Cognitive psychology

From Scholarpedia
Zhong-Lin Lu and Barbara Anne Dosher (2007), Scholarpedia, 2(8):2769. doi:10.4249/scholarpedia.2769 revision #88969 [link to/cite this article]
Jump to: navigation, search

Cognitive psychology is the scientific investigation of human cognition, that is, all our mental abilities – perceiving, learning, remembering, thinking, reasoning, and understanding. The term “cognition” stems from the Latin word “ cognoscere” or "to know". Fundamentally, cognitive psychology studies how people acquire and apply knowledge or information. It is closely related to the highly interdisciplinary cognitive science and influenced by artificial intelligence, computer science, philosophy, anthropology, linguistics, biology, physics, and neuroscience.



Cognitive psychology in its modern form incorporates a remarkable set of new technologies in psychological science. Although published inquiries of human cognition can be traced back to Aristotle’s ‘’De Memoria’’ (Hothersall, 1984), the intellectual origins of cognitive psychology began with cognitive approaches to psychological problems at the end of the 1800s and early 1900s in the works of Wundt, Cattell, and William James (Boring, 1950).

Cognitive psychology declined in the first half of the 20th century with the rise of “behaviorism" –- the study of laws relating observable behavior to objective, observable stimulus conditions without any recourse to internal mental processes (Watson, 1913; Boring, 1950; Skinner, 1950). It was this last requirement, fundamental to cognitive psychology, that was one of behaviorism's undoings. For example, lack of understanding of the internal mental processes led to no distinction between memory and performance and failed to account for complex learning (Tinklepaugh, 1928; Chomsky, 1959). These issue led to the decline of behaviorism as the dominant branch of scientific psychology and to the “Cognitive Revolution”.

The Cognitive Revolution began in the mid-1950s when researchers in several fields began to develop theories of mind based on complex representations and computational procedures (Miller, 1956; Broadbent, 1958; Chomsky, 1959; Newell, Shaw, & Simon, 1958). Cognitive psychology became predominant in the 1960s (Tulving, 1962; Sperling, 1960). Its resurgence is perhaps best marked by the publication of Ulric Neisser’s book, ‘’Cognitive Psychology’’, in 1967. Since 1970, more than sixty universities in North America and Europe have established cognitive psychology programs.


Cognitive psychology is based on two assumptions: (1) Human cognition can at least in principle be fully revealed by the scientific method, that is, individual components of mental processes can be identified and understood, and (2) Internal mental processes can be described in terms of rules or algorithms in information processing models. There has been much recent debate on these assumptions (Costall and Still, 1987; Dreyfus, 1979; Searle, 1990).


Very much like physics, experiments and simulations/modelling are the major research tools in cognitive psychology. Often, the predictions of the models are directly compared to human behaviour. With the ease of access and wide use of brain imaging techniques, cognitive psychology has seen increasing influence of cognitive neuroscience over the past decade. There are currently three main approaches in cognitive psychology: experimental cognitive psychology, computational cognitive psychology, and neural cognitive psychology.
Experimental cognitive psychology treats cognitive psychology as one of the natural sciences and applies experimental methods to investigate human cognition. Psychophysical responses, response time, and eye tracking are often measured in experimental cognitive psychology. Computational cognitive psychology develops formal mathematical and computational models of human cognition based on symbolic and subsymbolic representations, and dynamical systems. Neural cognitive psychology uses brain imaging (e.g., EEG, MEG, fMRI, PET, SPECT, Optical Imaging) and neurobiological methods (e.g., lesion patients) to understand the neural basis of human cognition. The three approaches are often inter-linked and provide both independent and complementary insights in every sub-domain of cognitive psychology.

Sub-domains of Cognitive Psychology

Traditionally, cognitive psychology includes human perception, attention, learning, memory, concept formation, reasoning, judgment and decision-making, problem solving, and language processing. For some, social and cultural factors, emotion, consciousness, animal cognition, evolutionary approaches have also become part of cognitive psychology.

