Team Cognition and Complex Collaborative Problem Solving

Total Page:16

File Type:pdf, Size:1020Kb

Team Cognition and Complex Collaborative Problem Solving Cognitive Sciences Laboratory Dr. Stephen M. Fiore, Director * IST http://csl.ist.ucf.edu Professor, Cognitive Sciences * Department of Philosophy University of Central Florida * 3100 Technology Parkway, Suite 140 * Orlando, FL 32826 407-882-0298 * [email protected] Team Cognition and Complex Collaborative Problem Solving The Cognitive Science laboratory has pursued a form of scientific stewardship in the development of the field of team cognition, a melding of cognition with research on how humans interact socially and with technology. This includes the development of a number of edited volumes that have brought together researchers from different disciplines to present their perspectives on team cognition (Salas & Fiore, 2004), on complex collaborative problem solving (Letsky, Warner, Fiore, & Smith, 2008), and on interdisciplinary approaches for studying shared cognition (Salas, Fiore, & Letsky, 2012). We have also pursued the development of journal special issues in order to reach targeted audiences who may not be familiar with certain disciplinary perspectives on collaborative cognition (Fiore & Salas, 2006; Salas, Fiore, Letsky, & Warner, 2010). These efforts contributed to the overall field of team cognition by bringing together experts in varied fields to present their work in a unified volume or issue to new audiences. We have focused on the development of a theory of collaborative problem solving that unites concepts and methods from a variety of disciplines. This work broke new ground in that we critically analyzed and synthesized existing literature across multiple domains, ranging from the psychological and organizational sciences to computer science and human factors. From this we integrated—into a concise theory—our approach for examining complex forms of collaborative cognition in teams. Building off of earlier work on team problem solving (Fiore & Schooler, 2004), and based upon this synthesis, we published a major theoretical article that positioned our macrocognition in teams model (MITM) within the broader literature on shared cognition (Fiore, Rosen, Smith-Jentsch, Salas, Letsky, & Warner, 2010). This model integrates three theoretical elements. First, it is multi-level in that it encompasses individual and team level factors. Second, it addresses internalized and externalized cognitive functions. Finally, it incorporates temporal characteristics to examine phases of collaborative problem-solving and how these alter process and performance. We additionally published a detailed delineation of the measures and metrics associated with assessing the processes arising in macrocognition in teams (Fiore, Smith-Jentsch, Salas, Warner, & Letsky, 2010). In support of this, we articulated a means for conceptualizing the knowledge building process and how problem solvers transform data to information to knowledge (Fiore, Elias, Salas, Warner, & Letsky, 2010) as well as how teams communicate to manage uncertainty when solving complex problems (Fiore, Rosen, & Salas, 2010). Our research is now examining this in the context of emergent and dynamic processes arising from collaboration to study phase transitions in problem solving using the processes identified in the MITM (Wiltshire, Butner, & Fiore, 2017), Our theoriZing was used to study collaborative problem solving at NaSa’s Johnson Space Center. We worked with NaSa’s Mission Control Center (MCC), which is responsible for control of the International Space Station (ISS), and which responds to problems that obstruct the functioning of the ISS. We used the MITM to assess collaborative problem solving processes in the MCC and documented the interplay between team knowledge building processes and internalized and externalized team knowledge in this complex domain (Fiore, Wiltshire, Oglesby, Okeefe, & Salas, 2014). In collaboration with NaSA, we developed a method for eliciting the knowledge necessary to understand the team’s collaborative problem solving processes, documented these findings, and made recommendations for training based upon this (e.g., Wiltshire, Rosch, Fiorella, & Fiore, 2014). Last, this line of work also encompasses team cognition in the context of technology (Bocklman Morrow & Fiore, 2013; Fiore, 2012; Wiltshire & Fiore, 2014), and how to develop and test technologies that can augment