Examining the information processing model: A comprehensive evaluation of the paradigm’s success in representing cognition’s broad scope
An essay I wrote for my Introduction to Cognitive Science module taken in first year.
Abstract
This essay is a comprehensive critical assessment of the information processing model’s ability to effectively study the full breadth of cognition in its constituent aspects. It will do this through three sections: First, a comprehensive description of the different aspects of cognition. Secondly, it provides a balanced analysis of the information processing models’ strengths and limitations in studying the previously defined aspects of cognition in two distinct subsections. Lastly, it will compare the successes and failures of the information processing paradigm against alternative models for cognition, providing a balanced and well-informed perspective.
Introduction
Cognition is a multifaceted approach to understanding the operation of the human mind. Cognitive science rejects historically accepted psychological approaches towards cognition, namely Skinner’s radical behaviourism, which holds that all animal behaviours, including cognition, are products of conditioning and that said conditioning depends upon processes of association and reinforcement (Bermúdez, 2022). Instead, cognitive science holds that we should attempt to understand the processes and decision-making within the brain and avoid treating it as a ‘black box’.
The information processing (IP) model is a framework used by cognitive scientists, modelling the mind and its cognitive processes – and therefore behaviour – as a computer, taking input through sensory experience, processing through cognitive operation, and outputting through behavioural response. IP was introduced as a method of modelling cognition in the 1950s, with a 1948 publication by Claude Shannon, “A Mathematical Theory of Communication”, considered the seminal work on the topic (Bermúdez, 2022). Further work by George Miller (Miller, 1956) proved pivotal in applying IP to psychology. In the following years, many academics developed new cognitive models – approximations of cognitive processes and behaviours using finite time sequences of symbols (David Klahr, 2022) – that implemented the IP paradigm.
To properly evaluate the claim that the IP model successfully represents cognition, we should first dissect its different aspects. By doing this, we can go on to recognise the IP model’s success in studying each aspect.
Section 1 – Cognition as a multidimensional study
Now that we have become familiar with the study of cognition as being wholly multifaceted, we can begin to investigate some of the main factors that constitute cognition. In doing this, we give some necessary context and understanding to assess this essay’s central thesis sufficiently.
Cognition’s wide range of aspects manifest themselves in processes. One of these is perception – usually defined as the processing of external information by the sensory system (C. Montemayor, 2017). Another aspect of cognition is memory – the ability to store and retrieve data in sensory, short-term and long-term forms (E. Camina, 2017). Attention is another core component, being the concentration of awareness on some phenomenon to the exclusion of other stimuli (McCallum, 2022). Additionally, problem-solving, often referred to as “executive functions”, is an aspect of cognition concerned with top-down mental processes that cannot be executed by relying on instinct or intuition – they require concentration(Diamond, 2013). Lastly, language comprehension is the cognitive process that connects sentences in a natural language to facts about one’s environment (Winograd, 1980).
Within the following subsection, we will begin to consider the IP paradigm’s success in modelling cognition by recognising its accomplishments in studying the different aspects of cognition that we have discussed so far.
Section 2.1 – IP’s successes
Following the previous section’s descriptions of the different aspects of cognition, we can begin to evaluate the IP approach’s success in modelling these aspects.
The IP paradigm is generally considered successful in accounting for perception. The IP schema (cognitive framework) for perception is a serial input-process-output sequence, with the mind taking sensory information input and matching this stimulus information with previously acquired knowledge in the process of pattern recognition (M. W. Eysenck, 2003). This schema is effective for several reasons. Firstly, neuroscientific studies have discovered that neurons act in a stimulus-selective pattern (Montgomery, 2022), evidencing the IP model’s concept of selectively matching a stimulus to a response. Moreover, psychological research has shown that perception, in the form of object recognition, shows that the brain is adept at identifying ‘distinctive features’ to recognise faces and writing (Matlin, 2017). Lastly, research concerning visual working memory illusions, such as the Ponzo illusion, suggests perception is influenced or even governed, by the mind’s processes (M. Shen, 2015), falling in line with the IP model’s understanding of cognition as a sequential, rule-based system.
