Cognitive neuroscience can support public health approaches to minimise the harm of ‘losses disguised as wins’ in multiline slot machines. A growing body of evidence is uncovering how key design features of multiline slot machines produce an inflated experience of reward, despite the fact that these features offer no overall. This article outlines a framework of creativity based on functional neuroanatomy. Recent advances in the field of cognitive neuroscience have identified distinct brain circuits that are involved in specific higher brain functions. To date, these findings have not been applied to research on creativity. It is proposed that there are four basic types of creative insights, each mediated by a.

ACT-R (pronounced /ˌækt ˈɑr/; short for 'Adaptive Control of Thought—Rational') is a cognitive architecture mainly developed by John Robert Anderson and Christian Lebiere at Carnegie Mellon University. Like any cognitive architecture, ACT-R aims to define the basic and irreducible cognitive and perceptual operations that enable the human mind. In theory, each task that humans can perform should consist of a series of these discrete operations.

Most of the ACT-R's basic assumptions are also inspired by the progress of cognitive neuroscience, and ACT-R can be seen and described as a way of specifying how the brain itself is organized in a way that enables individual processing modules to produce cognition.

Inspiration[edit]

ACT-R has been inspired by the work of Allen Newell, and especially by his lifelong championing the idea of unified theories as the only way to truly uncover the underpinnings of cognition.^[1]In fact, John Anderson usually credits Allen Newell as the major source of influence over his own theory.

What ACT-R looks like[edit]

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Like other influential cognitive architectures (including Soar, CLARION, and EPIC), the ACT-R theory has a computational implementation as an interpreter of a special coding language. The interpreter itself is written in Common Lisp, and might be loaded into any of the Common Lisp language distributions.

This means that any researcher may download the ACT-R code from the ACT-R website, load it into a Common Lisp distribution, and gain full access to the theory in the form of the ACT-R interpreter.

Also, this enables researchers to specify models of human cognition in the form of a script in the ACT-R language. The language primitives and (i.e., programs) in ACT-R.These models reflect the modelers' assumptions about the task within the ACT-R view of cognition. The model might then be run.

Running a model automatically produces a step-by-step simulation of human behavior which specifies each individual cognitive operation (i.e., memory encoding and retrieval, visual and auditory encoding, motor programming and execution, mental imagery manipulation). Each step is associated with quantitative predictions of latencies and accuracies. The model can be tested by comparing its results with the data collected in behavioral experiments.

In recent years, ACT-R has also been extended to make quantitative predictions of patterns of activation in the brain, as detected in experiments with fMRI.In particular, ACT-R has been augmented to predict the shape and time-course of the BOLD response of several brain areas, including the hand and mouth areas in the motor cortex, the left prefrontal cortex, the anterior cingulate cortex, and the basal ganglia.

Brief outline[edit]

ACT-R's most important assumption is that human knowledge can be divided into two irreducible kinds of representations: declarative and procedural.

Within the ACT-R code, declarative knowledge is represented in the form of chunks, i.e. vector representations of individual properties, each of them accessible from a labelled slot.

Chunks are held and made accessible through buffers, which are the front-end of what are modules, i.e. specialized and largely independent brain structures.

There are two types of modules:

• Perceptual-motor modules, which take care of the interface with the real world (i.e., with a simulation of the real world). The most well-developed perceptual-motor modules in ACT-R are the visual and the manual modules.
• Memory modules. There are two kinds of memory modules in ACT-R:
  • Declarative memory, consisting of facts such as Washington, D.C. is the capital of United States, France is a country in Europe, or 2+3=5
  • Procedural memory, made of productions. Productions represent knowledge about how we do things: for instance, knowledge about how to type the letter 'Q' on a keyboard, about how to drive, or about how to perform addition.

All the modules can only be accessed through their buffers. The contents of the buffers at a given moment in time represents the state of ACT-R at that moment. The only exception to this rule is the procedural module, which stores and applies procedural knowledge. It does not have an accessible buffer and is actually used to access other module's contents.