cognition in complex operational environments. This includes improving cognition for teams more generally (Fiore & Wiltshire, 2016) as well as improving decision making when faced with uncertainty (Fiore et al., 2017; Newton, Fiore, & Laviola, 2017). Many of these ideas were integrated in comprehensive review papers in which we developed a theoretical framework for the design of technology to support cognition and collaboration in distributed teams (Fiore, McDaniel, & Jentsch, 2009; Fiore, RodrigueZ, & Carstens, 2012), as well as training to accelerate the development of team cognitive readiness (Fiore, Ross, & Jentsch, 2012). REFERENCES - Team Cognition and Complex Collaborative Problem Solving * Indicates current or former student co-authors *Bockelman Morrow, P. & Fiore, S. M. (2013). Team Cognition: Coordination across Individuals and Machines. In J. D. Lee and a. Kirlik (Eds.), The Oxford Handbook of Cognitive Engineering, Vol. 1: Foundations, Perspectives and Cognitive Issues (pp. 200-215). New York: Oxford University Press. Fiore, S. M., Elias, J., Salas, E., Warner, N. & Letsky, M. (2010). From Data, to Information, to Knowledge: Measuring Knowledge Building in the Context of Collaborative Cognition. In E. Patterson and C. Miller (Eds.), Macrocognition Metrics and Scenarios: Design and Evaluation for Real-World Teams (pp. 179-200). ashgate Publishing: United Kingdom. Fiore, S. M., McDaniel, R., & Jentsch, F. (2009). Narrative-Based Collaboration Systems for Distributed Teams: Nine Research Questions for Information Managers. Information Systems Management, 26, 1, 28–38. Fiore, S. M., Rosen, M. a., Smith-Jentsch, K. a., Salas, E., Letsky, M. & Warner, N. (2010). Toward an Understanding of Macrocognition in Teams: Predicting Processes in Complex Collaborative Contexts. Human Factors, 52, 2, 203-224. Fiore, S. M., Ross, K., & Jentsch, F. (2012). a Team Cognitive Readiness Framework for Small Unit Training. Journal of Cognitive Engineering and Decision Making, 6(3), 325-349. Fiore, S. M., Smith-Jentsch, K. a., Salas, E., Warner, N., & Letsky, M. (2010). Toward an Understanding of Macrocognition in Teams: Developing and Defining Complex Collaborative Processes and Products. Theoretical Issues in Ergonomic Science, 11, 4, 250-271. Fiore, S. M., Wiltshire, T. J., Oglesby, J. M., O’Keefe, W. S., & Salas, E. (2014). Complex Collaborative Problem Solving in Mission Control. Aviation, Space, & Environmental Medicine, 85(4), 456-461. Fiore, S.M. (2012). Cognition and Technology: Interdisciplinarity and the Impact of Cognitive Engineering Research on OrganiZational Productivity. In S. KoZlowski (Ed.). Oxford Handbook of Industrial and Organizational Psychology (pp. 1306-1322). Oxford University Press. Fiore, S.M. & *Wiltshire, T.J. (2016). Technology as Teammate: Examining the Role of External Cognition in Support of Team Cognitive Processes. Frontiers in Psychology: Cognitive Science. 7:1531. doi: 10.3389/fpsyg.2016.01531. Fiore, S.M. & Salas, E. (2006). Team Cognition and Expert Teams: Emerging Insights into Learning and Performance for Exceptional Teams. International Journal of Sports and Exercise Psychology, Volume 4. Fiore, S.M., *Warta, S., *Best, a., *Newton, O., & Laviola, J. (2017). Developing a Theoretical Framework of Task Complexity for Research on VisualiZation in Support of Decision Making Under Uncertainty. Proceedings of the 61st Annual Meeting of the Human Factors and Ergonomics Society (pp. 1193-1197). Santa Monica, Ca: Human Factors and Ergonomics Society. Fiore, S.M., RodrigueZ, W., & Carstens, (2012). uCollaborator: Framework for STEM Project Collaboration among Geographically- Dispersed Student/Faculty Teams. Journal of STEM Education, 13, 2, 84-92. Fiore, S. M., *Rosen, M. a., & Salas, E. (2010). Uncertainty Management and Macrocognition in Teams: a Multi-disciplinary Review and Integration. In K. Mosier & U. Fischer (Eds.). Informed by Knowledge: Expert Performance in Complex Situations (pp. 247-260). East Sussex, UK: Psychology Press, Taylor & Francis Group Ltd. Fiore, S. M. & Schooler, J. W. (2004). Process mapping and shared cognition: Teamwork and the development of shared problem models. In E. Salas & S.M. Fiore (Editors). Team Cognition: Understanding the factors that drive process and performance (pp. 133-152). Washington, DC: american Psychological association. Letsky, M. Warner, N., Fiore, S.M., & Smith, C. (Eds.). (2008). Macrocognition in Teams: Theories and Methodologies. London: Ashgate