Another success of the IP model is in its account for memory. The IP model separately considers memory’s three stages (see section 1) and emphasises processes of encoding, storage, and retrieval. A landmark paper on short-term memory by George Miller proved pivotal in exemplifying the success of the IP model in its account for human memory and its limits(Miller, 1956), showing that it did follow a pattern of encoding, storage and retrieval, as well as limited capacity – these both being elements of the IP’s hypothesis on memory. Though considerably old in its field, Miller’s paper remains surprisingly relevant, though its conclusions have been subject to refinement in the years since.
Now that we have discussed some of IP’s successes, we will discuss the pitfalls of the IP paradigm within the following subsection.
Section 2.2 – IP’s shortcomings
One weakness of the IP approach comes with its assumption that cognitive processes are strictly sequential. The Stroop test is a neuropsychological test assessing human cognitive interference (F. Scarpina, 2017), where participants are tasked with identifying the colour of words. The researchers conducting the Stroop test found that tasks overlapping visual and semantic information (for instance, the word “blue” coloured red) led to increased times to produce answers on the semantic information. This data could be considered evidence to suggest that cognitive processes are, in fact, not sequential at all – going against the IP’s assumption (Sayood, 2018).
Furthermore, the IP model seems insufficient for properly studying creativity as a mode of cognition. Considered in recent history as a fully-fledged cognitive process (R. Khalil, 2019) (L. I. Perlovsky, 2012), creativity, defined as the ability to produce work that is “both novel and appropriate” (Zhou, 2018), is poorly studied through the IP lens for several reasons. Firstly, the IP’s sequential input-processing-output schema seems incompatible with the unconventional and divergent thinking involved with the creative process. Though heavy exponents of the IP model, such as Herbert Simon, attempted to account for creativity as no more than the normal problem-solving process (Csikszentmihalyi, 1988), many consider this explanation inadequate, arguing that creativity’s unique property – at least within scientific discovery – “is problem finding, not problem solving” (Csikszentmihalyi, 1988).
Now that we have assessed the successes and failures of the IP model’s ability to study different cognitive processes, we can investigate alternative models for cognition to see if they allow for studying various aspects of cognition more effectively.
Section 3 – Situatedness and issues with the cognitive approach
The situated model, championed by roboticist R. A. Brooks, is an alternative approach to cognition that IP can be contrasted with. The situated paradigm holds that cognition is “embodied and situated” (Brooks, 1990) – it arises from and is connected to the agent’s interaction with its physical environment (W. Roth, 2013). The situated model fundamentally goes against the emphasis and underlying principles of the IP model, maintaining that cognition is not a solely internal process. Instead, it holds that cognitive processes are intrinsically linked to the environment within which the agent exists, and cognitive processes such as perception emerge due to influence from the agent’s environment. This principle of ‘embodied cognition’ is more attractive than IP to many; it appears to be more representational of the human experience than the IP model's infinite, serial, input-process-output loop.
Lastly, a point for consideration when evaluating the success of any cognitive approach, IP or otherwise, is the limitations of cognitive science itself. William F. Battig revealed two main issues he took with studies of cognitive science. Firstly, he questioned cognitive science’s failure to benefit from other fields concerned with ‘non-cognitive’ research (Battig, 1975). This issue remains today (J. E. Humphries, 2017), with cognitive scientists paying little mind to studies on links between real-world factors and non-cognitive traits such as social skills and empathy (Tim T. Morris, 2021). Furthermore, Battig hypothesised that variance between two agents performing a task might not be due to the differences of the individual agents, as suggested by writing at the time and even today (Bruno R. Bocanegra, 2014), but rather differences in the processes of the agent (Battig, 1975). If Battig’s theory were to be accurate, it could be said to contradict an assumption of most cognitive models, including IP – that cognitive processes are uniform between cognitive agents (or at least human minds).
Conclusion
Ultimately, the IP model seems to be largely successful in studying most aspects of cognition, such as perception, memory and language. However, it is also apparent that the IP model is not flawless – it is ineffective in studying aspects such as embodiment and creativity, among others. Furthermore, intrinsic problems may exist with cognitive science itself. Nevertheless, though its dominance of the field – as seen in the early days of cognitive science – has lessened with the introductions of newer schemas and hybrid cognitive models (Gutknecht, 1992), IP continues to exist as a cornerstone of cognitive science worldwide – for an excellent reason.