Procedural knowledge is represented in form of productions. The term 'production' reflects the actual implementation of ACT-R as a production system, but, in fact, a production is mainly a formal notation to specify the information flow from cortical areas (i.e. the buffers) to the basal ganglia, and back to the cortex.

At each moment, an internal pattern matcher searches for a production that matches the current state of the buffers. Only one such production can be executed at a given moment. That production, when executed, can modify the buffers and thus change the state of the system. Thus, in ACT-R, cognition unfolds as a succession of production firings.

The symbolic vs. connectionist debate[edit]

In the cognitive sciences, different theories are usually ascribed to either the 'symbolic' or the 'connectionist' approach to cognition. ACT-R clearly belongs to the 'symbolic' field and is classified as such in standard textbooks and collections.^[2] Its entities (chunks and productions) are discrete and its operations are syntactical, that is, not referring to the semantic content of the representations but only to their properties that deem them appropriate to participate in the computation(s). This is seen clearly in the chunk slots and in the properties of buffer matching in productions, both of which function as standard symbolic variables.

Members of the ACT-R community, including its developers, prefer to think of ACT-R as a general framework that specifies how the brain is organized, and how its organization gives birth to what is perceived (and, in cognitive psychology, investigated) as mind, going beyond the traditional symbolic/connectionist debate. None of this, naturally, argues against the classification of ACT-R as symbolic system, because all symbolic approaches to cognition aim to describe the mind, as a product of brain function, using a certain class of entities and systems to achieve that goal.

A common misunderstanding suggests that ACT-R may not be a symbolic system because it attempts to characterize brain function. This is incorrect on two counts: First, all approaches to computational modeling of cognition, symbolic or otherwise, must in some respect characterize brain function, because the mind is brain function. And second, all such approaches, including connectionist approaches, attempt to characterize the mind at a cognitive level of description and not at the neural level, because it is only at the cognitive level at which important generalizations can be retained.^[3]

Further misunderstandings arise because of the associative character of certain ACT-R properties, such as chunks spreading activation to each other, or chunks and productions carrying quantitative properties relevant to their selection. None of these properties counter the fundamental nature of these entities as symbolic, regardless of their role in unit selection and, ultimately, in computation.

Theory vs. implementation, and Vanilla ACT-R[edit]

The importance of distinguishing between the theory itself and its implementation is usually highlighted by ACT-R developers.

In fact, much of the implementation does not reflect the theory. For instance, the actual implementation makes use of additional 'modules' that exist only for purely computational reasons, and are not supposed to reflect anything in the brain (e.g., one computational module contains the pseudo-random number generator used to produce noisy parameters, while another holds naming routines for generating data structures accessible through variable names).

Also, the actual implementation is designed to enable researchers to modify the theory, e.g. by altering the standard parameters, or creating new modules, or partially modifying the behavior of the existing ones.

Finally, while Anderson's laboratory at CMU maintains and releases the official ACT-R code, other alternative implementations of the theory have been made available. These alternative implementations include jACT-R^[4] (written in Java by Anthony M. Harrison at the Naval Research Laboratory) and Python ACT-R (written in Python by Terrence C. Stewart and Robert L. West at Carleton University, Canada).^[5]

Similarly, ACT-RN (now discontinued) was a full-fledged neural implementation of the 1993 version of the theory.^[6] All of these versions were fully functional, and models have been written and run with all of them.

Because of these implementational degrees of freedom, the ACT-R community usually refers to the 'official', Lisp-based, version of the theory, when adopted in its original form and left unmodified, as 'Vanilla ACT-R'.

Applications[edit]

Over the years, ACT-R models have been used in more than 700 different scientific publications, and have been cited in many more.

