EP2. Social learning Elena Pasquinelli Educa4on, cogni4on, cerveau Cogmaster 2010‐2011
Transmission of generic knowledge • “There is … a unique way to acquire generic • Induc4on problem: Humans are capable of knowledge from a single instance of transmiTng/extrac4ng general knowledge informa4on intake, namely, when it is from par4cular instances. transmiEed through human communica4on. • When such instances are repe44ve and • Moreover, the transmission of such generic frequent, sta$s$cal mechanisms* are knowledge is not restricted to linguis4c invoked. communica4on. • When this is not the case (single instance) we • … you acquire kind‐generalizable knowledge need a further mechanisms for explaining from a single manifesta4on. induc4on. • In such cases, the observer does not need to • Such a mechanisms is hypothesized to rely rely on sta4s4cal procedures to extract the on human‐human communica4on relevant informa4on to be generalized as this • Verbal and not verbal (demonstra4on) is selec4vely transmiEed to her by the communica4ve demonstra4on. • Such a short‐cut to generic knowledge acquisi4on relies heavily on the communica4ve coopera4on and epistemic benevolence of the communica4ve partner.” (Gergely & Csibra, p. 3)
A STEP BACK TO EARLY LEARNING MECHANISMS: ‐ STATISTICAL LEARNING ‐ IMPLICIT LEARNING ‐ EXPLANATORY LEARNING ‐ LEARNING BY ANALOGY ‐ LEARNING BY IMITATION
Learning = modiﬁca4on of behavior as a consequence of experience • “the modiﬁca$on of behavior in the light of • Learning is a common func4on to experience. Even simple organisms such as Aplysia learn according to this deﬁni4on. In diﬀerent animal species fact, a number of diﬀerent kinds of learning have been iden4ﬁed in work with animals. • Diﬀerent forms of learning: These include habitua$on, associa$ve – Habitua4on, associa4on, learning, social learning (e.g. by emula4ng others), and “insight” learning, where imita4on, explana4on‐analogy solu4ons to problems come “in a ﬂash”. Habitua4on and associa4ve learning in infants have already been discussed. In cogni)ve psychology, learning is usually measured in terms of what has been remembered as a result of learning, either via measures of recogni)on, or via measures of recall. We will examine learning by imita$on, learning by analogy, and explana$on‐based learning here, none of which are found in animals (apart perhaps from excep4onal animals such as language‐ reared chimps). Explana4on‐based learning is a form of causal learning. Causal learning is extremely important in cogni4ve development, and is found in animals in some forms…” (Goswami, 2008, p. 61‐62)
Early learning mechanisms • “The assump4on will be one of common learning mechanisms, namely • sta4s4cal learning, • learning by imita4on, • explana4on‐based or causal learning • and learning by analogy. • Using these simple learning mechanisms, the brain appears to build up complex representa4ons about how the world is.” (Goswami, 2008, p. 52) • “At least three types of learning also appear to be func4oning from very early in development. One is associa$ve learning. Babies appear to be able to make connec4ons between events that are reliably associated, even while in the womb. • Once outside the womb, they appear to be able to track sta$s$cal dependencies in the world, such as condi4onal probabili4es between visual events or between sounds. This turns out to be a very powerful learning mechanism.” • “The second type of learning that appears to be available early is learning by imita$on. This may be par4cularly important for the development of social cogni4on.” • “Finally, infants appear to be able to connect causes and eﬀects by using “explana$on based” learning. … The causal inferences made by infants provide an extremely powerful mechanism for learning about the world. Infants are not simply detec4ng causal regulari4es but appear to be construc4ng causal explana4ons for new phenomena on the basis of their prior knowledge. One mechanism they use is learning by analogy” (Goswami, 2008, p. 3‐4)
Sta4s4cal learning • « When we make inferences that are not • Sta4s4cal learning is involved in the processing of necessarily deduc4vely valid (when we go beyond interrela4ons between features and the the informa4on given) we are reasoning induc4vely. diﬀeren4a4on of prototypes … For example, when children learn about the • Experiments of Rosch, 1978; Younger & Cohen, category « birds », they may learn about one or two 1983; Younger, 1985; Kirkham et al., 2002 exemplars (e.g. the robins and sparrows in their back garden). However, they are happy to generalize • Kirkham, et al., 2002: a visual habitua4on task is proper4es like « lives in a nest » to other birds... based on simple colored geometric shapes (blue » (Goswami, 2008, p. xvii) cross, yellow circle, green triangle) presented as a con4nuous stream in a par4cular order; each infant • “Younger’s cartoon‐animal experiments saw a stream of 6 shapes with tree pairings; demonstrated that infants could code the following habitua4on the infants saw 6 test displays, correla4onal structure between the diﬀerent half of which comprised the familiar sequence and features being manipulated by the experimenters. half new sequences with diﬀerent transi4onal This suggests a form of sta4s4cal probabili4es. All groups looked signiﬁcantly longer learning.” (Goswami, 2008, p. 18) to the new sequences. • “Using the regulari4es in input to learn which features co‐occur together.” (Goswami, 2008, p. 18) • “… infants have an impressive ability to keep track of the sta4s4cal structure of the input” • “This experiment with geometrical shapes suggests that infants are able to learn about environmental structure at a fairly abstract level. • The ability to track condi4onal probabili4es provides a very powerful domain‐general learning mechanism for extrac4ng structure from the physical world of objects. ” (Goswami, 2008, p. 19)
Language acquisi4on • Language acquisi4on has provoked a debate • “Humans’ capacity for speech and language provoked classic debates on nature on nature (Chomsky) vs nurture (Skinner) versus nurture by strong proponents of na4vism (Chomsky, 1959) and learning • Cri4cal periods in language learning diﬀer in (Skinner, 1957). the three aspects of language: phone4cs • Language learning is a deep puzzle that our theories and machines struggle to solve (before 12 months), syntax (18‐36), lexicon but children accomplish with ease. How do infants discover the sounds and words (forever) used in their par4cular language(s) when the most sophis4cated computers cannot? • Why are children beEer than adults? What is it about the human mind that allows a young child, merely one year old, to • Kuhl, 2004: neural commitment understand the words that induce meaning in our collec4ve minds, and to begin to use those words to convey their innermost thoughts and desires? A child’s budding – Once perceptual systems are commiEed ability to express a thought through words is a breath‐taking feat of the human they ﬁlter new informa4on mind. – Commitment is done between 6 and 12 months (for phone4cs): before, children • Studies indicate, for example, that the cri4cal period for phone4c learning occurs dis4nguish all the phone4c units of all prior to the end of the ﬁrst year, whereas syntac4c learning ﬂourishes between 18 languages and 36 months of age. Vocabulary development ‘‘explodes’’ at 18 months of age, but does not appear to be as restricted by age as other aspects of language learning— one can learn new vocabulary items at any age. • How can children succeed in a diﬃcult task as iden4fying and grouping the more or less 40 • Work in my laboratory led me to advance the concept of neural commitment, the phonemes that compose their language? In idea that neural circuitry and overall architecture develops early in infancy to detect the middle of a great variability of speech? the phone4c and prosodic paEerns of speech (Kuhl, 2004; Zhang et al., 2005, 2009). This architecture is designed to maximize the efﬁciency of processing for the • Implicit learning processes commit the brain language(s) experienced by the infant. Once established, the neural architecture to the proper4es of na4ve language speech arising from French or Tagalog, for example, impedes learning of new paEerns that do not conform • Infants’ ability to learn which phone4c units are relevant in the language(s) they are exposed to, while decreasing or inhibi4ng their aEen4on to the phone4c units that do not dis4nguish words in their language, is the necessary step required to begin the path toward language. • These data led to a theore4cal argument that an implicit learning process commits the brain’s neural circuitry to the proper4es of na4ve‐language speech, and that neural commitment has bi‐direc4onal eﬀects – it increases learning for paEerns (such as words) that are compa4ble with the learned phone4c structure, while decreasing percep4on nonna4ve paEerns that do not match the learned scheme (Kuhl, 2004). (Kuhl, 2010)