  • Perception: Those studying perception seek to understand how we construct subjective interpretations of proximal information from the environment. Perceptual systems are composed of separate senses (e.g., visual, auditory, somatosensory) and processing modules (e.g., form, motion; Livingston & Hubel, 1988; Ungerleider & Mishkin, 1982; Julesz, 1971) and sub-modules (e.g., Lu & Sperling, 1995) that represent different aspects of the stimulus information. Current research also focuses on how these separate representations and modules interact and are integrated into coherent percepts. Cognitive psychologists have studied these properties empirically with psychophysical methods and brain imaging. Computational models, based on physiological principles, have been developed for many perceptual systems (Grossberg & Mingolla, 1985; Marr, 1982; Wandell, 1995).
  • Attention: Attention solves the problem of information overload in cognitive processing systems by selecting some information for further processing, or by managing resources applied to several sources of information simultaneously (Broadbent, 1957; Posner, 1980; Treisman, 1969). Empirical investigation of attention has focused on how and why attention improves performance, or how the lack of attention hinders performance (Posner, 1980; Weichselgartner & Sperling, 1987; Chun & Potter, 1995; Pashler, 1999). The theoretical analysis of attention has taken several major approaches to identify the mechanisms of attention: the signal-detection approach (Lu & Dosher, 1998) and the similarity-choice approach (Bundesen, 1990; Logan, 2004). Related effects of biased competition have been studied in single cell recordings in animals (Reynolds, Chelazzi, & Desimone, 1999). Brain imaging studies have documented effects of attention on activation in early visual cortices, and have investigated the networks for attention control (Kanwisher & Wojciulik, 2000).
  • Learning: Learning improves the response of the organism to the environment. Cognitive psychologists study which new information is acquired and the conditions under which it is acquired. The study of learning begins with an analysis of learning phenomena in animals (i.e., habituation, conditioning, and instrumental, contingency, and associative learning) and extends to learning of cognitive or conceptual information by humans (Kandel, 1976; Estes, 1969; Thompson, 1986). Cognitive studies of implicit learning emphasize the largely automatic influence of prior experience on performance, and the nature of procedural knowledge (Roediger, 1990). Studies of conceptual learning emphasize the nature of the processing of incoming information, the role of elaboration, and the nature of the encoded representation (Craik, 2002). Those using computational approaches have investigated the nature of concepts that can be more easily learned, and the rules and algorithms for learning systems (Holland, Holyoak, Nisbett, & Thagard, 1986). Those using lesion and imaging studies investigate the role of specific brain systems (e.g., temporal lobe systems) for certain classes of episodic learning, and the role of perceptual systems in implicit learning (Tulving, Gordon Hayman, & MacDonald, 1991; Gabrieli, Fleischman, Keane, Reminger, & Morell, 1995; Grafton, Hazeltine, & Ivry, 1995).
  • Memory: The study of the capacity and fragility of human memory is one of the most developed aspects of cognitive psychology. Memory study focuses on how memories are acquired, stored, and retrieved. Memory domains have been functionally divided into memory for facts, for procedures or skills, and working and short-term memory capacity. The experimental approaches have identified dissociable memory types (e.g., procedural and episodic; Squire & Zola, 1996) or capacity limited processing systems such as short-term or working memory (Cowan, 1995; Dosher, 1999). Computational approaches describe memory as propositional networks, or as holographic or composite representations and retrieval processes (Anderson, 1996, Shiffrin & Steyvers, 1997). Brain imaging and lesion studies identify separable brain regions active during storage or retrieval from distinct processing systems (Gabrieli, 1998).
