Temporal predictability enhances judgements of causality in elemental causal induction from both observation and intervention

When the temporal interval or delay separating cause and effect is consistent over repeated instances, it becomes possible to predict when the effect will follow from the cause, hence temporal predictability serves as an appropriate term for describing consistent cause-effect delays. It has been demonstrated that in instrumental action-outcome learning tasks, enhancing temporal predictability by holding the cause-effect interval constant elicits higher judgements of causality compared to conditions involving variable temporal intervals. Here, we examine whether temporal predictability exerts a similar influence when causal learning takes place through observation rather than intervention through instrumental action. Four experiments demonstrated that judgements of causality were higher when the temporal interval was constant than when it was variable, and that judgements declined with increasing variability. We further found that this beneficial effect of predictability was stronger in situations where the effect base-rate was zero (Experiments 1 and 3). The results therefore clearly indicate that temporal predictability enhances impressions of causality, and that this effect is robust and general. Factors that could mediate this effect are discussed.     

Collaboration facilitates abstract category learning

We examined the effects of collaboration (dyads vs. individuals) and category structure (coherent vs. incoherent) on learning and transfer. Working in dyads or individually, participants classified examples from either an abstract coherent category, the features of which are not fixed but relate in a meaningful way, or an incoherent category, the features of which do not relate meaningfully. All participants were then tested individually. We hypothesized that dyads would benefit more from classifying the coherent category structure because past work has shown that collaboration is more beneficial for tasks that build on shared prior knowledge and provide opportunities for explanation and abstraction. Results showed that dyads improved more than individuals during the classification task regardless of category coherence, but learning in a dyad improved inference-test performance only for participants who learned coherent categories. Although participants in the coherent categories performed better on a transfer test, there was no effect of collaboration.     

Audiovisual speech facilitates voice learning

In this research, we investigated the effects of voice and face information on the perceptual learning of talkers and on long-term memory for spoken words. In the first phase, listeners were trained over several days to identify voices from words presented auditorily or audiovisually. The training data showed that visual information about speakers enhanced voice learning, revealing cross-modal connections in talker processing akin to those observed in speech processing. In the second phase, the listeners completed an auditory or audiovisual word recognition memory test in which equal numbers of words were spoken by familiar and unfamiliar talkers. The data showed that words presented by familiar talkers were more likely to be retrieved from episodic memory, regardless of modality. Together, these findings provide new information about the representational code underlying familiar talker recognition and the role of stimulus familiarity in episodic word recognition.     

Temporal preparation facilitates perceptual identification of letters

Recent evidence has suggested that perceptual processing of single stimulus features improves when participants are temporally prepared for the occurrence of the stimuli. This study was conducted to investigate whether the benefit of temporal preparation generalizes to perceptual identification of more complex stimuli, such as letters. In three experiments, participants discriminated masked letters under high- and low-temporal-preparation conditions. Visual discrimination performance in all experiments improved when the participants were temporally prepared. Therefore, the present results support the notion that perception benefits from temporal preparation not only at the feature level, but also at subsequent levels at which feature information is integrated.     

D-cycloserine facilitates context-specific fear extinction learning

D-cycloserine (DCS) may facilitate fear extinction learning, but the behavioral consequences and mechanisms behind this effect are not well understood at present. In this paper, we re-analyze data from previously reported null result experiments and find that rats showing above-median extinction learning during DCS treatment benefited from the drug, whereas rats showing below-median (and in this case little) extinction learning did not. Two additional experiments found that DCS facilitated extinction learning when specifically combined with a moderate, but not a small, number of extinction trials. DCS thus facilitates extinction learning only if the behavioral procedure first engages the extinction learning process. The benefits of the drug, however, were specific to the context in which extinction was learned--i.e., DCS did not prevent or influence the renewal of fear observed when the extinguished cue was tested in the original conditioning context.     

