Identifying the precise locus of general cognitive ability (g) in the flow of information between perception and action is an important goal of differential psychology. To localize the negative correlation between g and reaction time to a specific processing stage, we administered a speeded number-comparison task to two groups differing in average g. The participants had to respond to two stimuli in each trial, which produced the well-known slowing of the second reaction time known as the psychological refractory period. The difference in the second reaction time favoring the high-g group doubled as the stimulus onsets became very close together. This finding affirms that the faster reaction times of higher-g individuals reflect an advantage exclusively in the serial bottleneck of central processing and not in the parallel peripheral stages.
ChabrisC. F. (2007). Cognitive and neurobiological mechanisms of the law of general intelligence. In RobertsM. J. (Ed.), Integrating the mind: Domain general versus domain specific processes in higher cognition (pp. 449–491). Hove, England: Psychology Press.
2.
CoralloG.SackurJ.DehaeneS.SigmanM. (2008). Limits on introspection: Distorted subjective time during the dual-task bottleneck. Psychological Science, 19, 1110–1117.
3.
DearyI. J.DerG.FordG. (2001). Reaction times and intelligence differences: A population-based cohort study. Intelligence, 29, 389–399.
4.
DearyI. J.PenkeL.JohnsonW. (2010). The neuroscience of human intelligence differences. Nature Reviews Neuroscience, 11, 201–211.
5.
DehaeneS. (1996). The organization of brain activations in number comparison: Event-related potentials and the additive-factors method. Journal of Cognitive Neuroscience, 8, 47–68.
6.
De JongR. (1993). Multiple bottlenecks in overlapping task performance. Journal of Experimental Psychology: Human Perception and Performance, 19, 965–980.
7.
DutilhG.VandekerckhoveJ.TuerlinckxF.WagenmakersE.-J. (2009). A diffusion model decomposition of the practice effect. Psychonomic Bulletin & Review, 16, 1026–1036.
8.
EfronB.TibshiraniR. J. (1993). An introduction to the bootstrap. Boca Raton, FL: Chapman and Hall/CRC.
9.
FerreiraV. S.PashlerH. (2002). Central bottleneck influences on the processing stages of word production. Journal of Experimental Psychology: Learning, Memory, and Cognition, 28, 1187–1199.
10.
FreyM. C.DettermanD. K. (2004). Scholastic assessment or g? The relationship between the Scholastic Assessment Test and general cognitive ability. Psychological Science, 15, 373–378.
11.
GoldJ. I.ShadlenM. N. (2007). The neural basis of decision making. Annual Review of Neuroscience, 30, 535–574.
12.
GrasmanR. P. P. P.WagenmakersE.-J.van der MaasH. L. J. (2009). On the mean and variance of response times under the diffusion model with an application to parameter estimation. Journal of Mathematical Psychology, 53, 55–68.
13.
GrayJ. R.ChabrisC. F.BraverT. S. (2003). Neural mechanisms of general fluid intelligence. Nature Neuroscience, 6, 316–322.
14.
HuntE. (2005). Information processing and intelligence: Where we are and where we are going. In SternbergR. J.PretzJ. E. (Eds.), Cognition and intelligence: Identifying mechanisms of mind (pp. 1–25). Cambridge, England: Cambridge University Press.
15.
JensenA. R. (2006). Clocking the mind: Mental chronometry and individual differences. Oxford, England: Elsevier.
16.
JensenA. R.CohnS. J.CohnC. M. G. (1989). Speed of information processing in academically gifted youths and their siblings. Personality and Individual Differences, 10, 29–33.
17.
JiangY.SaxeR.KanwisherN. (2004). Functional magnetic resonance imaging provides new constraints on theories of the psychological refractory period. Psychological Science, 15, 390–396.
18.
KamienkowskiJ. E.PashlerH.DehaeneS.SigmanM. (2011). Effects of practice on task architecture: Combined evidence from interference experiments and random-walk models of decision making. Cognition, 119, 81–95.