Sta4s4cal learning and language • Sta4s4cal learning (Saﬀran, et al, 1996) • “Sta4s4cal learning is computa4onal in nature, and reﬂects implicit rather than applies to the capacity to iden4fy phonemes explicit learning. It relies on the ability to automa4cally pick up and learn from the and to the capacity of segmen4ng words sta4s4cal regulari4es that exist in the stream of sensory informa4on we process, and – Japanese and English infants are both strongly inﬂuences both phone4c learning and early word learning. exposed to both /r/ and /l/ sounds, but in • To illustrate, adult speakers of English and Japanese produce both English r‐ and l‐like Japanese the sound /r/ is much more sounds, even though English speakers hear /r/ and /l/ as dis4nct and Japanese adults frequent hear them as iden4cal. Japanese infants are therefore exposed to both /r/ and /l/ – Babies spot the transi4onal probabili4es sounds, even though they do not represent dis4nct categories in Japanese. The between syllables presence of a par4cular sound in ambient language, therefore, does not account for infant learning. However, distribu4onal frequency analyses of English and Japanese show diﬀeren4al paEerns of distribu4onal frequency; in English, /r/ and /l/ occur very frequently; in Japanese, the most frequent sound of this type is Japanese /r/ which is related to but dis4nct from both the English variants. • studies indicate infants pick up the distribu4onal frequency paEerns in ambient speech, whether they experience them during short‐term laboratory experiments, or over months in natural environments, and can learn from them. • Sta4s4cal learning also supports word learning. Unlike wriEen language, spoken language has no reliable markers to indicate word boundaries in typical phrases. How do infants ﬁnd words? New experiments show that, before 8‐month‐old infants know the meaning of a single word, they detect likely word candidates through sensi4vity to the transi4onal probabili4es between adjacent syllables. In typical words, like in the phrase, ‘‘preEy baby,’’ the transi4onal probabili4es between the two syllables within a word, such as those between ‘‘pre’’ and ‘‘Ey,’’ and between ‘‘ba’’ and ‘‘by,’’ are higher than those between syllables that cross word boundaries, such and ‘‘Ey’’ and ‘‘ba.’’ Infants are sensi4ve to these probabili4es. When exposed to a 2 min string of nonsense syllables, with no acous4c breaks or other cues to word boundaries, they treat syllables that have high transi4onal probabili4es as ‘‘words’’ (Saﬀran et al., 1996) ” (Kuhl, 2010)
Language : sta4s4cal learning is not enough • Sta4s4cal learning can have strong and • At 9 months of age, the age at which the ini4al universal paEern of infant percep4on durable eﬀects on phone4cs at 9 months of has changed to one that is more language‐speciﬁc, infants were exposed to a foreign age, and with short‐4me exposure to language for the ﬁrst 4me (Kuhl et al., 2003). Nine‐month‐old American infants sta4s4cal regulari4es listened to 4 diﬀerent na4ve speakers of Mandarin during 12 sessions scheduled over – 9 months old children can learn to 4–5 weeks. The foreign language ‘‘tutors’’ read books and played with toys in dis4nguish Mandarin phonemes from sessions that were unscripted. A control group was also exposed for 12 sessions but exposure to play and interac4on with a heard only English from na4ve speakers. Ayer infants in the experimental Mandarin Mandarin speaking tutor exposure group and the English control group completed their sessions, all were • But is sta4s4cal learning enough? tested with a Mandarin phone4c contrast that does not occur in English. Both – 9 months old children cannot learn to behavioral and ERP methods were used. The results indicated that infants had a dis4nguish Mandarin phonemes from a remarkable ability to learn from the ‘‘live‐person’’ sessions – ayer exposure, they Mandarin speaking TV‐canned / performed signiﬁcantly beEer on the Mandarin contrast when compared to the audiotaped tutor control group that heard only English. In fact, they performed equivalently to infants • Social interac4on is required of the same age tested in Taiwan who had been listening to Mandarin for 10 months (Kuhl et al., 2003). The study revealed that infants can learn from ﬁrst‐4me natural exposure to a foreign language at 9 months, and answered what was ini4ally the experimental ques4on: can infants learn the sta4s4cal structure of phonemes in a new language given ﬁrst‐4me exposure at 9 months of age? If infants required a long‐ term history of listening to that language—as would be the case if infants needed to build up sta4s4cal distribu4ons over the ini4al 9 months of life—the answer to our ques4on would have been no. • Would infants learn if they were exposed to the same informa4on in the absence of a human being, say, via television or an audiotape? If sta4s4cal learning is sufﬁcient, the television and audio‐only condi4ons should produce learning. Infants who were exposed to the same foreign‐language material at the same 4me and at the same rate, but via standard television or audiotape only, showed no learning—their performance equaled that of infants in the control group who had not been exposed to Mandarin at all.” (Kuhl, 2010)
Language : sta4s4cal learning is not enough • Social interac4on • “social interac4on creates a vastly diﬀerent learning situa4on, one in can have an eﬀect which addi4onal factors introduced by a social context inﬂuence learning. Ga4ng could operate by increasing: (1) aEen4on and/ or on learning arousal, (2) informa4on, (3) a sense of rela4onship, and/or (4) ac4va4on of brain mechanisms linking percep4on and ac4on. through: • Infant aEen4on, measured in the original studies, was signiﬁcantly higher in response to the live person than to either inanimate source – Enhancement of (Kuhl et al., 2003). … AEen4on has been shown to play a role in the sta4s4cal learning studies as well.” aEen4on • during live exposure, tutors focused their visual gaze on pictures in the books or on the toys as they spoke, and the infants’ gaze tended to – Addi4onal follow the speaker’s gaze, as previously observed in social learning studies (Baldwin, 1995; Brooks and Meltzoﬀ, 2002). Referen4al informa4on (gaze informa4on is present in both the live and televised condi4ons, but it is to object) more difﬁcult to pick up via television, and is totally absent during audio‐only presenta4ons. … Infants who shiyed their gaze between the – Ac4va4on of tutor’s eyes and newly introduced toys during the Spanish exposure sessions showed a more nega4ve MMN (indica4ng greater neural mirror systems, discrimina4on) in response to the Spanish phone4c contrast. Infants who simply gazed at the tutor or at the toy, showing fewer gaze shiys, and other produced less nega4ve MMN responses. The degree of infants’ social engagement during sessions predicted both phone4c and word learning mechanisms for —infants who were more socially engaged showed greater learning as percep4on‐ac4on • reﬂected by ERP brain measures of both phone4c and word learning. Social interac4on may ac4vate brain mechanisms that invoke a sense of linking in the brain rela4onship between the self and other, as well as social understanding systems that link percep4on and ac4on “ (Kuhl, 2010)
