To explore hypotheses based on Stanovich’s proposal that analytic processing comprises a reflective-level, an algorithmic level, and specific mindware, 342 participants completed measures of thinking dispositions, general ability (GA), numeracy, and probabilistic and nonprobabilistic reasoning. In a control condition, numeracy predicted probabilistic reasoning at high levels of both thinking dispositions and GA, and GA predicted nonprobabilistic reasoning at high levels of thinking dispositions. In a logic instruction condition, numeracy predicted probabilistic reasoning when GA was high, and GA affected nonprobabilistic reasoning directly. Thinking dispositions moderated neither relationship. Instead, instructions facilitated reasoning for low thinking disposition/high-ability participants, suggesting that logic instructions cued low thinking disposition individuals to engage in higher order reflective processing. The evidence is consistent with the proposals that reflective processes are essential to the allocation of algorithmic resources, and algorithmic resources are necessary for effective mindware implementation.
AmselE.KlaczynskiP. A.JohnstonA.BenchS.CloseJ.SadlerE.WalkerR. (2008).
A dual-process account of the development of scientific reasoning: The nature and development of metacognitive intercession skills. Cognitive Development,
23, 452–471.
2.
BagoB.De NeysW. (2017).
Fast logic?: Examining the time course assumption of dual process theory. Cognition,
158, 90–109.
3.
De NeysW. (2012).
Bias and conflict: A case for logical intuitions. Perspectives on Psychological Science,
7, 28–38.
4.
De NeysW. (2015).
Heuristic bias and conflict detection during thinking. Psychology of Learning and Motivation,
62, 1–32.
5.
EpsteinS.LipsonA.HolsteinC.HuhE. (1992).
Irrational reactions of negative outcomes: Evidence for two conceptual systems. Journal of Personality and Social Psychology,
62, 328–339.
6.
EpsteinS.PaciniR.Denes-RajV.HeierH. (1995). Individual differences in rational and analytical information processing (Unpublished manuscript). University of Massachusetts, USA.
7.
EvansJ. S. B. T. (2008).
Dual-processing accounts of reasoning, judgment and social cognition. Annual Review of Psychology,
59, 255–278.
8.
EvansJ. S. B. T. (2011).
Dual process theories of reasoning: Contemporary issues and developmental applications. Developmental Review,
31, 86–102.
9.
EvansJ. S. B. T. (2012).
Dual-process theories of reasoning: Facts and fallacies. In K.HolyoakR. G.Morrison (Eds.), The Oxford handbook of thinking and reasoning (pp. 115–133).
New York, NY:
Oxford University Press.
10.
EvansJ. S. B. T.HandleyS. J.NeilensH.OverD. (2010).
The influence of cognitive ability and instructional set on causal conditional inference.TheQuarterly Journal of Experimental Psychology,
63, 892–909.
11.
EvansJ. S. B. T.StanovichK. E. (2013).
Dual-process theories of higher cognition: Advancing the debate. Perspectives on Psychological Science,
8, 223–241.
12.
FerreiraM. B.Garcia-MarquesL.ShermanS. J.ShermanJ. W. (2006).
Automatic and controlled components of judgment and decision making. Journal of Personality and Social Psychology,
91, 797–813.
13.
FreyD.JohnsonE. D.De NeysW. (2017).
Individual differences in conflict detection during reasoning. The Quarterly Journal of Experimental Psychology,
71, 1188–1208.
14.
GarfieldJ. B. (2003).
Assessing statistical reasoning. Statistics Education Research Journal,
2, 22–32.
15.
HandleyS. J.NewsteadS. E.TrippasD. (2011).
Logic, beliefs, and instruction: A test of the default interventionist account of belief bias. Journal of Experimental Psychology: Learning, Memory, and Cognition,
37, 28–43.
16.
HandleyS. J.TrippasD. (2015).
Dual processes and the interplay between knowledge and structure: A new parallel processing model. Psychology of Learning and Motivation,
62, 33–58.
17.
HayesA. F. (2013a). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach.
New York, NY:
Guilford Press.
HertzogC.BleckleyM. K. (2001).
Age differences in the structure of intelligence: Influences of information processing speed. Intelligence,
29, 191–217.
20.
IrwinK. C.IrwinR. J. (2005).
Assessing development in numeracy of students from different socio-economic areas: A Rasch analysis of three fundamental tasks. Educational Studies in Mathematics,
58, 283–298.
21.
KahnemanD.FrederickS. (2002). Representativeness revisited: Attribute substitution in intuitive judgment. In GilovichT.GriffinD.KahnemanD. (Eds.), Heuristics and biases: The psychology of intuitive judgment (pp. 49–81).
Cambridge, England:
Cambridge University Press.
22.
KayaF.DelenE.BulutO. (2012).
Test review: Shipley-2 manual. Journal of Psychoeducational Assessment,
30, 593–597.
23.
KirkpatrickL. A.EpsteinS. (1992).
Cognitive experiential self-theory and subjective probability: Further evidence for two conceptual systems. Journal of Personality and Social Psychology,
63, 534–544.
24.
KlaczynskiP. A. (2001a).
Analytic and heuristic processing influences on adolescent reasoning and decision-making.Child Development,
72, 844–861.
25.
KlaczynskiP. A. (2001b).
