Rational analysis of the adaptive and predictive nature of memory

In his target article, Klein (2013) makes the important point that many approaches to studying memory neglect the function of memory, in particular its capacity to help predict the future. Here, we complement Klein’s argument in two ways. First, we point to an existing and well-developed research program that formalizes a functional approach to memory, exploring its adaptive nature. Second, we illustrate how this approach can be applied to analyze regularities in social interactions, which memory might exploit to predict future interactions. John R. Anderson and colleagues (Anderson and Milson, 1989, Anderson and Schooler, 1991, Anderson and Schooler, 2000 and Schooler and Anderson, 1997) developed the rational analysis of memory, in which they argued that much of memory performance, including forgetting, might be understood in terms of adaptation to the structure of the environment. The first key assumption of the rational analysis is that environmental stimuli make informational demands on the cognitive system that are met by retrieving memory traces associated with those stimuli. The second assumption is that the memory system acts on the expectation that environmental stimuli tend to reoccur in predictable ways; the pattern of past encounters can, thus, predict the future need of information. The third assumption is that the memory system makes most accessible those traces that it predicts will be most useful in the future. Consequently, memory performance should reflect the patterns with which environmental stimuli occur and reoccur in the environment. For instance, more recently encountered stimuli will likely be encountered again. An adaptive memory system should make information about those stimuli more accessible because it is more likely to be needed. Conversely, the longer time interval since the last encounter, the less likely the information will be needed in the future, and so it can and should be forgotten

Reverse Engineering tools: development and experimentation of innovative methods for physical and geometrical data integration and post-processing

In recent years, the use of Reverse Engineering systems has got a considerable interest for a wide number of applications. Therefore, many research activities are focused on accuracy and precision of the acquired data and post processing phase improvements. In this context, this PhD Thesis deals with the definition of two novel methods for data post processing and data fusion between physical and geometrical information. In particular a technique has been defined for error definition in 3D points’ coordinates acquired by an optical triangulation laser scanner, with the aim to identify adequate correction arrays to apply under different acquisition parameters and operative conditions. Systematic error in data acquired is thus compensated, in order to increase accuracy value. Moreover, the definition of a 3D thermogram is examined. Object geometrical information and its thermal properties, coming from a thermographic inspection, are combined in order to have a temperature value for each recognizable point. Data acquired by an optical triangulation laser scanner are also used to normalize temperature values and make thermal data independent from thermal-camera point of view.L’impiego di tecniche di Ingegneria Inversa si è ampiamente diffuso e consolidato negli ultimi anni, tanto che questi sistemi sono comunemente impiegati in numerose applicazioni. Pertanto, numerose attività di ricerca sono volte all’analisi del dato acquisito in termini di accuratezza e precisione ed alla definizione di tecniche innovative per il post processing. In questo panorama, l’attività di ricerca presentata in questa tesi di dottorato è rivolta alla definizione di due metodologie, l’una finalizzata a facilitare le operazioni di elaborazione del dato e l’altra a permettere un agevole data fusion tra informazioni fisiche e geometriche di uno stesso oggetto. In particolare, il primo approccio prevede l’individuazione della componente di errore nelle coordinate di punti acquisiti mediate un sistema di scansione a triangolazione ottica. Un’opportuna matrice di correzione della componente sistematica è stata individuata, a seconda delle condizioni operative e dei parametri di acquisizione del sistema. Pertanto, si è raggiunto un miglioramento delle performance del sistema in termini di incremento dell’accuratezza del dato acquisito. Il secondo tema di ricerca affrontato in questa tesi consiste nell’integrazione tra il dato geometrico proveniente da una scansione 3D e le informazioni sulla temperatura rilevata mediante un’indagine termografica. Si è così ottenuto un termogramma in 3D registrando opportunamente su ogni punto acquisito il relativo valore di temperatura. L’informazione geometrica, proveniente dalla scansione laser, è stata inoltre utilizzata per normalizzare il termogramma, rendendolo indipendente dal punto di vista della presa termografica

Forgetting Constrains the Emergence of Cooperative Decision Strategies

Theoretical studies of cooperative behavior have focused on decision strategies that depend on a partner's last choices. The findings from this work assume that players accurately remember past actions. The kind of memory that these strategies employ, however, does not reflect what we know about memory. Here, we show that human memory may not meet the requirements needed to use these strategies. When asked to recall the previous behavior of simulated partners in a cooperative memory task, participants performed poorly, making errors in 10-24% of the trials. Participants made more errors when required to track more partners. We conducted agent-based simulations to evaluate how well cooperative strategies cope with error. These simulations suggest that, even with few errors, cooperation could not be maintained at the error rates demonstrated by our participants. Our results indicate that the strategies typically used in the study of cooperation likely do not reflect the underlying cognitive capacities used by humans and other animals in social interactions. By including unrealistic assumptions about cognition, theoretical models may have overestimated the robustness of the existing cooperative strategies. To remedy this, future models should incorporate what we know about cognition

Reflections of the Social Environment in Chimpanzee Memory: Applying Rational Analysis Beyond Humans

