The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19) Modelling Autobiographical Memory Loss across Life Span Di Wang,1 Ah-Hwee Tan,1,2 Chunyan Miao,1,2,3 Ahmed A. Moustafa4,5 1Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly 2School of Computer Science and Engineering 3Alibaba-NTU Singapore Joint Research Institute, Nanyang Technological University, Singapore 4School of Social Sciences and Psychology, Western Sydney University, Sydney, Australia 5Department of Social Sciences, College of Arts and Sciences, Qatar University, Doha, Qatar fwangdi,asahtan,[email protected], [email protected] Abstract the psychological basis presented by Conway and Pleydell- Pearce(2000), which has been widely accepted and sup- Neurocomputational modelling of long-term memory is a ported by neural imaging evidence (Addis et al. 2012). Our core topic in computational cognitive neuroscience, which is prior work (Wang, Tan, and Miao 2016) focuses on memory essential towards self-regulating brain-like AI systems. In this retrievals using imperfect cues and “wandering in reminis- paper, we study how people generally lose their memories and emulate various memory loss phenomena using a neuro- cence”, which refers to recalling a sequence of seemingly computational autobiographical memory model. Specifically, random but contextually connected memory across different based on prior neurocognitive and neuropsychology studies, episodes of life events. In that prior work, we assume that we identify three neural processes, namely overload, decay the memory formation and retrieval processes can always and inhibition, which lead to memory loss in memory for- be performed perfectly, which would rarely be true in real- mation, storage and retrieval, respectively. For model valida- world scenarios. Moreover, due to the hardware constraints tion, we collect a memory dataset comprising more than one in agents or robots, discard of certain portion of the stored thousand life events and emulate the three key memory loss memory is necessary in most complex application domains. processes with model parameters learnt from memory recall Therefore, with a totally different purpose, in this paper, we behavioural patterns found in human subjects of different age show AM-ART can accurately emulate various human mem- groups. The emulation results show high correlation with hu- man memory recall performance across their life span, even ory loss phenomena. with another population not being used for learning. To the Specifically, we employ three key processes in AM-ART best of our knowledge, this paper is the first research work to replicate the three widely studied memory loss phases, on quantitative evaluations of autobiographical memory loss namely during memory formation, storage and retrieval using a neurocomputational model. (Jahn 2013), respectively. Moreover, we introduce three novel parameters to AM-ART to regulate the corresponding memory loss processes, namely overload as the likelihood Introduction of being affected by cognitive overload during formation In recent years, many governments and agencies have in- (Daselaar et al. 2009), decay as the rate of long-term mem- vested a record-high amount of resources to look deeper into ory fading during storage (Rubin 1982), and inhibition as human brain’s functional dynamics. However, as of today, the likelihood of retrieval failure during retrieval (Storm and it is still difficult or impossible to quantitatively evaluate a Levy 2012). Our approach of using a neural network with wide range of brain dynamics at the neural network level. relevant control parameters to model memory loss aligns From the point of view of AI, neurocomputational models with cognitive experts’ opinion that “the individual pattern built upon neurocognitive and neuropsychology theories can of impaired memory functions correlates with parameters of provide insight into human behavioural processes in a rapid structural or functional brain integrity” (Jahn 2013). and quantitative manner. For example, according to Wang, For performance evaluations, we collect an autobiograph- Gauthier, and Cottrell(2016), “one advantage of computa- ical memory dataset comprising more than one thousand tional models is that we can analyse them in ways we cannot life events from public domains. However, because this col- analyse human participants to provide hypotheses as to the lected dataset does not span across one’s entire life (e.g., underlying mechanisms of an effect.” from childhood to 70s), in order to conduct relevant ex- In this paper, we evaluate how people generally lose periments, we alter the event dates so that the collected their memories by exploiting an established computational life events are equally