Individual recognition: it is good to be different

Tibbetts, Elizabeth A.; Dale, James

doi:10.1016/j.tree.2007.09.001

Cited by 664 publications

(675 citation statements)

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“…For example, Tibbetts (2002) showed that the paper wasp Polistes fuscatus can identify individual nestmates by unique facial features, as well as we humans recognize our own companions. In contrast, in the 'class-level' (Tibbetts & Dale 2007) or 'binary' individual recognition (Gherardi & Tiedemann 2004), the receiver associates the learned characteristics of the signaller with inferred class-specific information or matches the signaller's phenotype to an internal template associated with different classes (but see Steiger & Müller 2008). For example, while fighting with a conspecific, the hermit crab Pagurus longicarpus behaves following the simple rule: 'if I know the opponent, behave as before; if I do not know it, attack' (Gherardi & Tiedemann 2004).…”

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confidence: 94%

“…The ability to recognize conspecifics (individual recognition) is a critical skill for many animal species (Tibbetts & Dale 2007), being a key element in almost all social networks (reviewed in Zayan 1994). During individual recognition, the recognizer (or receiver) learns the distinctive 'signature' (Beecher 1982) of another individual (the signaller), associates it with specific information about the signaller, and, based on this association, classifies the other as a rival, friend, neighbour, mate, offspring or sibling (Tibbetts & Dale 2007).…”

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confidence: 99%

“…During individual recognition, the recognizer (or receiver) learns the distinctive 'signature' (Beecher 1982) of another individual (the signaller), associates it with specific information about the signaller, and, based on this association, classifies the other as a rival, friend, neighbour, mate, offspring or sibling (Tibbetts & Dale 2007).…”

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confidence: 99%

“…Many examples are reported in Tibbetts & Dale (2007). Sheep, Ovis aries, can recognize parents and offspring on an individual basis (Searby & Jouventin 2003); temperate-breeding hooded warblers, Wilsonia citrina, can remember their neighbours from the previous breeding season even after having overwintered in the tropics (Godard 1991); and yellow-bellied marmots, Marmota flaviventris, are able to assess the reliability of alarm calls based on the identity of the caller (Blumstein et al 2004).…”

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confidence: 99%

“…A dichotomy between 'true' individual recognition and 'class-level' or 'binary' individual recognition has been proposed. In 'true' individual recognition (Beecher 1989;Tibbetts & Dale 2007), the receiver learns the individually distinctive characteristics of the signaller and associates these characteristics with individual-specific information about it. For example, Tibbetts (2002) showed that the paper wasp Polistes fuscatus can identify individual nestmates by unique facial features, as well as we humans recognize our own companions.…”

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Visual recognition of conspecifics in the American lobster, Homarus americanus

et al. 2010

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Keywords:aggressive behaviour Homarus americanus individual recognition lobster sight For years, individual recognition has been the subject of many studies but, owing to the intrinsic complexity of the phenomenon, it has also been the source of much controversy. The sensory channel(s) used for recognition has also been much discussed. In aquatic invertebrates, vision has been one of the least understood media. We carried out two laboratory experiments using 49 pairs of adult male American lobsters. The first experiment was aimed at investigating the sensory channel/s (smell, sight or the two combined) used by lobsters to get information about the opponent, whereas in the second experiment we tested whether visual experience might allow lobsters to recognize the familiar opponent. Previous exposure to the sight of a conspecific induced lobsters either to avoid the opponent or to skip preliminaries (approaches and threats) and escalate the interaction. However, such changes in the dynamics of fighting were shown only when the opponent was the individual that the experimental lobster had previously seen rather than a generic lobster. This is the first study to provide evidence that lobsters can identify familiar conspecifics by sight, although this does not necessarily mean that they recognize them as individuals. Further studies are needed to clarify this issue. Ó

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Visual recognition of conspecifics in the American lobster, Homarus americanus

et al. 2010

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Environmental Enrichment

Young¹,

Schetini²,

Cipreste³

2019

Zoo Animal Learning and Training

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Nowadays, the advancements of wearable consumer devices have become a predominant role in healthcare gadgets. There is always a demand to obtain robust recognition of heterogeneous human activities in complicated IoT environments. The knowledge attained using these recognition models will be then combined with healthcare applications. In this way, the paper proposed a novel deep learning framework to recognize heterogeneous human activities using multimodal sensor data. The proposed framework is composed of four phases: employing dataset and processing, implementation of deep learning model, performance analysis, and application development. The paper utilized the recent KU-HAR database with eighteen different activities of 90 individuals. After preprocessing, the hybrid model integrating Extreme Learning Machine (ELM) and Gated Recurrent Unit (GRU) architecture is used. An attention mechanism is then included for further enhancing the robustness of human activity recognition in the IoT environment. Finally, the performance of the proposed model is evaluated and comparatively analyzed with conventional CNN, LSTM, GRU, ELM, Transformer and Ensemble algorithms. To the end, an application is developed using the Qt framework which can be deployed on any consumer device. In this way, the research sheds light on monitoring the activities of critical patients by healthcare professionals remotely. The proposed ELM-GRUaM model achieved supreme performance in recognizing multimodal human activities with an overall accuracy of 96.71% as compared with existing models.

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Aggressive Behaviour in Fish: Integrating Information about Contest Costs

Hsu

Earley

Wolf

2011

Fish Cognition and Behavior

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As the introductory chapter to the first edition of this book (Brown et al. 2006) noted, fish behaviour has long been viewed as stereotyped and not strongly influenced by context or experience. The recognition that experience and learning can influence fish behaviour coupled with observations of considerable variation in behaviour within and between individuals has led to an increasing awareness of the complexity of fish behaviour. Behavioural ecologists are interested in understanding the causes and effects of behavioural variation within individuals (e.g. over time), among individuals of a population and among populations. These causes and effects are related to the benefits and costs of the behaviour that influence the evolutionary success (fitness) of the individual.Over 40 years ago Tinbergen (1963) noted that behavioural variation could be examined from multiple perspectives. This variation could result, for example, from differences within and among individuals in genetics, age, size, sex, developmental history and morphological and physiological constraints. The variation could also be related to environmental situations encountered by an individual in the present or past and could result from the individual's past experiences. Behavioural variation presumably results from how each of these factors influences the benefits and costs of behaviour at a particular time and place. However, a more comprehensive understanding of behavioural variation will emerge only by integrating these different perspectives.Experiences could influence variables such as size, developmental history and physiological state that are predicted or observed to lead to behavioural differences. Experiences could also influence the individual's perception of its current environmental situation. For example, a fish that recently encountered a predator in a particular location may be less likely to engage in intense aggressive behaviour (Brick 1998(Brick , 1999 and spend more time scanning for potential predators than another fish at the same place that has never encountered a predator there.

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Individual recognition: it is good to be different

Cited by 664 publications

References 76 publications

Visual recognition of conspecifics in the American lobster, Homarus americanus

Visual recognition of conspecifics in the American lobster, Homarus americanus

Environmental Enrichment

Aggressive Behaviour in Fish: Integrating Information about Contest Costs

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