Implicit learning • “There is no doubt that many of our most fundamental abili4es, whether they • Implicit learning theories are based on the concern language, percep4on, motor skill, or social behavior, reﬂect some kind of capacity of extrac4ng regulari4es, e.g. from adapta4on to the regulari4es of the world that evolves without inten4on to learn, language: and without a clear awareness of what we know. This ubiquitous phenomenon was • Reber, 1967, 1989: implicit learning allows called ‘implicit learning’ (IL) by Reber 40 years ago.” the acquisi4on of complex, abstract • Origina4ng from a diﬀerent research tradi4on, the term ‘sta4s4cal learning’ (SL) knowledge without awareness and eﬀort was proposed 10 years ago by Saﬀran and collaborators to designate the ability of (extrac4on of abstract rules) infants to discover the words embedded in a con4nuous ar4ﬁcial language, and • Pacton & Perruchet, 2006: acquisi4on of this ﬁeld of research is now growing exponen4al. the ap4tude to correctly answering to • There are obvious similari4es between SL and IL. As in IL, par4cipants in SL certain situa4ons, without the inten4on of experiments are faced with structured material without being instructed to l earn. learning (no extrac4on of abstract rules; They learn merely from exposure to posi4ve instances, without engaging in the learning of rules requires explicit analy4cal processes or hypothesis‐tes4ng strategies.” learning) • “Introduc4on There is no doubt that many of our most fundamental abili4es, • It does not mean one can learn without whether they concern language, percep4on, motor skill, or social behavior, reﬂect aEen4on (concurrent aEen4onal tasks some kind of adapta4on to the regulari4es of the world that evolves without lower the capacity of implicit learning) inten4on to learn, and without a clear awareness of what we know. This • But the crucial variable is the exposi4on to ubiquitous phenomenon was called ‘implicit learning’ (IL) by Reber 40 years ago. regulari4es in the environment Since then, several studies have explored this form of learning with several experimental paradigms (mainly ﬁnite‐state grammars and serial reac4on 4me tasks; for reviews, see). • Ten years ago, it seemed possible to contrast IL and SL on their main issues of interest, namely syntax acquisi4on and lexicon forma4on, respec4vely. Indeed, the to‐be‐ learned material used in ar4ﬁcial grammar learning research is typically governed by rules, that is by organizing principles which are independent of the speciﬁc material used in a given instance. If par4cipants learned the rules, then this form of learning would be out of the scope of SL studies, in which the no4on of rules is a priori irrelevant. However, research from the past few years has made it increasingly clear that par4cipants in ar4ﬁcial grammar learning experiments do not need to extract the rules to perform well, even in situa4ons involving transfer across surface forms…” (Pacton & Perruchet, 2006, p. 1)
• It does not mean one can learn without aRen$on (concurrent aEen4onal tasks lower the capacity of implicit learning)
Implicit & explicit learning • “This form of learning is unconscious and con4nues • Perruchet & Pacton, 2006: Explicit learning throughout life.” (Goswami, 2008b, p. 5) completes implicit learning with rules • ‘In one of the most famous early studies comparing • Perruchet & Pacton, 2006: In any case, explicit the eﬀects of "learning a procedure" with "learning learning raises performances in comparison with with understanding," two groups of children implicit learning (school instruc4on demands more prac4ced throwing darts at a target underwater than above chance performances) (Scholckow and Judd, described in Judd, 1908; see a • Reber, 1989: introduc4on of explicit instruc4on is conceptual replica4on by Hendrickson and expecially useful when informa4on is provided Schroeder, 1941). before (rather than during or ayer the implicit • One group received an explana4on of refrac4on of learning phase), maybe because it helps direc4ng light, which causes the apparent loca4on of the aEen4on on mearningful aspects target to be decep4ve. The other group only • Bransford, Brown, & Cocking, 2000: Judd & prac4ced dart throwing, without the explana4on. Scholckow 1908’s experiment conﬁrms that explicit Both groups did equally well on the prac4ce task, instruc4on (before training) enhances performances which involved a target 12 inches under water. But for new situa4ons the group that had been instructed about the abstract principle did much beEer when they had to transfer to a situa4on in which the target was under only 4 inches of water. Because they understood what they were doing, the group that had received instruc4on about the refrac4on of light could adjust their behavior to the new task.” (Bransford, et al., 2000, p. 44)
Implicit learning of errors • “One concern about mul4ple‐choice tests is that • If implicit learning can happen by repeated they rou4nely expose students to wrong answers. If exposi4on (with aEen4on), then the repeated subjects read all choices carefully ,they read three exposi4on to errors favors the learning of errors (usually) plausible wrong answers and only one • Mul4ple choice tests enhance learning of good, and correct answer. Even if subjects pick the correct bad, answers answer, reading the wrong statements may make those answers seem true later. That is, simply repea4ng statements increases the probability that those statements will be judged true late r(Hasher, Goldstein,&Toppino,1977). Consistent with this analysis, tes4ng increases later ra4ngs of the truth of mul4ple‐choice lures, although they are s4ll rated as less true than known facts (Toppino&Brochin, 1989;Toppino& Luipersbeck,1993). Similarly, tes4ng increases the produc4on of mul4ple choice lures as answers to later cued recall ques4ons, even when students are strictly warned against guessing (Roediger&Marsh,2005). Speciﬁcally, mul4ple‐ choice lures were used to answer 5% of ques4ons when subjects had not been previously tested; tes4ng increased the use of these speciﬁc wrong answers to 12% on the later cued recall test.” Marsh, et al., 2007, p. 195)
Sta4s4cal learning & Extrac4on of causal structures • “… speciﬁc perceptual features of two objects in a “launching” event • In terms of neural sta4s4cal (where object A impacts object B, causing it to begin to move) may vary, learning, the infant brain is but spa4o‐temporal dynamics (and therefore causal structure, i.e., the essen4ally learning about fact that A causes B to move) will vary less. The perceptual “illusion” of dynamic spa4o‐temporal causality during launching and other visual events noted by MichoEe structure across sensory (1963) is one example of how perceptual covaria4on can yield causal modali4es structure (Scholl & Tremoulet, 2000). • The brain automa4cally generates • Most recently, it has been demonstrated that 6‐month‐old infants who causal inferences from observed watch geometric shapes (with eyes) that engage in self‐ini4ated mo4on events extract causal structure that an be interpreted as “moral” causal • Causal structures can be induced structure (“helping” versus “hindering”). For example, in one scenario, from sta4s4cal learning the babies watched as a blue circle with eyes tried to move up a mechanisms “hill” (piece of green apparatus), but repeatedly failed to get beyond a half‐way “plateau”. A yellow triangle with eyes then appeared and “pushed” the blue circle on up the hill (or a red square appeared and pushed the blue circle back down the hill). The babies were then allowed to reach for both the “helper” and the “hinderer”. Twelve out of 12 babies reached for the yellow triangle (the “helper”, see Hamlin, Wynn & Bloom, 2007). • The spa4o‐temporal structure of these objects and their “ac4ons” was suﬃcient for the infants to interpret the movements as goal‐directed ac4ons with moral content. The level of knowledge that can be abstracted from spa4o‐temporal structure (perceptual causal informa4on) about diﬀerent en44es has in important cases been transcended by modern physics and biology. A good example is the medieval “impetus” theory of mo4on, which has been supplanted by Newtonian physics (Kaiser, ProﬁE & McCloskey, 1985). According to the impetus theory of mo4on, every mo4on must have a cause. ” (Goswami, 2008b, p. 9)
