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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.

Assumptions

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).

Approaches

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).

Applications

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.

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