Memory, attention, and executive control[edit]

The ACT-R declarative memory system has been used to model human memory since its inception. In the course of years, it has been adopted to successfully model a large number of known effects. They include the fan effect of interference for associated information,^[7]primacy and recency effects for list memory,^[8] and serial recall.^[9]

ACT-R has been used to model attentive and control processes in a number of cognitive paradigms. These include the Stroop task,^[10][11]task switching,^[12][13] the psychological refractory period,^[14] and multi-tasking.^[15]

Natural language[edit]

A number of researchers have been using ACT-R to model several aspects of natural language understanding and production. They include models of syntactic parsing,^[16] language understanding,^[17] language acquisition ^[18] and metaphor comprehension.^[19]

Complex tasks[edit]

ACT-R has been used to capture how humans solve complex problems like the Tower of Hanoi,^[20] or how people solve algebraic equations.^[21] It has also been used to model human behavior in driving and flying.^[22]

With the integration of perceptual-motor capabilities, ACT-R has become increasingly popular as a modeling tool in human factors and human-computer interaction. In this domain, it has been adopted to model driving behavior under different conditions,^[23][24] menu selection and visual search on computer application,^[25][26] and web navigation.^[27]

Cognitive neuroscience[edit]

More recently, ACT-R has been used to predict patterns of brain activation during imaging experiments.^[28] In this field, ACT-R models have been successfully used to predict prefrontal and parietal activity in memory retrieval,^[29] anterior cingulate activity for control operations,^[30] and practice-related changes in brain activity.^[31]

Education[edit]

ACT-R has been often adopted as the foundation for cognitive tutors.^[32][33] These systems use an internal ACT-R model to mimic the behavior of a student and personalize his/her instructions and curriculum, trying to 'guess' the difficulties that students may have and provide focused help.

Such 'Cognitive Tutors' are being used as a platform for research on learning and cognitive modeling as part of the Pittsburgh Science of Learning Center. Some of the most successful applications, like the Cognitive Tutor for Mathematics, are used in thousands of schools across the United States.

Brief history[edit]

Early years: 1973–1990[edit]

ACT-R is the ultimate successor of a series of increasingly precise models of human cognition developed by John R. Anderson.

Its roots can be backtraced to the original HAM (Human Associative Memory) model of memory, described by John R. Anderson and Gordon Bower in 1973.^[34] The HAM model was later expanded into the first version of the ACT theory.^[35] This was the first time the procedural memory was added to the original declarative memory system, introducing a computational dichotomy that was later proved to hold in human brain.^[36] The theory was then further extended into the ACT* model of human cognition.^[37]

Integration with rational analysis: 1990–1998[edit]

In the late eighties, Anderson devoted himself to exploring and outlining a mathematical approach to cognition that he named Rational analysis.^[38] The basic assumption of Rational Analysis is that cognition is optimally adaptive, and precise estimates of cognitive functions mirror statistical properties of the environment.^[39] Later on, he came back to the development of the ACT theory, using the Rational Analysis as a unifying framework for the underlying calculations. To highlight the importance of the new approach in the shaping of the architecture, its name was modified to ACT-R, with the 'R' standing for 'Rational' ^[40]

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In 1993, Anderson met with Christian Lebiere, a researcher in connectionist models mostly famous for developing with Scott Fahlman the Cascade Correlation learning algorithm. Their joint work culminated in the release of ACT-R 4.0.^[41] Thanks to Mike Byrne (now at Rice University), version 4.0 also included optional perceptual and motor capabilities, mostly inspired from the EPIC architecture, which greatly expanded the possible applications of the theory.

Brain Imaging and Modular Structure: 1998–2015[edit]

After the release of ACT-R 4.0, John Anderson became more and more interested in the underlying neural plausibility of his life-time theory, and began to use brain imaging techniques pursuing his own goal of understanding the computational underpinnings of human mind.

The necessity of accounting for brain localization pushed for a major revision of the theory. ACT-R 5.0 introduced the concept of modules, specialized sets of procedural and declarative representations that could be mapped to known brain systems.^[42] In addition, the interaction between procedural and declarative knowledge was mediated by newly introduced buffers, specialized structures for holding temporarily active information (see the section above). Buffers were thought to reflect cortical activity, and a subsequent series of studies later confirmed that activations in cortical regions could be successfully related to computational operations over buffers.

