Prior Knowledge, Early Stage Perceptual Organization & Houston’s MetroRail

Human Factors in Information Design, Bentley University

HF700: Human Factors Foundations

Dr. WM Gribbons

November 9, 2020

Beginning in preattentive processing, cognitive structure building organizes and guides perception and interpretation of stimuli in the world through pattern and group identification. Top down processing, stored knowledge, and long term memory all refer to the developed ability of humans to compile and encode prior knowledge into early state perceptual organization (Almy, Genishi, 1979). The mind leverages knowledge accumulated through formal education and experience, which impacts expectation and performance (Gieseking, Mangold, 2014). In turn, designers must manage appropriate cognitive requests to predict and project user flow.

This paper will review the process of structuring of prior knowledge into early state perceptual organization. The human mind, unlimited by capacity and optimized for cognitive processing, can be understood in three primary high-level overlapping themes: the mind is highly organized, intricately interconnected, and constantly evolving. Through these themes we examine theories of how the mind optimizes access to essential knowledge at any given moment. These primary high-level themes share connections and characteristic sub-topics including: expertise, affordances and metaphors aesthetic response, task, and categorization theory. A design review case of public transit will elaborate on the sub-topic of expertise.

Long Term Memory

Human minds are highly adapted for optimal information processing. We evolved to subconsciously sample their environment to detect critical factors and immediately perceivable threats within the context of their surroundings (Sun, Chubb, Wright, Sperling, 2016). Humans “think fast” to prioritize responses within milliseconds; filtering by matching, modification, expectancy and priming to perceive environmental realities (Kahneman, 2011). However, this framework, applied in design, aids interpretation and decision making.

The mind cannot store every experience verbatim nor recall it in excruciating detail. Rather, our minds sample environments to offer a proposition, reduction, abstraction, or gist of the entity (Manktelow, Chung, 2004). The mind formulates hypotheses around these entities based on previously held assumptions from human memory, which “can be conceived as a network of associations among concepts.” (Anderson, 1978). Information architecture enables an efficient retrieval process via network relationships that are highly structured and based on organized levels of nomenclature (Cowan, 1988). Cognitive narrowing, a dominant way of thinking as a result of environmental sampling, allows humans to leverage learned information but simultaneously exacerbates preconceived notions or biases through selective omission and conformational bias (Lewkowicz, Ghazanfar, 2009). This points to the natural initial tendency of the mind to “focus on information that is explicit in their models” and reject foreign notions that refute the active hypothesis as the mind perceives these as a potential threat (Johnson-Laird et al., 1998).

However, these themes only posit a model for how researchers assume the mind works. Reality demands design methodologies such as user research, personas, taxonomy, and classification, align information architecture with unique individual motivations and desires. These human-oriented design solutions increase predictability of user interpretation and response to signals .

Highly Organized

The brain is highly organized, enabling humans to efficiently store and assess critical information. Given its infinite capacity, structure building enables connection and prioritization of prior knowledge and information. Entities are grouped and organized for greater accessibility and ease in retrieval of information (Robillard, 1999). This rapid automatic grouping operates at a low level of consciousness to achieve such significant speed and efficiency in retrieval that this process has been replicated in computer information organization (Mehler, 2001; Robillard, 1999).

Cognitive research suggests that the human mind organizes knowledge into semantic networks, which are embedded in information architecture, classification and taxonomy, to provide a framework to design interfaces (Norman, 2002, 115). Adapted from Collins and Quillian, the notion of semantic and propositional or abstraction networks as concept or cognitive maps include schema, frames, and propositional encoding. These are “spatial representations of concepts and their interrelationships that are intended to represent the knowledge structures that humans store in their minds” (Jonassen, 1993). Extending from this model, some of the dominant theories that describe how information is stored in the brain include schema and frame theory. In schema theory, related units of knowledge, called schemata, are connected to form a knowledge structure of an entity (Rousseau, 2001). Scripts are one type of schema that offer a framework for sequential, chronological, and procedural actions to enable structured and codified work, tasks, and processes. Frame theory reinforces schema theory in application (Barsalou, 1992). Frame theory posits the mind uses “frames” to structure memory (Minsky, 1974). Minsky notes that frame is a “data structure” that operates as a “remembered framework to be adapted to fit reality by changing details as necessary.”