  • Concept Formation: Concept or category formation refers to the ability to organize the perception and classification of experiences by the construction of functionally relevant categories. The response to a specific stimulus (i.e., a cat) is determined not by the specific instance but by classification into the category and by association of knowledge with that category (Medin & Ross, 1992). The ability to learn concepts has been shown to depend upon the complexity of the category in representational space, and by the relationship of variations among exemplars of concepts to fundamental and accessible dimensions of representation (Ashby, 2000). Certain concepts largely reflect similarity structures, but others may reflect function, or conceptual theories of use (Medin, 1989). Computational models have been developed based on aggregation of instance representations, similarity structures and general recognition models, and by conceptual theories (Barsalou, 2003). Cognitive neuroscience has identified important brain structures for aspects or distinct forms of category formation (Ashby, Alfonso-Reese, Turken, and Waldron, 1998).
  • Judgment and decision: Human judgment and decision making is ubiquitous – voluntary behavior implicitly or explicitly requires judgment and choice. The historic foundations of choice are based in normative or rational models and optimality rules, beginning with expected utility theory (von Neumann & Morgenstern 1944; Luce, 1959). Extensive analysis has identified widespread failures of rational models due to differential assessment of risks and rewards (Luce and Raiffa, 1989), the distorted assessment of probabilities (Kahneman & Tversky, 1979), and the limitations in human information processing (i.e., Russo & Dosher, 1983). New computational approaches rely on dynamic systems analyses of judgment and choice (Busemeyer & Johnson, 2004), and Bayesian belief networks that make choices based on multiple criteria (Fenton & Neil, 2001) for more complex situations. The study of decision making has become an active topic in cognitive neuroscience (Bechara, Damasio and Damasio, 2000).
  • ‘’’Reasoning:’’’ Reasoning is the process by which logical arguments are evaluated or constructed. Original investigations of reasoning focused on the extent to which humans correctly applied the philosophically derived rules of inference in deduction (i.e., A implies B; If A then B), and the many ways in which humans fail to appreciate some deductions and falsely conclude others. These were extended to limitations in reasoning with syllogisms or quantifiers (Johnson-Laird, Byne and Schaeken, 1992; Rips and Marcus, 1977). Inductive reasoning, in contrast, develops a hypothesis consistent with a set of observations or reasons by analogy (Holyoak and Thagard, 1995). Often reasoning is affected by heuristic judgments, fallacies, and the representativeness of evidence, and other framing phenomena (Kahneman, Slovic, Tversky, 1982). Computational models have been developed for inference making and analogy (Holyoak and Thagard, 1995), logical reasoning (Rips and Marcus, 1977), and Bayesian reasoning (Sanjana and Tenenbaum, 2003).
  • Problem Solving: The cognitive psychology of problem solving is the study of how humans pursue goal directed behavior. The computational state-space analysis and computer simulation of problem solving of Newell and Simon (1972) and the empirical and heuristic analysis of Wickelgren (1974) together have set the cognitive psychological approach to problem solving. Solving a problem is conceived as finding operations to move from the initial state to a goal state in a problem space using either algorithmic or heuristic solutions. The problem representation is critical in finding solutions (Zhang, 1997). Expertise in knowledge rich domains (i.e., chess) also depends on complex pattern recognition (Gobet & Simon, 1996). Problem solving may engage perception, memory, attention, and executive function, and so many brain areas may be engaged in problem solving tasks, with an emphasis on pre-frontal executive functions.
  • Language Processing: While linguistic approaches focus on the formal structures of languages and language use (Chomsky, 1965), cognitive psychology has focused on language acquisition, language comprehension, language production, and the psychology of reading (Kintsch 1974; Pinker, 1994; Levelt, 1989). Psycholinguistics has studied encoding and lexical access of words, sentence level processes of parsing and representation, and general representations of concepts, gist, inference, and semantic assumptions. Computational models have been developed for all of these levels, including lexical systems, parsing systems, semantic representation systems, and reading aloud (Seidenberg, 1997; Coltheart, Rastle, Perry, Langdon, & Ziegler, 2001; Just, Carpenter, and Woolley, 1982; Thorne, Bratley & Dewar, 1968; Schank and Abelson, 1977; Massaro, 1998). The neuroscience of language has a long history in the analysis of lesions (Wernicke, 1874; Broca, 1861), and has also been extensively studied with cognitive imaging (Posner et al, 1988).


