W. (1986). Response bias, criteria settings, and the fast-same phenomenon. A reply to Ratcliff. Psychological Review, 93(4), 473–477. https://doi.org/10.1037/0033-295X.93.4.473.
Ratcliff, R. (1978). A theory of memory retrieval. Psychological Review, 85(2), 59–108. https://doi.org/10.1037/0033-295X.85.2.59.
Ratcliff, R. (1985). Theoretical interpretations of the speed and accuracy of positive and negative responses. Psychological Review, 92(2), 212–225. https://doi.org/10.1037/0033-295X.92.2.212.
Ratcliff, R. (1987). More on the speed and accuracy of positive and negative responses. Psychological Review, 94(2), 277–280. https://doi.org/10.1037/0033-295X.94.2.277.
Ratcliff, R., & McKoon, G. (1982). Speed and accuracy in the processing of false statements about semantic information. Journal of Experimental Psychology: Learning, Memory, and Cognition, 8(1), 16–36. https://doi.org/10.1037/0278-7393.8.1.16.
Ratcliff, R., & Rouder, J. N. (1998). Modeling response times for two-choice decisions. Psychological Science, 9(5), 347–356. https://doi.org/10.1111/1467-9280.00067.
Ratcliff, R., & Smith, P. L. (2004). A comparison of sequential sampling models for two-choice reaction time. Psychological Review, 111(2), 333–367. https://doi.org/10.1037/0033-295X.111.2.333.
Ratcliff, R., McKoon, G., & Gomez, P. (2004a). A diffusion model account of the lexical decision task. Psychological Review, 111(1), 159–182. https://doi.org/10.1037/0033-295X.111.1.159.
Ratcliff, R., Perea, M., Colangelo, A., & Buchanan, L. (2004b). A diffusion model account of normal and impaired readers. Brain and Cognition, 55(2), 374–382. https://doi.org/10.1016/j.bandc.2004.02.051.
Ratcliff, R., Voskuilen, C., & McKoon, G. (2018). Internal and external sources of variability in perceptual decision-making. Psychological Review, 125(1), 33–46. https://doi.org/10.1037/rev0000080.
Schneider, W., & Shiffrin, R. M. (1977). Controlled and automatic human information processing: I. Detection, search, and attention. Psychological Review, 84(1), 1–66. https://doi.org/10.1037/0033-295X.84.1.1.
Sewell, D. K., & Smith, P. L. (2012). Attentional control in visual signal detection: Effects of abrupt-onset and no-onset stimuli. Journal of Experimental Psychology: Human Perception and Performance, 38(4), 1043–1068. https://doi.org/10.1037/a0026591.
Shiffrin, R. M., & Lightfoot, N. (1997). Perceptual learning of alphanumeric-like characters. The psychology of learning and motivation, vol 36. Perceptual learning, 45–81.
Shiffrin, R. M., & Schneider, W. (1977). Controlled and automatic human information processing: II. Perceptual learning, automatic attending and a general theory. Psychological Review, 84(2), 127–190. https://doi.org/10.1037/0033-295X.84.2.127.
Smith, P. L. (1995). Psychophysically principled models of visual simple reaction time. Psychological Review, 102(3), 567–593. https://doi.org/10.1037/0033-295X.102.3.567.
Smith, P. L., & Lilburn, S. D. (2020). Vision for the blind: Visual psychophysics and blinded inference for decision models. Psychonomic Bulletin & Review, 27, 882–910. https://doi.org/10.3758/s13423-020-01742-7.
Smith, P. L., & Ratcliff, R. (2009). An integrated theory of attention and decision making in visual signal detection. Psychological Review, 116(2), 283–317. https://doi.org/10.1037/a0015156.
Smith, P. L., & Wolfgang, B. J. (2004). The attentional dynamics of masked detection. Journal of Experimental Psychology. Human Perception and Performance, 30(1), 119–136. https://doi.org/10.1037/0096-1523.30.1.119.
Smith, P. L., Ellis, R., Sewell, D. K., & Wolfgang, B. J. (2010). Cued detection with compound integration-interruption masks reveals multiple attentional mechanisms. Journal of Vision, 10(5), 1–28. https://doi.org/10.1167/10.5.3.
Smith, P. L., Ratcliff, R., & McKoon, G. (2014). The diffusion model is not a deterministic growth model: Comment on Jones and Dzhafarov (2014). Psychological Review, 121(4), 679–688. https://doi.org/10.1037/a0037667.
Thapar, A., Ratcliff, R., & McKoon, G. (2003). A diffusion model analysis of the effects of aging on letter discrimination. Psychology and Aging, 18(3), 415–429. https://doi.org/10.1037/0882-7974.18.3.415.
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 and Review, 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(4), 321–359. https://doi.org/10.1006/jmps.1995.1033.
Townsend, J. T., & Wenger, M. J. (2004). A theory of interactive parallel processing: New capacity measures and predictions for a response time inequality series. Psychological Review, 111(4), 1003–1035. https://doi.org/10.1037/0033-295X.111.4.1003.
Turner, B. M., & Sederberg, P. B. (2012). Approximate Bayesian computation with differential evolution. Journal of Mathematical Psychology, 56(5), 375–385. https://doi.org/10.1016/j.jmp.2012.06.004.
Usher, M., & McClelland, J. L. (2001). The time course of perceptual choice: The leaky, competing accumulator model. Psychological Review, 108(3), 550–592. https://doi.org/10.1037/0033-295X.108.3.550.
Wagenmakers, E.-J., Steyvers, M., Raaijmakers, J. G. W., Shiffrin, R. M., van Rijn, H., & Zeelenberg, R. (2004). A model for evidence accumulation in the lexical decision task. Cognitive Psychology, 48(3), 332–367. https://doi.org/10.1016/j.cogpsych.2003.08.001.
White, C. N., Ratcliff, R., & Starns, J. J. (2011). Diffusion models of the flanker task: Discrete versus gradual attentional selection. Cognitive Psychology, 63(4), 210–238. https://doi.org/10.1016/j.cogpsych.2011.08.001.
Yu, A. J., Dayan, P., & Cohen, J. D. (2009). Dynamics of attentional selection under conflict: Toward a rational Bayesian account. Journal of Experimental Psychology: Human Perception and Performance, 35(3), 700–717. https://doi.org/10.1037/a0013553.
Zeelenberg, R., Wagenmakers, E.-J., & Shiffrin, R. M. (2004). Nonword repetition priming in lexical decision reverses as a function of study task and speed stress. Journal of Experimental Psychology: Learning, Memory, and Cognition, 30(1), 270–277. https://doi.org/10.1037/0278-7393.30.1.270.
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Harding, S.M., Cousineau, D. & Shiffrin, R.M. Dynamic Perception of Well-Learned Perceptual Objects. Comput Brain Behav 4, 497–518 (2021). https://doi.org/10.1007/s42113-021-00107-0
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DOI: https://doi.org/10.1007/s42113-021-00107-0