Framing effects on adolescent task representations, analytic and heuristic processing, and decision making: Implications for the normative-descriptive gap. Journal of Applied Developmental Psychology,
22, 289–309.
26.
KlaczynskiP. A. (2014).
Heuristics and biases: Interactions among numeracy, ability, and reflectiveness predict normative responding. Frontiers in Cognitive Science,
5. doi:10.3389/fpsyg.2014.00665
27.
LehmanD. R.LempertR. O.NisbettR. E. (1988).
The effects of graduate training on reasoning: Formal discipline and thinking about everyday-life events. American Psychologist,
43, 431–442.
28.
LiberaliJ. M.ReynaV. E.FurlanS.SteinL. M.PardoS. T. (2011).
Individual differences in numeracy and cognitive reflection, with implications for biases and fallacies in probability judgment. Journal of Behavioral Decision Making,
25, 361–381.
29.
LipkusI. M.SamsaG.RimerB. K. (2001).
General performance on a numeracy scale among highly educated samples. Medical Decision Making,
21, 37–44.
30.
MacphersonR.StanovichK. E. (2007).
Cognitive ability, thinking dispositions, and instructional set as predictors of critical thinking. Learning and Individual Differences,
17, 115–127.
31.
MorsanyiK.PrimiC.ChiesiF.HandleyS. (2009).
The effects and side-effects of statistics education: Psychology students’(mis-) conceptions of probability. Contemporary Educational Psychology,
34, 210–220.
32.
NeilensH. L.HandleyS. J.NewsteadS. E. (2009).
Effects of training and instruction on analytic and belief-based reasoning processes. Thinking & Reasoning,
15, 37–68.
33.
NewmanI. R.GibbM.ThompsonV. A. (2017).
Rule-based reasoning is fast and belief-based reasoning can be slow: Challenging current explanations of belief-bias and base-rate neglect.Journal of Experimental Psychology: Learning, Memory, and Cognition,
43, 1154–1170.
34.
PattonJ.StanfordM.BarrattE. (1995).
Factor structure of the Barratt Impulsiveness Scale. Journal of Clinical Psychology,
51, 768–774.
35.
PennycookG.FugelsangJ. A.KoehlerD. J. (2015).
What makes us think? A three-stage dual-process model of analytic engagement. Cognitive Psychology,
80, 34–72.
36.
PetersE. (2012).
Beyond comprehension: The role of numeracy in judgments and decisions. Current Directions in Psychological Science,
21, 31–35.
37.
ReynaV. F.BrainerdC. J. (2008).
Numeracy, ratio bias, and denominator neglect in judgments of risk and probability. Learning and Individual Differences,
18, 89–107.
38.
ReynaV. F.LloydF. J.BrainerdC. J. (2003). Memory, development, and rationality: An integrative theory of judgment and decision making. In SchneiderS. L.ShanteauJ. (Eds.), Emerging perspectives on judgment and decision research (pp. 201–245).
New York, NY:
Cambridge University Press.
39.
ShipleyW. C.GruberC.MartinT.KleinA. M. (2010). Shipley institute of living scale (2nd ed.).
Los Angeles, CA:
Western Psychological Services.
40.
StanovichK. E. (2009). Is it time for a tri-process theory? Distinguishing the reflective and the algorithmic mind. In EvansJ. S. B. T.FrankishK. (Eds.), In two minds: Dual processes and beyond (pp. 55–88).
Oxford, England:
Oxford University Press.
41.
StanovichK. E. (2011). Rationality and the reflective mind.
New York, NY:
Oxford University Press.
42.
StanovichK. E. (2018).
Miserliness in human cognition: The interaction of detection, override and mindware. Thinking & Reasoning,
24.
43.
StanovichK. E.WestR. F. (2008).
On the relative independence of thinking biases and cognitive ability. Journal of Personality and Social Psychology,
94, 672–695.
44.
Stanovich, K. E., West, R. F., & Toplak, M. E. (2014). Rationality, intelligence, and the defining features of Type 1 and Type 2 processing. In Sherman, J. W., Gawronski, B. & Trope, Y. (Eds), Dual-Process Theories of the Social Mind (pp. 80–91). New York, NY: The Guilford Press.
45.
TerrellD. (1994).
A test of the gambler’s fallacy—Evidence from pari-mutuel games. Journal of Risk and Uncertainty,
8, 309–317.
46.
ThompsonV. A. (2013).
Why it matters: The implications of autonomous processes for dual process theories—Commentary on Evans & Stanovich (2013). Perspectives on Psychological Science,
8, 253–256.\
47.
ThompsonV. A.EvansJ. S. B. T.CampbellJ. I. (2013).
Matching bias on the selection task: It's fast and feels good. Thinking & Reasoning,
19, 431–452.
48.
ThompsonV. A.JohnsonS. C. (2014).
Conflict, metacognition, and analytic thinking. Thinking & Reasoning,
20, 215–244.
49.
ThompsonV. A.PennycookG.TrippasD.EvansJ. S. B. (2018).
Do smart people have better intuitions?Journal of Experimental Psychology: General,
147, 945–961.
50.
ThurstoneT. G. (1962). Primary mental abilities for grades (pp. 9–12).
Chicago, IL:
Science Research Associates.
51.
ToplakM. E.WestR. F.StanovichK. E. (2014).
Assessing miserly information processing: An expansion of the Cognitive Reflection Test. Thinking & Reasoning,
20, 147–168.
