Additive Factors Do Not Imply Discrete Processing Stages: A Worked Example Using Models of the Stroop Task

Previously, it has been shown experimentally that the psychophysical law known as Piéron’s Law holds for color intensity and that the size of the effect is additive with that of Stroop condition (Stafford et al., 2011). According to the additive factors method (Donders, 1868–1869/1969; Sternberg, 1998), additivity is assumed to indicate independent and discrete processing stages. We present computational modeling work, using an existing Parallel Distributed Processing model of the Stroop task (Cohen et al., 1990) and a standard model of decision making (Ratcliff, 1978). This demonstrates that additive factors can be successfully accounted for by existing single stage models of the Stroop effect. Consequently, it is not valid to infer either discrete stages or separate loci of effects from additive factors. Further, our modeling work suggests that information binding may be a more important architectural property for producing additive factors than discrete stages.

[1]  Roger Ratcliff,et al.  The Diffusion Decision Model: Theory and Data for Two-Choice Decision Tasks , 2008, Neural Computation.

[2]  Derek Besner,et al.  The stroop effect and the myth of automaticity , 1997, Psychonomic bulletin & review.

[3]  Colin M. Macleod,et al.  Interdimensional interference in the Stroop effect: uncovering the cognitive and neural anatomy of attention , 2000, Trends in Cognitive Sciences.

[4]  James L. McClelland On the time relations of mental processes: An examination of systems of processes in cascade. , 1979 .

[5]  B A Reddi Decision making: The two stages of neuronal judgement , 2001, Current Biology.

[6]  F. Donders,et al.  Over de snelheid van psychische Processen , 1868 .

[7]  Colin M. Macleod Half a century of research on the Stroop effect: an integrative review. , 1991, Psychological bulletin.

[8]  M. Platt,et al.  Neural correlates of decisions , 2002, Current Opinion in Neurobiology.

[9]  D. Pins,et al.  On the relation between stimulus intensity and processing time: Piéron’s law and choice reaction time , 1996, Perception & psychophysics.

[10]  I. J. Myung,et al.  When a good fit can be bad , 2002, Trends in Cognitive Sciences.

[11]  J Miller,et al.  Discreteness and continuity in models of human information processing. , 1990, Acta psychologica.

[12]  R. Ratcliff,et al.  A comparison of macaque behavior and superior colliculus neuronal activity to predictions from models of two-choice decisions. , 2003, Journal of neurophysiology.

[13]  R. Duncan Luce,et al.  Response Times: Their Role in Inferring Elementary Mental Organization , 1986 .

[14]  C. Bruce,et al.  Neural circuitry of judgment and decision mechanisms , 2005, Brain Research Reviews.

[15]  G. Kane Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol 1: Foundations, vol 2: Psychological and Biological Models , 1994 .

[16]  T. Stafford,et al.  Modelling Natural Action Selection: Biologically constrained action selection improves cognitive control in a model of the Stroop task , 2011 .

[17]  James L. McClelland,et al.  On the control of automatic processes: a parallel distributed processing account of the Stroop effect. , 1990, Psychological review.

[18]  安藤 広志,et al.  20世紀の名著名論:David Marr:Vision:a Computational Investigation into the Human Representation and Processing of Visual Information , 2005 .

[19]  James L. McClelland,et al.  The time course of perceptual choice: the leaky, competing accumulator model. , 2001, Psychological review.

[20]  J. Ridley Studies of Interference in Serial Verbal Reactions , 2001 .

[21]  A. Sanders Issues and trends in the debate on discrete vs. continuous processing of information , 1990 .

[22]  Tom Stafford,et al.  The role of response mechanisms in determining reaction time performance: Piéron’s law revisited , 2004, Psychonomic bulletin & review.

[23]  Roger Ratcliff,et al.  A Theory of Memory Retrieval. , 1978 .

[24]  S. Sternberg Discovering mental processing stages: The method of additive factors. , 1998 .

[25]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[26]  Jonathan D. Cohen,et al.  The physics of optimal decision making: a formal analysis of models of performance in two-alternative forced-choice tasks. , 2006, Psychological review.

[27]  Kevin Gurney,et al.  Neural networks for perceptual processing: from simulation tools to theories. , 2007, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[28]  J. Gold,et al.  Banburismus and the Brain Decoding the Relationship between Sensory Stimuli, Decisions, and Reward , 2002, Neuron.

[29]  J. Townsend,et al.  The serial-parallel dilemma: A case study in a linkage of theory and method , 2004, Psychonomic bulletin & review.

[30]  E. Stoffels On stage robustness and response selection routes: Further evidence , 1996 .

[31]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[32]  Kevin N. Gurney,et al.  Piéron's Law Holds During Stroop Conflict: Insights Into the Architecture of Decision Making , 2011, Cogn. Sci..

[33]  M. W. van der Molen,et al.  Additive factors analysis of inhibitory processing in the stop-signal paradigm. , 2004, Brain and cognition.

[34]  Saul Sternberg,et al.  The discovery of processing stages: Extensions of Donders' method , 1969 .

[35]  James L. McClelland,et al.  Levels indeed! A response to Broadbent , 1985 .

[36]  R. Ratcliff Putting noise into neurophysiological models of simple decision making , 2001, Nature Neuroscience.

[37]  M. H. Pirenne,et al.  The sensations : their functions, processes and mechanisms , 1952 .

[38]  H Pashler,et al.  How persuasive is a good fit? A comment on theory testing. , 2000, Psychological review.

[39]  J. Gold,et al.  Neural computations that underlie decisions about sensory stimuli , 2001, Trends in Cognitive Sciences.

[40]  Robin D. Thomas Processing time predictions of current models of perception in the classic additive factors paradigm , 2006 .

[41]  G. Woodman,et al.  The Effect of Visual Search Efficiency on Response Preparation , 2008, Psychological science.