A number of ways of taxonomizing human learning have ben proposed. We examine the evidence for one such proposal, namely, that there exist independent explicit and implicit learning systems. This combines two further distinctions, (1) between learning that takes place with versus without concurrent awareness, and (2) between learning that involves the encoding of instances (or fragments) versus the induction of abstract rules or hypotheses. Implicit learning is assumed to involve unconscious rule learning. We examine the evidence for implicit learning derived from subliminal learning, conditioning, artificial grammar learning, instrumental learning, and reaction times in sequence learning. We conclude that unconscious learning has not been satisfactorily established in any of these areas. The assumption that learning in some of theses tasks (e.g., artificial grammar learning) is predominantly based on rule abstraction is questionable. When subjects cannot report the ''implicitly learned'' rules that govern stimulus selection, this is often because their knowledge consists of instances or fragments of the training stimuli rather than rules. In contrast to the distinction between conscious and unconscious learning, the distinction between instance and rule learning is a sound and meaningful way of taxonomizing human learning. We discuss various computational models of these two forms of learning.

Characteristics of dissociable human learning systems

The past 50 years have seen an accumulation of evidence suggesting that associative learning depends on high-level cognitive processes that give rise to propositional knowledge. Yet, many learning theorists maintain a belief in a learning mechanism in which links between mental representations are formed automatically. We characterize and highlight the differences between the propositional and link approaches, and review the relevant empirical evidence. We conclude that learning is the consequence of propositional reasoning processes that cooperate with the unconscious processes involved in memory retrieval and perception. We argue that this new conceptual framework allows many of the important recent advances in associative learning research to be retained, but recast in a model that provides a firmer foundation for both immediate application and future research.

/pdf/the-propositional-nature-of-human-associative-learning-1l9gvlzemy.pdf

The propositional nature of human associative learning.

Floral scent constitutes an ancient and important channel of communication between flowering plants, their pollinators, and enemies. Fragrance is a highly complex component of floral phenotype, with dynamic patterns of emission and chemical composition. The information content of specific volatile compounds is highly context dependent, and scent can function in direct and indirect ways from landscape to intrafloral scales. Floral scent promotes specialization in plant–pollinator relationships through private channels of unusual compounds, unique ratios of more widespread compounds, or through multicomponent floral filters. Floral scent also promotes outcrossing and reproductive isolation through floral constancy, via appetitive conditioning and discrimination on the basis of diverse mechanisms, including pheromone mimicry, odor intensity, complexity, composition, and synergy with visual stimuli. Finally, floral scent is a sexual signal and should be subject to the same selective pressures and modes of sig...

Wake Up and Smell the Roses: The Ecology and Evolution of Floral Scent

https://repub.eur.nl/pub/120844/Repub_120844_O-A.pdf

Don't fear 'fear conditioning': Methodological considerations for the design and analysis of studies on human fear acquisition, extinction, and return of fear

https://www.cns.nyu.edu/~david/courses/sm12/Readings/Olsen-Neuron2010.pdf

Divisive Normalization in Olfactory Population Codes

Natural olfactory stimuli occur as mixtures of many single odors. We studied whether the representation of a mixture in the brain retains single-odor information and how much mixture-specific information it includes. To understand mixture representation in the honeybee brain, we used in vivo calcium imaging at the level of the antennal lobe, and systematically measured odor-evoked activity in 24 identified glomeruli in response to four single odorants and all their possible binary, ternary and quaternary mixtures. Qualitatively, mixture-induced activity patterns always contained glomeruli belonging to the pattern of at least one of the components, suggesting a high conservation of component information in olfactory mixtures. Quantitatively, glomerular activity saturated quickly and increasing the number of components resulted in an increase of cases in which the response of a glomerulus to the mixture was lower than that to the strongest component (‘suppression’). This shows global inhibition in the antennal lobe, probably acting as overall gain control. Single components were not equally salient (in terms of number of active glomeruli) and mixture activity patterns were always more similar to the more salient components, in a way that could be predicted linearly. Thus, although a gain control system in the honeybee antennal lobe prevents saturation of the olfactory system, mixture representation follows essentially elemental rules.