Explana4on‐based learning • “Explana4on‐based learning … is the core • Children use previous (domain) knowledge in order mechanism used by infants to iden4fy new variables to construct explana4ons for new situa4ons as they build their knowledge of the physical world. (generaliza4on) • As infants experience more and more events, more • Iden4fy variables that are relevant for events to elaborate representa4ons are developed in which happen in a certain way variables that are relevant to the events’ outcomes • It is essen4ally causal learning are iden4ﬁed and represented, such as degree of contact for support events. This process whereby infants iden4fy new variables in event categories is thought to be explana4on‐based learning. • In the ﬁeld of machine learning, explana4on‐based learning depends on construc4ng causal explana4ons for phenomena on the basis of speciﬁc training examples, using prior domain knowledge. • If infants were merely learning condi4on‐outcome rela4ons, as in associa4ve learning, then they would be unable to make predic4ons about novel events. • However, infants who understand why (for example, short covers cannot conceal tall objects should be able to reason about height informa4on in any covering event, even if this event is very remote in perceptual terms form the learning events. • The infants, like the machines, would be able to formulate valid generaliza4ons from single instances.” (Goswami, 2008, p. 66)
Learning by analogy • “Finding correspondences between two events, situa4ons, or domains • Children learn by analogy of knowledge and transferring knowledge from one to • This is a speciﬁcally human another.” (Goswami, 2008, p. 52) capacity • “In learning by analogy, “we face a situa4on, we recall a similar • It can be found in children before situa4on, we match them up, we reason, and we learn” (Winston, language but is powered by 1980). We may decide whether a dog has a heart by thinking about language whether people have hearts (young children use “personiﬁca4on analogies” to learn about biological kinds, see Inagaki & Hatano, 1988), or we may solve a mathema4cal problem about the interac4on of forces by using an analogy to a tug‐of‐war (young children use familiar physical systems to reason about unfamiliar ones, see Pauen, 1996). Reasoning by analogy has usually been measured in children aged 3 years or older (see Goswami, 1992, 2001, for reviews), but can also be demonstrated in infancy. However, so far, analogy has not been found in the animal kingdom, sugges4ng that it is especially important for human learning. • Early analogies tend to depend on func4onal or causal rela4ons, but once language is acquired analogies can be quite abstract (e.g. 3‐year‐ old children deciding how animals can evade predators by using diﬀerent forms of mimicry, see Brown, 1989). The use of analogy depends crucially on the knowledge base. Children can only use analogies based on familiar rela4ons, rela4ons that they have experienced or that they understand. ” (Goswami, 2008b, p.13‐14)
Learning by imita4on • “Learning by imita4on can be deﬁned as B learns from A • Infants imitate adults’ behavior some part of the form of a behavior… One example is • Children learn by imita4on, e.g. learning the use of a novel tool by imita4ng the ac4ons of the use of tools another user with that tool. Most deﬁni4ons of imita4on require that something new is learned, and such learning • Learning by imita4on is present has proved remarkably diﬃcult to dis4nguish in animals … in the human baby by the age of (Goswami, 2008, p. 62‐63) at least 9 months (Meltzoﬀ, 1988) • Learning by imita4on is another cri4cal form of early learning. Here the infant or child reproduces observed ac4ons as a way of understanding them beEer. The importance of reproducing observed ac4ons was core to Piaget’s theory of the “sensory motor stage” (0 – 2 years) of cogni4on. (Goswami, 200b8, p. 11) • Piaget argued that inten4onal imita4on emerged at around 18 months, but it has since been shown that babies as young as 1 hour old can imitate facial ac4ons (Meltzoﬀ & Moore, 1983). In Meltzoﬀ and Moore’s classic 1983 study, adults modelled gestures like tongue protrusion and mouth opening in a quiet environment, and the infants reproduced these gestures. By around 9 months, babies can learn how to manipulate novel objects such as experimenter‐built toys by watching others manipulate them (Meltzoﬀ, 1988). (Goswami, 2008b, p. 11) • Older babies can even imitate intended acts which are never observed. Meltzoﬀ
Learning by imita4on & TV • “Meltzoﬀ (1988) has evidence that infants of 14 moths of age can indeed learn • 14 months’ babies can learn the same novel ac4ons from watching television.” (Goswami, 2008, p. 62‐63) ac4ons from real experimenters and from experimenters canned in a TV video (on live) • But they learn less than from live ac4on (video deﬁcit eﬀect) • “Empirical research conducted using a number of diﬀerent experimental paradigms has demonstrated that infants, toddlers, and preschool children learn – Maybe because the processing of 2D less from television and 2D s4ll images than from live face‐to‐face interac4ons … s4muli is poorer than the processing of 3D s4muli This has been termed the video deﬁcit eﬀect: Infants’ ability to transfer learning from television to real life situa4ons is rela4vely poor … compared to their – Or because 2D s4muli are poorly impressive transfer of learning from a live demonstra4on to a diﬀerent understood and their rela4on to 3D real objects is not granted situa4on” (Zack, et al. 2009, p. 14) – Or because of poor representa4onal ﬂexibility (and memory requirements) • Is that because of 2D/3D encoding diﬀerences? What happens with 3D models? – An experiments conduced by Zack and coll. would show that the limit comes from the transfer of informa4on from one dimension to another (live adult demonstra4on) – Infants do just as well imita4ng 2D/2D than 3D/3D: 2D is not as impoverished as to block imita4on, and 2D does not represent a poorly understood condi4on in comparison with 3D (but live adult demonstra4on could help the understanding) – Representa4onal ﬂexibility seems to be the problem, thus memory would be the key
Imita4on, social cogni4on & mirror neurons • “Social cogni4on is currently an ac4ve area of research in • Among the studies on social developmental cogni4ve neuroscience. Interest has focussed on a cogni4on, mirror neurons have neural system called the “mirror neuron system”, which is known to gained lot of aEen4on be important for ac4on and imita4on. Mirror neurons were • Mirror neurons are involved in the discovered in monkey research on the representa4on of ac4on. These representa4on of an ac4on neurons were found to become ac4ve when the monkey performed object‐directed ac4ons such as tearing, grasping, holding and • Mirror neurons are ac4vated when manipula4ng. Furthermore, the same neurons became ac4ve when observing an ac4on, independently the animal observed someone else performing these ac4ons, such as from the speciﬁc motor realiza4on of someone else tearing paper. Mirror neurons were even ac4vated by the ac4on the sound of an ac4on, such as the sound of paper ripping (RizzolaT • Mirror neurons are related to the & Craighero, 2004). RizzolaT and his colleagues pointed out that an goal, and the agent ac4on implies a goal and an agent, and therefore argued that mirror • Mirror neurons could be involved in neurons may play an important role in understanding inten4ons. It the understanding of others’ has since been shown that mirror neurons are ac4ve during imita4on, inten4ons and are only ac4vated by biological ac4ons (e.g., a human hand • Specula4vely, in empathy grasping, Tai et al., 2004). • Mirror neurons are not ac4vated by mechanical ac4ons such as a robot hand grasping, and Meltzoﬀ has shown that babies will imitate ac4ons on objects made by human hands but not iden4cal ac4ons made by mechanical hands (Meltzoﬀ, 1995). • It is therefore thought that the mirror neuron system may be a neural substrate for understanding the ac4ons and internal states of others. Interes4ngly, children with disorders of social cogni4on such as au4sm appear to have very liEle mirror neuron ac4vity (DapreEo et al., 2006). It is thus speculated that the mirror neuron system plays a role in the development of empathy.” (Goswami, 2008b, p. 23)