Publishers. *Newton, O., Fiore, S.M., & Laviola, J. (2017). an External Cognition Framework for VisualiZing Uncertainty in Support of Situation Awareness. Proceedings of the 61st Annual Meeting of the Human Factors and Ergonomics Society (pp. 1198-1202). Santa Monica, Ca: Human Factors and Ergonomics Society. Salas, E., & Fiore, S. M. (Eds.). (2004). Team Cognition: Understanding the factors that drive process and performance. Washington, DC: American Psychological Association. Salas, E., Fiore, S. M., & Letsky, M. (Eds.). (2012). Theories of Team Cognition: Cross-Disciplinary Perspectives. New York & London: Routledge. Salas, E., Fiore, S. M., Letsky, M., & Warner, N. (2010). Shared Cognition in Complex Environments: Emerging Theoretical Issues
Recommended publications
  • Compare and Contrast Two Models Or Theories of One Cognitive Process with Reference to Research Studies
    ! The following sample is for the learning objective: Compare and contrast two models or theories of one cognitive process with reference to research studies. What is the question asking for? * A clear outline of two models of one cognitive process. The cognitive process may be memory, perception, decision-making, language or thinking. * Research is used to support the models as described. The research does not need to be outlined in a lot of detail, but underatanding of the role of research in supporting the models should be apparent.. * Both similarities and differences of the two models should be clearly outlined. Sample response The theory of memory is studied scientifically and several models have been developed to help The cognitive process describe and potentially explain how memory works. Two models that attempt to describe how (memory) and two models are memory works are the Multi-Store Model of Memory, developed by Atkinson & Shiffrin (1968), clearly identified. and the Working Memory Model of Memory, developed by Baddeley & Hitch (1974). The Multi-store model model explains that all memory is taken in through our senses; this is called sensory input. This information is enters our sensory memory, where if it is attended to, it will pass to short-term memory. If not attention is paid to it, it is displaced. Short-term memory Research. is limited in duration and capacity. According to Miller, STM can hold only 7 plus or minus 2 pieces of information. Short-term memory memory lasts for six to twelve seconds. When information in the short-term memory is rehearsed, it enters the long-term memory store in a process called “encoding.” When we recall information, it is retrieved from LTM and moved A satisfactory description of back into STM.
    [Show full text]
  • Cognition, Affect, and Learning —The Role of Emotions in Learning
    How People Learn: Cognition, Affect, and Learning —The Role of Emotions in Learning Barry Kort Ph.D. and Robert Reilly Ed.D. {kort, reilly}@media.mit.edu formerly MIT Media Lab Draft as of date January 2, 2019 Learning is the quintessential emotional experience. Our species, Homo Sapiens, are the beings who think. We are also the beings who learn, and the beings who simultaneously experience a rich spectrum of affective emotional states, including a selected suite of emotional states specifically and directly related to learning. This proposal reviews previously published research and theoretical models relating emotions to learning and cognition and presents ideas and proposals for extending that research and reducing it to practice. Our perspective The concept of affect in learning (i.e., emotions in learning) is the same pedagogy applied by an athletic coach at a sporting event. A coach recognizes the affective state of an athlete, and, for example, exhorts that athlete toward increased performance (e.g., raises the level of enthusiasm), or, redirects a frustrated athlete to a productive affective state (e.g., instills confidence, or pride). A coach recognizes that an athlete’s affective state is a critical factor during performance; and, when appropriate, a coach will intervene with a meaningful strategy or tactic. Athletic coaches are skilled at recognizing affective states and intervening appropriately. Educators can have the same impact on a learner by understanding a learner’s affective state and intervening with appropriate strategies or tactics that will meaningfully manage and guide a person’s learning journey. There are several learning theories and a great deal of neuroscience/affective research.