Cognitive psychology research has produced an extensive body of principles, representations, and algorithms. Successful applications range from custom-built expert systems to mass-produced software and consumer electronics: (1) Development of computer interfaces that collaborate with users to meet their information needs and operate as intelligent agents, (2) Development of a flexible information infrastructure based on knowledge representation and reasoning methods, (3) Development of smart tools in the financial industry, (4) Development of mobile, intelligent robots that can perform tasks usually reserved for humans, (5) Development of bionic components of the perceptual and cognitive neural system such as cochlear and retinal implants.


  • Anderson, J.R. (1996) The architecture of Cognition. Mahwah, NJ: L. Erlbaum Associates.
  • Ashby, F. G. (2000) A stochastic version of general recognition theory. Journal of Mathematical Psychology 44: 310-329.
  • Ashby,F. G., Alfonso-Reese, L. A., Turken, A. U., & Waldron, E. M. (1998) A neuropsychological theory of multiple systems in category learning. Psychological Review 105: 442-481.
  • Barsalou, L.W. (2003) Abstraction in perceptual symbol systems. Philosophical Transactions of the Royal Society of London: Biological Sciences 358:.
  • Bechara, A., Damasio, H. and Damasio, A. (2000) Emotion, decision making and the orbitofrontal cortex. Cerebral Cortex 10: 295-307.
  • Boring, E. G. (1950). A history of experimental psychology (2nd ed.). New York: Appleton-Century-Crofts.
  • Broadbent, D. E. (1957) A mechanical model for human attention and immediate memory. Psychological Review 64: 205-215.
  • Bundesen, C. (1990) A Theory Of Visual-Attention. Psychological Review 97: 523-547.
  • Busemeyer, J. R., & Johnson, J. G. (2004). Computational models of decision making. In D. Koehler & N. Harvey (Eds.), Handbook of judgment and decision making (pp. 133–154). Oxford, England: Blackwell.
  • Chomsky, N. (1959) Review of Verbal Behavior, by B.F. Skinner. Language 35: 26-57.
  • Chomsky, N. (1965) Aspects of the theory of syntax. Cambridge, MA: MIT Press. |
  • Chun, M. M. and Potter, M. C. (1995) A two-stage model for multiple target detection in rapid serial visual presentation. Journal of Experimental Psychology: Human Perception & Performance 21: 109-127.
  • Coltheart, M., Rastle, K., Perry, C., Langdon, R., & Ziegler, J. (2001) DRC: A Dual Route Cascaded model of visual word recognition and reading aloud. Psychological Review , 108, 204 - 256.
  • Costall, A. and Still, A. (eds) (1987) Cognitive Psychology in Question. Brighton: Harvester Press Ltd.
  • Cowan, N. (1995) Attention and memory: an integrated framework, New York: Oxford University Press.
  • Craik, F. I. M. (2002) Levels of processing: Past, present... and future? Memory 10: 305-318.
  • Dosher, B.A. (1999) Item interference and time delays in working memory: Immediate serial recall. International Journal of Psychology Special Issue: Short term/working memory, 34: 276-284.
  • Dreyfus, H. L. (1979) What computers can't do: the limits of artificial intelligence, New York : Harper and Row.
  • Estes, W. K. (1969) Reinforcement in human learning. In J. Tapp (Ed.), Reinforcement and behavior. New York: Academic Press.
  • Fenton, N. and Neil, M. (2001) Making Decisions: using Bayesian nets and MCDA, Knowledge-based Systems 14: 307-325.
  • Gabrieli, J. D. E. (1998) Cognitive neuroscience of human memory. Annual Review of Psychology 49: 87-115.
  • Gabrieli, J.D.E., Fleischman, D.A., Keane, M.M., Reminger, S.L. and Morrell, F. (1995) Double dissociation between memory systems underlying explicit and implicit memory in the human brain. Psychological Science 6: 76-82.
