Unified Modeling of Proactive Interference and Memorization Effort: A new mathematical perspective within ACT-R theory

Unified Modeling of Proactive Interference and Memorization Effort: A new mathematical perspective within ACT-R theory Arindam Das, Wolfgang Stuerzlinger Computer Science and Engineering, York University, Toronto, Canada {arindam, wolfgang} @cse.yorku. ca Following this idea, we propose a simple mathematical model of visuo-spatial learning that combines the effect of “practice” in terms of practice time, the effect of “decay” in terms of a small numeric constant, the effect of “proactive interference” in terms of visual search cost, and the effect of “memorization” effort in terms of a newly introduced model parameter, an effort factor, explained later. All these effects are expressed in a single equation of memory activation. To achieve this goal, we adapt an existing memory activation equation of ACT-R theory developed by Anderson et al. (1998). We focus on the cognitive aspects of interaction more than the perceptual-motor control complexities in our model. Therefore, we leverage the empirically proven axioms of ACT-R theory that the time cost of a visual encoding is a constant and that a motor response can be modeled as an average value, according to the task specific behavior, such as a mouse movement. Guided by Altmann et al. (2002), we implement our mathematical model in a spreadsheet and validate it against previous empirical data collected by others. Abstract We parsimoniously model the effect of proactive interference and memorization effort in learning stable graphical layouts. We model the visual search cost, i.e. the number of distractors visually encoded while looking for a target item, as a reasonable surrogate of onscreen proactive interference. Further, we show that a novel quantity that we term “effort factor” is an acceptable estimate for comparing the memorization effort across different access cost of onscreen information during the early stages of practice. Keywords: ACT-R declarative memory, Interference, Memorization Effort, User Interface Proactive Introduction Onscreen information is an important part of daily life today – On one hand, they are prevalent in handheld devices like smart-phones and tablets; On the other hand, they can also be found in critical displays in aircraft and other machinery. The screens usually display a structured set of items for the user to interact with. When interacting, it is rare that users remember the position of every item in the set perfectly. One explanation for this forgetting effect is proactive interference caused by distractor items seen during the visual search for the desired item. Proactive interference causes loss of memory activation. In contrast, explicit memorization of item locations helps to mitigate the effect of such interference. People exert mental effort in order to accomplish such memorization. A study in flight simulation training (Waldron et al., 2008) found that temporarily decreasing the availability of onscreen information for pilots orients pilots more towards memory-based interaction strategies. This in turn helps them better remember critical information such as the aircrafts’ location. The study established that an increase in information access cost increases the perceptual-motor effort. This normally encourages users to choose the highest performance option of using fewer perceptual-motor operations but more memory operations, even if memory retrieval is imperfect. Rowe et al. (2008) empirically suggested that “practice” and “memorization” positively influence visuo-spatial learning while “proactive interference” impacts it negatively. On the other hand, Altmann et al. (2002) proposed a theory that not only holds proactive interference but also “decay” (i.e. loss of memory activation with passage of time) responsible for forgetting. Taking into account the mutually constraining effects of “practice”, “memorization” effort, “decay” and “proactive interference”, an integrated, yet simple and easily applicable performance model is possible that would reflect the effect of these phenomena on visuo-spatial learning. ACT-R Theory of Declarative Memory The ACT-R theory by Anderson et al. (1998) describes a modular system that aims to replicate the human mind. The theory is a framework of mathematical equations that models the neural computations in order to realize human dynamic behavior. The core of ACT-R declarative memory builds upon the notion of memory activation. It posits that memory encodings of items have different levels of activation to reflect their past use: items that have been used recently or items that are used very often receive a high activation. This activation decays over time if the item is not used. When the cognitive system needs to retrieve an item, memory returns the one with the highest activation at that instant. The job of memory retrieval is complicated by the noise in activation levels, which can temporarily make an item more active than the current one, or which can temporarily push all items below a threshold, thereby making the cognitive system transiently unable to recall information (Altmann et al., 2008; p. 604). Furthermore, the activation of an item controls its speed of retrieval. We focus on the following three equations behind the ACT-R declarative memory system that we leverage in our current work. ACT-R Activation Equation The equation describing the activation, A, of an item in the memory is given by A = B +  Activation Equation