Evan J. Livesey

Negative patterning is easier than a biconditional discrimination.

Two groups of rats were trained for 50 days on different discriminations in a magazine approach paradigm. One group was trained with a negative patterning schedule and a positive patterning schedule concurrently: they received intermixed trials of A+, B+, AB-, C-, D-, CD+ (A, B, C, and D are four distinct stimuli; the plus sign denotes reinforcement with food, and the minus sign denotes nonreinforcement). The second group of rats was trained with the same four stimuli arranged as compounds and reinforced according to the biconditional schedule AB+, CD+, AC-, and BD-. The first group learned the positive patterning schedule much more quickly than the negative patterning schedule, but they learned the negative patterning schedule more effectively than the second group learned the biconditional schedule. The authors discuss the implications of these findings for models of stimulus representation.

Comparing patterning and biconditional discriminations in humans.

In two experiments, human participants performed a causal judgment task that simultaneously comprised two reciprocal patterning discriminations and a biconditional discrimination. They learned both patterning discriminations more quickly than the biconditional discrimination. Postdiscrimination tests were used to identify participants who had, or had not, learned to apply the patterning rules, as well as participants who continued to expect summation when presented with two cues that predicted the same outcome. All groups were faster to learn the patterning than the biconditional discriminations. These results are inconsistent with models of stimulus representation that invoke configural representations (e.g., Pearce, 1987, 1994; Rescorla & Wagner, 1972) because these models solve biconditional discriminations more readily than patterning discriminations.

Outcome additivity, elemental processing and blocking in human causality judgements

Informing participants in a causal judgement task that outcomes are additive can increase blocking effects (Experiment 1). Outcome additivity information emphasizes the fact that the outcome following a compound is the sum of the effects of its elements. We suggest that the effect of providing outcome additivity information is to encourage elemental processing and thereby enhance blocking. Experiment 2 showed that blocking could be enhanced by factors encouraging elemental processing, and Experiment 3 demonstrated that blocking was reduced by manipulating the visual presentation of cues to encourage configural processing. While these experiments do not rule out the role of inference in causal judgement tasks, the results are most parsimoniously explained by associative accounts that allow flexibility in the encoding of compound cues.

Can expectancies produce placebo effects for implicit learning

The placebo effect is an important phenomenon whereby real changes occur in response to an otherwise inert intervention. Despite increasing research attention, it remains unclear exactly which processes are amenable to placebo effects. The current study tested whether an instructional manipulation could produce placebo effects on a nonconscious cognitive task, namely implicit learning. Four hundred and sixty-four university students completed a visual search task while smelling an odor or no odor, in alternating blocks. Unknown to them, the task contained a contingency whereby on half the trials the target’s location was cued by the pattern of distractors, which was achieved by repeating some configurations of targets and distractors. Prior to the task, participants received positive, negative, or no information about the odor’s possible effects on performance. Those given positive information demonstrated faster reaction times on cued trials than other participants. Those given negative information showed slower reaction times on cued trials compared with participants given no information. Further, the cuing effect appeared to be nonconscious, with participants’ ability to recognize the repeated configurations equivalent to chance and no evidence that performance on a recognition test was related to the magnitude of the cuing effect. This suggests that instructional manipulations can produce placebo effects on some nonconscious processes.

Attentional changes during implicit learning: signal validity protects a target stimulus from the attentional blink.

Participants in 2 experiments performed 2 simultaneous tasks: one, a dual-target detection task within a rapid sequence of target and distractor letters; the other, a cued reaction time task requiring participants to make a cued left-right response immediately after each letter sequence. Under these rapid visual presentation conditions, it is usually difficult to identify the 2nd target when it is presented in temporal proximity of the 1st target-a phenomenon known as the attentional blink. However, here participants showed an advantage for detecting a target presented during the attentional blink if that target predicted which response cue would appear at the end of the trial. Participants also showed faster reaction times on trials with a predictive target. Both of these effects were independent of conscious knowledge of the target-response contingencies assessed by postexperiment questionnaires. The results suggest that implicit learning of the association between a predictive target and its outcome can automatically facilitate target recognition during the attentional blink and therefore shed new light on the relationship between associative learning and attentional mechanisms.

An attention-modulated associative network.

We present an elemental model of associative learning that describes interactions between stimulus elements as a process of competitive normalization. Building on the assumptions laid out in Harris (2006), stimuli are represented as an array of elements that compete for attention according to the strength of their input. Elements form associations among each other according to temporal correlations in their activation but restricted by their connectivity. The model moves beyond its predecessor by specifying excitatory, inhibitory, and attention processes for each element in real time and describing their interaction as a form of suppressive gain control. Attention is formalized in this model as a network of mutually inhibitory units that moderate the activation of stimulus elements by controlling the level to which the elements are suppressed by their own inhibitory processes. The model is applied to a range of complex discriminations and related phenomena that have been taken as evidence for configural-learning processes.