Human imita4on • Infants understand and • Tomasello has argued that humans diﬀer profoundly from apes in their imitate adults’ inten4ons skills of imita4on and imita4ve learning, because the ability to learn novel • This seems to be a speciﬁcally behaviors via imita4on depends on the ability to understand the inten4ons human learning capacity of others. • Learning by imita4on seems • Most of our knowledge about imita4ve learning in infants comes from the to require the understanding pioneering work of Meltzoﬀ … Many of his more recent experiments of others’ inten4ons depend on the use of deferred imita4on … to see whether infants can (Tomasello, 1990) reproduce a novel ac4on that they have observed previously even if they are not allowed access to the cri4cal materials at the 4me of learning.” • Older babies can even imitate intended acts which are never observed. Meltzoﬀ manipulated a number of novel events (e.g., inser4ng a string of beads into a cylindrical container) so that the adult demonstrator accidentally failed to demonstrate the event (e.g. fumbled the beads so that they missed the opening). The observing infants took the beads and put them into the container successfully (Meltzoﬀ, 1995). • Empirical studies such as these show that the infants are going beyond what is observed and are aEribu4ng goals and inten4ons to the demonstrator (see also Tomasello and colleagues, e.g. Carpenter, Call & Tomasello, 2005). Understanding the goals of another person transforms their ac4ons into purposive behaviour (Gergely et al., 2002).
Understanding human inten4ons • Three levels of imita4on/understaninding • “Ac4ng animately. An observer perceives that the actor has generated his mo4on autonomously; that is, she others’ ac4ons & reading of inten4ons) dis4nguishes animate self‐produced ac4on from inanimate, caused mo4on. There is no understanding that – Perceiving others as actors that the actor has a goal, and so means and ends are not dis4nguished, nor are successful and unsuccessful produce their ac4ons (6 months old ac4ons. Although observers may learn from experience what animate actors typically do in familiar children) situa4ons, predic4ng behavior in novel circum‐ stances is basically impossible. – Perceiving others as having goals for their ac4ons (9 months) • Pursuing goals. An observer perceives and understands that the actor has a goal and behaves with persistence un4l reality matches the goal; that is, she understands that the actor recognizes the success or – Perceiving others as making plans for reaching their goal, and choosing the failure of his ac4ons with respect to the goal and con4nues to act in the face of failure. This understanding most ra4onal ac4on (14 months) implies that the observer also knows that the actor sees things (e.g., objects with respect to which he has (Tomasello, et al. 2005) goals, poten4al obstacles to goals, the results of ac4ons) and that this helps to guide ac4on and determine sa4sfac4on with results. Understanding ac4on in this way enables observers to predict what actors will do in at least some novel situa4ons. • Choosing plans. An observer perceives and under‐ stands that the actor considers ac4on plans and chooses which of them to enact in inten4onal ac4on (and these may be more or less ra4onal depending on their ﬁt with perceived reality). She also understands that in ac4ng toward a goal the actor chooses which en44es in its perceptual ﬁeld to aEend to. In general, the observer understands that actors act and aEend to things for reasons, which enables her to predict what an actor will do in a wide variety of novel situa4ons. (All elements of Fig. 1 present.) Children’s understanding of these diﬀerent aspects of inten4onal ac4on and percep4on emerge, in this order, at diﬀerent points in infancy“ • “Six‐month‐old infants perceive animate ac4on and follow gaze direc4on, which enables them to build up experiences on the basis of which they predict people’s ac4ons in familiar contexts. By 9 months of age, infants understand that that people have goals and persist in behaving un4l they see that their goal has been reached (avoiding obstacles and persis4ng past accidents and failures in the process) –be‐ ing happy when the goal is reached and disappointed if it is not. By 14 months of age, infants begin to understand full‐ ﬂedged inten4onal ac4on –including the rudiments of the way people make ra4onal decisions in choosing ac4on plans for accomplishing their goals in par4cular reality contexts and selec4vely aEending to goal‐ relevant aspects of the situa4on.“ (Tomasello, et al., 2005)
Engaging in shared inten4ons • 3 levels of engagement in • “Human infants are extremely sensi4ve to social con4ngencies. In their face‐to‐face interac4ons shared inten4ons: with adults, infants from just a few months of age display the ability to take turns in the sense of – Dyadic engagement: ac4ng when the adult is more passive and being more passive when the adult is ac4ng face to face (Trevarthen 1979). When these con4ngencies are broken –for example, in experiments in which interac4ons and the adult’s behavior is preprogrammed (or played to the infant over delayed video) –infants protoconversa4ons show various signs of being out of sorts (for reviews, see Gergely & Watson 1999 and Rochat & with shared emo4ons Striano 1999). Infants’ early social interac4ons thus clearly show mutual responsiveness on the – Tryadic engagement: behavioral level. But there is another dimension to these interac4ons that goes beyond simple doing things together, 4ming and con4ngency. Human infants and adults interact with one another dyadically in what but without assigning are called protoconversa4ons. These are social interac4ons in which the adult and infant look, roles for the reaching touch, smile, and vocalize toward each other in turn‐taking sequences. But as most observers of of the goal; sharing infants have noted, the glue that holds proto‐ conversa4ons together is not just con4ngency but percep4on and goals (9‐12 months) the exchange of emo4ons (Hobson 2002; Trevarthen 1979). – Collabora4ve • At around 9 to 12 months of age, as infants are beginning to understand other persons as goal engagement = sharing directed, they also begin to engage with them in ac4vi4es that are triadic in the sense that they ac4on plans (12‐15 involve child, adult, and some outside en4ty to‐ ward which they both direct their ac4ons. These months) are ac4vi4es such as giving and taking objects, rolling a ball back and forth, building a block tower together, puTng away toys together, “pretend” games of ea4ng or drinking, “reading” books, and poin4ng‐and‐naming games (Hay 1979; Hay & Murray 1982; Verba 1994). During these ac4vi4es, infants’ looking becomes coordinated with that of the other person triadically toward the relevant outside objects as well. When researchers focus on this aspect of the joint ac4vity, it is most oyen called “joint aEen4on” (e.g., see papers in Moore & Dunham 1995) – what we will call at this level joint percep4on. • At around 12 to 15 months of age, infants’ triadic engagements with others undergo a At around 12 to 15 months of age, infants’ triadic engagements with others undergo a signiﬁcant qualita4ve change. In a classic longitudinal study, Bakeman and Adamson (1984) categorized infants’ interac4ons with their mothers as involving, among other things, either “passive joint engagement” or “coordinated joint engagement.” Passive joint engagement referred to triadic interac4ons in general, whereas coordinated joint engagement referred to triadic interac4ons in which the infant was much more ac4ve in the interac4on –not just following adult leads, but also some4mes direc4ng adult behavior and aEen4on as well in a more balanced manner. The empirical ﬁnding was that al‐ though 9‐month‐old infants engaged in much passive joint engagement, it was not un4l 12 to 15 months of age that infants engaged in signiﬁcant amounts of coordinated joint engagement.