Causal imprinting in causal structure learning

Suppose one observes a correlation between two events, B and C, and infers that B causes C. Later one discovers that event A explains away the correlation between B and C. Normatively, one should now dismiss or weaken the belief that B causes C. Nonetheless, participants in the current study who observed a positive contingency between B and C followed by evidence that B and C were independent given A, persisted in believing that B causes C. The authors term this difficulty in revising initially learned causal structures "causal imprinting." Throughout four experiments, causal imprinting was obtained using multiple dependent measures and control conditions. A Bayesian analysis showed that causal imprinting may be normative under some conditions, but causal imprinting also occurred in the current study when it was clearly non-normative. It is suggested that causal imprinting occurs due to the influence of prior knowledge on how reasoners interpret later evidence. Consistent with this view, when participants first viewed the evidence showing that B and C are independent given A, later evidence with only B and C did not lead to the belief that B causes C.     

Second-order conditioning of human causal learning

This article provides the first demonstration of a reliable second-order conditioning (SOC) effect in human causal learning tasks. It demonstrates the human ability to infer relationships between a cause and an effect that were never paired together during training. Experiments 1a and 1b showed a clear and reliable SOC effect, while Experiments 2a and 2b demonstrated that first-order extinction did not affect SOC. These results were similar to those found in animal and human conditioning and suggested that a similar associative mechanism could explain these effects. However, they can also be used to look into the underlying causal mental model people build and store while they are learning this task. From a cognitive view, overall results suggest that an independent rather than a chain causal mental model is stored after second-order learning in human causal tasks.     

Cognitive biases in human causal learning

The main aim of this work was to look for cognitive biases in human inference of causal relationships in order to emphasize the psychological processes that modulate causal learning. From the effect of the judgment frequency, this work presents subsequent research on cue competition (overshadowing, blocking, and super-conditioning effects) showing that the strength of prior beliefs and new evidence based upon covariation computation contributes additively to predict causal judgments, whereas the balance between the reliability of both, beliefs and covariation knowledge, modulates their relative weight. New findings also showed "inattentional blindness" for negative or preventative causal relationships but not for positive or generative ones, due to failure in codifying and retrieving the necessary information for its computation. Overall results unveil the need of three hierarchical levels of a whole architecture for human causal learning: the lower one, responsible for codifying the events during the task; the second one, computing the retrieved information; finally, the higher level, integrating this evidence with previous causal knowledge. In summary, whereas current theoretical frameworks on causal inference and decision-making usually focused either on causal beliefs or covariation information, the present work shows how both are required to be able to explain the complexity and flexibility involved in human causal learning.     

Expectations and interpretations during causal learning

In existing models of causal induction, 4 types of covariation information (i.e., presence/absence of an event followed by presence/absence of another event) always exert identical influences on causal strength judgments (e.g., joint presence of events always suggests a generative causal relationship). In contrast, we suggest that, due to expectations developed during causal learning, learners give varied interpretations to covariation information as it is encountered and that these interpretations influence the resulting causal beliefs. In Experiments 1A-1C, participants' interpretations of observations during a causal learning task were dynamic, expectation based, and, furthermore, strongly tied to subsequent causal judgments. Experiment 2 demonstrated that adding trials of joint absence or joint presence of events, whose roles have been traditionally interpreted as increasing causal strengths, could result in decreased overall causal judgments and that adding trials where one event occurs in the absence of another, whose roles have been traditionally interpreted as decreasing causal strengths, could result in increased overall causal judgments. We discuss implications for traditional models of causal learning and how a more top-down approach (e.g., Bayesian) would be more compatible with the current findings.     

Sound symbolism facilitates early verb learning

Some words are sound-symbolic in that they involve a non-arbitrary relationship between sound and meaning. Here, we report that 25-month-old children are sensitive to cross-linguistically valid sound-symbolic matches in the domain of action and that this sound symbolism facilitates verb learning in young children. We constructed a set of novel sound-symbolic verbs whose sounds were judged to match certain actions better than others, as confirmed by adult Japanese- as well as English speakers, and by 2- and 3-year-old Japanese-speaking children. These sound-symbolic verbs, together with other novel non-sound-symbolic verbs, were used in a verb learning task with 3-year-old Japanese children. In line with the previous literature, 3-year-olds could not generalize the meaning of novel non-sound-symbolic verbs on the basis of the sameness of action. However, 3-year-olds could correctly generalize the meaning of novel sound-symbolic verbs. These results suggest that iconic scaffolding by means of sound symbolism plays an important role in early verb learning.     