A new version of the code, completely rewritten, was presented in 2005 as ACT-R 6.0. It also included significant improvements in the ACT-R coding language. This included a new mechanism in ACT-R production specification called dynamic pattern matching. Unlike previous versions which required the pattern matched by a production to include specific slots for the information in the buffers, dynamic pattern matching allows the slots to be matched to also be specified by the buffer contents. A description and motivation for the ACT-R 6.0 is given in Anderson (2007).^[43]

ACT-R 7.0: 2015-Present[edit]

At the 2015 workshop, it was argued that software changes required an increment in the model numbering to ACT-R 7.0, A major software change was removal of the requirement that chunks must be specified based on predefined chunk-types. The chunk-type mechanism was not removed, but changed from being a required construct of the architecture to being an optional syntactic mechanism in the software. This allowed for more flexibility in knowledge representation for modeling tasks that require learning novel information and extended the functionality provided through dynamic pattern matching now allowing models to create new 'types' of chunks. This also lead to a simplification of the syntax required for specifying the actions in a production because all the actions now have the same syntactic form. The ACT-R software has also been subsequently updated to include a remote interface based on JSON RPC 1.0. That interface was added to make it easier to build tasks for models and work with ACT-R from languages other than Lisp, and the tutorial included with the software has been updated to provide Python implementations for all of the example tasks performed by the tutorial models.

Spin-offs[edit]

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The long development of the ACT-R theory gave birth to a certain number of parallel and related projects.

The most important ones are the PUPS production system, an initial implementation of Anderson's theory, later abandoned; and ACT-RN,^[6] a neural network implementation of the theory developed by Christian Lebiere.

Lynne M. Reder, also at Carnegie Mellon University, developed in the early nineties SAC, a model of conceptual and perceptual aspects of memory that shares many features with the ACT-R core declarative system, although differing in some assumptions.

Notes[edit]