Humanness • “We propose that the crucial diﬀerence between human cogni4on and that of other species is the ability to par4cipate with others in • At the origin of human collabora4ve ac4vi4es with shared goals and inten4ons: shared culture and cogni4on inten4onality. Par4cipa4on in such ac4vi4es requires not only especially stand two capaci4es: powerful forms of inten4on reading and cultural learning, but also a • ‐ mind reading, and in unique mo4va4on to share psychological states with oth‐ ers and unique par4cular: the capacity of forms of cogni4ve representa4on for doing so. The result of par4cipa4ng perceiving and in these ac4vi4es is species‐unique forms of cultural cogni4on and understanding others’ evolu4on, enabling everything from the crea4on and use of linguis4c inten4ons symbols to the construc4on of social norms and individual beliefs to the • ‐ a mo4va4on for establishment of social ins4tu4ons. In support of this proposal we argue engaging in shared and present evidence that great apes (and some children with au4sm) inten4on ac4vi4es understand the basics of inten4onal ac4on, but they s4ll do not par4cipate in ac4vi4es involving joint inten4ons and aEen4on (shared inten4onality). Human children’s skills of shared inten4onality develop • So: shared inten4onality gradually during the ﬁrst 14 months of life as two ontogene4c pathways is what makes humans intertwine: (1) the general ape line of understanding others as animate, special in the animal goal‐directed, and inten4onal agents; and (2) a species‐unique mo4va4on reign to share emo4ons, experience, and ac4vi4es with other persons. The • (Tomasello, 2005) develop‐ mental outcome is children’s ability to construct dialogic cogni4ve representa4ons, which enable them to par4cipate in earnest in the collec4vity that is human cogni4on” (Tomasello, et al., 2005)
Cultural intelligence hypothesis • Baby humans diﬀer • “Some other ape species transmit some behaviors socially or culturally , but their from primates on species‐ typical cogni4on does not depend on par4cipa4ng in cultural interac4ons in social abili4es the same way as it does in humans, who must • Humans have • (i) learn their na4ve language in social interac4ons with others, developed special • (ii) acquire necessary subsistence skills by par4cipa4ng with experts in established cogni4ve skills as a cultural prac4ces, and result of the • (iii) (in many cultures) acquire skills with wriEen language and mathema4cal symbols development of specialized skills for through formal schooling. absorbing • In the end, human adults will have all kinds of cogni4ve skills not possessed by other knowledge and primates, but this outcome will be due largely to children’s early emerging, prac4ces of their specialized skills for absorbing the accumulated skillful prac4ces and knowledge of social group their social group (so that a child growing up outside of any human culture would develop few dis4nc4vely human cogni4ve skills). Humans’ especially powerful skills of social‐cultural cogni4on early in ontogeny thus serve as a kind of “bootstrap” for the dis4nc4vely complex development of human cogni4on in general. We may call this the cultural intelligence hypothesis” • “However, we should note that because the children were somewhat more skillful than the apes in the causality tasks not involving ac4ve tool manipula4on, as well as in the tasks of social cogni4on, it is possible that what is dis4nc4vely human is not social‐cultural cogni4on as a specialized domain, as we have hypothesized. Rather, what may be dis4nc4ve is the ability to understand unobserved causal forces in general, including (as a special case) the mental states of others as causes of behavior. Even in this case, however, it is a plausible hypothesis that understanding hidden causal forces evolved ﬁrst to enable humans to understand the mental states of other persons, and this generalized only later to the physical domain”. (Herrmann, et al., 2007)
NATURAL PEDAGOGY: ‐ THE INDUCTION PROBLEM ‐ THE CONDITIONS FOR NATURAL PEDAGOGY
Natural pedagogy • “… human communica4on is speciﬁcally • Development of natural pedagogy: adapted to fulﬁl the funciton of transmiTng generic knowledge between • Development of tools’ making prac4ces individuals.” (Gergely & Csibra, p. 3) represents an evolu4ve pressure • “A new type of communica4ve learning • Because these prac4ces cannot be learned/ system based on ostensive‐referen4al transmiEed by other, available mechanisms demonstra4ons of knowledge … expert user of learning from imita$on/observa$on* ac4vely guide the novice by selec4vely manifes4ng the informa4on to be acquire • Because they represent opaque contents for and generalized. cogni4on • … children … are always novices with respect • Thus, humans have evolved mechanisms that to the accumulated knowledge of their serve the pedagogical func4on of culture. transmiTng cogni4vely opaque contents • This is why we call the speciﬁc aspects of • These mechanisms are part of the more human communica4on that allow and general communica4on system facilitate the transfer of generic knowledge • They consist of demonstra4on acts: to novices Natural Pedagogy. ” (Gergely & ostensive‐referen4al demonstra4ons Csibra, p. 4)
Adults/children natural pedagogical system • “When children are shown an ac4on • Children observe and imitate adults performed in a par4cular style leading to a – Children spontaneously imitate causal ac4ons clear end state (e.g. a mouse is hopping that lead to achieve goals, and ignore other across the table into a house), they tend to components of the global ac4on reproduce only the end state (put the mouse – The others components of the ac4on are into the house), oyen ignoring the manner of opaque to children’s cogni4on ac4on (hopping). However, if the relevant – But, when the “teacher” makes it clear that informa4on concerning the end state is these components of the ac4on are relevant, communicated to them verbally by the actor children do pay aEen4on, and imitate before the demonstra4on (“the mouse lives • Adults use their communica4on system to in the house”), they reproduce the ac4on facilitate children’s learning style more oyen. • Young children are recep4ve to adult’s • Ostensive communica4on does not only ostensive demonstra4on before they are able make children pay more aEen4on to the to use it for learning demonstra4on but they also see it as a special opportunity to acquire generalizable knowledge.” (Gergely & Csibra, p. 5) • Ostensive signals allow to • “recent studies ...demonstrate this – Disambiguate the nature of the ac4on preparadness in the form of three kinds of (communica4on, not just using the tool) early perceptual and cogni4ve biases: – Disambiguate the target of the communica4on (you)
Ostensive signals • Preference for ostensive • 1. preferen4al aEen4on for signals : the sources of ostensive – Gaze contact signals • Newborns preferen4ally look at schema4c face‐like paEerns with direct gaze vs averted gaze; preference disappears when faces are upside‐down; preference disappears when the typical iris/sclera paEers of eyes is inverted • Same neural ac4va4on for infants and adults in response to direct gaze and common neural ac4va4on for two diﬀerent ostensive s4muli (direct gaze & eye‐brow raise) – Motherese – Mo4onese