19.
KaneM. J.ConwayA. R. A.HambrickD. Z.EngleR. W. (2007). Variation in working memory capacity as variation in executive attention and control. In ConwayA. R. A.JarroldC.KaneM. J.MiyakeA.TowseJ. N. (Eds.), Variation in working memory (pp. 21–48). New York, NY: Oxford University Press.
20.
LienM.-C.RuthruffE.JohnstonJ. C. (2006). Attentional limitations in doing two tasks at once: The search for exceptions. Current Directions in Psychological Science, 15, 89–93.
21.
LienM.-C.SchweickertR.ProctorR. W. (2003). Task switching and response correspondence in the psychological refractory period paradigm. Journal of Experimental Psychology: Human Perception and Performance, 29, 697–712.
22.
LuceR. D. (1986). Response times: Their role in inferring elementary mental organization. New York, NY: Oxford University Press.
23.
O’ReillyR. C. (2006). Biologically based computational models of high-level cognition. Science, 314, 91–94.
24.
PashlerH. (1998). The psychology of attention. Cambridge, MA: MIT Press.
25.
PashlerH.JohnstonJ. C. (1989). Chronometric evidence for central postponement in temporally overlapping tasks. Quarterly Journal of Experimental Psychology, 41A, 19–45.
26.
RatcliffR.McKoonG. (2008). The diffusion decision model: Theory and data for two-choice decision tasks. Neural Computation, 20, 873–922.
27.
RatcliffR.RouderJ. M. (1998). Modeling response times for two-choice decisions. Psychological Science, 9, 347–356.
28.
RatcliffR.ThaparA.McKoonG. (2010). Individual differences, aging, and IQ in two-choice tasks. Cognitive Psychology, 60, 127–157.
29.
RatcliffR.ThaparA.McKoonG. (2011). Effects of aging and IQ on item and associative memory. Journal of Experimental Psychology: General, 140, 464–487.
30.
SandersA. F. (1998). Elements of human performance: Reaction processes and attention in human skill. Mahwah, NJ: Erlbaum.
31.
SchmiedekF.OberauerK.WilhelmO.SüßH.-M.WittmanW. W. (2007). Individual differences in components of reaction time distributions and their relations to working memory and intelligence. Journal of Experimental Psychology: General, 136, 414–429.
32.
SchweickertR.TownsendJ. T. (1989). A trichotomy: Interactions of factors prolonging sequential and concurrent mental processes in stochastic discrete mental (PERT) networks. Journal of Mathematical Psychology, 33, 328–347.
SigmanM.DehaeneS. (2008). Brain mechanisms of serial and parallel processing during dual-task performance. Journal of Neuroscience, 28, 7585–7598.
36.
SmuldersF. T. Y.MillerJ. O. (2012). The lateralized readiness potential. In LuckS. J.KappenmanE. S. (Eds.), The Oxford handbook of event-related potential components (pp. 209–229). New York, NY: Oxford University Press.
37.
TombuM. N.AsplundC. L.DuxP. E.GodwinD.MartinJ. W.MaroisR. (2011). A unified attentional bottleneck in the human brain. Proceedings of the National Academy of Sciences, USA, 108, 13426–13431.
38.
van RavenzwaaijD.OberauerK. (2009). How to use the diffusion model: Parameter recovery of three methods: EZ, fast-dm, and DMAT. Journal of Mathematical Psychology, 53, 463–473.
39.
WilhelmO.OberauerK. (2006). Why are reasoning ability and working memory capacity related to mental speed? An investigation of stimulus-response compatibility in choice reaction time tasks. European Journal of Cognitive Psychology, 18, 18–50.
40.
WongK. F. E. (2002). The relationship between attentional blink and psychological refractory period. Journal of Experimental Psychology: Human Perception and Performance, 28, 54–71.
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