1. ^Newell, Allen (1994). Unified Theories of Cognition. Cambridge, Massachusetts: Harvard University Press. ISBN0-674-92101-1.
2. ^Polk, T. A.; C. M. Seifert (2002). Cognitive Modeling. Cambridge, Massachusetts: MIT Press. ISBN0-262-66116-0.
3. ^Pylyshyn, Z. W. (1984). Computation and Cognition: Toward a Foundation for Cognitive Science. Cambridge, Massachusetts: MIT Press. ISBN0-262-66058-X.
4. ^Harrison, A. (2002). jACT-R: Java ACT-R. Proceedings of the 8th Annual ACT-R WorkshopPDFArchived September 7, 2008, at the Wayback Machine
5. ^Stewart, T. C. and West, R. L. (2006) Deconstructing ACT-R. Proceedings of the seventh international conference on cognitive modelingPDF
6. ^ ^abLebiere, C., & Anderson, J. R. (1993). A connectionist Implementation of the ACT-R production system. In Proceedings of the Fifteenth Annual Conference of the Cognitive Science Society (pp. 635–640). Mahwah, NJ: Lawrence Erlbaum Associates
7. ^Anderson, J. R. & Reder, L. M. (1999). The fan effect: New results and new theories. Journal of Experimental Psychology: General, 128, 186–197.
8. ^Anderson, J. R., Bothell, D., Lebiere, C. & Matessa, M. (1998). An integrated theory of list memory. Journal of Memory and Language, 38, 341–380.
9. ^Anderson, J. R. & Matessa, M. P. (1997). A production system theory of serial memory. Psychological Review, 104, 728–748.
10. ^Lovett, M. C. (2005) A strategy-based interpretation of Stroop. Cognitive Science, 29, 493–524.
11. ^Juvina, I., & Taatgen, N. A. (2009). A repetition-suppression account of between-trial effects in a modified Stroop paradigm. Acta Psychologica, 131(1), 72–84.
12. ^Altmann, E. M., & Gray, W. D. (2008). An integrated model of cognitive control in task switching. Psychological Review, 115, 602–639.
13. ^Sohn, M.-H., & Anderson, J. R. (2001). Task preparation and task repetition: Two-component model of task switching. Journal of Experimental Psychology: General.
14. ^Byrne, M. D., & Anderson, J. R. (2001). Serial modules in parallel: The psychological refractory period and perfect time-sharing. Psychological Review, 108, 847–869.
15. ^Salvucci, D. D., & Taatgen, N. A. (2008). Threaded cognition: An integrated theory of concurrent multitasking. Psychological Review', 130(1)', 101–130.
16. ^Lewis, R. L. & Vasishth, S. (2005). An activation-based model of sentence processing as skilled memory retrieval. Cognitive Science, 29, 375–419
17. ^Budiu, R. & Anderson, J. R. (2004). Interpretation-Based Processing: A Unified Theory of Semantic Sentence Processing. Cognitive Science, 28, 1–44.
18. ^Taatgen, N.A. & Anderson, J.R. (2002). Why do children learn to say 'broke'? A model of learning the past tense without feedback. Cognition, 86(2), 123–155.
19. ^Budiu R., & Anderson J. R. (2002). Comprehending anaphoric metaphors. Memory & Cognition, 30, 158–165.
20. ^Altmann, E. M. & Trafton, J. G. (2002). Memory for goals: An activation-based model. Cognitive Science, 26, 39–83.
21. ^Anderson, J. R. (2005) Human symbol manipulation within an integrated cognitive architecture. Cognitive Science, 29(3), 313–341.
22. ^Byrne, M. D., & Kirlik, A. (2005). Using computational cognitive modeling to diagnose possible sources of aviation error. International Journal of Aviation Psychology, 15, 135–155. doi:10.1207/s15327108ijap1502_2
23. ^Salvucci, D. D. (2006). Modeling driver behavior in a cognitive architecture. Human Factors, 48, 362–380.
24. ^Salvucci, D. D., & Macuga, K. L. (2001). Predicting the effects of cellular-phone dialing on driver performance. In Proceedings of the Fourth International Conference on Cognitive Modeling, pp. 25–32. Mahwah, NJ: Lawrence Erlbaum Associates.
25. ^Byrne, M. D., (2001). ACT-R/PM and menu selection: Applying a cognitive architecture to HCI. International Journal of Human-Computer Studies, 55, 41–84.
26. ^Fleetwood, M. D. & Byrne, M. D. (2002) Modeling icon search in ACT-R/PM. Cognitive Systems Research, 3, 25–33.
27. ^Fu, Wai-Tat; Pirolli, Peter (2007). 'SNIF-ACT: A cognitive model of user navigation on the World Wide Web'(PDF). Human-Computer Interaction. 22 (4): 355–412. Archived from the original(PDF) on 2010-08-02.
28. ^Anderson, J.R., Fincham, J. M., Qin, Y., & Stocco, A. (2008). A central circuit of the mind. Trends in Cognitive Sciences, 12(4), 136–143
29. ^Sohn, M.-H., Goode, A., Stenger, V. A, Carter, C. S., & Anderson, J. R. (2003). Competition and representation during memory retrieval: Roles of the prefrontal cortex and the posterior parietal cortex, Proceedings of National Academy of Sciences, 100, 7412–7417.
30. ^Sohn, M.-H., Albert, M. V., Stenger, V. A, Jung, K.-J., Carter, C. S., & Anderson, J. R. (2007). Anticipation of conflict monitoring in the anterior cingulate cortex and the prefrontal cortex. Proceedings of National Academy of Science, 104, 10330–10334.
31. ^Qin, Y., Sohn, M-H, Anderson, J. R., Stenger, V. A., Fissell, K., Goode, A. Carter, C. S. (2003). Predicting the practice effects on the blood oxygenation level-dependent (BOLD) function of fMRI in a symbolic manipulation task. Proceedings of the National Academy of Sciences of the United States of America. 100(8): 4951–4956.
32. ^Lewis, M. W., Milson, R., & Anderson, J. R. (1987). The teacher's apprentice: Designing an intelligent authoring system for high school mathematics. In G. P. Kearsley (Ed.), Artificial Intelligence and Instruction. Reading, MA: Addison-Wesley. ISBN0-201-11654-5.
33. ^Anderson, J. R. & Gluck, K. (2001). What role do cognitive architectures play in intelligent tutoring systems? In D. Klahr & S. M. Carver (Eds.) Cognition & Instruction: Twenty-five years of progress, 227–262. Lawrence Erlbaum Associates. ISBN0-8058-3824-4.
34. ^Anderson, J. R., & Bower, G. H. (1973). Human associative memory. Washington, DC: Winston and Sons.
35. ^Anderson, J. R. (1976) Language, memory, and thought. Mahwah, NJ: Lawrence Erlbaum Associates. ISBN0-89859-107-4.
36. ^Cohen, N. J., & Squire, L. R. (1980). Preserved learning and retention of pattern-analyzing skill in amnesia: dissociation of knowing how and knowing that. Science, 210(4466), 207–210
37. ^Anderson, J. R. (1983). The architecture of cognition. Cambridge, Massachusetts: Harvard University Press. ISBN0-8058-2233-X.
38. ^Anderson, J. R. (1990) The adaptive character of thought. Mahwah, NJ: Lawrence Erlbaum Associates. ISBN0-8058-0419-6.
39. ^Anderson, J. R., & Schooler, L. J. (1991). Reflections of the environment in memory. Psychological Science, 2, 396–408.
40. ^Anderson, J. R. (1993). Rules of the mind. Hillsdale, NJ: Lawrence Erlbaum Associates. ISBN0-8058-1199-0.
41. ^Anderson, J. R., & Lebiere, C. (1998). The atomic components of thought. Hillsdale, NJ: Lawrence Erlbaum Associates. ISBN0-8058-2817-6.
42. ^Anderson, J. R., et al. (2004) An integrated theory of the mind. Psychological Review, 111(4). 1036–1060
43. ^Anderson, J. R. (2007). How can the human mind occur in the physical universe? New York, NY: Oxford University Press. ISBN0-19-532425-0.