  • Gobet, F. and Simon, H. A. (1996) Recall of random and distorted chess positions: implications for the theory of expertise. Memory & cognition 24: 493-503.
  • Grafton, S. T., Hazeltine, E., and Ivry, R. (1995) Functional mapping of sequence learning in normal humans. Journal of Cognitive Neuroscience, 7: 497–510.
  • Grossberg, S. and Mingolla, E. (1985) Neural dynamics of form perception: boundary completion, illusory figures, and neon color spreading. Psychological Review, 92: 173-211.
  • Holland, J. H., Holyoak, K. J., Nisbett, R. E., and Thagard, P. R. (1986) Induction. Cambridge, MA: MIT Press.
  • Holyoak, K. J. and Thagard, P. (1995) Mental leaps analogy in creative thought, Cambridge, MA: MIT Press.
  • Hothersall, David (1984) History of Psychology, NY: Random House.
  • Johnson-Laird, P. N., Byrne, R. M. J. and Schaeken, W. (1992) Propositional reasoning by model, Psychology Review 99: 418-439.
  • Julesz, B. (1971) Foundations of cyclopean perception. Chicago: University of Chicago Press.
  • Just, M. A., Carpenter, P. A., & Woolley, J. D. (1982) Paradigms and processes and in reading comprehension. Journal of Experimental Psychology: General 3: 228-238.
  • Kahneman, D. and Tversky, A. (1979) Prospect Theory: An Analysis of Decision under Risk. Econometrica 47: 263-292.
  • Kahneman, D., Slovic, P. and Tversky, A. (1982) Judgment under uncertainty: heuristics and biases, New York: Cambridge University Press.
  • Kandel, E. R. (1976) Cellular basis of behavior: An introduction to behavioural neurobiology. San Francisco: W. H. Freeman.
  • Kanwisher N and Wojciulik E. (2000) Visual attention: Insights from brain imaging. Nature Review Neuroscience 1: 91-100.
  • Kintsch, W. (1974) The representation of meaning in memory, Hillsdale, NJ: Lawrence Erlbaum Associates.
  • Livingstone, M.S. and Hubel, D.H. (1988) Segregation of form, colour, movement and depth: Anatomy, physiology and perception. Science 240: 740–749.
  • Levelt, W. J. M. (1989) Speaking: From Intention to Articulation, Cambridge, MA: The MIT Press.
  • Logan, G.D. (2004) Cumulative progress in formal theories of attention. Annual Review Of Psychology 55: 207-234.
  • Lu, Z.-L., & Dosher, B.A. (1998) External noise distinguishes attention mechanisms. Vision Research 38:.
  • Lu, Z.-L., & Sperling, G. (1995) The functional architecture of human visual motion perception, Vision Research 35:.
  • Luce, D. R. (1959) Individual choice behavior; a theoretical analysis, New York: Wiley.
  • Luce, R. D. and Raiffa, H. (1989) Games and decisions : introduction and critical survey. New York: Dover Publications
  • Marr, D. (1982) Vision. San Francisco: W. H. Freeman.
  • Massaro, D. W. (1998) Perceiving talking faces: from speech perception to a behavioral principle, Cambridge, MA: The MIT Press.
  • McClelland, J. L. and Rumelhart, D. E. (1981) An Interactive Activation Model of Context Effects in Letter Perception: Part 1, Psychological Review 88: 375-407.
  • Medlin, D. L. and Ross, B. H. (1992) Cognitive psychology. Fort Worth: Harcourt Brace Johanovich.
  • Medlin, D. L. (1989) Concepts and conceptual structure. American Psychologist 44: 1469–1481.
  • Miller, G.A. (1956) The magical number seven, plus or minus two. Psychological Review 63: 81–97.
  • Neisser, U (1967) Cognitive psychology. New York: Appleton-Century-Crofts.
  • Newell, A., and Simon, H. A. (1972) Human Problem Solving, Englewood Cliffs, NJ: Prentice-Hall.
  • Newell, A., Shaw, J. C., and Simon, H. A. (1958) Elements of a Theory of Human Problem Solving. Psychological Review 23: 342-343.
  • Pashler, H. E. (1999) The psychology of attention, Cambridge, MA: MIT Press,
  • Pinker, S. (1994) The language instinct, New York: W. Morrow and Co.