Referen4al expecta4ons – Infants follow the gaze of interac4ng • 2. Referen4al expecta4on adults to iden4fy what they are looking induced by ostensive contexts at, before they can understand language • Eight‐months olds observed – Useful for sampling parts of someone on a computer screen the world that others found ostensively looking at and interes4ng, and present in gree4ng them before shiying her other animals gaze to llok behind one of two – Human infants followgaze barriers. Following this, an object shiys only when these are preceded by ostensive signals was revealed either at the (gree4ng, gaze contact) targeted or at the other occluded – Infants expect to ﬁnd an object at the loca4on. Infants’ looking paEern “end” of a gaze‐following in an ostensive suggested that they expected to context ﬁnd an object at the loca4on – 13 months old Infants expect to where the person’s gaze wwas ﬁnd the named object (if its name directed at, just like older infants is part of their vocabulary) do in similar live – But not if the gesture and word are situa4ons.” (Gergely & Csibra, p. emiEed by diﬀerent persons 5‐8)
Interpreta4on bias – Not only infants are prepared to receive ostensive–referen4al • 3. interpreta4on bias to communica4on, but they do expect to learn something generalizable from it (and not just a par4cular instance) = to learn about referent preferen4ally encode the kinds – When infants (18 months old) observe adults expressing content of ostensive‐ emo4onal valence in rela4onship to an object in a non‐ communica4ve context, they infer that person’s par4cular preference (she does not like it). But when the same paEern referen4al communica4on as of valence expression is inserted in a communica4ve context, infants aEach the expressed value to the object and represen4ng generalizable expect that other people will react in the same manner to the object (it is disgus4ng for everybody) knowledge” – Infants (9 months old) shiy their encoding paEern from loca4on to appearance features when the situa4on shiys from non‐communica4ve to communica4ve. • “this is what dis4nguishes our – They are more likely to detect change in loca4on in hypothesis in the ﬁrst place a non‐communica4ve situa4on, but detect more oyen features change in a communica4ve situa4on and neglect loca4on; and this happens even in from compe4ng proposals, situa4ons in which loca4on is important, pragma4cally, such as hiding games according to which human – This bias could explain A not‐B task errors: children stop being interested in loca4on and do not mind communica4on originates about the new loca4on, because the communica4ve contexts has made them focus on evolu4onarily and the features of the object. In fact, once communica4ve cues are removed, the errors ontogene4cally from a basic – diminish. Appearance features are beEer candidates for mo4ve to cooperate with later use and object iden4ﬁca4on, thus for generaliza4on. others to achieve shared – Communica4on has evolved not only for collabora4on‐purposes but goals.” (Gergely & Csibra, p. also under the pressure of learning/teaching purposes 5‐9)
Social learning mechanisms • “There are many types of social learning • Social learning mechanisms are common to several mechanisms in the animal kingdom, and they all animal species involve some form of observa4onal learning, where • Learning generalizable knowledge from social the observa4on of an adap4ve behavior of another interac4ons seems to be speciﬁc to humans individual makes it more likely that the observer will • Natural pedagogy seems to be universal, thus produce the same or similar behaviors in the future. In this sense, social learning represents transmission “natural” of general knowledge or skills from one individual to another. • Non‐human animals communicate about episodic, non‐generalizable informa4on (that applies only to the here and now), and learn new skills by observa4on or scaﬀolded individual learning, they do not seem to use communica4on to pass on generalizable knowledge to others.” • “ This discrepancy between general claims about the absence of teaching and the actual reports is likely to reﬂect the enormous diﬀerences between teaching in Western socie4es and in more tradi4onal cultures. It is not just that Western educa4on relies heavily on formal schooling but also that it aims to provide verbal explana4on and jus4ﬁca4on for what is being taught. … however, Natural Pedagogy … seems to be universal.” (Gergely & Csibra, 2009, p. 12‐14)
• “Child development is today conceptualized as an essen4ally social process, based on incremental knowledge acquisi4on driven by cultural experience and social context. We have “social” brains.” (Goswami, 2008b, p. 1)
Socially distributed cogni4on • Distributed “ If we want to explain the informa4on processing proper4es cogni4on: of individuals, we have no choice but to aEempt to infer what – The unit of is inside the individual’s mind. Cogni4ve scien4sts do this by analysis of construc4ng carefully selected contexts for elici4ng behavior cogni4ve from which they can aEribute internal states to actors. performanc However, if we take the cockpit system as the unit of analysis, es should be we can look inside it and directly observe many of the extended phenomena of interest. In par4cular, we can directly observe beyond the the many representa4ons that are inside the cockpit system, individual yet outside the heads of the pilots. We can do a lot of so as to research on the cogni4ve proper4es of such a system (i.e., we encompass social and can give accounts of the system’s behavioral proper4es in material terms of its internal representa4ons), without saying interac4on anything about the processes that operate inside individual s with tools actors (Hutchins, 1990, 1991, 1995). This suggests that rather than trying to map the ﬁndings of cogni4ve psychological studies of individuals directly onto the individual pilots in the cockpit, we should map the conceptualiza4on of the cogni4ve system onto a new unit of analysis: the cockpit as a whole. ” (Hutchins, 1995, p. 267)
Socially distributed cogni4on • Distributed • “Let us now apply the cogni4ve science frame to the cockpit as a cogni4ve cogni4on: system. How are the speeds represented in the cockpit? How are these – Remebember representa4ons transformed, processed, and coordinated with other ing the speed representa4ons in the descent, approach, and landing? How does the is the task cockpit system remember the speeds at which it is necessary to change and result of the conﬁgura4on of the wing in order to maintain safe ﬂight? cogni4ve processes • The observable representa4ons directly involved in the cockpit processes involving the that coordinate airspeed with ﬂap and slat seTngs are: the gross weight pilots of the display (Figure 2), the speed card booklet (Figure l), the two airspeed cockpit as indicator instruments with internal and external bugs (Figure 3), the well as speed select window of the ﬂight guidance control panel, and the speed‐ various instruments related verbal exchanges among the members of the crew. The speed‐ related verbaliza4ons may appear in the communica4on of the values from PNF to PF while seTng the speed bugs, in the ini4al slat extension cross‐check, in the sub‐ sequent conﬁgura4on changes, in the cross‐check phase of the before‐landing checklist performance, in the PNF’s approach progress report at 500 feet AFL, and in any required speed devia4on call outs on the ﬁnal approach segment ayer the selec4on of the landing ﬂap seTng. • In addi4on to the directly observable media listed earlier, we may also assume that some sort of representa4on of the speeds has been created in two media that are not directly observable: the memories of the two pilots, themselves. ” (Hutchins, 1995, p. 275)
