Draft:SPIDER Model

• Comment: In accordance with Wikipedia's Conflict of interest policy, I disclose that I have a conflict of interest regarding the subject of this article. RhSwain24 (talk) 00:44, 12 December 2025 (UTC)

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SPIDER Model

The SPIDER Model (Scanning, Predicting, Identifying, Deciding, and Executing a Response) is a theoretical framework developed by David L. Strayer and Donald L. Fisher to explain the cognitive processes involved in distracted driving. These processes rely on the limited capacity of attention and support situational awareness, which is the driver’s mental representation of their driving environment. The SPIDER model defines distracted driving as engaging in a secondary task that is unrelated to the safe operation of a motor vehicle (for example, talking or text messaging on a mobile phone). When drivers engage in such secondary tasks, attention is diverted away from driving, and the cognitive processes that support updating awareness of their driving environment are impaired.

Background

Driving requires sustained attention, quick decision-making, and coordination of multiple cognitive and perceptual processes. Drivers must continuously monitor the road, track the movement of other vehicles, follow traffic signs, and attend to pedestrians waiting to cross the road. Distractions such as texting, talking on the phone, or eating compete for limited attention and can reduce the ability to notice and react to changes in the environment. Distracted driving is a major contributor to traffic accidents in the United States (NHSTA, 2023). To examine how distractions affect driver behavior, researchers have developed models such as SPIDER to describe the cognitive processes involved in safe driving.

Model Components

Scanning

Visual attention plays a critical role in driving. Drivers must constantly shift attention between central and peripheral vision to maintain an up-to-date representation of the driving environment. They scan for potential hazards, monitor mirrors and dashboard instruments, and check road signs, lane markings, and pedestrians. The functional field of view (see also useful field of view) is the central region of vision from which information can be acquired without moving the head or eyes. Drivers rely on this region to process critical information directly in front of them while also using peripheral vision to detect potential hazards. Distractions can overload visual awareness, narrow the functional field of view and reducing scanning behavior. Even relatively less demanding tasks, such as taking on the phone while driving, have been found to cause drivers to narrowly focus on objects directly in front of them, further narrowing their functional field of view. Limiting information from peripheral vision affects scanning behavior in many ways. Drivers make fewer glances toward areas that may contain hazards, spend less time scanning the environment, and have more difficulty shifting their gaze between relevant locations (for example, the road ahead, a mobile phone, mirrors, and traffic signs).

Predicting

The predicting stage involves the driver’s ability to anticipate how the environment may change over time. Drivers need to anticipate the movement of other vehicles, the flow of traffic, pedestrians crossing the street, and hazards that are likely to appear in certain scenarios. When a driver scans their environment, they continuously update a mental representation of the road and use past experience to anticipate where potential hazards are likely to occur.

Experienced drivers tend to be more efficient in their visual scanning behaviors than inexperienced drivers, making more anticipatory glances and often sustaining them for longer durations of time. However, when drivers are distracted, both experienced and inexperienced drivers make fewer anticipatory glances and spend less time attending to cues that could signal an upcoming hazard. Novice drivers appear especially susceptible to the effects of in-vehicle distractions on anticipatory glances.

Identifying

Once potential hazards are detected, drivers must correctly identify them. Failure to identify hazards or objects within their useful field of view can lead to what is called inattentional blindness.

In one study, participant’s memory for items presented in a driving simulator was tested. Eye-tracking data were used to verity that items appeared within the driver’s field of view. Drivers who talked on a cell phone while driving showed reduced item recognition, meaning that they were unable to see and remember objects that were directly in front of them.

In a follow-up study participants took the same driving test while measuring the P300 component of event-related potentials. The P300 is a positive waveform elicited during cognitively demanding tasks and is associated with attention, memory, and executive function. The P300 is also sensitive to the allocation of attention towards a task, and larger P300 amplitudes are generally associated with better memory performance during the encoding. In this study, the P300 was used to examine whether errors were related to earlier stages of cognitive processing or to later encoding stages. Results showed a smaller P300 when participants were talking on the phone than when they were not. Reduced P300 amplitudes suggest that distracted drivers had difficulty allocating sufficient attention during encoding, which in turn impaired memory for objects directly in front of them. These results are consistent with the inattentional blindness hypothesis, according to which distracted drivers have difficulty remembering and identifying objects within their functional vision.