Bibliography
Battig, W. F., 1975. Within-individual differences in "cognitive" processes. In: R. L. Solso, ed. Information Processing and Cognition. s.l.:LEA Publishers, pp. 195-228.
Bermúdez, J. L., 2022. Cognitive Science: An Introduction to the Science of the Mind. 4th ed. Cambridge: Cambridge University Press.
Brooks, R. A., 1990. Elephants don't play chess. Robotics and Autonomous Systems, 6(1), pp. 3-15.
Bruno R. Bocanegra, B. H., 2014. When Cognitive Control Is Not Adaptive. Psychological Science, 25(6), pp. 1249-1255.
C. Montemayor, H. H. H., 2017. Perception and Cognition Are Largely Independent, but Still Affect Each Other in Systematic Ways: Arguments from Evolution and the Consciousness-Attention Dissociation. Front Psychol., 8(40).
Csikszentmihalyi, M., 1988. Motivation and Creativity: Towards a Synthesis of Structural and Energetic Approaches to Cognition. New Ideas in Psychology, Volume 6, pp. 159-176.
David Klahr, J. G. W., 2022. Cognitive Development: An Information-Processing View. s.l.:Routledge.
Diamond, A., 2013. Executive Functions. Annu. Rev. Psychol., Volume 64, pp. 135-168.
E. Camina, F. G., 2017. The Neuroanatomical, Neurophysiological and Psychological Basis of Memory: Current Models and Their Origins. Front. Pharmacol., Volume 8, p. 438.
F. Scarpina, S. T., 2017. The Stroop Colour and Word Test. Front. Psychol., Volume 8, p. 557.
Gutknecht, M., 1992. The 'Postmodern Mind': Hybrid Models of Cognition. Connection Science, 4(3), pp. 339-364.
J. E. Humphries, F. K., 2017. On the interpretation of non-cognitive skills - What is being measured and why it matters. Journal of Economic Behaviour and Organization, Volume 136, pp. 174-185.
L. I. Perlovsky, D. S. L., 2012. The drive for creativity and the escape from creativity: neurocognitive mechanisms. Cognit. Comput., Volume 4, pp. 292-305.
M. Shen, H. X. H. Z. R. S. M. Z. J. Z., 2015. The working memory Ponzo illusion: Involuntary integration of visuospatial information stored in visual working memory. Cognition, Volume 141, pp. 26-35.
M. W. Eysenck, M. T. K., 2003. Cognitive Psychology: A Student's Handbook. 4th ed. Hove: Taylor and Francis.
Matlin, F., 2017. Cognition. 9th ed. s.l.:John Wiley and Sons.
McCallum, W. C., 2022. Britannica. [Online]
Available at: britannica.com/science/attention
[Accessed 2 January 2024].
Miller, G. A., 1956. The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review, 63(2), pp. 81-97.
Montgomery, e. a., 2022. Stimulus-Selective Response Plasticity in Primary Visual Cortex: Progress and Puzzles. Frontiers in Neural Circuits, Volume 15.
R. Khalil, B. G. A. A. K., 2019. The Link Between Creativity, Cognition, and Creative Drives and Underlying Neural Mechanisms. Frontiers in Neural Circuits, 13(18).
S. E. Palmer, R. K., 1986. The information processing approach to cognition. Approaches to cognition: Contrasts and controversies, pp. 37-77.
Sayood, K., 2018. Information Theory and Cognition: A Review. Entropy (Basel), 20(9).
Tim T. Morris, G. D. S. G. v. d. B. N. M. D., 2021. Consistency of noncognitive skills and their relation to educational outcomes in a UK cohort. Translational Psychiatry, 11(1).
W. Roth, A. J., 2013. Situated Cognition. WIREs Cognitive Science, 4(5), pp. 453-582.
Winograd, T., 1980. What Does It Mean to Understand Language?. Cognitive Science, Volume 4, pp. 209-241.
Zhou, K., 2018. What Cognitive Neuroscience Tells Us About Creativity Education: A Literature Review. Global Education Review, 5(1), pp. 20-34.