Mental models provide a framework for the mind to confront new equipment and systems via comparison with existing previously-known entities (Wickens et al., 2004, 137). Mental models are composed of nodes of knowledge that enable the mind to formulate “‘small-scale models’ of reality to anticipate events, to reason, and to underlie explanation” (Craik, Johnson-Laird et al., 1998). These nodes connect to create larger networks in the mind, which form a comprehensive experience of an evolving environment (Anderson, Bower, 1974). Weighted membership rules in these networks maintain consistency of attributes or characteristics between connections. These connections depend on recency and frequency of activation and degree of commonality or association (Anderson, 1983). In this way, recency and frequency guide and facilitate cognitive availability to determine optimum interpretation and interaction. However, these scaled models of reality, formed without our awareness, may fail to fully conform with the parameters of an entity, creating a flawed hypothesis, and further are unconsciously influenced by erroneous individual biases and stereotypes.

Intricately Connected

The interconnected human mind contains nodes that represent bits of knowledge and form an intricate network, creating a web of connections and leaving nothing in isolation. These interconnected networks of nodes trigger activation of related information in correlated networks (Cowan, 1988; Mayer, 1995). Priming, activation, fanning, and strength of networks are unique factors that contribute to the distribution of access to knowledge along these networks. Priming is the notion that “once a particular pattern has been recognized, it will be much easier to identify in the next few minutes or even hours, and sometimes days” (Huber, O’Reilly 2003). This activated neural pathway, believed to be in a state of heighted receptivity or a case for visual learning, facilitated increased future activation (Huber, O’Reilly 2003).

 The cascading effect of connections as they are enabled across nodes is referred to as activation (Anderson, 1983). As one node is activated, the stimulation spreads to activate adjacent notes. Frequency of use and strength of activation further exacerbate connection between nodes and ability of recall and recognition (Anderson, Bower, 1974). The fan effect describes the tendencies of activation trajectories; operating based on proximity, adjacent nodes have the highest probability of significant activation while more distant nodes are less likely to be activated and more difficult to predict degree of activation, if at all (Anderson, 1983). At the terminal ends, far from a point of activation, the connections become weaker.

In particular, traumatic experiences highlight the efficiency of the node networks, naturally and rapidly triggering related information. Oftentimes these experiences seemed threatening, and since humans are predetermined to remember these experiences so as to avoid a recurrence, the mind classifies these as more poignant (Ware, 2010). However, a node cannot be activated in isolation; there is no way to circumvent or control possible connections due to the associative nature of the mind (Anderson, 1983). While precision in language offers guidance and ambivalence generates a wider range, there is no way to confine or limit the activation patterns across a user’s mind. We can review patterns and predict some connections but external factors will influence actual mapping.

Constantly Evolving

Constantly evolving, the mind modifies and learns as it codifies information from new situations. Any innovation and new learning demands significant cognitive effort, which the mind will adapt to or account for over time. Different kinds of learning require varying respective cognitive loads and efforts, which can be refined for optimal learning styles (Jonassen, 1997).

Piaget’s theory surrounding cognitive load and development suggests that the mind scaffolds schema in a way that optimizes initial interpretation of an entity by leveraging existing information when learning something new to obtain equilibrium between given schema and environment (Wadsworth, 1996). However, we then run into what some have termed the innovator's dilemma, which states that if we only meet people at their current knowledge set, though it may eliminate burden from the user, it inhibits advancements and ultimately stagnates potential innovation. Assimilation is the concept of the mind forming connections with new information and stored knowledge as a frame of reference (Piaget, 1971). Accommodation, which requires a significant cognitive load, asks that a user create an entirely new framework to understand an entity; the benefit of this outcome must warrant such investment (Mayer, 1995). Designers must balance significant innovation advancement with appropriate user burden. When designing for a typical customer, terminology, language, and content management are critical to user understanding and more flexible nomenclature may reduce cognitive load. To accommodate prior learning, given the human propensity for sampling information, varying cognitive loads of learning should be considered within the context of differing personas and profiles across professions, perceptions, cultures, generations, etc. Further, aesthetics of information may inform perception via past experience, culture, legibility, and density (Neale, Carroll, 1997). When considering encoding, culture mediates meaning in reading patterns, color and iconic symbolism, typographic and design tradition, concept of time and category width, and visual and verbal communication styles and patterns (Mayer, 1995).