    [Show full text]
  • Chapter 14: Individual Differences in Cognition 369 Copyright ©2018 by SAGE Publications, Inc
    INDIVIDUAL 14 DIFFERENCES IN COGNITION CHAPTER OUTLINE Setting the Stage Individual Differences in Cognition Ability Differences distribute Cognitive Styles Learning Styles Expert/Novice Differences or The Effects of Aging on Cognition Gender Differences in Cognition Gender Differences in Skills and Abilities Verbal Abilities Visuospatial Abilities post, Quantitative and Reasoning Abilities Gender Differences in Learning and Cognitive Styles Motivation for Cognitive Tasks Connected Learning copy, not SETTING THE STAGE .................................................................. y son and daughter share many characteristics, but when it comes Do to school they really show different aptitudes. My son adores - Mliterature, history, and social sciences. He ceremoniously handed over his calculator to me after taking his one and only college math course, noting, “I won’t ever be needing this again.” He has a fantastic memory for all things theatrical, and he amazes his fellow cast members and directors with how quickly he can learn lines and be “off book.” In contrast, my daughter is really adept at noticing patterns and problem solving, and she Proof is enjoying an honors science course this year while hoping that at least one day in the lab they will get to “blow something up.” She’s a talented dancer and picks up new choreography seemingly without much effort. These differences really don’t seem to be about ability; Tim can do statis- tics competently, if forced, and did dance a little in some performances, Draft and Kimmie can read and analyze novels or learn about historical topics and has acted competently in some school plays. What I’m talking about here is more differences in their interests, their preferred way of learning, maybe even their style of learning.
    [Show full text]
  • Cognitive Retention of Generation Y Students Through the Use of Games
    11 COGNITNE RETENTION OF GENERATION Y STUDENTS THROUGH THE USE OF GAMES AND SIMULATIONS A Dissertation Submitted to the Faculty of Argosy University - Sarasota In partial fulfillment of The requirements for the degree of Doctorate of Business Administration Accounting Major by Melanie A. Hicks Argosy University - Sarasota August 2007 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. III Abstract A new generation of students has begun to proliferate colleges and universities. Unlike previous generations, Generation Y students have been exposed to a variety of technological advancements, have different behaviors towards learning, and have been raised in a different environment. These differences may be causing conflict with traditional pedagogy in educational institutions, thereby creating, while it may be unintentional, an inability for Generation Y students to learn under the standard educational method of lecture presented to previous generations. The literature supports the position that additional teaching methods are needed in order to effectively educate Generation Y students (Prensky, 2001; Brozik & Zapalska, 1999; Albrecht, 1995). Consequently, the primary goal of this dissertation is to examine the ability of Generation Y students to achieve greater cognitive retention when the instructional material is conveyed with the assistance of or through the use of games andlor simulations. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. IV © Copyright 2007 by Melanie A. Hicks Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. v ACKNOWLEDGEMENTS This dissertation is dedicated to my husband, Scott Hicks, who has encouraged me and constantly pushed me to "seek first to understand, then to be understood".