In cognitive science, the rational analysis framework allows modelling of how physical and social environments impose information-processing demands onto cognitive systems. In humans, for example, past social contact among individuals predicts their future contact with linear and power functions. These features of the human environment constrain the optimal way to remember information and probably shape how memory records are retained and retrieved. We offer a primer on how biologists can apply rational analysis to study animal behaviour. Using chimpanzees (Pan troglodytes) as a case study, we modelled 19 years of observational data on their social contact patterns. Much like humans, the frequency of past encounters in chimpanzees linearly predicted future encounters, and the recency of past encounters predicted future encounters with a power function. Consistent with the rational analyses carried out for human memory, these findings suggest that chimpanzee memory performance should reflect those environmental regularities. In re-analysing existing chimpanzee memory data, we found that chimpanzee memory patterns mirrored their social contact patterns. Our findings hint that human and chimpanzee memory systems may have evolved to solve similar information-processing problems. Overall, rational analysis offers novel theoretical and methodological avenues for the comparative study of cognition

Reflections of the Social Environment in Chimpanzee Memory: Applying Rational Analysis Beyond Humans

In cognitive science, the rational analysis framework allows modelling of how physical and social environments impose information-processing demands onto cognitive systems. In humans, for example, past social contact among individuals predicts their future contact with linear and power functions. These features of the human environment constrain the optimal way to remember information and probably shape how memory records are retained and retrieved. We offer a primer on how biologists can apply rational analysis to study animal behaviour. Using chimpanzees (Pan troglodytes) as a case study, we modelled 19 years of observational data on their social contact patterns. Much like humans, the frequency of past encounters in chimpanzees linearly predicted future encounters, and the recency of past encounters predicted future encounters with a power function. Consistent with the rational analyses carried out for human memory, these findings suggest that chimpanzee memory performance should reflect those environmental regularities. In re-analysing existing chimpanzee memory data, we found that chimpanzee memory patterns mirrored their social contact patterns. Our findings hint that human and chimpanzee memory systems may have evolved to solve similar information-processing problems. Overall, rational analysis offers novel theoretical and methodological avenues for the comparative study of cognition

Ecological Rationality: A Framework for Understanding and Aiding the Aging Decision Maker

The notion of ecological rationality sees human rationality as the result of the adaptive fit between the human mind and the environment. Ecological rationality focuses the study of decision making on two key questions: First, what are the environmental regularities to which people’s decision strategies are matched, and how frequently do these regularities occur in natural environments? Second, how well can people adapt their use of specific strategies to particular environmental regularities? Research on aging suggests a number of changes in cognitive function, for instance, deficits in learning and memory that may impact decision-making skills. However, it has been shown that simple strategies can work well in many natural environments, which suggests that age-related deficits in strategy use may not necessarily translate into reduced decision quality. Consequently, we argue that predictions about the impact of aging on decision performance depend not only on how aging affects decision-relevant capacities but also on the decision environment in which decisions are made. In sum, we propose that the concept of the ecological rationality is crucial to understanding and aiding the aging decision maker

The Recognition Heuristic: A Review of Theory and Tests

The recognition heuristic is a prime example of how, by exploiting a match between mind and environment, a simple mental strategy can lead to efficient decision making. The proposal of the heuristic initiated a debate about the processes underlying the use of recognition in decision making. We review research addressing four key aspects of the recognition heuristic: (a) that recognition is often an ecologically valid cue; (b) that people often follow recognition when making inferences; (c) that recognition supersedes further cue knowledge; (d) that its use can produce the less-is-more effect – the phenomenon that lesser states of recognition knowledge can lead to more accurate inferences than more complete states. After we contrast the recognition heuristic to other related concepts, including availability and fluency, we carve out, from the existing findings, some boundary conditions of the use of the recognition heuristic as well as key questions for future research. Moreover, we summarize developments concerning the connection of the recognition heuristic with memory models. We suggest that the recognition heuristic is used adaptively and that, compared to other cues, recognition seems to have a special status in decision making. Finally, we discuss how systematic ignorance is exploited in other cognitive mechanisms (e.g., estimation and preference)

The zoo of models of deliberate ignorance

This chapter looks at deliberate ignorance from a modeling perspective. Standard economic models cannot produce deliberate ignorance in a meaningful way; if there were no cost for acquisition and processing, data could be looked at privately and processed perfectly. Here the focus is on cases where the standard assumptions are violated in some way. Cases are considered from an individual’s perspective, without game-theoretic (strategic) aspects. Different classes of “not wanting to know” something are identified: aside from the boring case of the cost of information acquisition being too high, an individual may prefer to not know some information (e.g., when knowledge would reduce the enjoyment of other experiences) or may want to not use some information (e.g., relating to a lack of self-control). In addition, strategic cases of deliberate ignorance are reviewed, where obtaining information would also signal to others that information acquisition has occurred, and thus it may be better to remain ignorant. Finally, the possibility of deliberate ignorance emerging in population-level models is discussed, where there seems to be a relative dearth of models of the phenomenon at present. Throughout, the authors make use of examples to summarize different classes of models, ideas for how deliberate ignorance can make sense, and gaps in the literature for future modeling

We’ll Meet Again: Revealing Distributional and Temporal Patterns of Social Contact

What are the dynamics and regularities underlying social contact, and how can contact with the people in one’s social network be predicted? In order to characterize distributional and temporal patterns underlying contact probability, we asked 40 participants to keep a diary of their social contacts for 100 consecutive days. Using a memory framework previously used to study environmental regularities, we predicted that the probability of future contact would follow in systematic ways from the frequency, recency, and spacing of previous contact. The distribution of contact probability across the members of a person’s social network was highly skewed, following an exponential function. As predicted, it emerged that future contact scaled linearly with frequency of past contact, proportionally to a power function with recency of past contact, and differentially according to the spacing of past contact. These relations emerged across different contact media and irrespective of whether the participant initiated or received contact. We discuss how the identification of these regularities might inspire more realistic analyses of behavior in social networks (e.g., attitude formation, cooperation)