distributed across the life stages and autobiographical memory model (Wang, Tan, and Miao the ratio among pleasant, neutral and unpleasant memories 2016), named Autobiographical Memory-Adaptive Reso- in each life stage conforms to the distribution reported by nance Theory network (AM-ART). AM-ART is built upon Berntsen and Rubin(2002). Moreover, it has been found that people of all ages tend to recall more pleasant memories Copyright ⃝c 2019, Association for the Advancement of Artificial rather than unpleasant ones, although the voluntarily non- Intelligence (www.aaai.org). All rights reserved. recalled unpleasant memories are still retained. We model 1368 Figure 1: Network structure of AM-ART. All its input channels in F1 and the F2 and F3 layers match specific brain regions. this tendency based on the memory survey data reported ory loss is essential towards self-regulating systems to ac- by Rubin and Berntsen(2003). Subsequently, we perform commodate physical memory constraints. For example, to model evaluations based on the memory recall data reported achieve better efficiency, deep reinforcement learning agents by Berntsen and Rubin(2002). Specifically, we learn the normally perform mini-batch learning based on the experi- memory loss parameter values by emulating the memory re- ence replay strategy (Lin 1993). Other than the improvement call performance of human subjects in different age groups in time-wise learning efficiency, experience replay also pos- and further use the learnt parameter values to predict the sesses the following perk: “the behavior distribution is aver- performance of human subjects in the subsequent life stage. aged over many of its previous states, smoothing out learn- The emulation results show high correlation, even with the ing and avoiding oscillations or divergence in the parame- memory recall performance of another population reported ters” (Mnih et al. 2013). However, by performing random by Rubin and Schulkind(1997). sampling, the conventional experience replay strategy ig- As such, we show that AM-ART can accurately capture nores the importance or the quality of different experiences. the characteristics of human autobiographical memory loss. To incorporate the quality of the experiences during sam- Therefore, we provide a useful tool to analyse various mem- pling, various experience replay techniques, such as prior- ory loss phenomena that may be difficult or impossible in itized (Schaul et al. 2015), hindsight (Andrychowicz et al. human subjects. To the best of our knowledge, this paper is 2017) and dual (Wei et al. 2018), have been proposed in the the first research work on quantitative evaluations of autobi- literature. Nonetheless, these extended strategies are built ographical memory loss using a neurocomputational model. upon purely goal-orientated mechanisms, without any neu- rocognitive basis. Although not being the focus of this paper, Related Work it will be quite stimulating to implement autonomous agents For the same purpose of using a neurocomputational model that are able to emulate human memory recall behaviours. to verify neurocognitive theories and perform quantitative evaluations, Wang, Gauthier, and Cottrell(2016) use PCA AM-ART Model and Its Dynamics (principal component analysis) and MLP (multi-layer per- The network structure of Autobiographical Memory- ceptron) with one hidden layer, wherein different number of Adaptive Resonance Theory (AM-ART) model is shown in hidden neurons are used to represent the corresponding level Figure1. AM-ART is a three-layer neural network, wherein of the human participants’ pattern recognition ability. Their the event-specific knowledge of autobiographical memory model supports the “experience moderation effect” observed is presented to the bottom layer F1 to encode life events in by Gauthier et al.(2014). In this paper, we use AM-ART as the middle layer F2 and a sequence of related events in F2 the neurocomputational model to replicate human memory are encoded into an episode in the top layer F3. AM-ART is loss phenomena in different age groups. consistent with the hierarchical model established by Con- Many well-established cognitive models, such as Soar way and Pleydell-Pearce(2000), which is supported by neu- (Laird 2012), ACT-R (Anderson et al. 2004) and Icarus ral imaging evidence (Addis et al. 2012), in terms of both (Langley 2006), employ functionally specific memory mod- the network architecture and functional dynamics (Wang, ules. Moreover, few such cognitive models further inves- Tan, and Miao 2016). Furthermore, we find that the circuit tigate the dynamics of long-term memory forgetting, e.g., of AM-ART network may reside in the temporal lobe of the Derbinsky and Laird(2013) heuristically