    [Show full text]
  • Rethinking Co-Cognition Contents 1. Introduction
    This paper was published in Mind & Language, 13, 1998, 499-512. Archived at Website for the Rutgers University Research Group on Evolution and Higher Cognition. Rethinking Co-cognition Shaun Nichols Department of Philosophy College of Charleston Charleston, SC 29424 [email protected] and Stephen Stich Department of Philosophy and Center for Cognitive Science Rutgers University New Brunswick, NJ 08901 [email protected] Contents 1. Introduction 2. Points of Agreement 3. The Co-Cognition Thesis and a Friendly Amendment 4. A Critique of the Co-Cognition Thesis 1. Introduction In cognitive science and philosophy of mind, there has been a wealth of fascinating work trying to tease out the cognitive mechanisms that are involved in understanding other minds or "mindreading" (e.g., Baron-Cohen, 1995; Bartsch & Wellman, 1995; Fodor, 1995; Goldman, 1992; Gopnik, 1993; Harris, 1991; Leslie, 1991; Perner, 1991). This research has focused on evaluating the empirical evidence for various accounts of mindreading, predicting the results of future experiments, and carrying out experiments that might distinguish between the available theories. Our own previous work adopted this naturalistic approach (Stich & Nichols, 1992, 1995, 1997; Nichols et al., 1996; Nichols et al., 1995). In contrast to the naturalistic exploration of mindreading, Jane Heal has argued that simulation theorists have discovered an a priori truth about mindreading (Heal, 1994, 1995). In Heal's most recent paper (this issue), which is largely a response to an earlier paper of ours (Stich & Nichols, 1997), she maintains that we are committed to a view that conflicts with a simulationist thesis which is a priori true.
    [Show full text]
  • Memory & Cognition
    / Memory & Cognition Volume 47 · Number 4 · May 2019 Special Issue: Recognizing Five Decades of Cumulative The role of control processes in temporal Progress in Understanding Human Memory and semantic contiguity and its Control Processes Inspired by Atkinson M.K. Healey · M.G. Uitvlugt 719 and Shiffrin (1968) Auditory distraction does more than disrupt rehearsal Guest Editors: Kenneth J. Malmberg· processes in children's serial recall Jeroen G. W. Raaijmakers ·Richard M. Shiffrin A.M. AuBuchon · C.l. McGill · E.M. Elliott 738 50 years of research sparked by Atkinson The effect of working memory maintenance and Shiffrin (1968) on long-term memory K.J. Malmberg · J.G.W. Raaijmakers · R.M. Shiffrin 561 J.K. Hartshorne· T. Makovski 749 · From ·short-term store to multicomponent working List-strength effects in older adults in recognition memory: The role of the modal model and free recall A.D. Baddeley · G.J. Hitch · R.J. Allen 575 L. Sahakyan 764 Central tendency representation and exemplar Verbal and spatial acquisition as a function of distributed matching in visual short-term memory practice and code-specific interference C. Dube 589 A.P. Young· A.F. Healy· M. Jones· L.E. Bourne Jr. 779 Item repetition and retrieval processes in cued recall: Dissociating visuo-spatial and verbal working memory: Analysis of recall-latency distributions It's all in the features Y. Jang · H. Lee 792 ~1 . Poirier· J.M. Yearsley · J. Saint-Aubin· C. Fortin· G. Gallant · D. Guitard 603 Testing the primary and convergent retrieval model of recall: Recall practice produces faster recall Interpolated retrieval effects on list isolation: success but also faster recall failure IndiYiduaLdifferences in working memory capacity W.J.
    [Show full text]
  • Models of Memory
    To be published in H. Pashler & D. Medin (Eds.), Stevens’ Handbook of Experimental Psychology, Third Edition, Volume 2: Memory and Cognitive Processes. New York: John Wiley & Sons, Inc.. MODELS OF MEMORY Jeroen G.W. Raaijmakers Richard M. Shiffrin University of Amsterdam Indiana University Introduction Sciences tend to evolve in a direction that introduces greater emphasis on formal theorizing. Psychology generally, and the study of memory in particular, have followed this prescription: The memory field has seen a continuing introduction of mathematical and formal computer simulation models, today reaching the point where modeling is an integral part of the field rather than an esoteric newcomer. Thus anything resembling a comprehensive treatment of memory models would in effect turn into a review of the field of memory research, and considerably exceed the scope of this chapter. We shall deal with this problem by covering selected approaches that introduce some of the main themes that have characterized model development. This selective coverage will emphasize our own work perhaps somewhat more than would have been the case for other authors, but we are far more familiar with our models than some of the alternatives, and we believe they provide good examples of the themes that we wish to highlight. The earliest attempts to apply mathematical modeling to memory probably date back to the late 19th century when pioneers such as Ebbinghaus and Thorndike started to collect empirical data on learning and memory. Given the obvious regularities of learning and forgetting curves, it is not surprising that the question was asked whether these regularities could be captured by mathematical functions.