Deciding

After hazards are identified, drivers must then select an appropriate response. Some driving decisions involve basic control of the vehicle, such as when to accelerate and when to slow down. Others involve more complex judgments, such as determining when it is safe to make a left hand turn on a busy road.

When making a left-handed turn, drivers base their decision on the gap between an approaching lead vehicle and the following vehicle. Cooper and Zheng examined how distracting affects decision-making when turning across oncoming traffic. Their study found that distractions led drivers to misjudge both the size of the gap between the lead vehicle and the following vehicle and the speed of oncoming traffic. When drivers are distracted, they are less accurate in these judgments, which can impair decision-making during critical driving situations.

Executing a Response

The final stage of the SPIDER model concerns the physical execution of driving maneuvers. Driving performance depends on the quality of drivers’ decisions and their ability to translate those decisions into timely and accurate actions. Drivers must maintain an appropriate speed, stay within the lane boundaries, stop at red lights, and apply the correct amount of brake pressure at the right time.

High mental workload can impair the ability to make decisions and respond quickly when the environment requires rapid and precise action. A study found that distracted drivers had slower response times to traffic light changes, applied the brakes more intensely when approaching intersections, and were more likely to run red lights than drivers who were not distracted. Another study reported that driver-related distractions increased the variability in lane position (suggesting the driver had trouble staying within lane boundaries), led to missed lane changes, and increased brake reaction times, speed variability, and variations in steering wheel movements (consistent with swerving behavior.

Future Research

According to the SPIDER model, situational awareness depends on the limited capacity of attention. When attention is divided between competing tasks (for example, texting while driving) attentional resources are depleted and need time to recover. This “residual cost of attention” refers to the time it for attention to return to baseline after a secondary task. In one study, this residual cost was estimated at roughly 25 seconds.

The potential costs of task switching have become a focus in distracted driving research. Recent studies have examined the residual cost of task switching in the context of automated vehicles. Mok and colleagues investigated how long it takes to regain control of an automated vehicle after engaging in an active secondary task, such as playing a game on a tablet. They found that most participants took approximately 8 seconds to regain control of the vehicle.

Limitations

Although the SPIDER model has been used in the study of distracted driving, several limitations have been raised regarding its predictive power, validity, and real-world application. First, some researchers argue that the model lacks explanatory power because it does not differentiate4 between different types of distraction. As a result, it can be difficult to predict why different forms of distraction produce different levels of inattentive driving. A possible explanation is that the SPIDER model is based on attentional capacity and cognitive demand. Distractions might differ not only in terms of stimuli but also in salience, such as whether texting is more distracting than a phone call because it demands more attention (rather than because the stimulus itself is inherently more distracting).

Others argue that treating all distractions as equivalent oversimplifies the model and does not capture the complex nature of cognition. Objective evidence supports this concern. Research on attentional set and situation awareness shows that when drivers are distracted, they tend to keep their attention narrowly focused on what fits their current attentional set, or what they expect to encounter in a typical driving environment. When attention is divided, drivers are more likely to rely on schema-driven processes, such as expectations and familiar patterns, rather than noticing new or unexpected details.

The SPIDER model assumes a self-regulatory loop in which the drivers monitor their performance and adjust behavior, as needed, and it assumes that drivers can maintain enough attention focused on carrying out SPIDER-related processes. The model proposes that distractions disrupt or “break down” this loop, but that the process is still intact. Some research, however, suggests that distraction does not simply reduce awareness but can change how information is processed. These findings have been interpreted as challenging the SPIDER model’s assumption that scanning and predicting remain intact under distraction. They indicate that a focus on overall attention may overlook how distraction alters what drivers notice, leading them to rely on expectations and potentially miss important events.

Second, most validation studies of the SPIDER model rely on driving simulator data rather than real-world driving data. Some researchers argue that this approach may not fully capture how drivers decide to engage in secondary tasks while driving and how distractions interact with external demands such as traffic conditions and roadway characteristics. This reliance on simulation may limit the external validity and real-world applicability of the findings.