  • Posner, M.I. (1980). Orienting of attention. Quarterly Journal of Experimental Psychology 32: 3-25.
  • Posner, M. I., Petersen, S. E., Fox, P. T. and Raichle, R. E. (1988) Localization of cognitive operations in the human brain, Science 240:.
  • Reynolds, J.H., Chelazzi, L., & Desimone, R. (1999). Competitive mechanisms subserve attention in macaque areas V2 and V4. Journal of Neuroscience 19:.
  • Rips, L. J., & Marcus, S. L. (1977). Suppositions and the analysis of conditional sentences. In M. A. Just & P. A. Carpenter (Eds.), Cognitive processes in comprehension. Hillsdale, NJ: Erlbaum.
  • Roediger III, H. L. (2002) Processing approaches to cognition: The impetus from the levels-of-processing framework. Memory 10: 319-332.
  • Russo, J. E. & Dosher, B. A. (1983) Strategies for multiattribute binary choice. J Exp Psychology Learning, Memory & Cognition 9: 676-696.
  • Sanjana, N. E. & Tenenbaum, J. B. (2003) Bayesian models of inductive generalization. Advances in Neural Information Processing Systems 15: 59-66.
  • Schank, R. C. & Abelson, R. P. (1977) Scripts, plans, goals, and understanding : an inquiry into human knowledge structures, Hillsdale, NJ: L. Erlbaum Associates.
  • Searle, J. R. (1990) Is the brain a digital computer APA Presidential Address.
  • Seidenberg, M. S. (1997) Language Acquisition and Use: Learning and Applying Probabilistic Constraints. Science 275:.
  • Shiffrin, R. M., & Steyvers, M. (1997). A model for recognition memory: REM – retrieving effectively from memory. Psychonomic Bulletin & Review 4: 145-166.
  • Skinner, B. F. (1950) Are theories of learning necessary? Psychological Review 57: 193-216.
  • Sperling, G. (1960). The information available in brief visual presentations. Psychological Monographs, 74 1-29.
  • Squire, L. R., Zola, S. M. (1996) Structure and function of declarative and non-declarative memory systems. Proceedings of the National Academy of Sciences 93:.
  • Thompson, R. F. (1986) The neurobiology of learning and memory. Science 29: 941 – 947.
  • Thorne, J., Bratley, P. & Dewar, H. (1968) The syntactic analysis of English by machine. In Michie, D. (Ed), Machine Intelligence, New York: American Elsevier.
  • Tinklepaugh, O. L. (1928) An experimental study of representative factors in monkeys, Journal of Comparative Psychology 8: 197–236.
  • Treisman, A. M. (1969) Strategies and models of selective attention. Psychological Review 76: 282-299.
  • Tulving, E. (1962). Subjective organization in free recall of “unrelated” words. Psychological Review 69: 344-354.
  • Tulving, E., Gordon Hayman, C. A. and MacDonald, C. A. (1991) Long-lasting perceptual priming and semantic learning in amnesia, A case experiment. Journal of Experimental Psychology 17: 595-617.
  • Ungerleider, L.G. and Mishkin, M. (1982) In D.J. Ingle, M.A. Goodale, and R.J.W. Mansfield (Eds.), Analysis of visual behavior. Cambridge, MA: MIT Press.
  • von Neumann, J. and Morgenstern, O. (1944) Theory of Games and Economic Behavior, Princeton, NJ: Princeton Univ. Press.
  • Wandell, B. (1995) Foundations of vision, Sunderland, MA: Sinauer Associates.
  • Watson, J.B. (1913) Psychology as the behaviorist views it, Psychological Review 20: 158-177.
  • Weichselgartner, E. and G. L. U. S. Sperling (1987) Dynamics of automatic and controlled visual attention. Science 238: 778-780.
  • Wickelgren, W. A. (1974) How to solve problems. New York: W. H. Freeman.
  • Zhang, J. (1997) The nature of external: Representations in problem solving. Cognitive science 21: 179-217.

Internal references

External Links

See Also

Cognitive Neuropsychology, Evolutionary Psychology, Neuropsychology

Personal tools
Focal areas