    [Show full text]
  • The Shifting Border Between Perception and Cognition,” Nous, 53(2),316-346, Which Has Been Published in Final Form At
    This is the pre-peer reviewed version of the following article: 2019, “The shifting border between perception and cognition,” Nous, 53(2),316-346, which has been published in final form at https://doi.org/10.1111/nous.12218. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. The Shifting Border Between Perception and Cognition Ben Phillips [email protected] Abstract. The distinction between perception and cognition has always had a firm footing in both cognitive science and folk psychology. However, there is little agreement as to how the distinction should be drawn. In fact, a number of theorists have recently argued that, given the ubiquity of top-down influences (at all levels of the processing hierarchy), we should jettison the distinction altogether. I reject this approach, and defend a pluralist account of the distinction. At the heart of my account is the claim that each legitimate way of marking a border between perception and cognition deploys a notion I call ‘stimulus-control.’ Thus, rather than being a grab bag of unrelated kinds, the various categories of the perceptual are unified into a superordinate natural kind (mutatis mutandis for the complementary categories of the cognitive). 1 Introduction Is there a viable distinction to be drawn between perception and cognition? There certainly seems to be a difference in kind between hearing a balloon pop and thinking about the square root of -1. But common sense is not the only area in which the distinction is gainfully employed. As Firestone and Scholl (2016, 4) observe, the distinction is “woven so deeply into cognitive science as to structure introductory 1 courses and textbooks, differentiate scholarly journals, and organize academic departments.” Contemporary philosophy of mind is certainly brimming with debates that presuppose a perception/cognition border.
    [Show full text]
  • Recognition and the Perception-Cognition Divide
    Recognition and the Perception-Cognition Divide (forthcoming in Mind & Language) §1 Introduction Recent discussions in philosophy and psychology have focused on the distinction between perception and cognition.1 This interest is not entirely new. Philosophers dating back to Aristotle have found the categories of perception and cognition to be theoretically fruitful ways of carving up the mind. At least intuitively, the distinction is not difficult to appreciate. There is clearly some difference between seeing, touching, or tasting apple juice, on one hand, and thinking, reasoning, or making judgments about it, on the other. Intuition is at least partially vindicated by the success of scientific psychology, which readily employs such a distinction. What is more difficult to appreciate is how recognition should be understood in light of the perception-cognition distinction. As a first pass, the sense of recognition in question involves a sensitivity to particulars from one’s past. Recognizing a familiar person (e.g., a colleague from work) is one instance of this, as is recognizing a place or thing that one has viewed before (e.g., a lake one visited as a child or one’s jacket on a restaurant coatrack). It is not immediately apparent where recognition falls along the perception-cognition divide. With a few notable exceptions, the topic of recognition (in the aforementioned sense) has been largely ignored in the philosophy of mind. This is quite surprising, given philosophers’ interests in closely related matters, such as perceptual learning, imagination, and attention. One of the aims of this paper is to reignite philosophical interest in the topic.