References[edit]

• Anderson, J. R. (2007). How can the human mind occur in the physical universe? New York, NY: Oxford University Press. ISBN0-19-532425-0.
• Anderson, J. R., Bothell, D., Byrne, M. D., Douglass, S., Lebiere, C., & Qin, Y . (2004). An integrated theory of the mind. Psychological Review, 1036–1060.

External links[edit]

• Official ACT-R website – with a lot of online material, including the source code, list of publications, and tutorials
• jACT-R – a Java re-writing of ACT-R
• ACT-R: The Java Simulation & Development Environment – another open-source Java re-implementation of ACT-R
• PythonACT-R – a Python implementation of ACT-R
• pyactr – another Python implementation of ACT-R

How does the brain represent scenes, places, objects, and events? How is this information used to guide spatial navigation and action?
In our lab, we use functional magnetic resonance imaging (fMRI) and behavioral methods to investigate these questions.
Our lab is within the Department of Psychology at the University of Pennsylvania. We are affiliated with mindCORE, the Penn Brain Sciences Center, the Center for Functional Neuroimaging (CfN), and the Computational Neuroscience Initiative (CNI).

If you are interested in participating in a research study, please visit the CCN Experimetrix or Experiments@Penn to sign up for available slots.
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Penn Researchers Provide New Insights Into How People Navigate Through the World
Michael Bonner (postdoctoral fellow) and Russell Epstein (principal investigator) discuss how the brain analyses a scene to find what possible paths exist in it. In two experiments (one using well controlled artificial stimuli, the other using naturalistic real-world stimuli), the researchers found that the occipital place area automatically enocdes the navigational affordances of a scene. Many organisms, including humans, can effortlessly see a scene and extract what paths exist to navigate through, yet the method in which the brain does this was poorly understood. Read how these researchers leveraged neuronal patterns from fMRI experiments to understand how the brain does this crucial computation.
Click here to read the Penn News article.

Penn Psychologists Find Photos, Videos Result in Similar Understanding of Actions
Alon Hafri (graduate student), John Trueswell (collaborator), and Russell Epstein (principal investigator) discuss how the brain represents actions in an abstract manner. Though both videos and still images were presented to subjects, a certain brain network is able to represent actions abstractly across these two different mediums. Read how these researchers used state of the art pattern similarity analyses to understand how the brain represents actions.
Click here to read the Penn News article.