Bayesian generic priors for causal learning

The article presents a Bayesian model of causal learning that incorporates generic priors--systematic assumptions about abstract properties of a system of cause-effect relations. The proposed generic priors for causal learning favor sparse and strong (SS) causes--causes that are few in number and high in their individual powers to produce or prevent effects. The SS power model couples these generic priors with a causal generating function based on the assumption that unobservable causal influences on an effect operate independently (P. W. Cheng, 1997). The authors tested this and other Bayesian models, as well as leading nonnormative models, by fitting multiple data sets in which several parameters were varied parametrically across multiple types of judgments. The SS power model accounted for data concerning judgments of both causal strength and causal structure (whether a causal link exists). The model explains why human judgments of causal structure (relative to a Bayesian model lacking these generic priors) are influenced more by causal power and the base rate of the effect and less by sample size. Broader implications of the Bayesian framework for human learning are discussed.     

Categorical congruence facilitates multisensory associative learning

Learning about objects often requires making arbitrary associations among multisensory properties, such as the taste and appearance of a food or the face and voice of a person. However, the multisensory properties of individual objects usually are statistically constrained, such that some properties are more likely to co-occur than others, on the basis of their category. For example, male faces are more likely to co-occur with characteristically male voices than with female voices. Here, we report evidence that these natural multisensory statistics play a critical role in the learning of novel, arbitrary associative pairs. In Experiment 1, we found that learning of pairs consisting of human voices and gender-congruent faces was superior to learning of pairs consisting of human voices and gender-incongruent faces or of pairs consisting of human voices and pictures of inanimate objects (plants and rocks). In Experiment 2, we found that this “categorical congruency” advantage extended to nonhuman stimuli, as well—namely, to pairs of class-congruent animal pictures and vocalizations (e.g., dogs and barks) versus class-incongruent pairs (e.g., dogs and bird chirps). These findings suggest that associating multisensory properties that are statistically consistent with the various objects that we encounter in our daily lives is a privileged form of learning.     

Unconscious reward facilitates motion perceptual learning

Reward signal plays an important role in guiding human learning behaviour. Recent studies have provided evidence that reward signal modulates perceptual learning of basic visual features. Typically, the reward effects on perceptual learning were accompanied with consciously presented reward during the learning process. However, whether an unconsciously presented reward signal that minimizes the contribution of attentional and motivational factors can facilitate perceptual learning remains less well understood. We trained human subjects on a visual motion detection task and subliminally delivered a monetary reward for correct response during the training. The results showed significantly larger learning effect for high reward-associated motion direction than low reward-associated motion direction. Importantly, subjects could neither discriminate the relative values of the subliminal monetary reward nor correctly report the reward-direction contingencies. Our findings suggest that reward signal plays an important modulatory role in perceptual learning even if the magnitude of the reward was not consciously perceived.     

Temporal information and children's and adults' causal inferences

Three experiments examined whether children and adults would use temporal information as a cue to the causal structure of a three-variable system, and also whether their judgements about the effects of interventions on the system would be affected by the temporal properties of the event sequence. Participants were shown a system in which two events B and C occurred either simultaneously (synchronous condition) or in a temporal sequence (sequential condition) following an initial event A. The causal judgements of adults and 6–7-year-olds differed between the conditions, but this was not the case for 4-year-olds' judgements. However, unlike those of adults, 6–7-year-olds' intervention judgements were not affected by condition, and causal and intervention judgements were not reliably consistent in this age group. The findings support the claim that temporal information provides an important cue to causal structure, at least in older children. However, they raise important issues about the relationship between causal and intervention judgements.     