    [Show full text]
  • Situated Cognition and the Culture of Learning
    Situated Cognition and the Culture of Learning by John Seely Brown, Allan Collins and Paul Duguid Educational Researcher; v18 n1, pp. 32-42, Jan-Feb 1989. Abstract: Many teaching practices implicitly assume that conceptual knowledge can be abstracted from the situations in which it is learned and used. This article argues that this assumption inevitably limits the effectiveness of such practices. Drawing on recent research into cognition as it is manifest in everyday activity, the authors argue that knowledge is situated, being in part a product of the activity, context, and culture in which it is developed and used. They discuss how this view of knowledge affects our understanding of learning, and they note that conventional schooling too often ignores the influence of school culture on what is learned in school. As an alternative to conventional practices, they propose cognitive apprenticeship (Collins, Brown, Newman, in press), which honors the situated nature of knowledge. They examine two examples of mathematics instruction that exhibit certain key features of this approach to teaching. The breach between learning and use, which is captured by the folk categories "know what" and "know how," may well be a product of the structure and practices of our education system. Many methods of didactic education assume a separation between knowing and doing, treating knowledge as an integral, self-sufficient substance, theoretically independent of the situations in which it is learned and used. The primary concern of schools often seems to be the transfer of this substance, which comprises abstract, decontextualized formal concepts. The activity and context in which learning takes place are thus regarded as merely ancillary to learning---pedagogically useful, of course, but fundamentally distinct and even neutral with respect to what is learned.
    [Show full text]
  • Chapter 3 Cognitive Psychology the Word
    Chapter 3 Cognitive Psychology The word ‘cognition’ is derived from the Latin word cognoscere, meaning “to know” or “to come to know”. Thus, cognition includes the activities and processes concerned with the acquisition, storage, retrieval and processing of knowledge. In other words, it might include the processes that help us to perceive, attend, remember, think, categorize, reason, decide, and so on. Cognitive psychology, as the name suggests, is that branch of psychology that deals with cognitive mental processes. Sternberg (1999) defined Cognitive psychology as that which deals with how people perceive, learn, remember, and think about information.” In 2005, Solso gave another definition of Cognitive psychology as the study of processes underlying mental events. In general, Cognitive psychology can thus be defines as that branch of psychology that is concerned with how people acquire, store, transform, use and communicate language. The cognitive psychologists study the various cognitive processes that make up this branch. These processes include attention, the process through which we focus on some stimulus; perception, the process through which we interpret sensory information; pattern recognition, the process through which we classify stimuli into known categories; and memory, the process through which information is stored for later retrieval, and so on. Thus, the work of cognitive psychologists is extended to a number of areas, which can be depicted as follows – A Brief History of Cognitive Psychology The roots of cognitive psychology can be traced back much further, and is intimately intertwined with the history of experimental psychology. This leads back to the time period when the empiricist, rationalist, and structuralist schools of thought which included philosophical works of Plato, Aristotle that dealt with the philosophy of mind, and also to the later works of Wundt, and Titchner involving introspection.
    [Show full text]
  • Cognition Does Not Affect Perception: Evaluating the Evidence for “Top-Down” Effects
    BEHAVIORAL AND BRAIN SCIENCES (2016), Page 1 of 77 doi:10.1017/S0140525X15000965, e229 Cognition does not affect perception: Evaluating the evidence for “top-down” effects Chaz Firestone Department of Psychology, Yale University, New Haven, CT 06520-8205 chaz.fi[email protected] Brian J. Scholl Department of Psychology, Yale University, New Haven, CT 06520-8205 [email protected] Abstract: What determines what we see? In contrast to the traditional “modular” understanding of perception, according to which visual processing is encapsulated from higher-level cognition, a tidal wave of recent research alleges that states such as beliefs, desires, emotions, motivations, intentions, and linguistic representations exert direct, top-down influences on what we see. There is a growing consensus that such effects are ubiquitous, and that the distinction between perception and cognition may itself be unsustainable. We argue otherwise: None of these hundreds of studies – either individually or collectively – provides compelling evidence for true top-down effects on perception, or “cognitive penetrability.” In particular, and despite their variety, we suggest that these studies all fall prey to only a handful of pitfalls. And whereas abstract theoretical challenges have failed to resolve this debate in the past, our presentation of these pitfalls is empirically anchored: In each case, we show not only how certain studies could be susceptible to the pitfall (in principle), but also how several alleged top-down effects actually are explained by the pitfall (in practice). Moreover, these pitfalls are perfectly general, with each applying to dozens of other top-down effects. We conclude by extracting the lessons provided by these pitfalls into a checklist that future work could use to convincingly demonstrate top-down effects on visual perception.
    [Show full text]