Distributed cogni4on • Distributed • “We will advocate an externalism about mind, but one that is in no way cogni4on: grounded in the debatable role of truth‐condi4ons and reference in ﬁxing – Performance the contents of our mental states. Rather, we advocate an “ac4ve s typically externalism”, based on the ac4ve role of the environment in driving described as cogni4ve processes.” cogni4ve are signiﬁcantly worst in • “The informa4on in OEos notebook, for example, is a central part of his absence of iden4ty as a cogni4ve agent. What this comes to is that OEo himself is interac4on best regarded as an extended system, a coupling of biological organism with tools, others, or of and external resources. epistemic • The informa4on in OEos notebook, for example, is a central part of his ac4ons that iden4ty as a cogni4ve agent. What this comes to is that OEo himself is have no best regarded as an extended system, a coupling of biological organism other aim and external resources.” (Clark & Chalmers, 1998) than favoring a beEer knowledge of the world
Social neurosciences • Strong accent on social cogni4on, in • “Panoramic photographs of the earth from space cogni4ve sciences and in the new reveal agricultural runoﬀs that stretch hundreds of science of learning miles out to sea …From this ionospheric perspec4ve, – Social neuroscience: importance of one could easily visualize eﬀects that could not be mul4level, integra4ve analysis of fully comprehended from a closer focal point. This complex psychological phenomena simple example from space sciences illustrates a principle that seems so obvious … but that oyen appears incomprehensible in the psychological sciences and neurosciences. There are phenomena that may be explicable in terms of events at a microlevel of analysis but that are more easily studied and more fully comprehended by reference to broader and mul4ple levels analysis.” (Cacioppo & Berentson, 1992, p. 1019) • “Cogni4ve behavioral and developmental neuroscience, for instance, are all ac4ve areas of research, but social neuroscience strikes some as being an oxymoron (see ScoE, 1991). It is not…” (Cacioppo & Berentson, 1992, p. 1020)
Integra4on of levels of analysis • Social neuroscience: importance of mul4level, integra4ve • “… the brain does not exist in isola4on but rather is a fundamental but analysis of complex psychological phenomena interac4ng component of a developing or aging individual who is a mere actor in the larger theater of life. This theater is undeniably social, – 1. Neurochemical events inﬂuence social processes; beginning with prenatal care, mother‐infant aEachment, and early Social processes inﬂuence neurochemical events childhood experiences, and ending with loneliness or social support and • Diﬃculty in the integra4on of neuroscience and social with familiar or societal decisions about care for the elderly. … Social psychology levels of analysis: diﬀerent scales into which brain psychology, with its panoramic focus on the eﬀects of human associa4on and behavior can be represented and the impact of society on the individual, is therefore a fundamental • The level of organiza4on of psychological phenomena vary from molecular the organism set into a physical environment although some4mes unaknowledged complement to the and a socio‐cultural context neurosciences.” (Cacioppo & Berentson, 1992, p. 1020) • Neurosciences generally encompass the lower level of the spectrum, social psychology the higher one • Integra4on means that analyses at each level of organiza4on • “Cogni4ve behavioral and developmental neuroscience, for instance, are can inform, reﬁne or constrain inferences in the other levels all ac4ve areas of research, but social neuroscience strikes some as being – 2. The study of the elements of the system can fall short an oxymoron (see ScoE, 1991). It is not…” (Cacioppo & Berentson, 1992, of useful and comprehensive explana4ons p. 1020) • In other sciences, the existence of diﬀerent levels of explana4on (protons/rocks) does not lead to considering • “… these ﬁelds diﬀer in the level at which behavioral phenomena are geology as a folk theory when compared with molecular level uni4zed, although they need not diﬀer in terms of the behavioral models. phenomenon under inves4ga4on. … Consequently, the conceptual units • Dis4nc4ve levels of analysis are complementary, not alterna4ve and dimensions of one level seldom map isomorphically into those of – 3. A set of neural events can be a suﬃcient cause for another. producing a psychological phenomenon, without being a • Thus, social psychological analyses focus on social structures and necessary one processes that characterize func4onal aspects of neurophysiological • E.g., lying rubustly produces certain electrodermal responses ; mechanisms, but a par4cular func4on cannot be readily characterized in but other condi4ons can produce the same electrodermal the terminology and concepts of neurophysiology. Moreover, a given responses • E.g. schizophrenia is reliably associated with elevated func4on can be implemented by one or more neurophysiological dopamine levels (elevated dopamine levels produce mechanisms whose boundaries may not be obvious, at least ini4ally, from schizophrenia‐like symptoms) but excessive levels of anatomical considera4ons.” dopamine are not necessarily involved in all cases of schizophrenia • Important advances have been made and will con4nue to be made using – However, when other neurochemical mechanisms are iden4ﬁed that produce schizophrenia‐like symptoms with single levels of analysis. … there is an addi4onal beneﬁt to be gained, a diﬀerent neurochemical basis, it is possible to part the however, from a mul4level analysis of the phenomenon … from various psychological term “schizophrenia” in diﬀerent pathologies. structural scales or perspec4ves, ranging from the neuroscien4ﬁc • In the case of mul4ple determinants of a certain behavior, (“microscopic”) to the social psychological (“macroscopic”).” (Cacioppo & studies on the suﬃciency of a certain neurophysiological Berentson, 1992, p. 1021) condi4on in causing a certain phenomenological phenomenon are impôrtant but lack generalizing power.
from medicine to educa4on • “… no single level of behavioral organiza4on is best for all psychological ques4ons. • An example can be found in the rela4ve u4lity of specifying the sociocogni4ve versus the neurophysiological basis of pa4ent delay following the onset of gynecologic cancer. Women can now survive most gynecologic cancers if the disease is diagnosed and treated early. … The form of the representa4on of pa4ent delay oﬀered by neuroscien4ﬁc analyses of pa4ent delay, although perhaps contribu4ng to more complete understanding of the phenomenon, is not op4mal for iden4fying the determinants of pa4ent delay or for developing eﬀec4ve interven4ons to minimize such delay. Huge savings in resources and human suﬀering are there to be reaped not through a speciﬁca4on of the brain circuits underlying pa4ent delay, but by well‐ conceived public health campaings that iden4fy the early signs of cancer… ” (Cacioppo & Berentson, 1992, p. 1022) • “It follows … that an exclusive focus on a reduc4onis4c (e.g. neurophysiological, molecular, gene4c) level of analysis can mask contribu4ons of other levels of organiza4on to mental order and disorder and thereby constrain theore4cal accounts of psychological phenomena.” • “Hence, without aEen4on to basic social psychological factors and processes, the decade of the brain may yield some spectacular images and experimental eﬀects but rather limited answers to the problems of mental health.” (Cacioppo & Berentson, 1992, p. 1025)
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