/pdf/neural-representation-of-olfactory-mixtures-in-the-honeybee-4xxgvc5ht3.pdf

Neural representation of olfactory mixtures in the honeybee antennal lobe

In an appetitive context, honeybees (Apis mellifera) learn to associate odors with a reward of sucrose solution. If an odor is presented immediately before the sucrose, an elemental association is formed that enables the odor to release the proboscis extension response (PER). Olfactory conditioning of PER was used to study whether, beyond elemental associations, honeybees are able to process configural associations. Bees were trained in a positive and anegative patterning discrimination problem. In the first problem, single odorants were nonreinforced whereas the compound was reinforced. In the second problem, single odorants were reinforced whereas the compound was nonreinforced. We studied whether bees can solve these problems and whether the ratio between the number of presentations of the reinforced stimuli and the number of presentations of the nonreinforced stimuli affects discrimination. Honeybees differentiated reinforced and nonreinforced stimuli in positive and negative patterning discriminations. They thus can process configural associations. The variation of the ratio of reinforced to nonreinforced stimuli modulated the amount of differentiation. The assignment of singular codes to complex odor blends could be implemented at the neural level: When bees are stimulated with odor mixtures, the activation patterns evoked at the primary olfactory neuropile, the antennal lobe, may be combinations of the single odorant responses that are not necessarily fully additive.

/pdf/configural-olfactory-learning-in-honeybees-negative-and-1lb7ecxp80.pdf

Configural Olfactory Learning in Honeybees: Negative and Positive Patterning Discrimination

Stimulus coding in human associative learning: flexible representations of parts and wholes.

Three experiments on classical differential conditioning of the human skin conductance response to elemental and compound stimuli are reported. Subjects in Experiment 1 received both positive and negative patterning training, followed by either positive or negative patterning transfer tests on new stimuli. In positive patterning, a compound of two stimuli is reinforced and its elements are nonreinforced. In negative patterning, the elements are reinforced and the compound is nonreinforced. Subjects in Experiments 2 and 3 received either positive or negative patterning during training, followed by transfer tests on new stimuli. In Experiment 2, the transfer series began with new elements, after which their compound was presented; in Experiment 3, the new compound was presented first in the transfer series, and then the separate elements were administered. All three experiments provided evidence of the acquisition of positive patterning, while negative patterning was found only in Experiments 2 and 3. Positive patterning transferred to new stimuli, indicating that it was not attributable solely to summation of sub-threshold excitation conditioned to the elements on reinforced compound trials. This finding, coupled with the negative patterning found in Experiments 2 and 3, provided support for the unique cue hypothesis. It was concluded that the assumed unique cue constituted a learned “rule,” and that the actual elemental stimuli were neither perceptually nor otherwise modified during the conditioning process.

/pdf/positive-and-negative-patterning-in-human-classical-skin-ri5emx9v6z.pdf

Positive and negative patterning in human classical skin conductance response conditioning

We investigated the capability of honeybees to discriminate between single odorants, binary olfactory mixtures, and ternary olfactory mixtures in olfactory conditioning of the proboscis extension reflex. In Experiment 1, three single odorants (A+, B+, and C+) and three binary mixtures of these odors (AB+, AC+, and BC+) were reinforced while the ternary compound, consisting of all three odors (ABC-), was nonreinforced. In Experiment 2, only one single odorant (A+) and one binary olfactory compound (BC+) were reinforced while the ternary compound (ABC-) consisting of the single odor and the binary compound was nonreinforced. We studied whether bees can solve these problems and whether the course of differentiation can be predicted by the unique cue theory, a modified unique cue theory, or Pearce's configural theory. Honeybees were not able to differentiate reinforced from nonreinforced stimuli in Experiment 1. However, summation to ABC observed at the beginning of training contradicts the predictions of Pearce's configural theory. In Experiment 2, differentiation between the single odorant A and the ternary compound developed more easily than between the binary compound BC and ABC. This pattern of differentiation is in line with a modified unique cue theory and Pearce's configural theory. Summation to ABC at the beginning of training, however, again was at odds with Pearce's configural theory. Thus, olfactory compound processing in honeybees can best be explained by a modified unique cue theory.

/pdf/a-modified-version-of-the-unique-cue-theory-accounts-for-2gtmsge4kr.pdf

Harald Lachnit

Papers

Neural representation of olfactory mixtures in the honeybee antennal lobe

Configural Olfactory Learning in Honeybees: Negative and Positive Patterning Discrimination

Stimulus coding in human associative learning: flexible representations of parts and wholes.

Positive and negative patterning in human classical skin conductance response conditioning

A modified version of the unique cue theory accounts for olfactory compound processing in honeybees