Publications

Abstract

Humans are nature’s most intelligent and prolific users of external props and aids (such as written texts, slide-rules and software packages). Here we introduce a method for investigating how people make active use of their task environment during problem-solving and apply this approach to the non-verbal Raven Advanced Progressive Matrices test for fluid intelligence. We designed a click-and-drag version of the Raven test in which participants could create different external spatial configurations while solving the puzzles. In our first study, we observed that the click-and-drag test was better than the conventional static test at predicting academic achievement of university students. This pattern of results was partially replicated in a novel sample. Importantly, environment-altering actions were clustered in between periods of apparent inactivity, suggesting that problem-solvers were delicately balancing the execution of internal and external cognitive operations. We observed a systematic relationship between this critical phasic temporal signature and improved test performance. Our approach is widely applicable and offers an opportunity to quantitatively assess a powerful, although understudied, feature of human intelligence: our ability to use external objects, props and aids to solve complex problems.

Abstract

In a Turing test, a judge decides whether their conversation partner is either a machine or human. What cues does the judge use to determine this? In particular, are presumably unique features of human language actually perceived as humanlike? Participants rated the humanness of a set of sentences that were manipulated for grammatical construction: linear right-branching or hierarchical center-embedded and their plausibility with regard to world knowledge.

We found that center-embedded sentences are perceived as less humanlike than right-branching sentences and more plausible sentences are regarded as more humanlike. However, the effect of plausibility of the sentence on perceived humanness is smaller for center-embedded sentences than for right-branching sentences.

Participants also rated a conversation with either correct or incorrect use of the context by the agent. No effect of context use was found. Also, participants rated a full transcript of either a real human or a real chatbot, and we found that chatbots were reliably perceived as less humanlike than real humans, in line with our expectation. We did, however, find individual differences between chatbots and humans.

Abstract

There is mounting evidence that the ease of producing and understanding language depends not only on the frequencies of individual words but also on the frequencies of word combinations. However, in two picture description experiments, Janssen and Barber (2012) found that French and Spanish speakers' speech onset latencies for short phrases depended exclusively on the frequencies of the phrases but not on the frequencies of the individual words. They suggested that speakers retrieved phrase-sized units from the mental lexicon. In the present study, we examined whether the time required to plan complex noun phrases in Dutch would likewise depend only on phrase frequencies. Participants described line drawings in phrases such as rode schoen [red shoe] (Experiments 1 and 2) or de rode schoen [the red shoe] (Experiment 3). Replicating Janssen and Barber's findings, utterance onset latencies depended on the frequencies of the phrases but, deviating from their findings, also depended on the frequencies of the adjectives in adjective-noun phrases and the frequencies of the nouns in determiner-adjective-noun phrases. We conclude that individual word frequencies and phrase frequencies both affect the time needed to produce noun phrases and discuss how these findings may be captured in models of the mental lexicon and of phrase production

Abstract

Background

Transcutaneous vagus nerve stimulation (tVNS) is a new, non-invasive technique being investigated as an intervention for a variety of clinical disorders, including epilepsy and depression. It is thought to exert its therapeutic effect by increasing central norepinephrine (NE) activity, but the evidence supporting this notion is limited.
Objective

In order to test for an impact of tVNS on psychophysiological and hormonal indices of noradrenergic function, we applied tVNS in concert with assessment of salivary alpha amylase (SAA) and cortisol, pupil size, and electroencephalograph (EEG) recordings.
Methods

Across three experiments, we applied real and sham tVNS to 61 healthy participants while they performed a set of simple stimulus-discrimination tasks. Before and after the task, as well as during one break, participants provided saliva samples and had their pupil size recorded. EEG was recorded throughout the task. The target for tVNS was the cymba conchae, which is heavily innervated by the auricular branch of the vagus nerve. Sham stimulation was applied to the ear lobe.
Results

P3 amplitude was not affected by tVNS (Experiment 1A: N=24; Experiment 1B: N=20; Bayes factor supporting null model=4.53), nor was pupil size (Experiment 2: N=16; interaction of treatment and time: p=0.79). However, tVNS increased SAA (Experiments 1A and 2: N=25) and attenuated the decline of salivary cortisol compared to sham (Experiment 2: N=17), as indicated by significant interactions involving treatment and time (p=.023 and p=.040, respectively).
Conclusion

These findings suggest that tVNS modulates hormonal indices but not psychophysiological indices of noradrenergic function.

Abstract

The present study identified two aspects of complexity that have been manipulated in the implicit learning literature and investigated how they affect implicit and explicit learning of artificial grammars. Ten finite state grammars were used to vary complexity. The results indicated that dependency length is more relevant to the complexity of a structure than is the number of associations that have to be learned. Although implicit learning led to better performance on a grammaticality judgment test than did explicit learning, it was negatively affected by increasing complexity: Performance decreased as there was an increase in the number of previous letters that had to be taken into account to determine whether or not the next letter was a grammatical continuation. In particular, the results suggested that implicit learning of higher order dependencies is hampered by the presence of longer dependencies. Knowledge of first-order dependencies was acquired regardless of complexity and learning mode.

Abstract

Actively searching for the rules of an artificial grammar has often been shown to produce no more knowledge than memorising exemplars without knowing that they have been generated by a grammar. The present study investigated whether this ineffectiveness of intentional learning could be overcome by removing dual task demands and providing participants with more specific instructions. The results only showed a positive effect of learning intentionally for participants specifically instructed to find out which letters are allowed to follow each other. These participants were also unaffected by a salient feature. In contrast, for participants who did not know what kind of structure to expect, intentional learning was not more effective than incidental learning and knowledge acquisition was guided by salience.

Abstract

When assessing dangerousness of mentally ill persons with the objective of making a decision on civil commitment, medical and legal experts use information typically belonging to their professional frame of reference. This is investigated in two studies of the commitment decision. It is hypothesized that an ‘expertise bias’ may explain differences between the medical and the legal expert in defining the dangerousness concept (study 1), and in assessing the seriousness of the danger (study 2). Judges define dangerousness more often as harming others, whereas psychiatrists more often include harm to self in the definition. In assessing the seriousness of the danger, experts tend to be more tolerant with regard to false negatives, as the type of behavior is more familiar to them. The theoretical and practical implications of the results are discussed.

Abstract

Participants performed an artificial grammar learning task, in which the standard finite
state grammar (J. Verb. Learn. Verb. Behavior 6 (1967) 855) was extended with a recursive
rule generating self-embedded sequences. We studied the learnability of such a rule in two experiments.
The results verify the general hypothesis that recursivity can be learned in an artificial
grammar learning task. However this learning seems to be rather based on recognising
chunks than on abstract rule induction. First, performance was better for strings with more
than one level of self-embedding in the sequence, uncovering more clearly the self-embedding
pattern. Second, the infinite repeatability of the recursive rule application was not spontaneously
induced from the training, but it was when an additional cue about this possibility was
given. Finally, participants were able to verbalise their knowledge of the fragments making up
the sequences––especially in the crucial front and back positions––, whereas knowledge of the
underlying structure, to the extent it was acquired, was not articulatable. The results are discussed
in relation to previous studies on the implicit learnability of complex and abstract rules.