Dissociations Between Expectancy and Performance in Simple and Two-Choice Reaction-Time Tasks: A Test of Associative and Nonassociative Explanations

Perruchet, Cleeremans, and Destrebecqz (2006) reported a striking dissociation between trends in the conscious expectancy of an event and the speed of a response that is cued by that event. They argued that this indicates the operation of independent processes in human associative learning. However, there remains a strong possibility that this dissociation is not a consequence of associative learning and is instead caused by changes in vigilance or sensitivity based on the recency of events on previous trials. Three experiments tested this possibility with versions of a cued reaction time task in which trends in performance could not be explained by these nonassociative factors. Experiment 1 introduced a dual-response version of the task, in which response-related vigilance should be held relatively constant, and Experiments 2 and 3 used a differential conditioning procedure to separate the influence of recent response cue presentation from the recent associative history of the trial events. In all experiments, similar trends in reaction time were evident, suggesting a genuine influence of associative learning on response performance. Experiment 3 demonstrated that the associative contribution to these trends was not caused by commensurate changes in expectancy of the response cue.

A dissociation between causal judgement and the ease with which a cause is categorized with its effect

The associative view of human causal learning argues that causation is attributed to the extent that the putative cause activates, via an association, a mental representation of the effect. That is, causal learning is a human analogue of animal conditioning. We tested this associative theory using a task in which a fictitious character suffered from two allergic reactions, rash (O1) and headache (O2). In a conditioned inhibition design with each of these two outcomes (A–O1/AX– and B–O2/BY–), participants were trained that one herbal remedy (X) prevented O1 and that the other (Y) prevented O2. These inhibitory properties were revealed in a causal judgement summation test. In a subsequent categorization task, X was most easily categorized with O1, and Y with O2. Thus, the categorization data indicated an excitatory X–O1 and Y–O2 association, the reverse of the inhibitory relationship observed on the causal judgement measure. A second experiment showed that this pattern of excitation and inhibition is dependent on intermixed A–O1 and AX– trials. These results are problematic for the standard application of associative activation theories to causal judgement. We argue instead that the inhibition revealed in the causal judgement task reflects inferential reasoning, which relies, in part, on the ability of the cue in question to excite a representation of the outcome, as revealed in the categorization test.

Automaticity and conscious control in single and choice response time versions of the Perruchet effect.

The Perruchet effect refers to a dissociation between conscious expectation of an event and the strength or speed of the response elicited by that event, which is revealed through sequential analyses of consecutive trials. For instance in a cued go/no-go task, over a run of consecutive go responses, participants perform the go task with increasing speed but display decreasing expectancy of the go response when asked to rate the likelihood of its occurrence, in line with a gamblers fallacy. The effect has conventionally been shown in procedures that measure expectancy and responding in separate blocks of trials (separate measurement) or measure both on the same trial (concurrent measurement). Here we directly compared the trends produced in separate and concurrent measurement procedures, using single response and two-choice response procedures. While the single response showed the same trends regardless of measurement style, the Perruchet dissociation disappeared when concurrent measurement was used with the choice response task. Furthermore, in the concurrent measurement versions of both tasks, those participants who produced a consistent gamblers fallacy did not show the standard effects of positive recency that underpin the Perruchet effect in response time. The results suggest that explicit expectation of events and facilitation based on recent trial history have dissociable but competing effects on response performance.

Automaticity and cognitive control in the learned predictiveness effect.

In novel contexts, learning is biased toward cues previously experienced as predictive compared with cues previously experienced as nonpredictive. This is known as learned predictiveness. A recent finding has shown that instructions issued about the causal status of cues influences the expression of learned predictiveness, suggesting that controlled, volitional processes play a role in this effect. Three experiments are reported further investigating the effects of instructional manipulations on learned predictiveness. Experiment 1 confirms the influence of inferential processes, extending previous work to suggest that instructions affect associative memory as well as causal reasoning. Experiments 2 and 3 used a procedure designed to tease apart inferential and automatic contributions to the bias by presenting instructed causes that were previously predictive and previously nonpredictive. The results demonstrate that the prior predictiveness of cues influences subsequent learning over and above the effect of explicit instruction. However, it appears that the relationship between explicit instruction and predictive history is interactive rather than additive. Potential explanations for this interactivity are discussed.