Expertise in Design

A user must manage contributing factors within prior knowledge: mental models; expertise; task, profiles and goals, media and information design experience, conventions of typography, media, and visualization; affordances and metaphors; culture, object recognition; fixed elements; and aesthetics. Don Norman has expanded Piaget’s notion of cognitive development within the context of expertise; experts with their robust training and familiarity from repetition can experiment with application of principles in new frameworks, while novices must apply new concepts in a controlled manner. Expertise in a field has varying ramifications for frequency of use and expectation of users (Ware, 2010). A designer must set appropriate expectations based on application of continuum of user expertise (Norman 1990). The following design review analyzes the environmental design of the public transit station at the Altic/Howard Hughes of the Houston, Texas MetroRail light rail system within the domain of expertise to provide recommendations for improvement (Figure 01, 02).

Figure 01: Street View Altic/Howard Hughes MetroRail Station, Google Maps

Figure 02: Street View Altic/Howard Hughes MetroRail Station, Google Maps

A rider at this station requires guidance to not only purchase a ticket to ride the rail, but also to determine appropriate route, understand timetables, implement ticket, and navigate the physical layout of the platform space. This experience is considered through three user types—each with varying degrees of expertise depending upon a visitor’s familiarity with a given station, a METRO kiosk/pay station interface, and greater rail network/city layout understanding.

Figure 04: Photograph Resident at Harrisburg Blvd Green Station in “Long awaited, and long delayed, Metro's Green Line set to open,” Chron.com

Figure 03: MetroRail Expansion Map

A resident of this neighborhood is likely familiar with the station and may even use the light rail for regular commuting. Oftentimes, frequent users or experts are expected to overcome significant learning curves as they experience a higher rate of exposure to the interface (Ware, 2010). A design can exploit the capability of an expert to infer by minimizing use of energy and resources and implementing interaction accelerators (Mayer, 1995). In this way, the Metro system enables experts to entirely bypass the physical kiosk interface and purchase recurring passes appropriate for their needs via an online portal—a more efficient customizable option. Information density does not intimidate an expert, equipped to manage technical language, review progress, build strategies, and extrapolate inferences from their environment (Norman 1988). A regular commuter will likely want to examine specific timetables in more detail, an option appropriately only available online. However, variation across user profiles challenges the notion of a singular rule and given that the system needs to serve not only the regular and occasional commuters but also the general public the design must prioritize the most novice user who may be unfamiliar with the city.

Figure 05: Photograph Arts in Transit Altic / Howard Hughes, METRO

Figure 06: Decorative Elevation Metro Solutions Arts in Transit, METRO Solutions

A visitor to the city should be viewed as a novice to this environmental interface. Novices must employ verbal and visual working models to activate relevant information and connect to known metaphors, examples, and analogies. Novices require support in monitoring their own progress as they manage a heavy cognitive load while building out new schema (Jonassen, 1997). This support can be seen in the elevated scrolling digital timetable, which indicates time/date, station name, and arrival times. Dual coding offers redundancy; the greater number of connections increases the likelihood of relative recall and helps with preferred learning styles (Alvermann, 1981). Providing redundancy in this signal such as an audible reading of this information offers greater awareness to users and eliminates issues for those with visual disability. Visual intimidation easily deters novices as information density reduces understanding and therefore increases anxiety (Norman 1988). While interaction and information of the kiosk and the map are appropriately legible, navigation to enter the platform fails to signal physical entry for a first time rider. When designing this system for novice riders, a designer must clarify the entire process for a successful user experience.

To conclude, since knowledge in the mind is highly organized, intricately interconnected, and constantly evolving, designers must determine what an individual already knows, then design to mirror and support that experience. By delivering compatible and intuitive solutions, a designer meets the user at an appropriately familiar language and process, which offers design transparency.

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