Higher-order retrospective revaluation in human causal learning

Previous studies demonstrated that participants will retrospectively adjust their ratings about the relation between a target cue and an outcome on the basis of information about the causal status of a competing cue that was previously paired with the target cue. We demonstrate that such retrospective revaluation effects occur not only for target cues with which the competing cue was associated directly, but also for target cues that were associated indirectly with the competing cue. These second-order and third-order retrospective revaluation effects are compatible with certain implementations of the probabilistic contrast model and with a modified, extended comparator model, but cannot be explained on the basis of a revised Rescorla-Wagner model or a revised SOP model.     

Aging and retrospective revaluation of causal learning

In a 2-stage causal learning task, young and older participants first learned which foods presented in compound were followed by an allergic reaction (e.g., STEAK-BEANS→ REACTION) and then the causal efficacy of 1 food from these compounds was revalued (e.g., BEANS→ NO REACTION). In Experiment 1, unrelated food pairs were used, and although there were no age differences in compound- or single-cue-outcome learning, older adults did not retrospectively revalue the causal efficacy of the absent target cues (e.g., STEAK). However, they had weaker within-compound associations for the unrelated foods, and this may have prevented them from retrieving the representations of these cues. In Experiment 2, older adults still showed no retrospective revaluation of absent cues even though compound food cues with pre-existing associations were used (e.g., STEAK-POTATO), and they received additional learning trials. Finally, in Experiment 3, older adults revalued the causal efficacy of the target cues when small, unobtrusive icons of these cues were present during single-cue revaluation. These findings suggest that age-related deficits in causal learning for absent cues are due to ineffective associative binding and reactivation processes.     

Cue competition effects in human causal learning

Five experiments involving human causal learning were conducted to compare the cue competition effects known as blocking and unovershadowing, in proactive and retroactive instantiations. Experiment 1 demonstrated reliable proactive blocking and unovershadowing but only retroactive unovershadowing. Experiment 2 replicated the same pattern and showed that the retroactive unovershadowing that was observed was interfered with by a secondary memory task that had no demonstrable effect on either proactive unovershadowing or blocking. Experiments 3a, 3b, and 3c demonstrated that retroactive unovershadowing was accompanied by an inflated memory effect not accompanying proactive unovershadowing. The differential pattern of proactive versus retroactive cue competition effects is discussed in relationship to amenable associative and inferential processing possibilities.     

Retrieval during learning facilitates subsequent memory encoding

In multiple-list learning, retrieval during learning has been suggested to improve recall of the single lists by enhancing list discrimination and, at test, reducing interference. Using electrophysiological, oscillatory measures of brain activity, we examined to what extent retrieval during learning facilitates list encoding. Subjects studied 5 lists of items in anticipation of a final cumulative recall test and did either a retrieval or a no-retrieval task between study of the lists. Retrieval was from episodic memory (recall of the previous list), semantic memory (generation of exemplars from an unrelated category), or short-term memory (2-back task). Behaviorally, all 3 forms of retrieval enhanced recall of both previously and subsequently studied lists. Physiologically, the results showed an increase of alpha power (8-14 Hz) from List 1 to List 5 encoding when no retrieval activities were interpolated but no such increase when any of the 3 retrieval activities occurred. Brain-behavior correlations showed that alpha-power dynamics from List 1 to List 5 encoding predicted subsequent recall performance. The results suggest that, without intermittent retrieval, encoding becomes ineffective across lists. In contrast, with intermittent retrieval, there is a reset of the encoding process for each single list that makes encoding of later lists as effective as encoding of early lists.     

Asymmetry of generalization decrement in causal learning

Two experiments required volunteers to learn which of various “planes” caused high levels of pollution. Novel test items were then rated as causes of pollution. Items created by adding novel features were rated at the same level as that of the original training items but items created by removing features received reduced ratings. This asymmetry of generalization decrement was not predicted by a well-known configural model of stimulus representation (Pearce, 1987, 1994) but was predicted by a recently proposed model of stimulus representation, the replaced-elements model (Brandon, Vogel, & Wagner, 2000).