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Michael Bonner (postdoctoral fellow) and Josh Julian (graduate student) have organized a nanosymposium for SFN this year titled Scene Perception and Spatial Navigation. Michael and Josh will serve as the co-chairs of the session. The nanosymposium will take place from 8am to 12pm on Wednesday 11/16.
Stay tuned for more information and a list of speakers! Click here to reach the SFN website.

Mental GPS? There's an app for that! Josh Julian (graduate student) and Peter Bryan (former undergraduate research assistant) describe iJRD (a judgment of relative direction game), the app that takes research from the lab out into the real world! By playng iJRD anonymously on your iPhone, you can learn about how your navigation abilities compare to other people around the world all while aiding researchers make discoveries that will have important implications for basic research in psychology, neuroscience, urban planning, and for advancing early detection methods and targeted therapeutic approaches for diseases such as Alzheimer's disease, where spatial memory deficits occur.
Click here to read the article, and here to visit the official website of the app.

OPA! The occipital place area: crucial for using boundaries to navigate
Josh Julian (graduate student), Jack Ryan (former lab manager), Roy Hamilton (collaborator), and Russell Epstein (principal investigator) discuss the crucial role of the occipital place area in perceiving boundaries. Indeed, boundaries are something fairly unique to scenes; faces and objects do not have the same boundaries that a city street or natural landscape contain. Read how these researchers used TMS to discover that the occipital place area is determining where boundaries are during navigation.
Click here to read the Penn News article, and here for the Science Daily coverage.

Brain's Compass Relies on Geometric Relationships
Steven Marchette (postdoctoral fellow) and Russell Epstein (principal investigator) discuss the importance of their latest research on geometry and memory. In order to get from point A to point B, you need to know which direction you are facing; you need a mental compass of sorts. Read how our lab has shown how the brain anchors this mental compass using geometry in the retrosplenial complex.
Click here to read the Penn News article, and here for the Science Daily coverage.

Mental ‘Map’ and ‘Compass’ Are Two Separate Systems
Josh Julian (graduate student), Alexander Keinath (graduate student), Isabel Muzzio (collaborator), and Russell Epstein (principal investigator) describe their research separating the cognitive map from the cognitive compass. Read how these researchers were able to tease the two systems apart using two different chambers with unique contextual clues. These clues allowed the mice to know which of the two chambers they were in, but they used geometry instead to orient themselves.
Click here to read the Penn News article, and here for the Science Daily coverage.

Penn study sees changing faces of beauty
Teresa Pegors (former graduate student) and Russell Epstein (principal investigator) discuss their research on the role of context and beauty judgments. Previous studies on the role of context in estimating beauty have led to contradictory responses: in some studies, the preceding attractive face causes the next face to be more attractive, but in others, the preceding attractive face causes the next to seem less attractive. Our researchers got to the bottom of this seeming contradiction. Read the article to find out the role of timing in influencing judgments of attractiveness and how no percept is seperable from recent history.
Click here to read the Penn Current article.

Beyond the Nobel: What Scientists Are Learning About How Your Brain Navigates
Read this article describing the current state of the field in the cognitive neuroscience of navigation. Under discussion is work from our colleagues - the Mosers, John O'Keefe, and Eleanor Maguire - and from our own Russell Epstein (principal investigator). Read the article to find out more about the theorized roles of the hippocampus, parahippocampal place area, and retrosplenial cortex in storing and retrieving cognitive maps, in landmark based navigation, and in triangulating the position of different landmarks in relation to each other.
Click here to read the article from Wired.

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A New Perspective on Brain Function
Sean MacEvoy (collaborator) and Russell Epstein (principal investigator) discuss evidence of a new way to consider how the brain processes and recognizes a person's surroundings. Read to find out how the brain may use objects from within scenes to identify that scene.
Click here to read the Boston College Chronicle article and here for the Science Daily article.